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- Raft Thickness Rational Design for Megatall Skyscrapers: Case Studies
Authors: Hoa Cao Van
Pages: 14781 - 14787
Abstract: The design process for tall buildings involves three main steps: Estimating roughly the sizes of foundation and superstructure components, verifying the determined sizes with full consideration of the interactions between soil, piles, raft, and superstructure to ensure the bearing capacity and deformation of all elements, and optimizing the design with parametric analysis. However, the thickness of the rafts in existing buildings appears to be very thick and varies to the point of confusion. It is noticeable that some buildings have a considerable height but a relatively small raft thickness and vice versa. To address this issue, a simplified graphical method is proposed to determine the raft thickness for the initial design phase. As megatall skyscrapers become increasingly common, a more comprehensive study of rafts is necessary. This article explores the process of designing and constructing rafts for tall and megatall skyscrapers. The study aims to validate and extend the graphical method and establish a basis for the raft thickness optimization process. The research shows that the number of floors strongly affects the thickness of the rafts. However, the elastic modulus is significantly influenced when the ratio of the raft thickness to the number of floors is less than 5% and vice versa.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7285
Issue No: Vol. 14, No. 4 (2024)
- An Enhanced Framework to Mitigate Post-Installation Cyber Attacks on
Android Apps
Authors: Vijay Koka, Kireet Muppavaram
Pages: 14788 - 14792
Abstract: The widespread use of smartphones worldwide has led to a corresponding rise in the number of mobile applications available for Android devices. These apps offer users convenient ways to perform various daily tasks, but their proliferation has also created an environment in which attackers can steal sensitive information. Insecure options employed by many app developers create vulnerabilities that can be exploited by attackers to gain access to most smartphones. While existing methods can detect malware during app installation, they do not sufficiently address post-installation attacks, such as those resulting from fake apps or Man-in-the-Disk (MitD) attacks. To address this issue, the current study conducted research on post-installation attacks, including data leakage, malware injection, repackaging, reverse engineering, privilege escalation, and UI spoofing. MitD attacks are particularly challenging to counter, so, to mitigate this risk, the Post-Installation App Detection Method is proposed to monitor and regulate sensitive information flow and prevent MitD attacks.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7467
Issue No: Vol. 14, No. 4 (2024)
- Mapping Graduate Skills to Market Demands: A Holistic Examination of
Curriculum Development and Employment Trends
Authors: Abdulsamad Ebrahim Yahya, Wael M. S. Yafooz, Atef Gharbi
Pages: 14793 - 14800
Abstract: The number of unemployed computer science graduates has increased significantly over the last few years. The primary reason for this problem is the skill gap between the graduates and what is required on the job market. The current study aims to address the challenge of aligning the skills of computer science graduates with the evolving demands of the job market. To achieve this objective, the current research leverages Machine Learning (ML) and Deep Learning (DL) techniques to predict the skills required by employers and those possessed by graduates. The dataset used in this study has been carefully curated and annotated by experts in the field. It entails 18 features that capture various aspects of a graduate’s skillset, such as programming languages, technical expertise, and soft skills. Additionally, the dataset includes information on the most common job positions in the computer science industry (i.e. a total of 8 roles). A sample size of 3,831 computer science graduates was sourced from alumina surveys and reputable hiring agencies. The dataset provides a comprehensive view of the skills landscape in the computer science domain. Several ML classifiers, ensemble methods, and DL approaches were utilized in a series of experiments. The correlations and important skills and jobs in the market were given focus. The experimental results indicate that support vector machines and neural networks achieved high accuracies of 82% and 88%, respectively. By analyzing the results, this study seeks to uncover patterns and trends that can guide the development of educational programs and curricula, ensuring they are aligned with the evolving needs of the industry.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7454
Issue No: Vol. 14, No. 4 (2024)
- Maximizing DRL-based Energy Efficiency in IRS-NOMA using a DDPG Algorithm
for the Next Generation of Wireless Communications
Authors: Kamil Audah, Nor K. Noordin, Wala'a Hussein, Mod Fadlee B. A. Rasid, Aduwati Sali, Aymen Flah
Pages: 14801 - 14810
Abstract: Combining Intelligent Reflecting Surfaces (IRSs) with Non-Orthogonal Multiple Access (NOMA) effectively enhances communication. This study introduces a NOMA-assisted Downlink Transmission (DT) system, emphasizing Energy Efficiency (EE) optimization. EE, crucial in Wireless Communications (WCs), measures data transmission relative to energy consumption. This study focuses on a Deep Deterministic Policy Gradient (DDPG) algorithm that intelligently adjusts IRS phase-shift matrices and access point beamforming in NOMA DT. Beamforming directs signals to users for optimal strength and quality, while phase shift control enhances signal coverage and quality. Strategic IRS placement improves user signal transmissions. The simulation results demonstrate significantly improved EE compared to other algorithms, such as Deep Q Network (DQN) and Proximal Policy Optimization (PPO), showcasing the effectiveness of the combined IRS and NOMA approach in enhancing communication systems' EE.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7536
Issue No: Vol. 14, No. 4 (2024)
- Computer Architectures Empowered by Sierpinski Interconnection Networks
utilizing an Optimization Assistant
Authors: Muhammad Waseem Iqbal, Nizal Alshammry
Pages: 14811 - 14818
Abstract: The current article discusses Sierpinski networks, which are fractal networks with certain applications in computer science, physics, and chemistry. These networks are typically used in complicated frameworks, fractals, and recursive assemblages. The results derived in this study are in mathematical and graphical format for particular classes of these networks of two distinct sorts with two invariants, K-Banhatti Sombor (KBSO) and Dharwad, along with their reduced forms. These results can facilitate the formation, scalability, and introduction of novel interconnection network topologies, chemical compounds, and VLSI processor circuits. The mathematical expressions employed in this research offer modeling insights and design guidelines to computer engineers. The derived simulation results demonstrate the optimal ranges for a certain network. The optimization assistant tool deployed in this work provides a single maximized value representing the maximum optimized network. These ranges can be put into service to dynamically establish a network according to the requirements of this paper.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7572
Issue No: Vol. 14, No. 4 (2024)
- Numerical Simulation for Strength and Stability of RC Tapered Columns
Authors: Jabbar Abdalaali Kadhim, Salah R. Al.Zaidee
Pages: 14819 - 14824
Abstract: This study investigates the strength and stability of Reinforced Concrete (RC) linearly tapered square columns. Evaluating the slenderness ratio of an RC column requires the radius of gyration of its cross-section, which is well-defined for a prismatic column but not for a non-prismatic one. This study primarily investigates the application of the ACI Code formulae to evaluate the slenderness ratio of RC columns with the studied geometry. Validated numerical models, using Abaqus, were employed to perform nonlinear first- and second-order analyses on the investigated columns subjected to eccentric axial loads. The concrete damaged plasticity model was employed to simulate the nonlinear behavior of concrete. The static Riks solver, available in Abaqus, was utilized for nonlinear analyses: first, with an inactivated geometric nonlinearity for a first-order analysis, and second, with an activated geometric nonlinearity to consider the effects of secondary moments (p-δ effects). The findings indicate the reliability of defining the slenderness ratio of an RC linearly tapered column based on the ACI Code formulae, using the average cross-section of the tapered column.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7228
Issue No: Vol. 14, No. 4 (2024)
- Improvement of Torque Control for an Assistant Electric Power Steering
System using a Type-2 Fuzzy Logic Controller
Authors: Vo Thanh Ha
Pages: 14825 - 14831
Abstract: This article explains how a type 2 fuzzy logic controller can improve the torque of a 3-phase Permanent Magnet Synchronous Motor (PMSM) in an electric power steering system. The goal is to ensure effective electric power steering under various road conditions and speeds. The implementation of type-2 FLS involves fuzzification, inference, and output processing. Type-reduction methods are more advanced than type-1 defuzzification methods, handling more rule uncertainties. While computationally demanding, a simple type-reduction computation process is outlined for interval type-2 fuzzy sets. The type 2 fuzzy logic controller algorithm manages the PMSM motor using Field-Oriented Control (FOC), adjusting the motor voltage based on torque, speed, and steering angle sensor inputs. The study's results provide a solid basis for future research on designing and controlling electric power steering systems with precision and efficiency. This research sets the stage for improved electric power steering systems, contributing to the development of intelligent automotive technologies.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7494
Issue No: Vol. 14, No. 4 (2024)
- Utilizing Ant Colony Optimization for Result Merging in Federated Search
Authors: Adamu Garba, Shah Khalid, Aliya Aleryni, Irfan Ullah, Nasser Mansoor Tairan, Habib Shah, Diyawu Mumin
Pages: 14832 - 14839
Abstract: Federated search or distributed information retrieval routes the user's search query to multiple component collections and presents a merged result list in ranked order by comparing the relevance score of each returned result. However, the heterogeneity of the component collections makes it challenging for the central broker to compare these relevance scores while fusing the results into a single ranked list. To address this issue, most existing approaches merge the returned results by converting the document ranks to their ranking scores or downloading the documents and computing their relevance score. However, these approaches are not efficient enough, because the former methods suffer from limited efficacy of result merging due to the negligible number of overlapping documents and the latter are resource intensive. The current paper addresses this problem by proposing a new method that extracts features of both documents and component collections from the available information provided by the collections at query time. Each document and its collection features are exploited together to establish the document relevance score. The ant colony optimization is used for information retrieval to create a merged result list. The experimental results with the TREC 2013 FedWeb dataset demonstrate that the proposed method significantly outperforms the baseline approaches.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7302
Issue No: Vol. 14, No. 4 (2024)
- Advancing IoT Security: Integrative Machine Learning Models for Enhanced
Intrusion Detection in Wireless Sensor Networks
Authors: Bhargavi Mopuru, Yellamma Pachipala
Pages: 14840 - 14847
Abstract: This paper introduces a breakthrough approach to enhancing intrusion detection capabilities within Wireless Sensor Networks (WSNs) by implementing the Enhanced Wireless Intrusion Detection System (EW-IDS). Leveraging a sophisticated blend of Machine Learning (ML) algorithms, including Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), the proposed model effectively streamlines feature selection, resulting in a robust detection framework. Extensive evaluations demonstrate that EW-IDS not only achieves a high accuracy rate of 96%, but also consistently surpasses traditional models in precision, recall, and F1 Score metrics. These achievements underscore the model’s superior ability to differentiate between benign and malicious network activities. The implementation of EW-IDS marks a significant advance in securing the Internet of Things (IoT) environments against a diverse range of cyber threats, enhancing both the security protocols and operational efficiency of WSNs. This study provides a novel intrusion detection solution and offers valuable insights into the application of ML in complex security settings.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7641
Issue No: Vol. 14, No. 4 (2024)
- Selection of Crankshaft Manufacturing Material by the PIV Method
Authors: Hong Son Nguyen, Tran Trung Hieu, Nguyen Manh Thang, Huynh Nhu Tan, Nguyen Tien Can, Pham Thi Thao, Nguyen Chi Bao
Pages: 14848 - 14853
Abstract: The type of material employed in crankshaft production has a great influence on the performance, durability, and product lifespan. There are many types of material that can be used to manufacture crankshafts, but choosing the best one is a complicated work. This study is carried out to select the best material type among four commonly deployed types, including 1080 steel, 18CrMo4 steel, 4130 steel, and S48C steel. Fifteen parameters (criteria) were chosen to describe each material. The weights of the criteria were determined by three methods, including the Mean weight method, the Entropy weight method, and the MEREC (Method based on the Removal Effects of Criteria) weight method. To rank the steel types, the PIV (Proximity Indexed Value) method was adopted, and it was demonstrated that the ranks did not depend on the weighting method followed. S48C is the best choice among the four types of steel generally utilized for crankshaft production.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7514
Issue No: Vol. 14, No. 4 (2024)
- Enhancing Enterprise Financial Fraud Detection Using Machine Learning
Authors: Mustafa Mohamed Ismail, Mohd Anul Haq
Pages: 14854 - 14861
Abstract: The aim of their research is to improve the detection of financial fraud in enterprises through the utilization of artificial intelligence (AI) methodologies. The framework employs machine learning algorithms and data analytics to accurately identify patterns, anomalies, and signs of fraudulent activity. They employed exploratory data analysis approaches to identify instances of missing values and imbalanced data. The selection of the Random Forest Classifier is based on its ability to consistently capture intricate patterns and efficiently tackle the problem of multicollinearity. The isolation forest approach yielded an accuracy of 99.7%, while the local outlier factor method achieved an accuracy of 99.8%. Similarly, the Random Forest algorithm demonstrated an accuracy of 99.9%. The objective of their study is to aid organizations in proactively identifying instances of fraud by utilizing artificial intelligence methodologies.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7437
Issue No: Vol. 14, No. 4 (2024)
- Utilization of Multi-Channel Hybrid Deep Neural Networks for Avocado
Ripeness Classification
Authors: Sumitra Nuanmeesri
Pages: 14862 - 14867
Abstract: Ripeness classification is crucial in ensuring the quality and marketability of avocados. This paper aims to develop the Multi-Channel Hybrid Deep Neural Networks (MCHDNN) model between Visual Geometry Group 16 (VGG16) and EfficientNetB0 architectures, tailored explicitly for avocado ripeness classification in five classes: firm, breaking, ripe, overripe, and rotten. Each feature extracted is concatenated in an early fusion-based to classify the ripeness. The image dataset used for each avocado fruit was captured from six sides: front, back, left, right, bottom, and pedicel to provide a multi-channel input image in of a Convolution Neural Network (CNN). The results showed that the developed fine-tuned MCHDNN had an accuracy of 94.10% in training, 90.13% in validation, and 90.18% in testing. In addition, when considering individual class classification in the confusion matrix of the training set, it was found that the 'ripe' class had the highest accuracy of 94.58%, followed by the 'firm' and 'rotten' classes with 94.50% and 93.75% accuracy, respectively. Moreover, compared with the single-channel model, the fine-tuned MCHDNN model performs 7.70% more accurately than the fine-tuned VGG16 model and 7.77% more accurately than the fine-tuned EfficientNetB0 model.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7651
Issue No: Vol. 14, No. 4 (2024)
- A New Model of Fault-Tolerant Predictive Current Control of Multilevel
Cascaded H-Bridge Inverters for Induction Motors
Authors: Mai Van Chung, Vo Thanh Ha
Pages: 14868 - 14875
Abstract: This study proposes a fault-tolerant method for controlling multilevel inverters using predictive control strategies to tackle semiconductor valve open circuit problems, making a substantial step towards ensuring smooth functionality and sustained performance. The proactive error detection mechanism, based on analyzing differences between the output voltage and the H-bridge control signals, offers a sophisticated approach to fault management. With an advanced SVM voltage modulation algorithm, the system efficiently handles potential faults by optimizing switching combinations to achieve standard voltage vectors. This method ensures maximum output voltage and maintains balanced operation across three phases, resulting in an optimal operational state. The viability and effectiveness of the proposed solution are conclusively established through a comprehensive analysis and rigorous testing. MATLAB simulations confirmed the integrity of the proposed method, demonstrating its ability to accurately address current, torque, and speed requirements. The findings highlight the competence of multilevel inverters in practice, presenting them as user-friendly, secure, and capable of meeting diverse quality standards.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7532
Issue No: Vol. 14, No. 4 (2024)
- The Effect of Fly Ash and Silica Fume on the Rheology of Cement Slurries
of Ordinary Portland Cement of Grade 43 and 53
Authors: Ahmad Waqar Khan, Sanjay Kumar
Pages: 14876 - 14881
Abstract: Cement slurry is the medium of dispersion of coarse and fine aggregates when preparing concrete. The flow behavior of the cement slurries is governed by rheological parameters. The lower the value of these parameters is, the better the flowability and homogeneity of the cement slurry are. Static shear stress (τs), dynamic shear stress (τd), and the thixotropic index (β) are the basic rheological parameters. The effect of fly ash and silica fume on the rheology of Ordinary Portland Concrete (OPC) 43 and OPC 53 was studied by conducting tests on a coaxial rotating-type viscometer. Fly ash dosage was increased from 10% to 30% by the weight of cement in increment steps of 5% in binary and ternary mixes. Silica fume was kept constant at 5% in the ternary mixes. It was found that τs increases with fly ash in the OPC 43 but remains almost constant for the OPC 53 in both binary and ternary mixes. τd was almost constant for both the cement slurries in both binary and ternary mixes. β increases with an increase in fly ash for OPC 43 in binary and ternary slurries but decreases in OPC 53 slurries. The increment of fly ash increases the reversible built in the OPC 43 slurries, which can be broken on the application of shear. Thus, OPC 43 is a better cement from the rheological point of view in the development of various concrete mixes.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7582
Issue No: Vol. 14, No. 4 (2024)
- A Mobile Robot Design for Home Security Systems
Authors: Thanh-Nam Pham, Duc-Tho Mai
Pages: 14882 - 14887
Abstract: Home Security Systems (HSSs) have received much attention and have been widely adopted for practical deployment. However, detection and warning accuracy still need to be improved, along with the range of the realm, making it challenging to satisfy the user demands. This study proposes a security monitoring system based on mobile robots and the Internet of Things (IoT), allowing users to monitor and control devices remotely. The mobile robot integrated sensor systems and surveillance cameras are utilized for unauthorized early intrusion detection and to give users instant warnings. The data collected by the robot were stored on the Firebase Cloud server, and a mobile application and a Telegram interface were integrated to manage and control the system. In addition, adaptive motion control was adopted to correct errors in the robot’s trajectory. The implementation results proved that this system operated effectively with a minimal response delay of 0.87–1.67 s and a high detection accuracy (96.25%) in two experimental cases, which makes it suitable for real-time applications.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7336
Issue No: Vol. 14, No. 4 (2024)
- Incorporation of High Volume Ground Granulated Slag From Blast Furnaces in
Pavement Quality Concrete
Authors: Vikram J. Patel, Jayesh Juremalani, Hemraj R. Kumavat
Pages: 14888 - 14893
Abstract: Supplementary cementitious materials (SCMs) are commonly introduced into the concrete mix to increase their properties, addressing the current need for durable and robust pavements. With every ton of Ordinary Portland Cement (OPC) produced through processes such as fossil fuel combustion and limestone fermentation, carbon dioxide is released into the atmosphere. Conversely, Ground Granulated Blast-furnace Slag (GGBS), being abundantly available, presents a viable more environmentally friendly alternative to cement for various concrete applications. This work studies the influence of GGBS, in combination with chemical admixtures, on M40 grade binary blended concrete mixtures. The aim of the study was to improve the strength characteristics at various stages of concrete curing. The results indicated that the GGBS-incorporated concretes (replacing 60%, 65%, and 70% of the cement) exhibited an increase in compressive strength after prolonged curing. The average density of fresh concrete mixes containing GGBS did not exhibited a noticeable increase. A marginal disparity in air content was observed in the replacement mix. Minimal length changes were observed in the drying shrinkage test after a curing duration of 360 days compared to conventional concrete mixtures.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7466
Issue No: Vol. 14, No. 4 (2024)
- Effect of Magnetic Treatment on Seawater Determined by Quartz Crystal
Microbalance: Mechanisms of Crystal Deposition
Authors: Fathi Alimi
Pages: 14894 - 14898
Abstract: This study investigated the effect of a magnetic field on the crystal deposition of treated seawater to determine the mechanism of calcium carbonate deposition on the quartz surface. Several samples of standard seawater (43 g/L) were circulated through a permanent magnetic field of 0.16 T at a fixed temperature, pH, and flow rate. Scaling experiments showed that magnetic treatment of seawater enhanced the precipitation of calcium carbonate and that two superposed phases were deposited. A first layer formed with crystals of aragonite covering the whole surface of the quartz, and then a second phase of calcite was deposited. In the untreated solutions, only homogeneous agglomerates of aragonite were deposited.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7482
Issue No: Vol. 14, No. 4 (2024)
- Assessing the Impact of Criterion Weights on the Ranking of the Top Ten
Universities in Vietnam
Authors: Duc Trung Do
Pages: 14899 - 14903
Abstract: This study focuses on evaluating the influence of criterion weights on the ranking of the top ten universities in Vietnam. Criteria weights were determined using four different methods, including the equal weight method, the weights of the Vietnam University Rankings (VNUR) system, the entropy weight method, and the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) weight method. Four university ranking methods were applied: Proximity Indexed Value (PIV), Ranking of Alternatives with Weights of Criterion (RAWEC), Root Assessment Method (RAM), and Simple Ranking Process (SRP). The results indicate that the use of different weight calculation methods does not significantly affect university rankings. The four leading universities in Vietnam consistently maintain their position in the rankings, regardless of the weight calculation or ranking methods used.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7607
Issue No: Vol. 14, No. 4 (2024)
- Improving Image Inpainting through Contextual Attention in Deep Learning
Authors: Ayoub Charef, Ahmed Ouqour
Pages: 14904 - 14909
Abstract: Image processing is vital in modern technology, offering a diverse range of techniques for manipulating digital images to extract valuable information or enhance visual quality. Among these techniques, image inpainting stands out, involving the reconstruction or restoration of missing or damaged regions within images. This study explores advances in image inpainting and presents a novel approach that integrates coarse-to-fine inpainting and attention-based inpainting techniques. The proposed method leverages deep learning methods to enhance the quality and efficiency of image inpainting, achieving robust and high-quality results that balance structural integrity and contextual coherence. A comprehensive evaluation and comparison with existing methods showed that the proposed approach had superior performance in maintaining structural integrity and contextual coherence within images.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7347
Issue No: Vol. 14, No. 4 (2024)
- Design and Simulation of a Fuel Cell-based Hybrid Underwater Vehicle
Propulsion System in Matlab/Simulink
Authors: Huy Chien Nguyen, Nguyen Ha Hiep
Pages: 14910 - 14915
Abstract: Remotely Operated Vehicles (ROVs) and Autonomous Underwater Vehicles (AUVs) have many applications in underwater missions. A recurring issue is managing the power source of the vehicle, especially air-independent propulsion sources, such as batteries, accumulators, and fuel cells, to increase diving depth, underwater endurance, and range. The current article proposes the use of both fuel cells and batteries simultaneously. The subject of the study is Pluto Plus ROVs, which are mine sweeping and counter-terrorism underwater vehicles. The considered system is simulated in Matlab/Simulink. The result was a block diagram that simulates a hybrid propulsion system for submersibles in general and serves as the basis for improving the propulsion system for Pluto Plus ROVs.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7550
Issue No: Vol. 14, No. 4 (2024)
- Deep Learning Techniques for Lung Cancer Recognition
Authors: Suseela Triveni Vemula, Maddukuri Sreevani, Perepi Rajarajeswari, Kumbham Bhargavi, Joao Manuel R. S. Tavares, Sampath Alankritha
Pages: 14916 - 14922
Abstract: Globally, lung cancer is the primary cause of cancer-related mortality. Higher chance of survival depends on the early diagnosis of lung nodules. Manual lung cancer screenings depends on the human factor. The variability in size, texture, and shape of lung nodules may pose a challenge for developing accurate automatic detection systems. This article proposes an ensemble approach to tackle the challenge of lung nodule detection. The goal was to improve prediction accuracy by exploring the performance of multiple transfer learning models instead of relying solely on deep learning models. An extensive dataset of CT scans was gathered to train the built deep learning models. This research paper is focused on the Convolutional Neural Networks' (CNNs') ability to automatically learn and adapt to discernible features in the lung images which is particularly beneficial for accurate classification, aiding in identifying true and false labels, and ultimately enhancing lung cancer diagnostic accuracy. This paper provides a comparative analysis of the performance of CNN, VGG-16, and VGG-19. Notably, the built transfer learning model VGG-16 achieved a remarkable accuracy of 95%, surpassing the baseline method.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7510
Issue No: Vol. 14, No. 4 (2024)
- Design of Dual Band Substrate Integrated Waveguide (SIW) Antenna with
Modified Slot for Ka-Band Applications
Authors: Saravanan Ramamoorthi Agilesh, Boddapati T. P. Madhav, A. Gangadhar, Sarada Sowjanya Chintalapati
Pages: 14923 - 14928
Abstract: This paper introduces a dual-feed substrate integrated waveguide antenna design with a modified H-Slot (DFH-SIW) for Ka-band 5G applications. In general, 5G base stations operating in Ka-band applications need large bandwidth-based antennas with high gain to solve the problem of path loss and high data capacity. To resonate with an ideal bandwidth at 28/38 GHz, this study proposes a high-gain antenna with a modified H-slot. This study used the CST EM tool to design the proposed antenna model and evaluate and optimize its characteristics. The proposed dual feed antenna has compact dimensions of 12×12×1.2 mm and was constructed on a Roger 5880 substrate with an appropriate permittivity of 2.2 and a minimal loss tangent of 0.0009. The proposed dual-feed antenna showed an ideal reflection coefficient of less than
-10dB and a bandwidth of 3.7 GHz and 3.23 GHz at operating frequencies of 28 and 38 GHz, respectively. The proposed design operates at the 28 GHz frequency with 73.2% efficiency and 4.3 dBi gain, and at 38 GHz with 83.68% efficiency and 6.7 dBi gain. This antenna is intended to be used in future 5G wireless networks and has outstanding overall performance in terms of return loss, gain, and wide bandwidth.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7620
Issue No: Vol. 14, No. 4 (2024)
- Application of TOPSIS-LP and New Routing Models for the Multi-Criteria
Tourist Route Problem: The Case Study of Nong Khai, Thailand
Authors: Wasana Phuangpornpitak, Wanita Boonchom, Kittanathat Suphan, Watchara Chiengkul, Thanawat Tantipanichkul
Pages: 14929 - 14938
Abstract: This study investigates the application of a new mathematical routing model, integrated with the TOPSIS Linear Programming (TOPSIS-LP) approach, to optimize tourist routes in Nong Khai, Thailand, within a Multi-Criteria Decision-Making (MCDM) framework. The research demonstrates the efficacy of TOPSIS-LP by consistently ranking the same alternative as the optimal route, achieving the highest rankings across various Multi-Attribute Decision Making (MADM) methods, including MOORA, WASPAS, and ARAS. These methods displayed significant consistency in outcome evaluation, with Spearman Correlation Coefficients (SCC) of 0.952 for MOORA WASPAS, and ARAS, indicating the influence of diverse weighting and aggregation strategies in route optimization. Moreover, the study confirmed a perfect alignment (SCC of 1.00) between TOPSIS-LP and the traditional TOPSIS method, affirming that the enhancements to the LP components maintained the integrity of the original model. The findings provide invaluable insights for tourism planners aiming to improve tourist satisfaction and operational efficiency and contribute to the academic discourse by highlighting the practical utility of sophisticated mathematical models in real-world scenarios. This research not only advances the methodological practices in tourist route optimization, but also sets a benchmark for future research aimed at enhancing the effectiveness, robustness, and adaptability of MADM methods in the tourism sector.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7523
Issue No: Vol. 14, No. 4 (2024)
- Machine Learning Techniques for Power Quality Enhancement of Power
Distribution Systems with FACTS Devices
Authors: Malladi Lakshmi Swarupa, Katuri Rayudu, Chava Sunil Kumar, Sree Lakshmi Gundebommu, P. Kamalakar
Pages: 14939 - 14944
Abstract: The power quality problem refers to the issues caused by the sudden rise of nonstandard voltage, current, or frequency. The problems that emerge from poor power quality due to non-linear loads are voltage sag, swell, interruptions, harmonics, and transients in distribution systems. Various compensation devices are used nowadays to improve power quality. The advances in power electronic technologies improve the reliability and functionality of power electronic-based controllers, resulting in increased applications of FACTS devices like DSTATCOM and Dynamic Voltage Restorer (DVR) which are fast, flexible, and efficient solutions to power quality problems. These devices are used to restore the source, load voltage, and current disturbances caused by different loads and faults. These devices were tested in a standard IEEE 14-bus system for Total Harmonic Distortion (THD) minimization while utilizing PI-based Artificial Neural Networks (ANNs) and Linear Regression (LR). The results were analyzed and compared.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7233
Issue No: Vol. 14, No. 4 (2024)
- Enhanced Chaos Game Optimization for Multilevel Image Thresholding through
Fitness Distance Balance Mechanism
Authors: Achraf Ben Miled, Mohammed Ahmed Elhossiny, Marwa Anwar Ibrahim Elghazawy, Ashraf F. A. Mahmoud, Faroug A. Abdalla
Pages: 14945 - 14955
Abstract: This study proposes a method to enhance the Chaos Game Optimization (CGO) algorithm for efficient multilevel image thresholding by incorporating a fitness distance balance mechanism. Multilevel thresholding is essential for detailed image segmentation in digital image processing, particularly in environments with complex image characteristics. This improved CGO algorithm adopts a hybrid metaheuristic framework that effectively addresses the challenges of premature convergence and the exploration-exploitation balance, typical of traditional thresholding methods. By integrating mechanisms that balance fitness and spatial diversity, the proposed algorithm achieves improved segmentation accuracy and computational efficiency. This approach was validated through extensive experiments on benchmark datasets, comparing favorably against existing state-of-the-art methods.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7713
Issue No: Vol. 14, No. 4 (2024)
- A Kinetic and Morphological Study of Barite Precipitation Reaction in the
Presence of Fe3+ and Mn2+ Ions
Authors: Lassaad Mechi
Pages: 14956 - 14960
Abstract: The precipitation mode of barium sulphate (BaSO4) in the presence of mineral additives plays an important role in many industrial processes. Therefore, in this paper, a study of the precipitation reaction of a saturated barium sulphate solution in the presence of metal ions Fe3+ and Mn2+, found in industrial waters and in the geochemical evolutions of paleoenvironments, is presented. XRD, conductivity, FTIR spectroscopy, and SEM were used to investigate the barite precipitation reaction in the presence of a known amount of Fe3+ and Mn2+ ions. Conductivity measurements showed that the presence of Fe3+ accelerated both induction and crystal growth stages. On the other hand, adding Mn2+ ions did not affect the kinetics of the precipitation reaction. Solid analysis showed that the barite lattice was doped with low levels of manganese.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7518
Issue No: Vol. 14, No. 4 (2024)
- Software Vulnerability Fuzz Testing: A Mutation-Selection Optimization
Systematic Review
Authors: Fatmah Yousef Assiri, Asia Othman Aljahdali
Pages: 14961 - 14969
Abstract: As software vulnerabilities can cause cybersecurity threats and have severe consequences, it is necessary to develop effective techniques to discover such vulnerabilities. Fuzzing is one of the most widely employed approaches that has been adapted for software testing. The mutation-based fuzzing approach is currently the most popular. The state-of-the-art American Fuzzy Lop (AFL) selects mutations randomly and lacks knowledge of mutation operations that are more helpful in a particular stage. This study performs a systematic review to identify and analyze existing approaches that optimize the selection of mutation operations. The main contributions of this work are to draw attention to the importance of mutation operator selection, identify optimization algorithms for mutation operator selection, and investigate their impact on fuzzing testing in terms of code coverage and finding new vulnerabilities. The investigation shows the effectiveness and advantages of optimizing the selection of mutation operations to achieve higher code coverage and find more vulnerabilities.
PubDate: 2024-08-02
DOI: 10.48084/etasr.6971
Issue No: Vol. 14, No. 4 (2024)
- Developing a Program for Practice Management and Productivity Improvement
of Infrastructure Projects by using Business Information Modeling
Authors: Saja Abd Alrazaq Khamees, Sawsan Rasheed Mohammed
Pages: 14970 - 14976
Abstract: The employment of Business Information Modeling (BIM) may assist the resident engineering department in reaching its long-term strategic goals. The suggested computer software was created and tested using the first package of new projects for roads, bridges, and tunnels aimed at solving the traffic congestion crisis taking place in the capital city of Iraq, Baghdad. In the context of the current study, questionnaires and personal interviews with specialists in the field of road, tunnel, and overpass construction were employed. The aim of implementing BIM is to assess the introduced program’s efficiency, verify its performance, and identify any errors, defects, or challenges that users may encounter during its application. The computer program underwent successful testing at several Resident Engineer's Offices (REOs) within the initial package of new projects aimed at addressing traffic congestion issues in Baghdad through independent verification checks. Initially, the program was utilized with the assistance of a user guide to facilitate its operation and provide a detailed description of each program feature which can provide effective decision-making and enhance REOs' computer skills.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7613
Issue No: Vol. 14, No. 4 (2024)
- Assessment of Wind Energy Potential for achieving Sustainable Development
Goal 7 in the Rural Region of Jeje, Nigeria
Authors: Youssef Kassem, Huseyin Camur, Terry Apreala
Pages: 14977 - 14987
Abstract: The implementation of a decentralized energy system has the potential to improve the life quality of the people who live in remote rural areas with limited or nonexistent power sources. Renewable energy technologies can be very important in the production of power. The main purpose of this study is to assess Jeje, Nigeria's wind energy potential, using a reanalysis and analysis dataset. To this aim, data on wind speed at a height of 10 m were gathered from a variety of sources, including EAR5, EAR5 Ag, EAR5 Land, CFSR, and MERRA-2. The Weibull distribution function, commonly employed to evaluate wind energy potential, was utilized. A maximum wind power density value of 15.75 W/m2 was obtained when the MERRA-2 dataset was implemented. The results indicate that large-scale wind turbines are not a viable alternative in this area. Thus, the performance of six wind turbines, expressed by output power with a cut in speed ranging between 1 and 1.5 m/s, was investigated. The results demonstrated that the AWI-E1000T is the most efficient wind turbine under consideration. In addition, it has been shown that each considered turbine can be installed in this area based on the data acquired from the MERRA-2 and CFSR.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7311
Issue No: Vol. 14, No. 4 (2024)
- Enhanced Intrusion Detection in IoT with a Novel PRBF Kernel and Cloud
Integration
Authors: Bhargavi Mopuru, Yellamma Pachipala
Pages: 14988 - 14993
Abstract: The proliferation of Internet of Things (IoT) devices in various sectors has increased the need for robust security solutions capable of addressing complex network vulnerabilities and sophisticated cyber threats. This study introduces a novel architecture that integrates cloud computing with advanced machine learning techniques to provide efficient and scalable security in IoT systems. A unique Polynomial Radial Basis Function (PRBF) kernel is proposed to enhance the classification accuracy of Support Vector Machine (SVM) beyond traditional Gaussian and polynomial kernels. This study compares the proposed PRBF-SVM with Logistic Regression, SVM, and XGBoost models, optimized through rigorous hyperparameter tuning, to demonstrate significant improvements in detection rates. Furthermore, the integration of cloud services facilitates the offloading of computationally intensive tasks, ensuring scalability and real-time response capabilities. The results highlight the superior performance of the proposed model in accuracy, efficiency, and computation time, making a compelling case for its application in safeguarding IoT environments against evolving threats.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7767
Issue No: Vol. 14, No. 4 (2024)
- Advancing Email Spam Classification using Machine Learning and Deep
Learning Techniques
Authors: Meaad Hamad Alsuwit, Mohd Anul Haq, Mohammed A. Aleisa
Pages: 14994 - 15001
Abstract: Email communication has become integral to various industries, but the pervasive issue of spam emails poses significant challenges for service providers. This research proposes a study leveraging Machine Learning (ML) and Deep Learning (DL) techniques to effectively classify spam emails. Methods such as Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and Artificial Neural Networks (ANNs) are employed to construct robust models for accurate spam detection. By amalgamating these techniques, the aim is to enhance efficiency and precision in spam detection, aiding email and IoT service providers in mitigating the detrimental effects of spam. Evaluation of the proposed models revealed promising outcomes. LR, RF, and NB achieved an impressive accuracy of 97% and an F1-Score of 97.5%, showcasing their efficacy in accurately identifying spam emails. The ANN model demonstrated slightly superior performance, with 98% accuracy and 97.5% F1-score, suggesting potential improvements in accuracy and robustness in spam filtering systems. These findings underscore the viability of both traditional ML algorithms and DL approaches in addressing the challenges of email spam classification, paving the way for more effective spam detection mechanisms in electronic communication platforms.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7631
Issue No: Vol. 14, No. 4 (2024)
- The Effect of Hybrid Fibers on Some Properties of Structural Lightweight
Self-Compacting Concrete by using LECA as Partial Replacement of Coarse
Aggregate
Authors: Salah Mahdi Ali, Hadeel K. Awad
Pages: 15002 - 15007
Abstract: Self-Compacting Concrete (SCC) is a concrete with high workability. It fills the molds and passes between the narrow openings of reinforcing steel bars without the need for any mechanical pressure or compaction and without the need of a vibrator. Structural Lightweight Self Compacting Concrete (SLWSCC) is an innovative concrete developed in recent years. This concrete type combines the characteristics of lightweight concrete and SCC. This study focused on preparing the appropriate mixture to obtain SLWSCC by using Lightweight Expanded Clay Aggregate (LECA) as a volumetric partial replacement of coarse aggregate by 20, 40, 60, and 80%, reinforced by volumetric ratios of single and hybrid Micro steel Fiber (MF) and Hooked steel Fiber (HF( of 1.5 MF, 0.75 HF+0.75 MF, 1 HF+0.5 MF, and 1.5 HF (%) to evaluate the fresh properties through slump flow, T500mm, V-funnel, L-box, and segregation tests. The results showed that all mixtures fell within the limits of EFNARC/2005. It was found that single and hybrid fiber addition reduces slump flow, L-box, and segregation, while the T500mm and V-funnel values increased. The hard properties of SLWSCC reinforced by fibers, such as compressive strength, flexural strength, splitting tensile strength, oven dry density, and water absorption were studied. The addition of fibers raises compressive, splitting tensile, and flexural strength, with the maximum augmentation of 21.4, 43.4, and 53.8%, respectively, occurring when adding 1 HF + 0.5 MF. The highest value of oven dry density was acquired when adding 1.5 MF and the highest water absorption rate was acquired after the addition of 1.5 HF.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7425
Issue No: Vol. 14, No. 4 (2024)
- Using the Delone and McLean Success Model to Evaluate Moodle's
Information System Success
Authors: Manal Y. Alduaij, Mariam A. Alterkait, Shaikhah Alainati
Pages: 15008 - 15015
Abstract: This study evaluated the Moodle Professional Learning Management System (PLMS), based on user experience during the global pandemic of COVID-19, to explore the impact of system, service, information, education, learner, and instructor qualities on user satisfaction (SAT), perceived ease of use (PEOU), and perceived usefulness (PU). Data were collected using previously validated scales. Using SmartPLS structural equation modeling, data from 403 college students were analyzed to test 22 hypotheses. The results show that system and information quality positively affect PEOU. Although service quality had a partially significant impact on PU, it negatively affected SAT and PEOU. The quality of the education system had a partially positive impact on PU but a negative impact on PEOU and SAT. Learner quality positively affected PEOU, PU, and SAT. Although PU and PEOU significantly and positively affected benefits (BEN), SAT had only a partially significant impact. The results support the need for continued integration of e-learning with traditional learning schemas. This comprehensive analysis demonstrates how quality dimensions affect user experiences and outcomes in a unique cultural and emergency context, thus contributing to the e-learning body of knowledge.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7300
Issue No: Vol. 14, No. 4 (2024)
- Chemical Composition, Fatty Acids, Total Phenolics and Antioxidant
Activity of the Desert Truffle Terfezia Boudieri Chatin in the Northern
Region of Saudi Arabia
Authors: Arbi Guetat, A. Khuzaim Alzahrani, Mohamed Habib Oueslati, Abd Ealrhman M. Elhaj, Jalloul Bouajila, Ismail M. A. Shahhat
Pages: 15016 - 15021
Abstract: Terfezia boudieri Chatin (T. boudieri), which belongs to the Terfeziaceae family, is a desert truffle growing naturally from mycorrhizal relationships with plants that inhabit the slightly moist sandy soils of some desert or semi-desert regions. The chemical examination of the methanolic extract of T. boudieri led to the isolation of a new ketone and the known compound mannitol. The structures of the 2 isolated compounds were resolved using spectroscopic analysis including 1D and 2D Nuclear Magnetic Resonance (NMR), Infrared (IR) spectroscopy and were supported by literature data. The GC-MS analysis of the Fatty Acids (FAs) of T. boudieri oils showed high Unsaturated FA (UFA) profile (78.72%). The major FAs were linoleic acid (62.36%) and oleic acid (14.7%). Comparing the extracts obtained, the methanolic extract revealed the highest levels of Total Phenolic Content (TPC) of 185.56 mg GAE/100 g of truffle, thus the same extract showed the best results for antioxidant activity.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7470
Issue No: Vol. 14, No. 4 (2024)
- Evaluating Surface Water Quality of Euphrates River in Al-Najaf Al-Ashraf,
Iraq with Water Quality Index (WQI)
Authors: Suhair Razzaq Al Sharifi, Hanadi H. Zwain, Zinah K. Hasan
Pages: 15022 - 15026
Abstract: The present study illustrates the rapid pollution of Euphrates River, inwards Al-Najaf Al-Ashraf governance (Al-Kufa River) in Iraq, which is one of the most important rivers in the region. The river faces formidable pressure due to encroachments, discharge of untreated domestic and industrial waste, drainage water from cultivated orchards, and dumping of solid waste. This study was conducted to assess the temporary and locative organic pollution in the region. Water Quality Index (WQI) of the Al-Kufa river was determined according to the Canadian Council of Ministers of Environment (CCME) method by calculating seven parameters (water temperature, DO, COD, EC, TDS, turbidity, and pH) in two sampling stations. Surface water samples were collected in a period of four months (September to December) in 2023. WQI level, correlation analysis between parameters, and Iraqi and CCME drinking water specification standards were employed to classify the surface water status. The results show that the ecological condition can be classified as marginal with WQI= 49 and poor with WQI= 42 in stations 1 and 2, respectively.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7681
Issue No: Vol. 14, No. 4 (2024)
- Leveraging Machine Learning for Android Malware Analysis: Insights from
Static and Dynamic Techniques
Authors: Mohd Anul Haq, Majed Khuthaylah
Pages: 15027 - 15032
Abstract: In this study, the domain of Android malware detection was explored with a specific focus on leveraging the potential of Machine Learning (ML). At the time of this study, Android had firmly established its dominance in the mobile landscape and IoT devices, necessitating a concerted effort to fortify its security against emerging malware threats. Static analysis methods were scrutinized as vital sources of feature extraction for ML, while dynamic analysis methods were employed to analyze the behavior of applications in real or simulated environments. Additionally, a hybrid method, combining both static and dynamic analyses, was investigated. The study evaluated four ML models: XGBoost, Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), revealing compelling insights into their performance metrics. Notably, RF achieved the highest accuracy of 0.99, closely followed by SVM with an accuracy of 0.96. These results underscore the potential effectiveness of ML techniques in bolstering Android malware detection and mitigating security risks. As the research progressed, it underscored the latent power of integrating ML into the framework of Android malware analysis. With an eye towards the future, the overarching goal was to empower enhanced security measures and foster a resilient mobile ecosystem through the insights gleaned from this investigation.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7632
Issue No: Vol. 14, No. 4 (2024)
- A Research on Passenger Carrying Capacity of an Innovative Electric
Traction Power Supply System based on ROCS of 750 V DC MRT
Authors: Nguyen Thai, Dong Doan Van
Pages: 15033 - 15038
Abstract: This article presents the results of a study on the feasibility of a Rigid Overhead Conductor-rail System (ROCS) for a Mass Rapid Transit (MRT) system using 750 V DC power based on a carrying capacity transport-supply voltage level relationship. In particular, peak load conditions often cause serious problems of voltage drops occurring along the contact line, affecting the reliability, flexibility, system safety, and efficiency performance of the MRT system. The potential at the pantograph of a train on the segment of power supply depends significantly on the structure of the traction power supply network, contact network type, and voltage level. Recently, there have been studies on the dynamics of ROCSs under the impact of train motion, thereby applying the design to several railway systems in the world in specific conditions such as tunnels, stations, or viaducts. To consolidate the advantages of this trend, this paper studies the operating voltage of an ROCS in a full-line MRT system with a voltage level of 750 V DC belonging to the third rail. Matlab R2017b/Railway Systems is a reliable software for simulating and analyzing the necessary data. The results exhibit the feasibility of the designed ROCS. The system has a passenger carrying capacity of up to 90,000 passengers per hour per direction (p/h/d) under both normal and fault conditions. In this case, this capacity is achieved with a single-end feed at a distance of 2 km from a Traction Power Station (TPS), with the minimum feeder voltage at the pantograph point being 532.7200 V. The lowest operational feeder voltage of the system is 523.6667 V, supplied from a double-end feed at a distance of up to 5 km from the TPS.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7625
Issue No: Vol. 14, No. 4 (2024)
- Simultaneous Influence of Imperfect Length and Load on the Dynamic
Buckling of Plane Trusses under Step Loading
Authors: Tran Thi Thuy Van, Dao Ngoc Tien, Ta Duy Hien
Pages: 15039 - 15044
Abstract: The influence of imperfections in element length and loading on the dynamic buckling of plane trusses is investigated in the present study. Finite element formulation and the Euler formula are employed to tackle the problem of large displacements. Equivalently, the Newmark integration method and the Newton–Raphson iteration algorithm are deployed to solve the nonlinear dynamic equilibrium equations. The dynamic applied load considered in this study is a step-imperfect load with an imperfection in the element length. The relationship between the load and maximum displacement is determined, and the simultaneous influence of the imperfect parameters on the dynamic limit load is discussed. The imperfect element length and loading significantly affect the dynamic limit load, demonstrating the need to consider both imperfections when studying the dynamic buckling of truss systems.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7626
Issue No: Vol. 14, No. 4 (2024)
- Evaluating the Effectiveness of Requirement Patterns for Data Intensive
Application Development
Authors: Renita Raymond, Margret Anouncia Savarimuthu
Pages: 15045 - 15049
Abstract: In the rapidly evolving landscape of data-intensive applications, the precision and clarity of software requirements play a pivotal role in these applications’ development process. This research focuses on the validation of a specifically designed Transformation Requirement Pattern (TFReqPat) for data-intensive applications, such as banking, e-commerce, and healthcare. The main objective is to ascertain the completeness, correctness, and unambiguity of requirements captured using this novel pattern. Traditional approaches to requirement documentation often suffer from inconsistencies leading to the exploration of structured patterns that promise enhanced clarity and reuse. This article focuses on acceptable verification and validation procedures deployed as concrete methods to validate the adequacy of the captured requirements. Through the generation of stringent acceptance criteria, it is ensured that the documented requirements were adherent to developmental standards with fewer ambiguities. As per the proposed validation process, the correctness and completeness of the specified requirements were determined through the acceptance criteria that check for the presence of process, domain dependency, behavior, and storage details in the specifications. Accordingly, the obtained evaluation score was 93.1%, 88.5%, and 75.2% for correctness and 94.8%, 92.9%, and 76.4%, respectively, for completeness. These findings significantly underscore the suitability of the proposed pattern for data-intensive applications, marking it as a more efficient and effective methodology compared to ad-hoc reuse approaches. This article not only contributes a validated requirement pattern to the field but also highlights the importance of structured requirement documentation in enhancing software development outcomes for data-intensive domains.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7469
Issue No: Vol. 14, No. 4 (2024)
- Blockchain-Inspired Lightweight Dynamic Encryption Schemes for a Secure
Health Care Information Exchange System
Authors: Etikala Aruna, Arun Sahayadhas
Pages: 15050 - 15055
Abstract: The telemedicine sector has entered a new phase marked by the integration of Internet of Things (IoT) devices to identify and then send patient health data to medical terminals for additional diagnostic and therapeutic procedures. Today, patients can receive prompt and expert medical care at home in comfortable settings. Due to the unique nature of these services, it is essential to verify patient healthcare data, as it contains a greater amount of personal information that is vulnerable to privacy violations and data breaches. Blockchain technology has attracted interest in addressing security concerns due to its decentralized, immutable, shared, and distributed characteristics. This study proposes lightweight dynamic blockchain-enabled encryption schemes to secure physiological data during authentication and exchange processes. The proposed scheme introduces the logistic Advanced Encryption Scheme (AES) that combines chaotic logistic maps to secure the data in the blockchain network and mitigate different attacks. The model was deployed on the Ethereum blockchain and performance metrics, such as computation and transaction time, were calculated and compared with other current blockchain-inspired encryption models. Furthermore, the NIST test was conducted to prove the strength of the proposed scheme. The proposed model exhibits high security and a shorter transaction time (0.964 s) than other existing schemes. Finally, the proposed model generates high-dynamic keys that are suitable for defending against unpredictable attacks on blockchain.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7390
Issue No: Vol. 14, No. 4 (2024)
- Compressive Strength Analysis of Renewable Mortar after Portland Cement
Replacement with Waste Ash
Authors: Muhammad Syarif, Abdul Rakhim Nanda, . Nurnawaty, Hamzah Al Imran, Nenny Karim, Andi Yusri
Pages: 15056 - 15061
Abstract: There are many environmental problems caused by factory waste. Sugar factory waste, in the form of bagasse ash, and PLTU factory waste, in the form of fly ash, are currently in the spotlight of science studies. This study used waste bagasse and fly ash to substitute Portland cement as the main ingredients for mortar. Baggash and fly ash waste were collected, processed, and then used to replace cement by up to 40% to determine to what extent they could be used in brick masonry work, wall plastering, and masonry paste. Experimental tests were carried out on mortar cube samples measuring 5×5×5 cm, comparing four types of samples consisting of Portland cement, bagasse ash, and fly ash. The compressive strength results were obtained after 28 days. Normal Mortar (MN=11.75 MPa) had higher compressive strength than the substitute mortar types MA (3.23 MPa), MB (3.09 MPa), and MC (2.98) MPa. According to SNI 6882-2014, MA, MB, and MC mortars can be used as O-type mortar (2.4 MPa). Therefore, they can be applied to wall plastering or walls not bearing loads.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7489
Issue No: Vol. 14, No. 4 (2024)
- Detection of Unsafe Behavior in conveying Vehicle Parts using Computer
Vision
Authors: Carlos Eduardo Vazquez-Monjaras, Leonor Adriana Cárdenas-Robledo, Carolina Reta
Pages: 15062 - 15067
Abstract: Deep Learning (DL) has experienced notable growth in various applications, which highlights its use in vision systems for object detection. The present work proposes a proof of concept for detecting unsafe acts in a vehicle assembly plant. The employment of Convolutional Neural Networks (CNNs) for either object or event detection was studied, and a vision system specifically trained for real-time detection of unsafe acts carried out by personnel while conveying car body parts was implemented. The intention of this research is to prevent workplace accidents and promote safety in the production environment by creating a personalized dataset composed of images that capture some incorrect ways of loading the car body doors, labeled as unsafe acts. For this purpose, a YOLOv8 DL model was trained to recognize unsafe behaviors, and after the test execution, the system efficiently identified safe and unsafe acts. Therefore, the proposal is feasible to be deployed to improve surveillance in daily operations, deliver automated reports for decision-making, and establish countermeasure actions.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7530
Issue No: Vol. 14, No. 4 (2024)
- A Case Study of Surface Roughness Improvement for C40 Carbon Steel and 201
Stainless Steel using Ultrasonic Assisted Vibration in Cutting Speed
Direction
Authors: Thanh Trung Nguyen, Truong Cong Tuan, Toan Thang Vu
Pages: 15068 - 15073
Abstract: The surface roughness of mechanical parts plays an important role in evaluating the machining performance. However, achieving fine surface finishes on small-diameter shafts through traditional lathes poses challenges due to low cutting speed and workpiece stiffness. To address this issue, in the present work, we applied ultrasonic-assisted vibration aligned with the cutting speed direction to enhance the turning process of small shafts made of C40 Carbon steel or 201 stainless steel. The workpieces were machined by Ultrasonic Assisted Turning (UAT) at three different cutting speeds, ranging from 15 to 36 m/min, while maintaining a constant feed rate and depth of cut. To facilitate comparison with conventional turning (CT), the cutting parameters remained consistent, and both methods were performed for the same duration. UAT necessitates the use of a specialized turning inserts’ fixture known as a horn to transmit ultrasonic vibrations from the generator to the tooltip. This study also presents the design methodology and the performance evaluation of the horn. Surface roughness was assessed using the arithmetical mean height, Ra. In UAT, the roughness Ra exhibited the most significant reduction for C40 Carbon steel, reaching a decrease of 308% at a cutting speed of 15 m/min, whereas for 201 stainless steel, Ra did not vary by more than 23% across different cutting speeds.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7552
Issue No: Vol. 14, No. 4 (2024)
- Cyberatttack Detection and Classification in IIoT systems using XGBoost
and Gaussian Naïve Bayes: A Comparative Study
Authors: Mordi Alenazi, Shailendra Mishra
Pages: 15074 - 15082
Abstract: The Industrial Internet of Things (IIoT) is experiencing rapid expansion, forming a vast network of interconnected devices, sensors, and machines that generate large volumes of data. In the context of Industry 5.0, ensuring the accuracy and reliability of this data is essential. This paper addresses the challenges of detecting and classifying cyberattacks within the IIoT by employing advanced analytical techniques. Specifically, we explore the application of Machine Learning (ML) algorithms, focusing on the comparison between the XGBoost and Naïve Bayes models. Our study uses the KDD-99 and NSL KDD datasets to evaluate the performance of these models in terms of accuracy, precision, recall, and F1 score. The results demonstrate that the XGBoost model significantly outperforms the Naïve Bayes model across all metrics, achieving an accuracy of 99%. This study contributes to the improvement of intrusion detection and classification of cyberattacks in IIoT environments.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7664
Issue No: Vol. 14, No. 4 (2024)
- Synthesis of an Orbit Tracking Controller for a 2DOF Helicopter based on
Sequential Manifolds with Stabilization Time in the Presence of
Disturbances
Authors: Nguyen Xuan Chiem, Le Tran Thang
Pages: 15083 - 15089
Abstract: This study presents the design of a controller for a two-degree-of-freedom (2-DOF) helicopter based on sequential invariant manifolds with exponential convergence. The system is decomposed into two subsystems for pitch and yaw angles, and exponentially stable manifolds are constructed for each subsystem. The control law is found based on sequential manifolds and the Analytical Design of Aggregated Regulators (ADAR) method. The controller is designed to increase the system's stability against disturbances while ensuring stability over a finite period of time. The response time of the system can be evaluated in advance through the parameters of the designed manifold. The robustness of the control law for external disturbances was proven using the Lyapunov function in the design process. Finally, the effectiveness of the proposed controller based on the synergetic control theory is demonstrated by numerical simulation results and a comparison with the backstepping controller.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7512
Issue No: Vol. 14, No. 4 (2024)
- Investigating the Impact of Leadership Quality and Educational Practices
on Student Outcomes through Teacher Attitude and Behavior in Pakistani
Educational Institutions: An Applied Science Perspective
Authors: Wei Xiang, Muhammad Rizwan Ullah
Pages: 15090 - 15098
Abstract: The present study stresses how leadership quality and educational practices affect student outcomes through the way teacher attitudes and behavior are expressed in Pakistani educational institutions. The quantitative data for this study were collected via random sampling from investors in different cities of Pakistan. A simple random sampling technique was adopted recruiting 1000 teachers from different levels of educational institutions in various areas of Pakistan for data analysis. Therefore, the study considered several factors to ensure that a representative sample, which reflects the large population of Pakistan’s teachers, was taken, allowing for more accurate generalizations based on the study findings. The Partial Least Squares (PLS) method was employed to analyze the data gathered for this study. The latter concluded that educational practices and leadership grades play a significant role in improving student learning. It emphasized the relationship between educational institution management, teachers’ mindsets and actions, and student learning results. These relationships can enhance educational outcomes, leadership development, and teacher preparation. Implications include policy considerations, teacher pedagogical training, leadership skills, and pursuing a more student-centered and holistic educational approach.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7690
Issue No: Vol. 14, No. 4 (2024)
- A Hybrid Genetic Algorithm Approach based on Patient Classification to
Optimize Home Health Care Scheduling and Routing
Authors: Radhia Zaghdoud, Olfa Ben Rhaiem, Marwa Amara, Khaled Mesghouni, Shahad Galet
Pages: 15099 - 15105
Abstract: This study aims to solve the multi-objective problem of home healthcare scheduling and routing. The former’s objectives are to upgrade the travel distance, the workload balance, and the waiting time of caregivers. A novel approach was proposed based on patient and caregiver clustering with the K-means++ algorithm in the first step and a hybrid genetic algorithm to optimize the global operation in the second step. The problem was solved regarding the deterministic and the uncertain aspect. The uncertain parameter investigated is the number of patients. A numeric study was conducted to prove the performance of the recommended approach using the Solomon Benchmark.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7649
Issue No: Vol. 14, No. 4 (2024)
- Development of a Novel Backup Fault Protection Algorithm for Low-Voltage
DC Microgrids based on Local Measurements and Chi-square Statistics
Authors: Duong Minh Bui, Duy Phuc Le, Hieu Minh Nguyen
Pages: 15106 - 15120
Abstract: A direct-current microgrid (MG) can be susceptible to extremely high fault currents contributed by the output filter capacitors of power converters and can also face protection challenges because of the non-zero crossing of fault currents. In a Low-Voltage Direct Current (LVDC) MG, low-fault-tolerance converters such as boost converters and bidirectional converters mostly require a fast and adaptable fault protection scheme that can detect and clear quickly faults irrespective of a wide range of fault impedances in the system. Several current- and voltage-derivative-based protection methods with communication support have been developed to primarily protect DC MGs due to their high sensitivity and selectivity. Over-current and under-voltage-based protection schemes are mostly suggested as backup protections for the DC MGs. To accurately detect and rapidly clear the faults even in the case of communication failure from the primary protection, this paper proposes a novel backup fault protection scheme with high selectivity, adaptability, and scalability for islanded LVDC MGs based on local measurements along with Chi-square-distribution-based statistics. Specifically, this developed backup protection not only applies a cumulative summation methodology for the locally measured signals to extract derivative and integral characteristics of the current and voltage, but also uses the Chi-square-distribution-based statistics to consistently calculate tripping thresholds for the effective detection of different fault events in the LVDC MG, regardless of variable fault resistances and the communication-link damage. As a result, the proposed backup protection is capable of accurately detecting various DC faults to secondarily protect the source and load branches of the system within the expected time frame of a few milliseconds and has been validated through multiple staged fault tests from an off-grid and ungrounded 1kW and 48VDC MG testbed.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7022
Issue No: Vol. 14, No. 4 (2024)
- The Effect of Technological Innovation and Knowledge Management Process on
Organisational Agility: A Systematic Literature Review
Authors: Saleh Mohammed Yousef Obaid Alkaabi, Nor Suzylah Binti Sohaimi, Aminurraasyid Bin Yatiban
Pages: 15121 - 15126
Abstract: Organizational agility has become essential and its importance has increased after COVID-19. There are inconsistent findings regarding the factors that affect organizational agility. This study focuses on the effect of technological innovation and the knowledge management process by reviewing the literature related to these variables. Three databases, Scopus, Web of Science (WoS), and Google Scholar, were used using certain search keywords, and a total of 30 articles were identified between 2010 and 2022 and reviewed. The findings showed that the number of articles has increased sharply during and after the COVID-19 pandemic. However, the use of theories to explain organizational agility is still emerging, with the resource-based view, the dynamic capability, and the knowledge-based view being the most used theories. The sample size is increasing to meet the structural equation modeling requirements. The effect of technological innovation and the knowledge management process is positive in most studies. More studies are needed to examine organizational agility as a dependent variable in different countries, contexts, and industries. In addition, future studies should examine other moderating variables in this context.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7691
Issue No: Vol. 14, No. 4 (2024)
- Test Case Generation Approach for Android Applications using Reinforcement
Learning
Authors: Asmau Usman, Moussa Mahamat Boukar, Muhammed Aliyu Suleiman, Ibrahim Anka Salihu
Pages: 15127 - 15132
Abstract: Mobile applications can recognize their computational setting and adjust and respond to actions in the context. This is known as context-aware computing. Testing context-aware applications is difficult due to their dynamic nature, as the context is constantly changing. Most mobile testing tools and approaches focus only on GUI events, adding to the deficient coverage of applications throughout testing. Generating test cases for various context events in Android applications can be achieved using reinforcement learning algorithms. This study proposes an approach for generating Android application test cases based on Expected State-Action-Reward-State-Action (E-SARSA), considering GUI and context events for effective testing. The proposed method was experimentally evaluated on eight Android applications, showing 48-96% line of code coverage across them, which was higher than Q-testing and SARSA.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7422
Issue No: Vol. 14, No. 4 (2024)
- Optimizing Quantum Key Distribution Protocols using Decoy State Techniques
and Experimental Validation
Authors: Sellami Ali, Benlahcene Djaouida
Pages: 15133 - 15140
Abstract: This paper simulated the operation of vacuum state and single decoy state protocols in the BB84 and SARG04 QKD schemes by utilizing the features of the commercial ID-3000 QKD system. Numerical modeling identified an optimal signal-to-decoy state ratio of 0.95:0.05 and an intensity of μ=0.85 for the signal state and ν1=0.05 for the decoy state, ensuring the highest key generation rate and a secure distance of up to 50 km. These protocols were validated experimentally over various transmission distances with standard telecom fiber, using the ID-3000 QKD system in a conventional bi-directional plug-and-play setup. Simulations predicted secure key rates of 1.2 × 10 5 bits/s for SARG04 and 8.5 × 104 bits/s for BB84 at 10 km, with secure distances of 45 km and 35 km, respectively. The experimental results confirmed these predictions, showing a 30% higher key rate and 20% longer secure distance compared to non-decoy methods. The SARG04 protocol surpassed BB84 in key rate and secure distance, highlighting the two-photon component's role in key generation. This study concludes that the decoy-state method significantly enhances key generation rates and secure distances, optimizing QKD protocols for secure quantum communication.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7521
Issue No: Vol. 14, No. 4 (2024)
- Flatness-based Motion Planning and Model Predictive Control of Industrial
Cranes
Authors: Hoa Bui Thi Khanh, Mai Hoang Thi, Luu Thi Hue, Tung Lam Nguyen, Danh Huy Nguyen
Pages: 15141 - 15148
Abstract: This study develops a new controller for an industrial crane system in a three-dimensional space. First, the dynamic model of the industrial crane system with two subsystems, the tower crane and the overhead crane is presented. A bidirectional mapping is established between the system's input and output, allowing for efficient trajectory generation. Additionally, the design process explicitly considers the system's kinematic constraints, ensuring safe and feasible motions. This designed trajectory serves as an input for Model Predictive Control (MPC). The MPC is designed with the dual objectives of trajectory tracking and payload anti-swing. Finally, simulations are conducted and the results are compared with those of other control strategies under different cases to demonstrate the effectiveness of the proposed method.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7662
Issue No: Vol. 14, No. 4 (2024)
- Assessment of Groundwater Quality and Heavy Metal Contamination in Rural
Areas of Duhok City, Iraq
Authors: Sarmad Salim, Rangeen Shihab Mohammed, Idrees Majeed Kareem
Pages: 15149 - 15153
Abstract: This study investigates the quality of groundwater sources in the rural areas of Duhok City, Iraq, with a particular focus on heavy metal contamination, chemical composition, and properties of water. Water samples from 14 wells, serving as the main water source for the surrounding areas, were collected and analyzed. Water quality parameters including calcium (Ca²⁺), magnesium (Mg²⁺), chloride (Cl⁻), potassium (K⁺), and sulfate (SO₄²⁻) values ranged from 24 to 105.6 mg/L, 6.832 to 50.752 mg/L, 18 to 34 mg/L, 1 to 7 mg/L, and 3.4 to 38 mg/L respectively. Common heavy metals like manganese (Mn), lead (Pb), copper (Cu), and cobalt (Co) exhibited varying concentrations. Most parameters meet the WHO standards, except for the elevated potassium in one sample, requiring attention. Additionally, 50% of the sampled wells showed elevated cadmium (Cd) levels. Possible sources of contamination include industrial activities, agricultural runoff, and geological factors, highlighting the importance of ongoing monitoring and targeted interventions to ensure access to clean and safe water.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7501
Issue No: Vol. 14, No. 4 (2024)
- A Techno-Economic Feasibility Study of Electricity and Hydrogen Production
in Hybrid Solar-Wind Energy Park. The Case Study of Tunisian Sahel
Authors: Slah Farhani, El Manaa Barhoumi, Haytham Grissa, Mohamed Ouda, Faouzi Bacha
Pages: 15154 - 15160
Abstract: This paper provides a comprehensive analysis of the potential for integrating renewable energy sources to meet the growing electricity and hydrogen demand in the Tunisian Sahel region, focusing particularly on solar and wind energies. The feasibility of installing a hybrid solar-wind energy system capable of producing both electricity and hydrogen is evaluated. With the help of the available solar and wind resources combined, the system not only generates electric power, but also produces hydrogen gas through electrolyzation, hence offering a multipurpose solution in terms of storage and supply. This flexibility is crucial due to the variability of renewable resources, which change daily and seasonally. The paper outlines the optimization process for designing the hybrid system deploying HOMER Pro software, according to local climatic conditions and demand profiles. The economic analysis reveals that the system can produce an average of 101.8 kg of hydrogen daily with a total photovoltaic capacity of 3,000 kWp, resulting in a project net cost estimation of approximately 5,494,912 euros. This analysis provides valuable insights for stakeholders considering similar projects, including the costs associated with photovoltaic systems, electrolyzers, and hydrogen storage solutions.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7394
Issue No: Vol. 14, No. 4 (2024)
- Development of an Ankle Sensor for Ground Reaction Force Measurement in
Intelligent Prosthesis
Authors: Ali Ihsan Bulbul, Umut Mayetin, Serdar Kucuk
Pages: 15161 - 15170
Abstract: In this study, a new low-cost, three-degree-of-freedom force sensor is developed to measure Ground Reaction Forces (GRFs) and to be used in the safe control of active transfemoral prosthesis. Initially, the proposed sensor was designed with the Finite Element Method (FEM). Then, the sensor's control board was developed to include an electronic circuit with its microcontroller module, four load cell amplifiers, and an orientation sensor. A test platform was also developed to conduct the sensor tests. To test the accuracy of the results obtained from the developed test platform, the same tests were also carried out with a commercial sensor and similar results were obtained, thus proving that the sensor is suitable for use in prosthetics.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7430
Issue No: Vol. 14, No. 4 (2024)
- Optimizing the Location and Capacity of DGs and SOPs in Distribution
Networks using an Improved Artificial Bee Colony Algorithm
Authors: Nguyen Tung Linh, Pham Vu Long
Pages: 15171 - 15179
Abstract: This study proposes an improved method of the Artificial Bee Colony (ABC) algorithm for the distribution network in scenarios where distributed generation sources and Soft Open Points (SOPs) are connected to optimize power control. Improvement is achieved by integrating the ABC algorithm with the Grenade Explosion Method and Cauchy to accelerate the ABC algorithm's speed. The objective function is considered to reduce power losses over a day. The proposed method was tested on the IEEE-33 bus test system under various scenarios: Case 1 with 3 DGs installed, Case 2 with 3 DGs and 1 SOP simultaneously installed in the distribution network, and Case 3 having the same configuration as Case 2 but operating for 24 hours. In addition to reducing power losses, the voltage at the nodes in the distribution grid was also improved, maintained above 0.95 pu and close to 1 pu. Case 3 showed that integrating a Wind Turbine (WT), two Photovoltaic (PV) generators, and one SOP during operation resulted in the lowest energy losses, smaller than a system with only one WT and two PVs, and significantly lower than the baseline system without any DGs and SOPs. Therefore, employing SOPs in a distribution network with integrated DGs can offer significant benefits in reducing energy losses.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7665
Issue No: Vol. 14, No. 4 (2024)
- Finite Element Modeling for Flexible Pavement Behavior under Repeated Axle
Load
Authors: Zainab M. Aljaleel, Nahla Yasoub, Yahya K. H. Atemim
Pages: 15180 - 15186
Abstract: Accurate assessment of flexible pavement behavior requires a computational model that is able to predict the permanent deformation of the pavement under heavy load and its response with different thicknesses. This study developed several realistic models using advanced Finite Element Analysis (FEA) techniques employing the ABAQUS/CAE finite element program. The model integrates measured tire pavement contact stresses, moving wheel loads, and the viscoelastic properties of the asphalt layer. The model undergoes fine-tuning through the utilization of implicit dynamic analysis and variance in thickness. The simulations demonstrate that the viscoelastic behavior is more susceptible to changes in thickness. Furthermore, variation in thicknesses showed different pavement and rut depth behavior. The thinner the thickness is, the less resistance is applied to loading pressure and when the number of load repetitions increases, the depth of the rut also increases, leading to permanent deformation and consolidation with each passage of a heavy vehicle.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7505
Issue No: Vol. 14, No. 4 (2024)
- NIST CSF-2.0 Compliant GPU Shader Execution
Authors: Nelson Lungu, Ahmad Abdulqadir Al Rababah, Bibhuti Bhusan Dash, Asif Hassan Syed, Lalbihari Barik, Suchismita Rout, Simon Tembo, Charles Lubobya, Sudhansu Shekhar Patra
Pages: 15187 - 15193
Abstract: This article introduces a mechanism for ensuring trusted GPU shader execution that adheres to the NIST Cybersecurity Framework (CSF) 2.0 standard. The CSF is a set of best practices for reducing cybersecurity risks. We focus on the CSF’s identification, protection, detection, and response mechanisms for GPU-specific security. To this end, we exploit recent advancements in side-channel analysis and hardware-assisted security for the real-time and introspective monitoring of shader execution. We prototype our solution and measure its performance across different GPU platforms. The evaluation results demonstrate the effectiveness of the proposed mechanism in detecting anomalous shader behaviors that only incur modest overhead at runtime. Integrating the CSF 2.0 principles into the proposed GPU shader pipeline leads to an organizational recipe for securing heterogeneous computing resources.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7351
Issue No: Vol. 14, No. 4 (2024)
- Enhancing Image SEO using Deep Learning Algorithms: A Research Approach
Authors: Marwa Amara, Radhia Zaghdoud, Olfa Ben Rhaiem, Ebtihal Althubiti
Pages: 15194 - 15200
Abstract: Visual content influences profoundly user interactions and search engine rankings. Traditional SEO techniques, while foundational, often fall short in the swiftly changing search landscape. This paper presents a cutting-edge solution that leverages the power of deep learning and generative AI to redefine image SEO. Through the innovative integration of the AWS Rekognition's image analysis and ChatGPT's advanced natural language processing, we automate and refine the creation of alt-texts, ensuring they are not only precise, but also SEO-enriched. The result is a harmonious blend of technology and strategy that significantly boosts online content discoverability. Using a specific website as a test environment, the proposed methodology demonstrated a profound impact on SEO performance. Initially, no images on the webpage had alt-texts. After applying the AI-driven approach, all images were equipped with descriptive, SEO-optimized alt attributes, markedly improving their visibility in search engine results. These results underscore the effectiveness of integrating advanced AI technologies in SEO strategies, providing a scalable and effective framework for enhancing digital content accessibility and search engine rankings.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7473
Issue No: Vol. 14, No. 4 (2024)
- Analyzing Safety Management Practices affecting Safety Performance in the
Electrical Industry: A Systematic Review
Authors: Omar Munaf Tawfeeq, Sivadass A. L. Thiruchelvam, Izham Bin Zainal Abidin
Pages: 15201 - 15208
Abstract: Workplace accidents can be avoided through meticulous planning, systematic organization, and through evaluating the efficacy of implemented control measures. Acquiring high standards in a safety management system can be achieved using models that facilitate the execution of proactive steps to mitigate work-related risks. This study aimed to ascertain the presence of effective safety practices throughout the implementation of safety performance measures. A systematic literature review was carried out following the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Thirty articles in English, published between 2010 and 2020, were identified by a comprehensive search in the Scopus, Science Direct, and Web of Science databases, based on certain criteria. The results demonstrated the efficacy of safety management practices and their impact on safety performance. The predominant focus of the evaluations was procedural safety, with relatively little emphasis placed on the elements of human safety that were significantly overlooked.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7569
Issue No: Vol. 14, No. 4 (2024)
- Detection of QR Code-based Cyberattacks using a Lightweight Deep Learning
Model
Authors: Mousa Sarkhi, Shailendra Mishra
Pages: 15209 - 15216
Abstract: Traditional intrusion detection systems rely on known patterns and irregularities. This study proposes an approach to reinforce security measures on QR codes used for marketing and identification. The former investigates the use of a lightweight Deep Learning (DL) model to detect cyberattacks embedded in QR codes. A model that classifies QR codes into three categories: normal, phishing, and malware, is proposed. The model achieves high precision and F1 scores for normal and phishing codes (Class 0 and 1), indicating accurate identification. However, the model's recall for malware (Class 2) is lower, suggesting potential missed detections in this category. This stresses the need for further exploration of techniques to improve the detection of malware QR codes. Despite the particular limitation, the overall accuracy of the model remains impressive at 99%, demonstrating its effectiveness in distinguishing normal and phishing codes from potentially malicious ones.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7777
Issue No: Vol. 14, No. 4 (2024)
- The Impact of E-Marketing on the Preference to Purchase Hybrid Cars by
Increasing Awareness: An Empirical Study of Hybrid Car Users in the
Northern Border Region, Saudi Arabia
Authors: Jamal Ali Arous, Bilal Louail, Samar Ahmed El Rabbat, Nadya Ali Hima, Shaima Ahmad Barakat
Pages: 15217 - 15225
Abstract: This research examines the impact of e-marketing on consumers' preference to purchase hybrid cars, through the conduction of a field study on the hybrid car users in the Northern Border Region in Saudi Arabia. The research aims to comprehend the relationship between e-marketing campaigns and increasing consumer awareness of the benefits of hybrid cars, and how their decisions are affected by this awareness when purchasing cars. The research aims to shed light on the role of e-marketing in encouraging the use of hybrid cars and identifying the factors influencing consumers' decisions in this context. The research aims to identify factors influencing users' responses to such information, including advertising campaigns, environmental attitudes, future orientations, and governmental incentives. A survey was conducted to a total of 385 hybrid car users and data analysis relied on structural equation modeling deploying the Amos software. The results indicate the existence of a relationship and impact between e-marketing campaigns and user awareness, leading to an increased preference for hybrid cars.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7668
Issue No: Vol. 14, No. 4 (2024)
- Effects of High Loss on Ignition Fly Ash as a Partial Replacement for Sand
on the Properties of Mortars
Authors: Nguyen Thi Bich Thuy, Somnuk Tangtermsirikul, Tran Van Mien, Bui Anh Kiet
Pages: 15226 - 15232
Abstract: This study investigates the effects of fly ash with a high unburned carbon content as a partial sand replacement material on the properties of mortars. Fly ash was added to the mortar in four different ratios: 0, 10, 30, and 50% by volume. This study examined two series of mortar mix proportions, one having a controlled water-to-cement ratio and the other having a controlled flow. The fresh properties and compressive strength of the mortars were investigated. The experimental results showed that the compressive strength increased with increasing fly ash-to-aggregate ratio (FA/A). However, the increase in FA/A resulted in higher water requirements, lower flow, and longer setting times. The calcium hydroxide content and total porosity of the mortars were also examined to support the results of the compressive strength tests. Based on the results, the FA/A ratio had a significant impact on the fresh properties and compressive strength of the mortars. To ensure fresh properties and compressive strength of mortars, a fly ash-to-sand ratio of up to 30% is recommended.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7782
Issue No: Vol. 14, No. 4 (2024)
- Modeling and Predicting Steam Power Plant Condenser Vacuum based on
Small-sized Operation Data
Authors: Aqli Mursadin, Andi Yuwenda Iriyanto
Pages: 15233 - 15238
Abstract: The condenser vacuum is an important variable in steam power plants. Monitoring and controlling this variable requires predicting its behavior. This paper develops further Autoregressive-Generalized Autoregressive Conditional Heteroscedasticity (AR-GARCH) models for this purpose, using lagged values of predictors. The predictors include the inlet temperature of the condenser cooling water and the active power of the generator. Models can be adequately trained with small-sized data, making them suitable for use in thermal plants, which are often regularly maintained with operating conditions being reset, rendering past data obsolete. Training and testing were carried out using operation data from an actual steam power plant generating unit during a period in which it faced the prospect of an emergency turbine shutdown. When the models pass all the required statistical tests, they tend to outperform other techniques, including autoregressive neural networks and support vector regression, in terms of prediction. This study also discusses an implementation scenario. The choice of training sizes and model variants can be flexible, enhancing the models' practicality for real operational situations. This study also provides additional directions for further research.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7574
Issue No: Vol. 14, No. 4 (2024)
- Vehicle-to-Home: Implementation and Design of an Intelligent Home Energy
Management System that uses Renewable Energy
Authors: Hanadi Alkhudhayr, Alanoud Subahi
Pages: 15239 - 15250
Abstract: Using energy storage technology, such as batteries and electric vehicles, is crucial in combating energy shortages. Wind turbines and solar panels are two prominent alternative energy sources. This study examines the impact of Vehicle-to-Home (V2H) technology, specifically during the hours when solar radiation is at its highest. V2H enables electric car batteries to be a primary solution for home energy needs. An accurate scheduling system has been established to improve the organization's energy sustainability. The proposed algorithm effectively controls the allocation, availability, and retention of energy transported by electric vehicles (EVs). The model incorporates constraints to ensure that the family's electricity needs are met regardless of the prevailing weather conditions, whether sunny or cloudy. The Intelligent Home Energy Management System (IHEMS) is being developed to regulate energy use efficiently across different applications and sources. A multi-agent system (MAS) is used to improve operational efficiency and effectively meet the energy needs of devices in the system. An experimental database in Saudi Arabia examines and monitors production costs and energy consumption, considering weather conditions and equipment utilization. The results demonstrate the great potential of V2H technology as a practical storage option that efficiently addresses energy shortages.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7273
Issue No: Vol. 14, No. 4 (2024)
- Emulation Structures and Control of Wind-Tidal Turbine Hybrid Systems for
Saudi Arabia Off-shore Development
Authors: Abdulaziz Alanazi, Ezzeddine Touti, Cristian Nichita, Ashglaf Mohamed
Pages: 15251 - 15256
Abstract: This paper presents the principles of developing an electromechanical emulator based on an original hybridization concept of a wind and tidal power system. Wind and tidal horizontal axis turbines showcase functional similarities and electromechanical coupling possibility. Tidal concepts are very close to those of wind power. Tidal turbine technology should thus reach maturity more quickly because it is possible for it to rely on a certain number of reliable and proven techniques developed for wind power. The proposed hybrid wind – tidal turbine system is electromechanically coupled on the axis of rotation of a single and common electric generator. An experimental simulation of the hybrid wind-tidal turbine system was carried out, using a developed architecture of an emulator system. The results are both numerical simulations carried out in the MATLAB/Simulink environment and tests obtained employing real-time emulators.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7800
Issue No: Vol. 14, No. 4 (2024)
- Evaluating the Ultimate Performance of Pylon-Head Joints with Numerical
Analysis
Authors: Wael A. Salah, Moustafa S. Darweesh
Pages: 15257 - 15261
Abstract: This study presents a comprehensive Finite Element (FE) model of the multiple-cable-to-pylon head joint within a specific cable roof structure. The analysis focuses on the upper part of the pylon substructure, particularly the pylon head joint, to examine its localized behavior under a set of internal forces derived from a simplified FE model. The steel tubular components of the pylon substructure were precisely simulated using thin shell elements. The designers of this particular roof structure proposed two solutions for reinforcing the pylon-head joint, while an additional novel strengthening technique was introduced, aimed at enhancing the joint's performance. These three strengthening methods, along with the original design joint, were modeled numerically, and the joint's effectiveness was assessed. The findings of the analysis indicate that the newly proposed strengthening technique exhibits greater potential for stiffening the considered pylon-head joint compared to the other introduced solutions. The study concludes with significant insights relevant to practical applications.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7652
Issue No: Vol. 14, No. 4 (2024)
- Optimizing Sliding Mode Controller in a DC Microgrid with Variant Constant
Power Loads
Authors: Ameen M. Al-Modaffer, ِAmer A. Chlaihawi, Dhulfiqar M. Shabeeb
Pages: 15262 - 15267
Abstract: The optimization of a suitable controlling method is a priority in running any DC/DC boost converter effectively. However, a problem may arise as the occurring oscillations in the microgrid caused by the incremental negative resistance of the Constant Power Poad (CPL) variation may lead to system instability. In order to tackle this intrinsic problem, three proposed Sliding Mode Control (SMC) methods were simulated and examined against multiple variations of CPL in MatLab/Simulink. Integral Sliding Mode Control (ISMC) and Two-variable Sliding Mode Control (TSMC) methods showed a better system performance than the Low Pass Filter SMC (LPFSMC) in terms of stability of output voltage in both steady state and transient conditions. The output voltages of ISMC and TSMC had a margin of error of approximately 1 V in the steady-state response and a minor overshoot of less than 1% in the transient response. The steady-state output voltage when using LPFSMC showed approximately 3 V of error and the transient state had a noticeable overshoot near 3%. However, all three controlling methods had a similar efficiency of around 98%. The outstanding robustness of ISMC exhibited the highest voltage stability with the lowest chattering in both steady state and transient responses through the compensation of adequate current to satisfy the CPL requirement.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7694
Issue No: Vol. 14, No. 4 (2024)
- Phase Field Modeling of Crack Propagation in Concrete Composite with
Imperfect Interface
Authors: Gia-Khuyen Le, Hoang-Quan Nguyen, Tien-Dung Nguyen
Pages: 15268 - 15273
Abstract: In this study, a phase-field model with imperfect interface is developed to simulate the crack behavior of concrete at the mesoscale level. Concrete is treated as a biphasic material, comprising aggregates, a cementitious matrix, and interfaces between them, which are characterized using a level set function. Both cracks and interfaces are represented in a smeared sense by scalar fields ranging from 0 to 1. On the other hand, the displacement jump at the interface is described by an auxiliary field over the entire domain. This model effectively captures the complex crack patterns in concrete, including debonding cracks and bulk cracks. Furthermore, the results show that a strong interface can significantly enhance the mechanical performance of the material.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7881
Issue No: Vol. 14, No. 4 (2024)
- Numerical Simulation of Gradually Varying Permanent Flows in a Prismatic
Open Channel for Four Geometric Shapes
Authors: Abderrahmane Benabid, Mazouz Badis, Fourar Ali, Mansouri Tarek, Mohammed Saadi
Pages: 15274 - 15282
Abstract: Standing flows in natural channels often cause phenomena that can be very serious, such as flooding, deformation of channel geometry, and destruction of infrastructure (dams, bridges, and culverts). This study focuses on the computation of gradually varying permanent flows (backwater curves) by two methods: direct integration (Chow) and successive approximation (depth variation). To solve the system of equations governing the problem of gradually varying one-dimensional stationary flows at a free surface, a large amount of data should be taken into account, namely, the flow rate, the water head, the mean flow velocity, the rugosity, and the slope. These parameters are very important, as they cause nonlinear behavior, making the problem and its mathematical solution complex. Digitizing these parameters can help to determine and visualize the longitudinal profile of the water line for known flow rates. This study aimed to: (1) determine the influence of rugosity on gradually varying steady flows and the overclassification of eddy curves in a prismatic channel, (2) study the effect of geometric shape on these flows, and (3) investigate and compare the effects of the calculation methods. The results reveal the great influence of rugosity on gradually varying permanent flows for four selected geometric shapes of the channel, as it has a direct influence on the normal depth and the critical slope. Each time the resistance of the bottom to the flow increases, these results increase. The influence of the geometric shape on these flows is less significant. The comparative study showed a difference between the results obtained.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7715
Issue No: Vol. 14, No. 4 (2024)
- Stern Flow Hydrodynamics around a Self-propelled Maneuvering VLCC Ship
Authors: Oana Marcu, Elena-Gratiela Robe-Voinea
Pages: 15283 - 15290
Abstract: The present research explores the stern flow hydrodynamics around a maneuvering ship. Utilizing Computational Fluid Dynamics (CFD) techniques, several flow scenarios including different drift angles and propulsion configurations are modeled for the benchmark ship KRISO Very Large Crude Carrier 2 (KVLCC2). The analysis depicts all vortical structures that appear in the propeller operating area, explaining their formation and evolution. Also, the mutual interactions between the turbulent flow and the propulsion unit are observed and examined. The detailed outcome is intended to provide valuable insights for both new ship design and retrofits, aiming to advance new and sustainable engineering practices.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7624
Issue No: Vol. 14, No. 4 (2024)
- A Secure and Reliable Framework for Explainable Artificial Intelligence
(XAI) in Smart City Applications
Authors: Mohammad Algarni, Shailendra Mishra
Pages: 15291 - 15296
Abstract: Living in a smart city has many advantages, such as improved waste and water management, access to quality healthcare facilities, effective and safe transportation systems, and personal protection. Explainable AI (XAI) is called a system that is capable of providing explanations for its judgments or predictions. This term describes a model, its expected impacts, and any potential biases that may be present. XAI tools and frameworks can aid in comprehending and trusting the output and outcomes generated by machine-learning algorithms. This study used XAI methods to classify cities based on smart city metrics. The logistic regression method with LIME achieved perfect accuracy, precision, recall, and F1-score, predicting correctly all cases.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7676
Issue No: Vol. 14, No. 4 (2024)
- A Review of the Surface Roughness Prediction Methods in Finishing
Machining
Authors: Van-Long Trinh
Pages: 15297 - 15304
Abstract: The desired Surface Roughness (SR) can be achieved via general machining methods by using a cutting tool to remove a material layer on the workpiece surface. Cutting Parameters (CP), cutting tool properties, and workpiece properties must be considered. The finishing machining methods that can be applied to produce the desired SR are turning, milling, grinding, boring, and polishing. The technological parameters must be tightly combined in the Machining Process (MP). The CP selection presents some issues regarding time, cost, and practical skill when considering different cutting methods, cutting tools, and workpiece materials. SR predicting methods of machined parts have the advantages of shortening the time of CP selection, reducing machining cost, and bringing the desired SR. This paper reviews the recent methods followed in predicting the SR of the MPs. The SR prediction methods will bring many benefits for MP, such as improved SR, reduced cost, improved cutting conditions, and enhanced quality.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7710
Issue No: Vol. 14, No. 4 (2024)
- Structural Behavior of Reinforced Concrete Flat Plates Strengthened by
Horizontal Reinforcement
Authors: Ali N. Ameen, Mohannad H. Al-Sherrawi
Pages: 15305 - 15311
Abstract: Flat plate structures consist of a slab supported directly by columns without beams or drop panels, resulting in a thinner slab with more efficient use of space. Despite these advantages, a flat plate slab is subjected to brittle punching shear. Sudden collapse may occur when a column pushes a piece of concrete from the slab above it. This paper displays Finite Element Analysis (FEA) using ABAQUS/ CAE 2019 to simulate the punching shear impact on a flat plate strengthened with horizontal steel bars of varying number and diameter, located at the compressive side of the slab. A numerical model was constructed with 8-noded hexahedral 3D brick elements for concrete and 2-noded linear 2D beam elements for steel reinforcement. The model was adapted based on experimental data. A parametric analysis was conducted to evaluate the impact of placing horizontal steel bars at the compression side of the flat plate and changing the quantity and size of these bars on the slab's performance. The results illustrate that the shear capacity increases from 17.07% to 28.13% as the bar diameter increases and from 19.17% to 54.82% as the number of bars increases.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7261
Issue No: Vol. 14, No. 4 (2024)
- Predictors of Blockchain Technology Acceptance in Medical Imaging: The
Mediating Role of Initial Trust
Authors: Zainab Amin Al-Sulami, Nor’ashikin Ali, Rohaini Ramli
Pages: 15312 - 15319
Abstract: Blockchain technology (BCT) is an emerging technology that has been used mainly in supply chain and financial technology. However, the use of this technology in Medical Imaging (MI) is still limited. This study investigates the acceptability of BCT in MI in public hospitals in Iraq. Based on relevant theories, the study proposed that Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), and Facilitating Condition (FC) significantly affect the acceptability of BCT in MI. Similarly, EE, PE, SI, and FC are expected to affect Initial Trust (IT), which in turn is proposed to mediate the effect of EE, PE, SI, and FC on the acceptability of BCT in MI. Data were collected from 136 doctors from public hospitals in Iraq. The results indicated that EE, PE, and SI positively affected the acceptability of BCT in MI. EE and PE positively affected IT. Furthermore, IT positively affected the acceptability of BCT in MI and mediated the effect of EE and PE. The study offers valuable insights for both theoretical and practical implementations, can guide future research, and informs strategies for the effective acceptability of BCT in MI in public hospitals.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7660
Issue No: Vol. 14, No. 4 (2024)
- Using Shunt Capacitors to Mitigate the Effects of Increasing Renewable
Energy Penetration
Authors: Abdulrahman Altarjami, Marwa Ben Slimene, Mohamed Arbi Khlifi
Pages: 15320 - 15324
Abstract: Over the past two decades, Renewable Energy Sources (RESs) have gained global popularity. Control issues are becoming more difficult as the system inertia decreases due to the absence of typical synchronous generators. Innovative methods like fault current limiters, energy storage devices, and alternative control systems are utilized to deal with these difficulties. This study provides a summary of the challenges associated with incorporating high-level RESs into the existing grid. The increased penetration of the RESs has a negative impact on the system oscillations and harmonics, generating the need for power quality improvement techniques, such as adaptive control, adding energy systems, power stream assessment, or weight stream examination. This paper presents a framework of the power stream issue, its arrangement as well as different game plan methods. The power stream model of a power structure can be built using the significant association, weight, and age data.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7519
Issue No: Vol. 14, No. 4 (2024)
- Dynamic Stability Enhancement of Wind Power Generation with Static VAR
Compensator using Multiobjective Optimization Algorithms
Authors: Ezzeddine Salah Touti, Mohamed Fterich, Aamir Ali
Pages: 15325 - 15329
Abstract: Renewable energy, particularly wind energy, is expected to contribute significantly to the overall power generation. Induction machines are extensively used as generators in wind power generation because of their multiple benefits, such as robustness, reliability, and low cost and maintenance. However, due to the reactive power demand from the system to which they are connected, this type of generator brings new problems related to power quality, generally consisting of voltage regulation and reactive power compensation. These problems may cause voltage drops and dynamic instability. This study presents a metaheuristic method to attain a microgrid system with an optimal distribution based on its different constraints. The numerical model of an induction generator constructed in MATLAB/Simulink was used, and the simulation results obtained demonstrate the efficacy of the proposed metaheuristic technique.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7732
Issue No: Vol. 14, No. 4 (2024)
- TQU-SLAM Benchmark Feature-based Dataset for Building Monocular VO
Authors: Van-Hung Le, Huu-Son Do, Van-Nam Phan, Trung-Hieu Te
Pages: 15330 - 15337
Abstract: This paper introduces the TQU-SLAM benchmark dataset, which includes 160,631 RGB-D frame pairs with the goal to be used in Dell Learning (DL) training of Visual SLAM and Visual Odometry (VO) construction models. It was collected from the corridors of three interconnected buildings with a length of about 230 m. The ground-truth data of the TQU-SLAM benchmark dataset, including the 6-DOF camera pose, 3D point cloud data, intrinsic parameters, and the transformation matrix between the camera coordinate system and the real world, were prepared manually. The TQU-SLAM benchmark dataset was tested based on the PySLAM framework with traditional features, such as SHI_TOMASI, SIFT, SURF, ORB, ORB2, AKAZE, KAZE, and BRISK and features extracted from DL LIKE VGG. Experiments were also conducted on DPVO for VO estimation. The camera pose estimation results were evaluated and presented in detail, while the challenges of the TQU-SLAM benchmark dataset were analyzed.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7611
Issue No: Vol. 14, No. 4 (2024)
- Nanocellulose-Based Adsorbent for Cu(II) Adsorption
Authors: Haziqatulhanis Ibrahim, Norazlianie Sazali, Kumaran Kadirgama, Wan Norharyati Wan Salleh, Triyanda Gunawan, Nurul Widiastuti, Afdhal Junaidi
Pages: 15338 - 15343
Abstract: This study addresses the critical issue of copper removal from wastewater due to environmental and health concerns. Choosing pandan leaves as a source of cellulose was a deliberate decision due to their abundant availability in nature and minimal ecological footprint. Through the utilization of these properties, this study synthesized nanocellulose with enhanced adsorption capabilities by employing chemical pretreatments, sulfuric acid hydrolysis, and acrylamide grafting with the aid of ceric ammonium nitrate (CAN) as an initiator. In order to thoroughly evaluate the synthesized material, X-Ray diffractometer (XRD) and Fourier transform infrared (FTIR) spectroscopy were used. These characterization methods revealed insights into the morphology, functionality, and crystallinity of nanocellulose. The removal of copper(II) ions is investigated by employing an atomic absorption spectrometer (AAS), focusing on three important factors: pH variation, initial concentration, and adsorbent dosage, which are carefully examined. Grafted nanocellulose demonstrates superior performance, achieving over 85% grafting efficiency. Optimal Cu(II) removal conditions are identified at pH 6, with an initial metal ion concentration of 30 ppm and an adsorbent dose of 2.2 g/L. This study not only addresses a critical concern in wastewater treatment, but also explores the potential of pandan leaf-derived nanocellulose as a sustainable solution for heavy metal removal.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7581
Issue No: Vol. 14, No. 4 (2024)
- A Detection Android Cybercrime Model utilizing Machine Learning Technology
Authors: Fahad M. Ghabban
Pages: 15344 - 15350
Abstract: The present study developed a Detection Android cybercrime Model (DACM), deploying the design science approach to detect different Android-related cybercrimes. The developed model consists of five stages: problem identification and data collection, data preprocessing and feature extraction, model selection and training, model evaluation and validation, and model deployment and monitoring. Compared to the existing cybercrime detection models on the Android, the developed DACM is comprehensive and covers all the existing detection phases. It provides a robust and effective way to spot cybercrime in the Android ecosystem by following Machine Learning (ML) technology. The model covers all the detection stages that are normally included in similar models, so it provides an integrated and holistic approach to combating cybercrime.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7218
Issue No: Vol. 14, No. 4 (2024)
- A Study of the Development Strategy of the Wind Power Sector in Vietnam
Authors: Luong Ngoc Giap, Nguyen Binh Khanh, Bui Tien Trung, Truong Nguyen Tuong An, Tran The Vinh, Le Tat Tu
Pages: 15351 - 15355
Abstract: Nowadays, the wind power market is growing rapidly, while the cost of wind power equipment is decreasing, so Vietnam is currently building many wind power projects to ensure green energy development in its power system. However, some barriers in price mechanisms, and economic and technical conditions have also caused disadvantages in the process of developing wind power projects. This paper studies the SWOT-TOWS analysis to evaluate in greater detail the strengths, weaknesses, opportunities, and threat factors linked to it. Then the former compares the internal and external factors influencing the wind power industry on the way it exploits potential strategies. In general, Vietnam has satisfactory wind power development potential, and the wind power sector has been supported by a number of strong but unstable policies in the past. The rapid development of wind power capacity in recent times has also led to great challenges for investors and managers in actual operating conditions. Currently, investment costs for wind power plants are still quite high, Feed in Tariff (FIT) prices are not stable, transmission grid capacity is limited, and environmental treatment issues during construction and operation have not been fully considered.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7760
Issue No: Vol. 14, No. 4 (2024)
- The Influence of Printing Materials on Shrinkage Characterization in Metal
3D Printing using Material Extrusion Technology
Authors: Thi Van Nga Tran, Dang Cao Long, Cuong Nguyen Van
Pages: 15356 - 15360
Abstract: This study investigates the shrinkage characteristics of various materials in metal 3D printing using Material Extrusion (ME) technology. The materials examined include 17-4PH Stainless Steel V1 and V2, Inconel 625, H13 Tool Steel V1, and A2 Tool Steel. Experiments reveal that shrinkage rates vary significantly among these materials, with 17-4PH Stainless Steel V1 exhibiting the lowest average shrinkage rate of 16.2%, while Inconel 625 shows the highest average shrinkage rate of 24.5%. These findings are critical for improving dimensional accuracy in metal 3D printing. Additionally, results demonstrate that print orientation affects shrinkage. The analysis of product accuracy reveals inconsistencies between printed dimensions and design specifications, likely influenced by printing parameters. The conclusion underscores the importance of selecting appropriate printing materials and optimizing parameters to ensure dimensional accuracy in 3D printed products.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7758
Issue No: Vol. 14, No. 4 (2024)
- ANN-weighted Distance Grey Wolf Optimizer for NOx Emission Optimization in
Coal Fired Boilers of a Thermal Power Plant
Authors: Mahmad Raphiyoddin Shaphiyoddin Malik, Viswambaran Saraswati Priya
Pages: 15361 - 15366
Abstract: This research work suggests the application of predictive modeling for the Nitrogen Oxide (NOx) emissions from the 210 MW pulverized boiler that burns coal. In order to lower the NOx emissions in the flue gas, it is necessary to optimize various operational parameters during combustion, including oxygen in flue gas, various damper opening positions, air-distribution system, nozzle tilt, and the temperature of the flue gas outlet. Information gathered from variable parametric field tests was used to create an Artificial Neural Network (ANN) model. The model estimated NOx emissions based on the parameters of coal combustion. The ANN model was put to the test under full load conditions and the results of its predictions were compared to the actual values. The trained ANN with its biases and weights in the form of arithmetical equations was given as a fitness function to the weighted distance Grey Wolf Optimizer (GWO) to improve operating conditions for decreased NOx emissions.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7847
Issue No: Vol. 14, No. 4 (2024)
- Trajectory Tracking Control of Pneumatic Cylinder-Actuated Lower Limb
Robot for a Gait Training System
Authors: Van-Thuc Tran, Ba-Son Nguyen, Tiendung Vu, Ngoc-Tam Bui
Pages: 15367 - 15372
Abstract: This article presents the design of a control strategy for a lower limb gait training system catering to patients with Spinal Cord Injury (SCI) or stroke. The system operates by driving the hip and knee joints individually through pneumatic cylinders. The focus lies on the study and development of a control strategy for the pneumatic actuators within the gait training system, specifically targeting trajectory tracking control of pneumatic double-acting cylinders utilizing a PID Controller. The experiment setup comprises a pneumatic cylinder regulated by a proportional valve, incorporating feedback via position and pressure sensors. The experimental results show that the system exhibits good trajectory-tracking performance, particularly at low frequencies.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7733
Issue No: Vol. 14, No. 4 (2024)
- A New Computational Envelope Solution for Helical Gear Disc Tool Profiling
Authors: Long Hoang, Thanh-Tuan Nguyen
Pages: 15373 - 15377
Abstract: This paper presents a new computational envelope solution for profiling the helical gear disc tool. It uses the normal projection of the disc tool axis onto the helical gear surface to generate the characteristic curve and then automatically computes the geometric data of the characteristic curve to create the disc tool in 3D solid form. As a popular profile, the ISO heliacal gear was a typical proper example to verify and clarify the proposed solution. The solution can quickly create the helical gear disc tool in 3D solid form with high accuracy. The 3D comparison average error of the helical gear disc tool surfaces generated by the proposed solution and the Boolean method is 0.004 mm, and the RMS error is 0.009 mm.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7814
Issue No: Vol. 14, No. 4 (2024)
- Performance Evaluation of Data Mining Techniques to Enhance the
Reusability of Object-Oriented (O-O) Systems
Authors: Bharti Bisht, Parul Gandhi
Pages: 15378 - 15383
Abstract: The software industry is evolving at a rapid pace, making it necessary to optimize efforts and accelerate the software development process. Software can be reused to achieve quality and productivity goals. Reusability is a crucial measure that can be used to increase the overall level of software quality in less time and effort. To better understand the necessity of enhancing the software reusability of Object-Oriented (O-O) systems, this study employed a semi-automated approach to measure the values of class-level software metrics on an input dataset collected from the MAVEN repository. This paper explored several previous studies, data strategies, and tools to predict reusability in O-O software systems. This study compares various data mining techniques to identify the most suitable approach for enhancing the reusability of O-O software systems. The analysis was based on performance parameters such as precision, MSE, and accuracy rates. Due to its higher precision and lower MSE, the SOM technique is considered one of the top data mining approaches to increase the reusability of O-O software systems. However, the results show that the different levels of reusability in O-O software systems are not adequately addressed in current solutions.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7213
Issue No: Vol. 14, No. 4 (2024)
- Transfer Learning Artificial Neural Network-based Ensemble Voting of Water
Quality Classification for Different Types of Farming
Authors: Sumitra Nuanmeesri, Chaisri Tharasawatpipat, Lap Poomhiran
Pages: 15384 - 15392
Abstract: This study aims to develop a model for characterizing water quality in seawater-influenced areas for salt farming, fish farming, and crop farming. The water quality classification model was based on transfer learning trained by the Multi-Layer Perceptron Neural Network (MLPNN) and then classified by conventional Machine Learning (ML) methods, such as Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). The results of each ML classification were ensemble voted together, comparing the efficiency between hard and soft voting. The collected imbalanced dataset had a difference ratio between the majority and minority classes of 1:0.0138. However, after 900% resampling by applying the k-mean SMOTE technique, the data ratio between the majority and minority classes was 1:0.9778. The results show that the proposed ensemble approach improved accuracy by up to 2.15% in classifying water quality for salt farming, fish farming, and crop farming in seawater-influenced areas.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7855
Issue No: Vol. 14, No. 4 (2024)
- A Comparison of the Surface Pressure Distribution of Circular Cables and
Helical Fillet Cables under Wind Attack: A Wind Tunnel Test Study
Authors: Duy Thao Nguyen, Duy Hung Vo, Viet Hai Do
Pages: 15393 - 15399
Abstract: This study examines the aerodynamic performance and surface pressure distribution features of circular and helical fillet stay cables when subjected to wind using wind tunnel testing. The research seeks to clarify the aerodynamic performance disparities between conventional circular stay cables and helical fillet cables, providing valuable insights into their appropriateness for cable-supported structures exposed to wind-induced vibrations. The study initially investigates the aerodynamic efficiency of circular and helical fillet cables. Afterward, the wind tunnel captures the distribution of surface pressure on both cable surfaces. The findings suggest that circular stay cables may undergo cable dry galloping, whereas helical fillet cables demonstrate stability when subjected to wind forces. Furthermore, there are noticeable differences in the surface pressure distribution patterns between circular stay cables and helical fillet cables. Circular stay cables provide a symmetric distribution of pressure, with uniform pressure magnitudes along their surfaces, forming a symmetric pattern. On the other hand, helical fillet cables exhibit modified airflow patterns, leading to asymmetric pressure on the cable surface. Furthermore, the dry galloping observed in circular cables is attributed to the presence of low-frequency components. In contrast, helical fillet cables exhibit a more regulated incidence of low-frequency vortices, making them less prone to wind-induced vibrations.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7602
Issue No: Vol. 14, No. 4 (2024)
- A Ransomware Early Detection Model based on an Enhanced Joint Mutual
Information Feature Selection Method
Authors: Tasnem Magdi Hassin Mohamed, Bander Ali Saleh Al-rimy, Sultan Ahmed Almalki
Pages: 15400 - 15407
Abstract: Crypto ransomware attacks pose a significant threat by encrypting users' data and demanding ransom payments, causing permanent data loss if not detected and mitigated before encryption occurs. The existing studies have faced challenges in the pre-encryption phase due to elusive attack patterns, insufficient data, and the lack of comprehensive information, often confusing the current detection techniques. Selecting appropriate features that effectively indicate an impending ransomware attack is a critical challenge. This research addresses this challenge by introducing an Enhanced Joint Mutual Information (EJMI) method that effectively assigns weights and ranks features based on their relevance while conducting contextual data analysis. The EJMI method employs a dual ranking system—TF for crypto APIs and TF-IDF for non-crypto APIs—to enhance the detection process and select the most significant features for training various Machine Learning (ML) classifiers. Furthermore, grid search is utilized for optimal classifier parameterization, aiming to detect ransomware efficiently and accurately in its pre-encryption phase. The proposed EJMI method has demonstrated a 4% improvement in detection accuracy compared to previous methods, highlighting its effectiveness in identifying and preventing crypto-ransomware attacks before data encryption occurs.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7092
Issue No: Vol. 14, No. 4 (2024)
- Fuzzy Logic Energy Management System-based Nonlinear Sliding Mode
Controller for the Stabilization of DC Microgrids
Authors: Sahbi Boubaker, Khalil Jouili
Pages: 15408 - 15414
Abstract: Access to energy is critical for improving living conditions in remote and isolated areas. The integration of Renewable Energy Sources (RESs) and energy storage technologies becomes critical for sustainable energy supply, particularly in distant locations without access to the main grid. The isolated operation of RESs may face numerous problems in operation and reliability, hence, investing Direct Current Microgrids (DCMGs) can be adopted as an effective solution allowing Renewable Energy (RE) integration and contributing to efficient system operation. However, several issues related to monitoring, control, and diagnosis may be encountered under such conditions. The control of a PV-based RE system and a battery/ supercapacitor-based energy storage system in a DCMG is examined in this research. For this aim, a hierarchical control method is proposed. The proposed approach is based on a Sliding Mode Controller (SMC) and the Lyapunov stability theory. To manage load and energy generation, an energy management system based on fuzzy logic was designed. Global asymptotic stability has been demonstrated using Lyapunov stability analysis. The overall system behavior, including the proposed DCMG and controllers, was simulated. The results indicated that the system performs well under varying output and loads.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7658
Issue No: Vol. 14, No. 4 (2024)
- Hyper-tuned Swarm Intelligence Machine Learning-based Sentiment Analysis
of Social Media
Authors: Nitesh Sureja, Nandini Chaudhari, Priyanka Patel, Jalpa Bhatt, Tushar Desai, Vruti Parikh
Pages: 15415 - 15421
Abstract: Natural Language Processing (NLP) uses Sentiment Analysis (SA) to determine text sentiment. SA is often used on text datasets to assess consumer demands, the sentiment of the customer for a product, and brand monitoring. Deep Learning (DL) is a subset of Machine Learning (ML) that mimics how humans learn. In this work, the Deep Learning Reptile Search Algorithm (SA-DLRSA) model is introduced for accurate automatic SA. The SA-DLRSA model utilizes Word2Vec word embedding to reduce language processing that is dependent on data pre-processing. The SA-DLRSA model utilizes SVM, CNN, RNN, BiLSTM, and BERT models for sentiment classification. Choosing the optimal hyperparameters is crucial for determining the model's architecture, functionality, performance, and accuracy. The Reptile Search Algorithm (RSA) is employed to find the best optimal hyperparameters to improve classification. A derived balanced dataset based on the tweets related to bitcoins was employed as a training dataset, which contains three sentiments, namely "neutral", "positive", and negative". The collection has 7 columns and 50058 rows, consisting of 21938 neutral, 22937 positive, and 5183 negative tweets. Precision, accuracy, recall, and F1 Score metrics were used to evaluate the effectiveness of the proposed approach. The results showed that the BERT and BiLSTM classifiers achieved superior performance in classifying sentiments in the tweets achieving accuracies of 99% and 98%, respectively. Due to the promising results of the proposed approach, it is anticipated to be used in solutions to social media problems, such as hate speech detection and emotion detection.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7818
Issue No: Vol. 14, No. 4 (2024)
- Control of Grid-connected Inverter using Carrier Modulation
Authors: Quang-Tho Tran
Pages: 15422 - 15428
Abstract: Multilevel inverters are becoming prevalent due to their remarkable attributes, including their ability to withstand high voltage shocks and accommodate high capacity. As a result, they find extensive applications in grid-connected inverter systems utilizing photovoltaic (PV) panels and electric drive systems for electric motors. However, their power quality is heavily reliant on current controls and inverter modulation techniques. Conventional modulation methods typically employ fixed frequency carriers for inverter modulation, lacking inherent control signal information. In response to this challenge, this study proposes a novel modulation method for grid-connected multilevel inverters utilizing frequency and phase-modulated carriers. The study findings demonstrate the effectiveness of the proposed approach in the nominal operation, showcasing a reduction in Total Harmonic Distortion (THD) by 15.92% and a 48.5% decrease in the highest individual harmonic amplitude compared to the conventional method using the modulation of phase opposite disposition. Moreover, the switching count is also decreased by 26.37%.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7789
Issue No: Vol. 14, No. 4 (2024)
- Removal of Diethylene Glycol from Wastewater by Photo Aeration
Authors: Ibrahim A. Alsayer
Pages: 15429 - 15432
Abstract: Photo air oxidation combines the use of ultraviolet (UV) light and air as an oxidant to degrade diethylene glycol (DEG) in wastewater. DEG is well known for its high chemical oxygen demand and has raised some serious environmental issues, since the conventional biological treatment package in plants may not be able to treat the waste in an effective manner. UV light generates highly reactive species, such as hydroxyl radicals (•OH), which can react with DEG and break it down into smaller, less harmful compounds. Air, specifically oxygen (O2), can act as an additional oxidant in the process, assisting in the degradation of DEG. The UV light source, such as low-pressure mercury lamps that emit at around 254 nm, is still needed to initiate the generation of hydroxyl radicals. The presence of oxygen in wastewater allows the hydroxyl radicals to react with DEG more effectively, improving the oxidation process. In the present research work, the effect of airflow rate, UV light intensities, oxygen partial pressure, and initial DEG concentration on photo-oxidation of DEG in wastewater was examined. We have also performed a kinetic study, from which the reaction order and the rate constant were determined.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7872
Issue No: Vol. 14, No. 4 (2024)
- An Image Processing-based and Deep Learning Model to Classify Brain Cancer
Authors: Amal Al-Shahrani, Wusaylah Al-Amoudi, Raghad Bazaraah, Atheer Al-Sharief, Hanan Farouquee
Pages: 15433 - 15438
Abstract: In recent years, the prevalence of cancer has increased significantly around the world. Cancer is considered one of the most dangerous diseases in humans. Cancer screening devices, such as Magnetic Resonance Imaging (MRI), X-ray imaging, ultrasound imaging, and others, play an important role in its early detection. This study aims to facilitate cancer tumor detection on mobile phones with high accuracy in a short period of time using deep learning techniques. A brain tumor dataset was used, consisting of 4,489 images and 14 classified types, and experiments were carried out using ResNet 12, DenseNet, YOLOv8, and MobileNet to evaluate them in terms of accuracy, speed, and model size. ResNet12, DenseNet, YOLOv8, and MobileNet results indicated satisfactory accuracy ranging from 88% to 97.3%. YOLOv8 was the most suitable model, as its fastest inference time of 1.8 ms, preprocessing time of 0.1 ms, highest accuracy of 97.3%, and compact model size make it ideal for real-time mobile applications.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7803
Issue No: Vol. 14, No. 4 (2024)
- Enhancing the Quality of Ambulance Crew Work by detecting Ambulance
Equipment using Computer Vision and Deep Learning
Authors: Jonab Hussain, Nada Al-Masoody, Asmaa Alsuraihi, Fay Almogbel, Asmaa Alayed
Pages: 15439 - 15446
Abstract: Ambulance crews play an important role in responding quickly to emergencies and rescuing patients by providing appropriate treatment. Typically, fully equipped emergency vehicles are used to transport ambulance personnel to emergency locations. The ambulance crew cleans, sterilizes, and prepares equipment after each patient transfer with great care. Additionally, they check more than 70 pieces of equipment twice a day using a checklist, which is a tedious, time-consuming, and error-prone task. This study uses computer vision and deep learning techniques to replace the manual checklist process for medical equipment to assist the crew and make the equipment availability check faster and easier. To accomplish this, a dataset containing 2099 images of medical equipment in ambulances was collected and annotated with 3000 labeled instances. An experimental study compared the performance of YOLOv9-c, YOLOv8n, and YOLOv7-tiny. YOLOv8n demonstrated the best performance with a mAP50 of 99.2% and a speed of 3.3 ms total time per image. Therefore, YOLOv8 was selected for the proposed system due to its high accuracy and detection speed, which make it suitable for mobile applications. The presence of an application integrated with computer vision and deep learning technologies in paramedic devices can assist in reviewing the equipment checklist, reducing human errors, speeding up the review process, and alleviating the burden on paramedics in their work.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7769
Issue No: Vol. 14, No. 4 (2024)
- A Novel Method for the Estimation of the Elastic Modulus of Ultra-High
Performance Concrete using Vibration Data
Authors: Huong Duong Nguyen, Samir Khatir, Quoc Bao Nguyen
Pages: 15447 - 15453
Abstract: The elastic modulus of concrete is one of the most important parameters in the analysis and design of concrete structures. However, determining the elastic modulus in civil structures using core-drilled samples is time-consuming and labor-intensive. Additionally, the elastic modulus of Ultra-High Performance Concrete (UHPC) varies significantly depending on its composition. This paper proposes an improved, non-destructive application to identify the elastic modulus of UHPC materials in in-service structures. The elastic modulus is estimated through calibration between a numerical model and experimental UHPC plate vibration test results, using frequency and mode shapes. This calibration involves solving an inverse problem using optimization techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search, and the YUKI algorithm. Updating the plate characteristics is made possible by the development of numerous iterations, where each iteration updates the elastic modulus, thickness, and width values in the term to find the best solution. The highest accuracies compared to experimental data natural frequency values were found in models updated by GA, PSO, YUKI, and Cuckoo algorithms, with errors of 10.77%, 6.58%, 6.87%, and 6.87%, respectively. An experimental sample was tested to determine the elastic modulus of the UHPC, and the proposed application showed a 0.55% error compared to the experimental value. Thus, the estimated elastic modulus value is highly accurate.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7859
Issue No: Vol. 14, No. 4 (2024)
- Application of the TOPSIS Method for Multi-Objective Optimization of a
Two-Stage Helical Gearbox
Authors: Huu-Danh Tran, Van-Thanh Dinh, Duc-Binh Vu, Duong Vu, Anh-Tung Luu, Ngoc Pi Vu
Pages: 15454 - 15463
Abstract: In order to design a high-efficiency two-stage gearbox to reduce power loss and conserve energy, a Multi-Criterion Decision-Making (MCDM) method is selected for solving the Multi-Objective Optimization Problem (MOOP) in this research. The study's objective is to determine the best primary design factors that will increase gearbox efficiency and decrease gearbox mass. To that end, the first stage's gear ratio and the first and second stages' Coefficients of Wheel Face Width (CWFW) were chosen as the three main design elements. Furthermore, two distinct goals were analyzed: the lowest gearbox mass and the highest gearbox efficiency. Additionally, the MOOP is carried out in two steps: phase 1 solves the Single-Objective Optimization Problem (SOOP) to close the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the TOPSIS approach was selected to address the MOOP. For the first time, an MCDM technique is used to solve the MOOP of a two-stage helical gearbox considering the power losses during idle motion. When designing the gearbox, the optimal values for three crucial design parameters were ascertained according to the study's results.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7551
Issue No: Vol. 14, No. 4 (2024)
- Performance Evaluation of PI and Sliding Mode Control for PMSM in
Applications for Electric Vehicles
Authors: Kamel Cherif, Abdelaziz Sahbani, Kamel Ben Saad
Pages: 15464 - 15470
Abstract: Electrical and mechanical subsystems are the main parts of the powertrain of an Electric Vehicle (EV). These parts include principally electric motors, inverters, batteries, wheels, axles, differentials, and transmissions. Permanent Magnet Synchronous Motor (PMSM) is one of the most popular used motors in the electric powertrain due to its several benefits over other AC motors, such as its small size, low weight, wide range of speed, elevated overload capacity, elevated power factor, and elevated efficiency. This paper compares the performance of PI and sliding-mode controllers for PMSM employed in electric vehicle applications with single-motor drive configurations. Dynamic performance and robustness are the main topics of the comparative analysis. The robustness of the drive train with sliding-mode controller is proven by simulation results.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7172
Issue No: Vol. 14, No. 4 (2024)
- High-force Mechanical Dynamic Investigation of Natural Fiber Composite
Sandwich Panels for Aerospace Design
Authors: Bader Alqahtani
Pages: 15471 - 15476
Abstract: In this paper, the non-destructive assessment of thermo-mechanical characteristics of extremely rigid structures made up of natural composites fibers is conducted. Higher-force dynamic mechanical investigation has allowed for new insights, which is not possible with the regularly employed static and impact test methods. Natural fibers made up of composite sandwich panels with aluminum and aramid honeycomb cores are studied. Various panel cores of equal stiffness and damping capabilities are compared over flight frequencies ranging from 1 to 100 Hz. It was discovered that the exhaustion of the natural fiber sandwich panels depends on both the core material and the applied static load. Additionally, temperature sweeps were carried out, and it was discovered that they can identify variations in the post processing of the natural fiber laminated panels and show the variations in the transition temperature of the matrix material.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7903
Issue No: Vol. 14, No. 4 (2024)
- Stance Detection in Hinglish Data using the BART-large-MNLI Integration
Model
Authors: Somasekhar Giddaluru, Sreerama Murty Maturi, Obulesu Ooruchintala, Mahendra Munirathnam
Pages: 15477 - 15481
Abstract: Real-time stance detection can be used in a wide range of applications like debate analysis, sentiment analysis, system feedback, etc. This study focuses on stance detection in political speeches, discerning whether the speaker is in favor, against, neutral, or lacking any stance on a given topic. The problem with this type of speeches dwells in the modification of the existing methods of stance detection to allow for the fine distinctions of Hinglish, a language mixture blending Hindi and English, as conveyed in human-edited texts. The proposed method utilizes the Bidirectional Auto-Regressive Transformers Multi-Genre Natural Language Inference (BART-large-MNLI) model with zero-Shot, few-Shot and N-Shot learning approaches. The proposed model is compared with the existing models of stance detection on Hinglish texts. For the pre-trained BART-based models, a limited number of labeled examples are utilized to determine the labels of test instances. For the other models, the train-test split method is adopted to get accurate results. The results indicate that the model surpasses the previous models.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7741
Issue No: Vol. 14, No. 4 (2024)
- Strengthening Reinforced Concrete Beams with Vertical Perforations by
using Steel-Plate Tubing
Authors: Masharaq A. Shneet, Amer F. Izzet
Pages: 15482 - 15487
Abstract: This study aimed to experimentally compare the bending behavior of reinforced concrete beams in the midspan zone of the maximum bending region with and without vertically perforated openings. Five rectangular cross-section beams were produced for this purpose: one solid specimen, one with an opening without reinforcing steel, and the remaining three had openings reinforced with steel tubes of thickness 1, 1.5, and 2 mm. The square opening width was 40 mm. All specimens were tested under a symmetric four-point loading until failure. The test results showed that all specimens failed in bending. The presence of an opening negatively affects the ultimate load-bearing capacity and bending. However, the bending behavior of the reinforced concrete beams exhibited superior performance as the thickness of the steel tube increased. Additionally, the maximum load-bearing capacity increased with increasing tube thickness.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7813
Issue No: Vol. 14, No. 4 (2024)
- Development of a Machine for Cleaning the Core of Grass Straws
Authors: Ngoc-Kien Nguyen, Hai-Nam Nguyen, Van-Tinh Nguyen
Pages: 15488 - 15493
Abstract: Nowadays, plastic waste is one of the most pressing issues, especially in developing countries. Plastic waste comes from everyday products such as plastic food boxes, plastic bottles, disposable products, or plastic straws causing negative effects on the environment. On the other hand, grass straws are eco-friendly and safe and can be easily found in Southeast Asian countries such as Vietnam, Indonesia, and Malaysia. However, the process of grass straw production is implemented manually, resulting in low productivity and unsafe labor conditions. Among the stages of producing grass straws, cleaning the core of the straw is the most important because it determines its quality. Aiming to automate the grass straw production process, this paper introduces the first grass straw core cleaning machine in the world.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7475
Issue No: Vol. 14, No. 4 (2024)
- Comparative Analysis of GDM2000, WGS84, and MRT68 Coordinate Reference
Systems based on Different Converter Modules
Authors: Alhaji Hussaini, Kelvin Tang Kang Wee, Amalina Izzati Abdul Hamid, Auwal Graba Abubakar, Chee Wee Tan
Pages: 15494 - 15498
Abstract: The employment of multiple data and coordinate systems in Malaysia has not only resulted in challenges in surveying and mapping purposes but has also caused data compatibility issues with the local positioning system. This study examines the disparities in coordinates converted from the localized Geocentric Datum of Malaysia 2000 (GDM2000) to the global coordinate system, the World Geodetic System 1984 (WGS84), and from GDM2000 to the Malayan Revised Triangulation 1968 (MRT68). Several coordinate converter tools, available in the mCOORD mobile app, Geodetic Datum Transformation System (GDTS), Global Mapper, and Quantum GIS (QGIS), were employed to analyze the variation between converted coordinates. The locally developed coordinate converter tools, mCOORD and GDTS, exhibit similar levels of accuracy and conform to the standards set by the local survey department. In contrast, the reliability of the coordinate conversion tools in Global Mapper and QGIS seems uncertain. It is recommended that each data revision should establish transparency to the latest geodetic reference frame, with publicly accessible transformation parameters.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7124
Issue No: Vol. 14, No. 4 (2024)
- A Surface Roughness Prediction Model for SKT4 Steel Milling
Authors: Chaiyakron Sukkam, Seksan Chaijit
Pages: 15499 - 15504
Abstract: Predicting surface roughness is critical in manufacturing processes like grinding, particularly for materials such as SKT4 steel, where a precise surface finish is imperative. Precise roughness prediction facilitates the optimization of process parameters to achieve the desired surface quality, consequently diminishing the need for supplementary operations such as grinding or polishing. This, in turn, decreases costs and lead times. This study aimed to develop a surface roughness prediction model tailored for milling SKT4 steel by designing experiments to analyze the influence of cutting parameters on surface roughness, collecting and analyzing data related to machining parameters in process modeling, and developing and validating the model. The analysis of variance (ANOVA) results highlights the significant influence of the interaction between rotational speed and cutting depth on skin roughness. The linear regression model demonstrates clear variability in the data (R2 of approximately 99.74%) and exhibits effective predictive capabilities (pred. R2 of approximately 83.56%). The maximum impact on skin roughness was observed at a rotational speed of 1500 RPM, a feed rate of 300 mm per minute, and a cutting depth of 0.2 mm. Increasing rotational speed leads to smoother skin, whereas higher feed rates result in decreased smoothness. However, skin roughness shows minimal fluctuation with changes in feed rate and cutting depth. The model accurately predicts the average skin roughness values.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7612
Issue No: Vol. 14, No. 4 (2024)
- Detecting and Mitigating Data Poisoning Attacks in Machine Learning: A
Weighted Average Approach
Authors: Yogi Reddy Maramreddy, Kireet Muppavaram
Pages: 15505 - 15509
Abstract: Adversarial attacks, in particular data poisoning, can affect the behavior of machine learning models by inserting deliberately designed data into the training set. This study proposes an approach for identifying data poisoning attacks on machine learning models, the Weighted Average Analysis (VWA) algorithm. This algorithm evaluates the weighted averages of the input features to detect any irregularities that could be signs of poisoning efforts. The method finds deviations that can indicate manipulation by adding all the weighted averages and comparing them with the predicted value. Furthermore, it differentiates between binary and multiclass classification instances, accordingly modifying its analysis. The experimental results showed that the VWA algorithm can accurately detect and mitigate data poisoning attacks and improve the robustness and security of machine learning systems against adversarial threats.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7591
Issue No: Vol. 14, No. 4 (2024)
- Reconstructing Health Monitoring Data of Railway Truss Bridges using
One-dimensional Convolutional Neural Networks
Authors: Nguyen Thi Cam Nhung, Hoang Bui Nguyen, Tran Quang Minh
Pages: 15510 - 15514
Abstract: Structural Health Monitoring (SHM) system uses sensors to collect information and evaluate the structure, aiming for early damage detection. For many reasons, data from sensors can be corrupted, affecting the assessment results. Reconstructing lost or corrupted data helps complete it, improves structural assessments, and ensures structural safety. Artificial Intelligence (AI) has emerged in recent years as a solution to data problems. This study proposes the use of a One-Dimensional Convolutional Neural Network (1DCNN) to reconstruct lost vibration data during SHM. A complete dataset was used to train the 1DCNN network. After completing the training, the 1DCNN network received incomplete data to return erroneous data. The results of the study show that the proposed method is able to reconstruct vibration sensor data.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7515
Issue No: Vol. 14, No. 4 (2024)
- Analysis of Multiple Input Multiple Output-Orthogonal Frequency Division
Multiplexing with Dynamic Optimal Power Allocation
Authors: Owk Srinivasulu, P. Rajesh Kumar
Pages: 15515 - 15521
Abstract: Multiple Input Multiple Output (MIMO) is a technology that combines multiple antennas and Orthogonal Frequency Division Multiplexing (OFDM) modulation to increase the data rate and spectral efficiency of wireless communication systems. Equalization problems are one of the key issues with MIMO-OFDM systems, which are caused by multiple antennas and subcarriers. Optimal Power Allocation (OPA) in MIMO-OFDM is a critical component for achieving high spectral efficiency and improving the overall performance of the system. OPA refers to the process of allocating power to the different subcarriers and antennas in MIMO-OFDM systems in an optimal way. This study proposes the Dynamic OPA (DOPA) algorithm for MIMO-OFDM systems to dynamically adjust power allocation based on the changing channel conditions and reduce pilot contamination issues. This approach allocates more power to subcarriers and antennas with better channel conditions and less power to subcarriers and antennas with worse channel conditions. DOPA in MIMO-OFDM systems is necessary to maximize data rates, minimize interference, improve system capacity, and reduce power consumption. Simulation results showed that the proposed MIMO-OFDM-DOPA had reduced bit error rates, mean square error, and transmitter power and increased energy efficiency and capacity compared to existing state-of-the-art methods.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7459
Issue No: Vol. 14, No. 4 (2024)
- Security Threat Exploration on Smart Living Style based on Twitter Data
Authors: Tahani AlSaedi, Misbah Mehmood, Asad Mahmood, Saif Ur Rehman, Mahwsh Kundi
Pages: 15522 - 15532
Abstract: The Internet of Things (IoT) has revolutionized individuals’ homes with smart devices, but it has also brought security worries due to the huge amounts of data they generate. This study aims to uncover common security problems, like malware, cyber-attacks, and data storage flaws, in such smart setups. To tackle these issues, this study suggests beefing up security measures and educating users about safe device practices. A new approach was followed in this study, using Convolutional Neural Networks (CNNs) instead of the traditional Natural Language Processing (NLP) methods. CNNs are great at understanding complex patterns in text, especially on platforms like Twitter where messages can be brief and unclear. By applying CNN to analyze Twitter data, specific entities linked to security issues could be pinpointed, giving a deeper insight into smart home security challenges. The findings showed that the employed CNN model was exceptionally efficient at sorting out tweets regarding security problems in smart homes. It achieved an accuracy of around 87%, precision of 76.78%, recall of 82.49%, and F1-score of 84.87% surpassing the other methods it was compared with. These findings underscore the CNN model's effectiveness in accurately classifying security-related tweets in diverse topics within smart living environments.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7257
Issue No: Vol. 14, No. 4 (2024)
- A Children's Psychological and Mental Health Detection Model by Drawing
Analysis based on Computer Vision and Deep Learning
Authors: Amal Alshahrani, Manar Mohammed Almatrafi, Jenan Ibrahim Mustafa, Layan Saad Albaqami, Raneem Abdulrahman Aljabri
Pages: 15533 - 15540
Abstract: Nowadays, children face different changes and challenges from an early age, which can have long-lasting impacts on them. Many children struggle to express or explain their feelings and thoughts properly. Due to that fact, psychological and mental health specialists found a way to detect mental issues by observing and analyzing different signs in children’s drawings. Yet, this process remains complex and time-consuming. This study proposes a solution by employing artificial intelligence to analyze children’s drawings and provide diagnosis rates with high accuracy. While prior research has focused on detecting psychological and mental issues through questionnaires, only one study has explored analyzing emotions in children's drawings by detecting positive and negative feelings. A notable gap is the limited diagnosis of specific mental issues, along with the promising accuracy of the detection results. In this study, different versions of YOLO were trained on a dataset of 500 drawings, split into 80% for training, 10% for validation, and 10% for testing. Each drawing was annotated with one or more emotional labels: happy, sad, anxiety, anger, and aggression. YOLOv8-cls, YOLOv9, and ResNet50 were used for object detection and classification, achieving accuracies of 94%, 95.1%, and 70.3%, respectively. YOLOv9 and ResNet50 results were obtained at high epoch numbers with large model sizes of 5.26 MB and 94.3 MB. YOLOv8-cls achieved the most satisfying result, reaching a high accuracy of 94% after 10 epochs with a compact model size of 2.83 MB, effectively meeting the study's goals.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7812
Issue No: Vol. 14, No. 4 (2024)
- Identification and Improvement of Image Similarity using Autoencoder
Authors: Suresh Merugu, Rajesh Yadav, Venkatesh Pathi, Herbert Raj Perianayagam
Pages: 15541 - 15546
Abstract: Identifying the similarity between fine-grained images requires sophisticated techniques. This study presents a deep learning approach to the image similarity problem as an unsupervised learning task. The proposed autoencoder, built on a Deep Neural Network (DNN), autonomously learns image representations by computing cosine similarity distances between extracted features. This paper presents several applications, including training the autoencoder, transforming images, and evaluating the DNN model. In each instance, the generated images exhibit sharpness and closely resemble natural photographs, demonstrating the effectiveness and versatility of the proposed deep learning framework in computer vision tasks. The results suggest that the proposed approach is well-suited for tasks that require accurate image similarity assessments and image generation, highlighting its potential for various applications in image retrieval, data augmentation, and pattern recognition. This study contributes to the advancement of the computer vision field by providing a robust and efficient method for learning image representations and evaluating image similarity in an unsupervised manner.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7548
Issue No: Vol. 14, No. 4 (2024)
- Fabrication of Sustainable Roller-compacted Concrete Pavement containing
Plastic Waste as Fine and Coarse Aggregate
Authors: Shahad Qais Abd Almajeed, Zena K. Abbas
Pages: 15547 - 15552
Abstract: The primary goal of this practical lab analysis was to obtain a sustainable and eco-friendly Roller-Compacted Concrete (RCC), by lowering the consumption of natural resources and energy and utilizing plastic waste. The experiment performed involved six RCC mixes with partial weight replacement of coarse or fine aggregate of specified percentages with waste plastic along with a reference mixture (R.M), utilizing different curing methods, namely spraying with water two times per day, immersing in water, and utilizing ISO SMART CURING W 1035 material. Three types of plastic were used in the analysis: polyvinyl chloride (PVC) as coarse aggregate replacement and polyethylene terephthalate (PET) and high-density polyethylene (HDPE) as fine aggregate replacement. The mixes were tested regarding compressive, flexural, and splitting tensile strength. The results of the study indicate that the RCC containing 10% PVC (CP10) exhibited a reduction in compressive strength of 5.25, 5.69, and 5.99% for water, spray, and coating curing, respectively at 28 days related to the R.M, followed by the mix including 20% PVC (CP20) with a decrease ratio of 12.79, 13.52, and 13.20%. Mixtures with 5% PET and HDPE (FP5, FH5) can be accepted, since their results were nearest to R.M with a percentage decrease of 4.16, 3.52, and 3.74% for PET and 3.18, 3.13, and 3.14%for HDPE. Treating with coating material achieved the best results, exhibiting improvement in compressive, flexural, and tensile splitting strength, while the water spray method performed worse than water curing.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7882
Issue No: Vol. 14, No. 4 (2024)
- An MPL-CCN Model for Real-time Health Monitoring and Intervention
Authors: Mohammad Khaja Nizamuddin, Syed Raziuddin, Mohammed Farheen, C. Atheeq, Ruhiat Sultana
Pages: 15553 - 15558
Abstract: The use of Artificial Intelligence (AI) in healthcare, particularly in real-time health monitoring and predictive interventions for chronic diseases, has many benefits but also many drawbacks. Existing health risk prediction algorithms face accuracy issues and, due to the wide variety of health profiles, general algorithm applicability is problematic. The proposed model solves this issue by using an advanced AI framework to improve the accuracy of the prediction of Chronic Kidney Disease (CKD) and eliminate false positives. Our hybrid Deep Learning (DL) method blends a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN), thus including real-time feedback to help the system learn from its predictions and results. The proposed system solves a major problem in this area and sets a new benchmark for AI applications in healthcare by directly addressing prediction accuracy. It offers more tailored, accurate, and responsive chronic disease management, improving patient outcomes and healthcare resource efficiency.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7684
Issue No: Vol. 14, No. 4 (2024)
- Intellectual Property Design with PUF-based Hardware Security
Authors: Devi Pradeep Podugu, A. Kamala Kumari, Srinivas Sabbavarapu
Pages: 15559 - 15563
Abstract: With the advent of networked systems in almost all current applications, security poses a great threat to the design industry. The participation of several people in different design abstract stages in the hierarchical design industry makes the design vulnerable to security threats. To address these security issues, this study used PUFs to create signatures on Intellectual Property (IP) to protect against malicious attacks. The proposed method exhibits significant resilience to ML-based attacks.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7413
Issue No: Vol. 14, No. 4 (2024)
- Solving the Multi-objective Travelling Salesman Problem by an Amalgam of
Fruit Fly Optimization and Ant Colony Optimization
Authors: Archana A. Deshpande, Seema Raut, Nalini V. Vaidya
Pages: 15564 - 15569
Abstract: In this article, the multi-objective Travelling Salesman Problem (TSP), which includes the optimization of two competing and incompatible goals, is taken into account. There is not a single ideal strategy that enhances all the objective functions at once. Usually, one of the goals is considered a constraint or both goals are combined into one objective function. This work provides an extremely efficient Ant Colony Optimization (ACO)-based multi-objective Fruit Fly Optimization Algorithm (FFOA). Using FFOA, which was normalized and initialized to the pheromone quantity for ACO, the present study first establishes a local solution. To evaluate the optimization results a combined method of FFOA and ACO is carried out.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7353
Issue No: Vol. 14, No. 4 (2024)
- Optimal Airport Selection for Iraq's Infrastructure Development: A
TOPSIS Analysis
Authors: Farah K. Naser, Tareq A. Khaleel
Pages: 15570 - 15574
Abstract: This study investigates the application of the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method to enhance Iraq's airport infrastructure. The research aims to analyze the performance of major airports based on data collected from the Central Statistical Organization (CSO) of Iraq. A model is proposed to prioritize the airport development projects based on essential criteria, such as aircraft movement (landing and takeoff) and passenger movement (arrivals and departures). The airports are ranked according to the priority they have in infrastructure development, which is linked to their economic growth. In terms of priority, the first is Baghdad International Airport, Basra Airport follows, and Najaf Airport ranks third. Erbil and Sulaymaniyah airports rank lower due to infrastructure limitations and administrative challenges. The research underscores the importance of ongoing investments in airport infrastructure to accommodate the increasing number of passengers and bolster economic expansion.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7773
Issue No: Vol. 14, No. 4 (2024)
- Microscale Thermal Management: A Review of Nanofluid Applications in
Microfluidic Channels
Authors: Lingenthiran Samylingam, Navid Aslfattahi, Kumaran Kadirgama, Devarajan Ramasamy, Norazlianie Sazali, Wan Sharuzi Wan Harun, Chee Kuang Kok, Nor Atiqah Zolpakar, Mohd Fairusham Ghazali
Pages: 15575 - 15580
Abstract: This critical review study focuses on the integration of nanofluids with microfluidic channels. This emerging field, which combines nanotechnology and microfluidics, has the potential to transform the control of temperatures and monitoring completely. Nanofluids, which are fluids containing nanoparticles like metals or oxides, greatly improve the heat management capabilities of base fluids. These materials are highly efficient in transferring and conducting heat, making them ideal for applications such as cooling electronics and medical diagnostics. The addition of nanofluids to microfluidic routes, typically measured in micrometers, greatly simplifies fluid flow and heat transfer regulation. The article includes several research studies demonstrating how nanofluids enhance the performance of microfluidic systems compared to conventional fluids. The benefits are examined, including the potential for reduced size and increased energy efficiency of heat exchanges and cooling systems. As a result, these technologies are better suited for implementation in the healthcare and industry sectors.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7547
Issue No: Vol. 14, No. 4 (2024)
- Safeguarding Identities with GAN-based Face Anonymization
Authors: Mahmoud Ahmad Al-Khasawneh, Marwan Mahmoud
Pages: 15581 - 15589
Abstract: Effective anonymous facial registration techniques are critical to address privacy concerns arising from facial recognition technology. This study presents an intelligent anonymity platform that incorporates blockchain with advanced privacy and uses a CIAGAN-powered approach. This solution addresses the immediate need for privacy in facial recognition technology. The proposed system uses advanced techniques to anonymously generate highly realistic and effective facial images. The widespread use of facial recognition systems places greater emphasis on privacy concerns, emphasizing the need for strong enrollment mechanisms. The proposed system uses CIAGAN to address this challenge and generate facial images while preserving important attributes. Blockchain storage ensures that data integrity and security are maintained. The process begins with detailed image preprocessing steps to improve data quality and eliminate unwanted noise. CIAGAN can generate anonymous face images with important facial attributes to complicate the recognition of specific objects. A dataset of 202,599 facial images was used. Performance metrics such as PSNR and SSIM indicate image quality and uniformity. The PSNR obtained was 35.0516, indicating a unique image anonymization process.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7527
Issue No: Vol. 14, No. 4 (2024)
- Analysis of Rooftop Solar Power Development in Northwest Vietnam using the
Analytic Hierarchy Process
Authors: Ngo Phuong Le, Giap Ngoc Giap, Nguyen Binh Khanh, Bui Tien Trung, Vu Minh Phap, Dai Hung Phi
Pages: 15590 - 15595
Abstract: Traditional energy sources are gradually being exhausted, which causes many negative effects on the environment and contributes to the climate change. Countries around the world, including Vietnam, are focusing on developing renewable energy sources, such as solar power, to combat climate change and create a foundation for sustainable development. The development of solar power, particularly rooftop solar power, is being encouraged in various provinces and cities in Vietnam. The Northwest is a mountainous region in the Northern part of Vietnam with considerable solar energy potential. However, developing rooftop solar power projects requires balancing economic, technical, and environmental goals. Currently, there is no comprehensive research in Vietnam that fully evaluates the sustainable development goals for rooftop solar power in the Northwest region. This paper focuses on identifying the factors that influence the decision to install and use rooftop solar power in the Northwest region of Vietnam with the support of the Analytic Hierarchy Process (AHP) method and Expert Choice software. This impact ranges to varying degrees. The most significant influencing factor is the solar energy development policy, with a priority of 36.1%, while the social factor has the lowest priority value of 7.7%. The primary factor affecting people's decisions is the future solar power development policy, with a weight value of 24.2.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7708
Issue No: Vol. 14, No. 4 (2024)
- Explainable AI-based Framework for Efficient Detection of Spam from Text
using an Enhanced Ensemble Technique
Authors: Ahmed Alzahrani
Pages: 15596 - 15601
Abstract: Today, identifying and preventing spam has become a challenge, particularly with the abundance of text-based content in emails, social media platforms, and websites. Although traditional spam filters are somewhat effective, they often struggle to keep up with new spam methods. The introduction of Machine Learning (ML) and Deep Learning (DL) models has greatly improved the capabilities of spam detection systems. However, the black-box nature of these models poses challenges to user trust due to their lack of transparency. To address this issue, Explainable AI (XAI) has emerged, aiming to make AI decisions more understandable to humans. This study combines XAI with ensemble learning, utilizing multiple learning algorithms to improve performance, and proposes a robust and interpretable system to detect spam effectively. Four classifiers were used for training and testing: Support Vector Machine (SVM), Logistic Regression (LR), Gradient Boost (GB), and Decision Tree (DT). To reduce overfitting, two independent spam email datasets were blended and balanced. The stacking ensemble technique, based on Random Forest (RF), was the best-performing model compared to individual classifiers, having 98% recall, 96% precision, and 97% F1-score. By leveraging XAI's interpretability, the model elucidates the reasoning behind its classifications, leading to the comprehension of hidden patterns associated with spam detection.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7901
Issue No: Vol. 14, No. 4 (2024)
- A Study of Single Stone Column Bearing Capacity from a Full-Scale Plate
Load Test in Long Son Project
Authors: Hong Lam Dang
Pages: 15602 - 15606
Abstract: The ultimate bearing capacity of stone columns is very important in the design of soil improvement. The bulging failure mechanism is the most common failure mechanism reported. However, it depends on the surrounding soil at the site, necessitating a site-specific study of single-stone column bearing capacity. The current paper presents the full-scale plate load test of the single stone column in the Long Son Petrochemical Project, Vietnam in order to verify the bearing capacity of single stone column. A single stone column of 800 mm diameter was installed at the site by vibroflot. An 800 m circle full-scale plate test was carried out on site. The stone properties followed the Vietnamese standard TCVN 7572. The settlement result of the plate load test verifies the single stone column bearing capacity of 882.5 kPa.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7698
Issue No: Vol. 14, No. 4 (2024)
- A Study of Cyberbullying Detection and Classification Techniques: A
Machine Learning Approach
Authors: Srinadh Unnava, Sankara Rao Parasana
Pages: 15607 - 15613
Abstract: The popularity of online social networks has increased the prevalence of cyberbullying, making it necessary to develop efficient detection and classification methods to mitigate its negative consequences. This study offers a comprehensive comparative analysis of various machine-learning techniques to detect and classify cyberbullying. Using various datasets and platforms, this study investigates and compares the performance of various algorithms, including both conventional and cutting-edge deep learning models. To determine the best practices in various scenarios, this study includes a thorough review of feature engineering, model selection, and evaluation measures. This study also examines how feature selection and data preprocessing affect classification precision and computational effectiveness. This study provides useful information on the advantages and disadvantages of various machine learning algorithms for detecting cyberbullying through experimentation and comparative research. The results of this study can help practitioners and researchers choose the best methods for particular applications and support ongoing efforts to make the Internet safer.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7621
Issue No: Vol. 14, No. 4 (2024)
- Leukemia Diagnosis using Machine Learning Classifiers based on MRMR
Feature Selection
Authors: Sipan M. Hameed, Walat A. Ahmed, Masood A. Othman
Pages: 15614 - 15619
Abstract: Early and accurate diagnosis of leukemia is crucial for effective treatment. Machine Learning (ML) offers promising tools for leukemia diagnosis classification, but the required high-dimensional datasets pose challenges. This study explores the effectiveness of ML algorithms for leukemia disease classification and investigates the impact of feature selection with the Minimum Redundancy Maximum Relevance (MRMR ) technique. MRMR was implemented to select informative features and evaluate four ML algorithms (Naïve Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs)) using feature subsets with varying levels of relevance based on MRMR scores. Our results demonstrate that MRMR effectively reduced dimensionality while maintaining and even improving classification accuracy. KNN and SVM achieved the highest accuracy (100% for 67, 30, and 24 feature subsets), suggesting the benefit of focusing on highly relevant features. NB exhibited consistent accuracy across all feature sets.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7720
Issue No: Vol. 14, No. 4 (2024)
- Design and Implementation of a High Gain Hexagon Loop Antenna for 5G and
WLAN Application
Authors: Ali Y. Al-Zahrani, Mohd Najim
Pages: 15620 - 15624
Abstract: In this work, the design and implementation of a high-gain Hexagon Loop Antenna (HLA) for 5G and WLAN application is presented. Initially, a hexagon patch was designed to resonate at 3.5 GHz. A loop-based radiator was tailored to obtain miniaturization without affecting the overall performance of the proposed antenna. A CPW feed was utilized to maintain the wide bandwidth with improved return loss. FR-4 material was used as a dielectric to retain the low cost of the antenna. The return loss was kept well below -10 dB, from 3.2 GHz to 6.4 GHz. The gain of the proposed Microstrip Hexagon Loop Antenna (MHLA) is 5.3 dBi at 3.5 GHz and 8.2 dBi at 5.8 GHz, respectively. To improve the gain and directivity of the proposed antenna, a square-shaped AMC unit cell was considered, and finally, a [4×4] ground plane AMC was integrated with the antenna. The radiation pattern of the antenna is stable and low cross-polarization is maintained for the desired band. A simulation of the proposed antenna was carried out in the ANSYS HFSS tool and the experimental results were measured inside an anechoic chamber. The experimental and simulated results of the proposed antenna are in good agreement. Its simple and low-profile structure, lower cost, and high gain ensure that the proposed high gain antenna is well suited for sub-6 GHz band and wireless application.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7323
Issue No: Vol. 14, No. 4 (2024)
- Development of a Risk Breakdown Structure in Mega Projects based on
Different Case Studies
Authors: Shamal Ali Othman, Dalshad Kakasor Ismael Jaff, Ahmet Oztas
Pages: 15625 - 15630
Abstract: Rapid urbanization, globalization, and population growth have led to an increase in megaprojects in recent decades. Consequently, as construction projects move forward, a wider range of risks arise. By looking into and managing risk factors in advance of their occurrence, it is vital to reduce their negative effects. In construction projects, risk management is seen as a critical procedure that helps meet project objectives in relation to schedule, budget, quality, safety, and sustainability concerns. This study aims to examine and gain a deeper understanding of project-related risks in mega projects. This study also develops a risk breakdown structure in mega projects based on a literature review. This will help project participants manage these risks in their projects properly. Another objective of this study is to examine the methodologies used in data collection for determining and categorizing risks in mega projects. Finally, it is concluded that the risk factors in mega projects can be divided into two categories: internal and external risks whereas the main risk factors in mega projects are categorized as execution, construction, technical, economic and financial, environmental, social, political, and other.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7585
Issue No: Vol. 14, No. 4 (2024)
- A Performance Study of Different Approaches of Digital Image Compression
Techniques
Authors: Wahida Ali Mansouri, Salwa Hamda Othman, Somia Asklany, Doaa Mohamed Elmorsi
Pages: 15631 - 15636
Abstract: Today, managing a large amount of information becomes increasingly crucial. Efficient storage and retrieval of digital data are essential for their effective utilization. This study investigates the efficacy of Spatial Domain Image Compression Techniques, which directly manipulate the original image to reduce its size by leveraging pixel spatial relationships. These techniques segment the image into blocks and process each block independently. Evaluation entails measuring perceptual quality through metrics, such as PSNR, WPSNR, NMSE, and SSIM applied to the compressed image. Experimental results provide a comparative analysis of the performance of these techniques.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7862
Issue No: Vol. 14, No. 4 (2024)
- Planning Optimization of a Standalone Photovoltaic/Diesel/Battery Energy
System for a Gold Mining Location in Mauritania
Authors: Kebbada Salihi, Mohammed Qasim Taha, Abdou Oubeidi, Mamoudou Ndongo, Sadok Ben Jabrallah, Bamba El Heiba
Pages: 15637 - 15644
Abstract: The greenhouse gas emissions resulting from the excessive use of Diesel Generators (DGs) in mining locations pose a threat to the environment and the macroeconomic sustainability of this industry. This paper aims to decrease or eliminate the use of DG units in gold mining areas to increase access to more clean Renewable Energy Sources (RESs) such as Photovoltaic (PV) systems. In order to evaluate PV potential at small-scale gold mining sites in Mauritania, ArcGIS software is utilized to analyze Chagatt gold mining location as a case study. The techno-economic viability of a PV/DG/battery Hybrid Energy System (HES) was examined and discussed. For yearly modeling, the PVsyst and HOMER Pro were employed to assess the performance of the ideal size of HES in terms of installation and energy costs. The findings indicate that Mauritania's gold mining locations are most suitable for PV energy harvesting. As a result, this industry may rely on clean PV energy.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7776
Issue No: Vol. 14, No. 4 (2024)
- Early Glaucoma Detection using LSTM-CNN integrated with Multi Class SVM
Authors: Vijaya Madhavi Vuppu, P. Lalitha Surya Kumari
Pages: 15645 - 15650
Abstract: Glaucoma, a progressive eye disease, is a major public concern on health due to its gradual onset and the possibility of irreversible vision loss. Early glaucoma detection is critical because it allows for timely intervention and management, lowering the risk of severe visual impairment. To address this pressing need, we present a comprehensive glaucoma detection methodology that focuses on image processing techniques and machine learning models. The initialization and preprocessing of retinal fundus images obtained from the DRIVE database is the first step in our approach. These images are resized to a standard size, grayscaled, and blurred with Gaussian blur to ensure consistency and noise reduction. Our methodology is built around feature extraction and modeling. We harness the power of deep learning, specifically Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), which we integrate seamlessly with multi-class Support Vector Machines (SVMs). This synergy enables our Deep Flex SVM-MC model to capture intricate data patterns during training while also demonstrating exceptional adaptability in multi-class classification tasks. The proposed model has a glaucoma detection accuracy of 97.2%, an exceptional sensitivity of 97.53%, indicating its proficiency in correctly identifying glaucoma cases, and a specificity of 96.4%.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7798
Issue No: Vol. 14, No. 4 (2024)
- Enhancing the Load-bearing Capacity of a Damaged Hydraulic Structure
Rehabilitated by Underwater Concreting
Authors: Bach Phuong Ho Thi, Viet Duc Nguyen, Quoc Vu Vuong, Hoang Minh Dang
Pages: 15651 - 15655
Abstract: Due to the severe environment, damages often occur early on hydraulic structures, even in parts submerged in water. This paper presents a study on how the load-bearing capacity of the hydraulic structure is enhanced, once it has been rehabilitated by underwater concreting. A column has been considered as the hydraulic structure. The damaged part of the column was submerged in water, hence, it was repaired by the underwater concrete. For this, the tremie casting method was taken into account. The compression test results pointed out that the damaged column after rehabilitation had a similar load-bearing capacity to the undamaged column, while the not rehabilitated column had reduced load-bearing capacity by 20%. The failure mode of the damaged column after the rehabilitation presented longitudinal cracks along the body of the column, which is similar to that occurred in the undamaged column. Besides, there was no sign of delamination between the protective layer and the existing concrete of the damaged column, proving the feasibility of the selected casting method and concrete mixture for rehabilitation.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7843
Issue No: Vol. 14, No. 4 (2024)
- Enhancing e-Commerce Strategies: A Deep Learning Framework for Customer
Behavior Prediction
Authors: Yasser D. Al-Otaibi
Pages: 15656 - 15664
Abstract: Today, the use of artificial intelligence (AI) to enhance the processes of online shopping is crucial for e-commerce as it uses the past purchasing behavior of customer-automated processes. Nevertheless, predicting or understanding customers’ buying behavior remains a major challenge. This research work attempts to put forward a new approach by utilizing Deep Learning (DL) models to identify whether a customer will buy or not depending on his age and salary. By employing lightweight dense layers in the DL architecture, the model is trained with the use of publicly available datasets and has great accuracy and performance metrics. This predictive model offers valuable lessons for e-commerce because the recommendation and marketing personalization methods it deploys can be integrated into the business to yield improved experience and performance for customers and users.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7945
Issue No: Vol. 14, No. 4 (2024)
- Deep Learning and Fusion Mechanism-based Multimodal Fake News Detection
Methodologies: A Review
Authors: Iman Qays Abduljaleel, Israa H. Ali
Pages: 15665 - 15675
Abstract: Today, detecting fake news has become challenging as anyone can interact by freely sending or receiving electronic information. Deep learning processes to detect multimodal fake news have achieved great success. However, these methods easily fuse information from different modality sources, such as concatenation and element-wise product, without considering how each modality affects the other, resulting in low accuracy. This study presents a focused survey on the use of deep learning approaches to detect multimodal visual and textual fake news on various social networks from 2019 to 2024. Several relevant factors are discussed, including a) the detection stage, which involves deep learning algorithms,
b) methods for analyzing various data types, and c) choosing the best fusion mechanism to combine multiple data sources. This study delves into the existing constraints of previous studies to provide future tips for addressing open challenges and problems.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7907
Issue No: Vol. 14, No. 4 (2024)
- Comparative Assessment of Fraudulent Financial Transactions using the
Machine Learning Algorithms Decision Tree, Logistic Regression, Naïve
Bayes, K-Nearest Neighbor, and Random Forest
Authors: Paiboon Manorom, Umawadee Detthamrong, Wirapong Chansanam
Pages: 15676 - 15680
Abstract: Today, fast-paced technology plays an important role in financial transactions, especially in payment-related digital habits. As fraud is a major concern in online payments, many machine-learning approaches have been proposed to detect and prevent fraudulent payment transactions. This study aimed to evaluate Decision Tree, Logistic Regression, Naïve Bayes, K-Nearest Neighbor, and Random Forest in detecting fraudulent payment transactions. The results show that Random Forest, K-Nearest Neighbor, Decision Tree, and Logistic regression achieved total accuracy rates exceeding 99%. However, such impressive results do not necessarily indicate satisfactory performance. The results highlight the need to detect fraudulent transactions and investigate specific improvements to effectively manage and minimize unexpected financial transaction fraud.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7774
Issue No: Vol. 14, No. 4 (2024)
- An Advanced Filter-based Supervised Threat Detection Framework on Large
Databases
Authors: Lakshmi Prasanna Byrapuneni, Maligireddy SaidiReddy
Pages: 15681 - 15685
Abstract: Adaptive and robust detection mechanisms are becoming more and more necessary as cyber threats become more complex. This study presents a framework to increase threat detection efficiency and address the complex problems posed by various dynamic cyber threats. This study focuses primarily on investigating a new algorithm for feature classification and selection in predictive modeling applications. Using a sizable real-time threat detection dataset, a hybrid filter-based feature ranking and cluster-based classification approach is proposed. A detailed analysis was carried out to investigate the performance of the proposed algorithm and compare it with various machine-learning models. This study also examines how well the algorithm scales to large-scale datasets and adapts to different data properties. The results highlight the algorithm's potential to enhance the efficiency of predictive modeling by optimizing feature selection procedures and reducing model complexity, thus making a substantial contribution to the field of data-driven decision-making and the wider range of machine-learning applications.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7779
Issue No: Vol. 14, No. 4 (2024)
- Automatic Call of CPO Toolpaths without Uncut Regions for 2.5 D Pocket
Milling
Authors: Elhachemi Bahloul, Khelifa Guerraiche
Pages: 15686 - 15694
Abstract: In order to achieve the automatic call of any toolpath generation in 2.5 D pocket milling, new tool paths with offset parallel to the contour for any concave or convex polygonal shape are proposed in this article. Focus is given on several methods for addressing issues related to trajectory generation: the disappearance of edges when transitioning from one contour to another, the residual between passes, and the center of the pocket. A few selected test cases are presented for validation. The obtained results reveal that the approach introduced offers automatic toolpath generation for any polygonal shape and ensures efficient machining simulation without the appearance of material residues between passes in the corners or at the center of the pocket.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7669
Issue No: Vol. 14, No. 4 (2024)
- Application of Synthetic Data on Object Detection Tasks
Authors: Huu Long Nguyen, Duc Toan Le, Hong Hai Hoang
Pages: 15695 - 15699
Abstract: Object detection is a computer vision task that identifies and locates one or more effective targets from image or video data. The accuracy of object detection heavily depends on the size and the diversity of the utilized dataset. However, preparing and labeling an adequate dataset to guarantee a high level of reliability can be time-consuming and labor-intensive, because the process of building data requires manually setting up the environment and capturing the dataset while keeping its variety in scenarios. There have been several efforts on object detection that take a long time to prepare the input data for training the models. To deal with this problem, synthetic data have emerged as a potential for the replacement of real-world data in data preparation for model training. In this paper, we provide a technique that can generate an enormous synthetic dataset with little human labor. Concretely, we have simulated the environment by applying the pyBullet library and capturing various types of input images. In order to examine its performance on the training model, we integrated a YOLOv5 object detection model to investigate the dataset. The output of the conducted model was deployed in a simulation robot system to examine its potential. YOLOv5 can reach a high accuracy of object detection at 93.1% mAP when solely training on our generated data. Our research provides a novelistic method to facilitate the understanding of data generation process in preparing datasets for deep learning models.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7929
Issue No: Vol. 14, No. 4 (2024)
- The Impact of Supply Chain Delays on Inventory Levels and Sale Demand
Fulfillment: Analyzing the Effects of Lead Times and In-Transit Quantities
Authors: Vipul Ladva, Madhu Shukla, Chetansinh Vaghela
Pages: 15700 - 15710
Abstract: Efficient inventory management is essential for maintaining a balance between supply and demand in various industries. This research study aims to quantitatively examine the impact of supply chain delays, with a specific emphasis on lead times and in-transit amounts, inventory levels, and the ability to meet sales demands. Mathematical modeling and statistical analysis are utilized to create prediction models that assess the impact of variations in lead time and quantities in transit on inventory stability and fulfillment rates. The study used regression analysis to ascertain the relationships between the indicated parameters and inventory outcomes. Also, machine learning algorithms like Random Forest and Linear Regression are applied to predict possible disruptions and optimize inventory levels. The methodology followed focuses on the Tri-Model Fusion Stacking approach, which combines various models to improve the predicted accuracy and offer a more comprehensive analysis. The main goal of this research is to provide practical insights that help organizations optimize their inventory management techniques, resulting in cost reduction and enhanced service levels. The findings aim to simplify the modification of inventory management techniques in light of up-to-date supply chain information, providing a notable improvement in the resources available to supply chain experts.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7834
Issue No: Vol. 14, No. 4 (2024)
- A Secure Framework based On Hybrid Cryptographic Scheme and Trusted
Routing to Enhance the QoS of a WSN
Authors: Mohammad Sirajuddin, Chittibabu Ravela, S. Rama Krishna, Shaik Khaleel Ahamed, S. Karimulla Basha, N. Md. Jubair Basha
Pages: 15711 - 15716
Abstract: Achieving Quality of Service (QoS) in Wireless Sensor Networks (WSNs) is challenging, due to their dynamic nature, and many parameters must be taken into account. The main objective of this work is to propose a hybrid cryptographic system with trust-based routing to improve the QoS. This study considers ways to improve QoS while maintaining security. To enhance the performance of the WSN, a framework that uses a hybrid cryptographic system based on a logistic map and the trust-based routing protocol CTBSR that can recognize and counteract a variety of security threats is presented. The findings of this study support the claim that the proposed framework ensures better security than the existing approaches in terms of confidentiality, integrity, and authentication. The performance of the framework introduced is evaluated by employing the NS2 simulator.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7633
Issue No: Vol. 14, No. 4 (2024)
- Semantic IoT Transformation: Elevating Wireless Networking Performance
through Innovative Communication Paradigms
Authors: Ibrahim R. Alzahrani
Pages: 15717 - 15723
Abstract: This paper addresses the privacy concerns inherent in semantic communication within the Internet of Things (IoT) and proposes a Secure Semantic Communication Framework (SSCF) to ascertain confidentiality and communication accuracy without compromising semantic integrity. The proposed framework uses the Advanced Encryption Standard (AES) for encryption to address privacy breaches in semantic communication. Additionally, it introduces a novel approach employing Deep Q-Networks (DQN) for adversarial training to maintain semantic communication accuracy in both unencrypted and encrypted modes. SSCF combines universality and confidentiality, ensuring secure and efficient semantic communication. Experimental evaluations showed that SSCF, with its adversarial encryption learning scheme, effectively ensures communication accuracy and privacy. Regardless of encryption status, SSCF significantly hinders attackers from restoring original semantic data from intercepted messages. The integration of heuristic algorithms enhances performance and security. The proposed framework is based on a shared database for training network modules. The originality of the proposed approach lies in the introduction of a DQN-based adversarial training technique to balance confidentiality and semantic communication accuracy, address key privacy concerns, and enhance the security and reliability of IoT communication systems.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7784
Issue No: Vol. 14, No. 4 (2024)
- Performance Evaluation of Asphalt Mixtures with Rediset LQ-1200 Additive
Authors: Thanh Len Nguyen, Van Phuc Le, Quang Phuc Nguyen, Gia Bao Dang, Minh Hong Nhan Nguyen
Pages: 15724 - 15728
Abstract: One of the primary causes of premature pavement deterioration in Asphalt Concrete (AC) mixtures is the breaking of the adhesive bond between the aggregate and the binder. The purpose of this study is to assess the performance characteristics of AC mixture utilizing the Rediset LQ-1200 additive. In order to achieve this goal, tests such as the Marshall Stability (MS), Marshall Stability Ratio (MSR), Indirect Tensile (IDT), indirect tensile cracking, and Wheel Tracking (WT) were deployed to evaluate the mechanical properties of AC mixtures. The findings demonstrated a considerable improvement in the physical parameters of the AC mixture with the Rediset LQ-1200 additive compared to the base AC mixture. The MS and MSR were improved by about 28% and 13%, respectively, while the AC mixture's resistance to rutting and cracking was efficiently increased.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7848
Issue No: Vol. 14, No. 4 (2024)
- A Privacy Recommending Data Processing Model for Internet of Vehicles
(IoV) Services
Authors: Ali Alqarni
Pages: 15729 - 15733
Abstract: The Internet of Vehicles (IoV) faces security challenges in maintaining privacy due to the presence of open networks and diverse services. Ensuring privacy is essential in transportation networks to provide users with a long-lasting driving, navigation, and communication experience. In this paper, the proposed Privacy Recommending Data Processing Model (PRDPM) is deployed to handle the huge amount of data accumulated in this field. The proposed model adopts data processing techniques that are dependent on user demand and are influenced by either neighboring entities or service providers. The various application requirements are analyzed to minimize the potential privacy consequences. The data from various intervals are utilized to validate the parameters in the operational plane. Thus, data balancing is performed using plane differentiation to prevent privacy leaks in either of the vehicular services. This is useful for neighbors and infrastructures across various applications/users.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7743
Issue No: Vol. 14, No. 4 (2024)
- Evaluation Strength of Materials of the Compressor Wheel and Engine Power
in the Turbocharger
Authors: Tran Huu Danh, Le Hong Ky, Pham Hoang Anh, Dang Thanh Tam, Nguyen Hoang Hiep
Pages: 15734 - 15738
Abstract: This paper presents the research results on the strength of materials and power of the Toyota 3C engine when changing the structure and number of blades of the compressor wheel in the turbocharger. 3D models of different compressor wheels were created using reverse engineering and then simulated in the ANSYS environment with turbine shaft rotation speeds of 10,000, 15,000, and 20,000 rpm, respectively, to examine the strength of the compression wheel materials. To evaluate engine power, compressor wheels were machined on a 5-axis CNC milling machine. The MP 100S specialized test bed was used to perform experiments and compare engine power when using the original and alternative compressor wheels of the CT9 turbocharger. The compressor wheels were made of aluminum alloy, with a structure and number of blades selected to ensure durability when working. The CT9 turbocharger has a four-pair blade compressor wheel that consistently delivers higher engine power than in other cases.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7891
Issue No: Vol. 14, No. 4 (2024)
- Influence of Magnetized Mixing Water on Different Levels of Concrete
Strength using Different Curing Processes
Authors: Dhuha M. Hussein, Zena K. Abbas
Pages: 15739 - 15744
Abstract: This study investigated the impact of using Magnetic Water (MW) in concrete mixes on the mechanical properties of three normal concrete strength grades (15 MPa, 27.5 MPa, and 40 MPa) cured with three different methods (normal curing, water spraying, and coating). Compressive, flexural, and splitting strengths were tested. Results revealed that for the 15 MPa concrete, water spraying reduced compressive strength by 15.76% at 28 days compared to normal curing while coating curing increased it by 15.63%. Similar trends were observed for the 27.5 MPa (13.98% decrease for spraying, 13.60% increase for coating) and 40 MPa (10.81% decrease for spraying, 10.60% increase for coating) concrete grades. Flexural and splitting strength tests followed a similar pattern. For all concrete grades, water spraying led to reduced strength, while coating curing improved it. Overall, coating curing yielded the most favorable results across all strength grades, with the 15 MPa concrete showing the most significant improvements. These findings highlight the potential benefits of utilizing magnetic water in combination with coating curing to enhance the mechanical properties of concrete.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7898
Issue No: Vol. 14, No. 4 (2024)
- Overcoming the Limitations of the RAPS Method by identifying Alternative
Data Normalization Methods
Authors: Nguyen Van Thien, Hoang Tien Dung, Do Duc Trung
Pages: 15745 - 15750
Abstract: This study proposes a new approach to improve the performance of the Ranking Alternatives by Perimeter Similarity (RAPS) method in Multi-Criteria Decision-Making (MCDM). RAPS has attracted attention but encounters difficulties when handling zero values in the decision matrix. This study suggests using alternative data normalization methods and assesses their suitability when combined with RAPS in various scenarios. The results identified three additional normalization methods that are appropriate for integration with RAPS. These findings provide a theoretical basis and specific guidelines for selecting data normalization methods when applying RAPS in MCDM.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7909
Issue No: Vol. 14, No. 4 (2024)
- Controlling Output Power to Enhance the Investment Efficiency of Wind
Farms by Maximizing the Capacity of Transmission Transformers and
Integrating Energy Storage Systems
Authors: Truong Viet Anh, Nguyen Tung Linh, Dinh Ngoc Sang
Pages: 15751 - 15756
Abstract: This study addresses inherent challenges stemming from uncertainty associated with the integration of wind energy into the electricity market. A novel approach is proposed to leverage the capabilities of dynamic transformers to optimize the utilization of uncertain wind power output, thereby enhancing financial investment efficiency for wind power stakeholders. The flexible combination of wind turbines (WTB), transmission transformers (TTS), and Energy Storage Systems (ESS) can actively reserve or provision electricity. Electricity generation control is based on optimal analysis results using linear integer programming algorithms that consider temperature fluctuations, lifespan of transformers, and electricity market prices. Maximizing the dynamic transformer's efficiency as proposed and optimizing revenue and costs from the fluctuating wind power output significantly improves financial performance metrics when investing in wind farm projects. Financial figures highlighted in the paper emphasize notable benefits, particularly for wind farm expansion projects. The potential return on investment ratio is expected to increase up to 5.64 times compared to conventional wind farm investment scenarios, with an improvement to increase from 4.4% to 24.8.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7688
Issue No: Vol. 14, No. 4 (2024)
- An Experimental Investigation on the Synthetic Ester Circulation for
Drying Cellulose Insulation in Distribution Transformers
Authors: Adilbek Tazhibayev, Yernar Amitov, Nurbol Arynov, Nursultan Shingissov, Askat Kural
Pages: 15757 - 15763
Abstract: Water can cause damage to power transformers by accelerating aging processes, reducing the dielectric margin, decreasing the partial-discharge inception voltage, and increasing the risk of unexpected failures. Modern electrical companies utilize a variety of drying techniques but sometimes do not comprehend them, making drying less effective. To address these challenges, this study proposes the application of synthetic ester to dry distribution transformers because water dissolves better in the ester than other dielectric liquids. An improved laboratory model of transformer insulation was used for the investigation. This model dried the ester using a molecular filter and carefully selected adsorbed weight. Pressboard strip water content before and after drying was analyzed to determine the drying efficacy of the cellulose insulation. The water content was measured using the Karl-Fischer titration method. The investigation proved that the drying procedure worked. At an ester moisture level of 105-120 ppm and an insulation system temperature of 70°C, samples dried for 5 days showed above 1% water loss. The experimental investigation demonstrated the high efficiency of the proposed drying method for distribution transformers.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7788
Issue No: Vol. 14, No. 4 (2024)
- Shearing Properties of Epoxy and Epoxy Bitumen as Bonding Material of
Asphalt Overlay on Ultra-High Performance Concrete Slab
Authors: Trung Quang Dinh, Thi Kim Dang Tran, Ngoc Quy Ngo
Pages: 15764 - 15770
Abstract: This article discusses the results of direct shear and fatigue shear tests on epoxy resin and epoxy bitumen bonding materials. Shearing properties, including shear strength, shear stiffness, shear energy, and post-failure energy, are analyzed using results from direct shear tests at 30°C and 60°C. The fatigue tests used a direct shearing test with a pulse load of 1 Hz frequency at 60°C to analyze the fatigue life and plateau value based on the ratio of dissipated energy change versus load cycles curve. At 30°C, the shearing properties of the tested epoxy resin were approximately 60-70% higher than those of the tested epoxy bitumen. The epoxy resin possesses an outstanding advantage against the epoxy bitumen at high temperatures when applying the shear energy approach. At 60°C, the shear energy of the epoxy resin was 30.5% higher than that of the epoxy bitumen, while its shear strength and shear stiffness were 18.5% and 79% lower than those of the epoxy asphalt, respectively. The shear fatigue life of the epoxy resin after the energy method was more than ten times that of the epoxy bitumen, and its plateau value was only 10% of the epoxy bitumen. Regression analysis was also performed using fatigue shear test data to provide a fatigue shear equation in the form of an exponential function.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7734
Issue No: Vol. 14, No. 4 (2024)
- A Dual-Step Approach for Implementing Smart AVS in Cars
Authors: Bachu Poornima, P. Lalitha Surya Kumari
Pages: 15771 - 15778
Abstract: The Smart Autonomous Vehicular System (AVS) is designed to combine technologies such as sensors, cameras, radars, and machine learning algorithms in cars. The implementation of Smart AVS in smart cars has the potential to revolutionize the automotive industry and transform the way we think about transportation. In this paper, the implementation of Smart AVS in smart cars includes two steps. Firstly, the architecture is designed using Microsoft Threat Modelling tool. Secondly, with the use of Engineering Software, smart cars are constructed and simulated to verify and validate algorithms related to autonomous driving, path planning, and other intelligent functionalities. Simulating these algorithms in a controlled virtual environment helps to identify and address issues before implementation on physical vehicles. The main advantages of using the proposed model are early detection of vulnerabilities, realistic simulation of sensor inputs, communication protocol testing, cloud integration validation, user interface, and consumer experience, and validation of compliance with security standards.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7844
Issue No: Vol. 14, No. 4 (2024)
- Feasibility Analysis of Wind Power Plant in South East Region, Vietnam
Authors: Nguyen Tuong An Truong, Nguyen Binh Khanh, Luong Ngoc Giap, Bui Tien Trung, Ngo Phuong Le, Tran The Vinh
Pages: 15779 - 15783
Abstract: The wind power market is expanding quickly and the cost of wind power equipment is decreasing, making wind power technology a key player in the world's energy transition. Assessing wind potential and selecting the right wind turbine site are crucial parameters for developing a wind farm. Vietnam focuses on onshore and nearshore wind power projects due to its promising wind power potential and supportive policies. However, Vietnam has diverse climate characteristics and wind patterns. Therefore, initial basic research is necessary to evaluate the feasibility of investing in wind power projects. This study examines the technical feasibility of a typical wind power project in the Southeast region of Vietnam using Wind Atlas Analysis and Application Program (WAsP) software. The results indicate that the wind turbine's type and installation location significantly affect wind power plants' efficiency. The total power output of the wind power project after factoring with losses at a rate of 17%, is 304,149 MWh.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7849
Issue No: Vol. 14, No. 4 (2024)
- Evaluating Nine Machine Learning Algorithms for GaN HEMT Small Signal
Behavioral Modeling through K-fold Cross-Validation
Authors: Neda Ahmad, Vandana Nath
Pages: 15784 - 15790
Abstract: This paper presents an investigation into the modeling of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) using multiple Machine Learning (ML) algorithms. Despite the documented use of various ML techniques, a thorough comparison and performance analysis under different operating conditions were lacking. This study fills this gap by conducting a rigorous evaluation of nine ML models using TCAD-generated data of Pseudomorphic AlGaN/InGaN/GaN HEMT. The research focuses on Small Signal Behavioral Modeling and examines regression techniques such as Multiple Linear Regression (MLR), Multivariate Linear Regression (MVLR), Ridge Regression (L2), Lasso Regression (L1), Elastic Net Regression (ENR), Decision Trees (DT), Random Forest (RF), Gradient Boosting Regression(GBR), and Support Vector Regression (SVR). These methods use biases, frequency, and device geometry as independent variables, with S-parameters being the dependent variables. K-fold cross-validation was employed to ensure model reliability and accuracy across diverse operating conditions. Results reveal that the RF, coupled with 10-fold cross-validation, exhibits superior performance giving 99.7% accurate results, with a Mean Squared Error (MSE) of 4.6375×10-5, and a coefficient of determination (R2) of 0.9977. Conversely, SVR, L1, and ENR fall short of expectations. This study underscores the significance of methodological advancements in ML-based GaN HEMT modeling and provides valuable insights for future research in this domain.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7726
Issue No: Vol. 14, No. 4 (2024)
- Investigating Efficient Thermal Distribution in a House Room by combining
Statistics with Computational Fluid Dynamics
Authors: Jairo Aparecido Martins, Adriano Francisco Siqueira, Estaner Claro Romao
Pages: 15791 - 15796
Abstract: The study of energy sources is an open subject due to constraints on the current energy global production versus the current and future energy demands. From the consumption perspective, houses pull considerable energy from the electrical grid. With that being said, this paper investigates the theoretical thermal distribution of the heat in the basement of a house and measures the theoretical temperatures throughout different points at the same height by using statistics and numerical simulation. The numerical simulation, such as Computational Fluid Dynamic Analysis by COMSOLTM combined with Statistics by MiniTabTM was utilized to determine the most economical settings for the variables in the heating system evaluation. It is understood that thermal comfort for householders is achieved when the heat is evenly distributed in the room. To have a more realistic model set-up, the air flow in the room was considered as a turbulent model. The studied variables were intake airflow, positioning of the vents (intakes), airflow temperature, and external temperature. The results showed the significance of the variables. The latter were ranked from the highest to the lowest as: external temperature, airflow velocity, inlet location, and temperature input, while the highest interaction was found between the external temperature and air inlet velocity. This study comes up with a superior understanding of the system and generates an efficient setting for the variables for energy-saving purposes.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7923
Issue No: Vol. 14, No. 4 (2024)
- Investigation of Strength and Long-Term Durability Properties of
stabilized Coal Mine Overburden Material for Base and Subbase Layer of
Pavement
Authors: Subhash Chandra, Sanjeev Sinha
Pages: 15797 - 15804
Abstract: The purpose of this study is to examine the possibility of using Coal Mine Overburden (CMOB) material as a secondary aggregate in low volume roadways' sub-base and/or base layer. Such roads usually experience less traffic, which means that weaker materials like CMOB could be used in various layers of the road after stabilization, either alone or in combination with cement or fly ash. After 7 and 28 days of curing, samples taken from Jharkhand mines were used to assess the strength characteristics of the stabilized samples, namely Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR). The findings demonstrate that the material, which has CBR of 80% or above and UCS of 3 MPa at 6% for Cement-Treated (CT-CMOB) and Cement-Fly Ash-Treated (CFA-CMOB) samples, may be utilized successfully as a secondary aggregate in low-volume road building. The results were then validated through standard acceptance as per IRC provisions and microstructural analysis. Additionally, correlations were established between the 7 and 28-day UCS properties of CT-CMOB and CFA-CMOB samples. This information can be beneficial for pavement engineers to estimate the strength properties associated with the base and subbase layer of pavement using CMOB as a suitable alternative to conventional aggregates.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7861
Issue No: Vol. 14, No. 4 (2024)
- Real-Time Rain Prediction in Agriculture using AI and IoT: A
Bi-Directional LSTM Approach
Authors: Radhika Peeriga, Dhruva R. Rinku, J. Uday Bhaskar, Rajeswaran Nagalingam, Fahd M. Aldosari, Hussain M. Albarakati, Ayman A. Alharbi, Amar Y. Jaffar
Pages: 15805 - 15812
Abstract: Accurate rain forecasting is crucial for optimizing agricultural practices and improving crop yields. This study presents a real-time rain forecasting model using a Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm for an on-device AI platform. The model uses historical weather data to predict rainfall, enabling farmers to make data-driven decisions in irrigation, pest control, and field operations. This model enables farmers to optimize water use, conserve energy, and improve overall resource management. Real-time capabilities allow immediate adjustments to agricultural activities, mitigating risks associated with unexpected weather changes. The Bi-LSTM model achieved a mean accuracy of 92%, significantly outperforming the traditional LSTM (85%) and ARIMA (80%) models. This high accuracy is attributed to the model's bidirectional processing capability, which captures comprehensive temporal patterns in the weather data. Implementing this model can enhance decision-making processes for farmers, resulting in increased productivity and profitability in the agricultural sector.
PubDate: 2024-08-02
DOI: 10.48084/etasr.8011
Issue No: Vol. 14, No. 4 (2024)
- An Advanced Deep Learning Approach for Precision Diagnosis of Cotton Leaf
Diseases: A Multifaceted Agricultural Technology Solution
Authors: Ashwathnarayan Nagarjun, Nagarajappa Manju, Abdulbasit A. Darem, Shivarudraswamy Siddesha, Abdulsamad E. Yahya, Asma A. Alhashmi
Pages: 15813 - 15820
Abstract: During the past few decades, cotton leaf diseases have become a significant challenge for farmers, leading to substantial losses in harvests, productivity, and financial resources. Traditional observation methods are often time-consuming, costly, and prone to inaccuracies, exacerbating the plight of farmers in detecting and identifying diseases in their early stages. The consequences of late detection are dire, and both crops and farmers are under the brunt of prolonged infections. This study proposes a method to improve the detection of cotton leaf diseases by applying advanced deep transfer learning techniques. Using models such as ResNet101, Inception v2, and DenseNet121, and fine-tuning parameters utilizing the Nesterov accelerated gradient, the proposed system offers a powerful tool for farmers to swiftly and accurately diagnose leaf diseases. This system allows users to simply upload an image of a cotton leaf. After sophisticated image processing techniques, a Convolutional Neural Network (CNN) is deployed to detect the presence of cotton leaf diseases with high precision and efficiency. The experimental results demonstrated the effectiveness of transfer learning approaches, with the CNN achieving an impressive accuracy of 99%, while ResNet101, Inception v2, and DenseNet121 achieved 75.36%, 97.32%, and 97.16%, respectively. These findings underscore the potential of deep learning techniques to revolutionize disease detection in agricultural contexts, offering farmers a powerful tool to mitigate the impact of diseases on their crops.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7535
Issue No: Vol. 14, No. 4 (2024)
- Optimizing TEM Image Segmentation: Advancements in DRU-Net Architecture
with Dense Residual Connections and Attention Mechanisms
Authors: M. Nagaraju Naik, Nagajyothi Dimmita, Vijayalakshmi Chintamaneni, P. Srinivasa Rao, Nagalingam Rajeswaran, Amar Y. Jaffar, Fahd M. Aldosari, Wesam N. Eid, Ayman A. Alharbi
Pages: 15821 - 15828
Abstract: This study introduces an innovative enhancement to the U-Net architecture, termed Modified DRU-Net, aiming to improve the segmentation of cell images in Transmission Electron Microscopy (TEM). Traditional U-Net models, while effective, often struggle to capture fine-grained details and preserve contextual information critical for accurate biomedical image segmentation. To overcome these challenges, Modified DRU-Net integrates dense residual connections and attention mechanisms into the U-Net framework. Dense connections enhance gradient flow and feature reuse, while residual connections mitigate the vanishing gradient problem, facilitating better model training. Attention blocks in the up-sampling path selectively focus on relevant features, boosting segmentation accuracy. Additionally, a combined loss function, merging focal loss and dice loss, addresses class imbalance and improves segmentation performance. Experimental results demonstrate that Modified DRU-Net significantly enhances performance metrics, underscoring its effectiveness in achieving detailed and accurate cell image segmentation in TEM images.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7994
Issue No: Vol. 14, No. 4 (2024)
- Performance of Modified Polymer as a Binder with Qarry Dust and Ballast in
the Production of Paving Blocks
Authors: Priscilla Jebotip Ngetich, Fundi Isaac Sanewu, Odero J. Brain S.
Pages: 15829 - 15835
Abstract: The city of Nairobi is in need of pedestrian and cyclist infrastructure as an answer to an increasing reliance on automobiles. Collaborative efforts are underway to reduce congestion, although challenges such as sand mining persist. The utilization of plastic waste and Quarry Dust (QD) may provide a sustainable solution. The proposed study aims to assess the mechanical properties and optimal blend design for the production of paving blocks using a modified polymer and a combination of ballast (B) and QD. The gradation curve constituted a pivotal stage in the research process, as it provided vital information regarding the ratio of B and QD required for the optimal mix design. The study methodically explored the optimal ratio of materials for the production of paving blocks incorporating a modified polymer, QD, and B. It was observed that there were minor reductions in strength under acidic conditions, which suggests that the proposed paving blocks may be suitable for use in a variety of outdoor applications. Additionally, it would be advantageous to investigate innovative solutions for Non-Motorized Transport (NMT) infrastructure.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7199
Issue No: Vol. 14, No. 4 (2024)
- Dynamic Adaptation in Deep Learning for Enhanced Hand Gesture Recognition
Authors: Abdirahman Osman Hashi, Siti Zaiton Mohd Hashim, Azurah Bte Asamah
Pages: 15836 - 15841
Abstract: The field of Human-Computer Interaction (HCI) is progressing quickly with the incorporation of gesture recognition, which requires advanced systems capable of comprehending intricate human movements. This study introduces a new Dynamic Adaptation Convolutional Neural Network (DACNN) that can adjust to different human hand shapes, orientations, and sizes. This allows for more accurate identification of hand gestures over a wide range of variations. The proposed model includes a thorough process of collecting and preparing data from the Sign Language MNIST dataset. This is followed by a strong data augmentation procedure that provides a wide variety of realistic variations. The architecture utilizes sophisticated convolutional layers to leverage the capabilities of deep learning to extract and synthesize essential gesture features. A rigorous training procedure, supplemented with a ReduceLROnPlateau callback, was used to assure the model's generalization and efficiency. The experimental findings provide remarkable results, showing a substantial accuracy of 99% in categorizing a wide range of hand movements. This study makes a significant contribution to the field of hand gesture recognition by introducing morphological operations, thus enriching input data quality and expanding the model's applicability in diverse HCI environments.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7670
Issue No: Vol. 14, No. 4 (2024)
- A Study on the Structural Safety of Buildings using Image Metrology
Authors: Alfred Sunday Alademomi, Eganoosi Esme Atojunere, Tosin Julius Salami, Abiodun Olawale Alabi, Olalekan Abeeb Jimoh, Sunday Amos Ishola, Inioluwa Victor Ayantayo-Ojo, Joseph Olayemi Odumosu
Pages: 15842 - 15847
Abstract: This research aims to explore the potential of image metrology as a substitute for monitoring building structural deformations. It utilizes stereo-photogrammetry and the Kalman filter in its approach. The ultimate goal is to reduce the damage to human lives and property caused by the collapse of building structures in urban areas. Image metrology and conventional geodetic surveying were both used in monitoring the deformation of a selected building prone to land subsidence. Four geodetic monitoring stations were established using the GNSS surveying technique, while 10 photo points were placed on the selected building for deformation monitoring. Simultaneous observation of photo points and acquisition of their images were carried out during the first three months of this study. The data acquired via geodetic survey were subjected to least square adjustment while the images acquired were subjected to stereo-photogrammetry and Kalman filtering for extraction and refinement of photo point coordinates. The preliminary results show that image metrology is comparable to conventional geodetic survey methods for monitoring building deformation down to 100 mm. The t statistic value of 1.524234 and t critical value of 1.7291333 justify the comparability.
PubDate: 2024-08-02
DOI: 10.48084/etasr.6951
Issue No: Vol. 14, No. 4 (2024)
- FEM Structural Analysis for Ship's Beam Modification: A Case Study
Authors: Adrian Popa, Mihaela-Greti Manea, Marian-Valentin Ristea-Komornicki
Pages: 15848 - 15853
Abstract: Classification is mandatory for all seagoing ships engaged in international trade worldwide, with regulations that require the strength of the hull construction to be confirmed. Upon completion of the ship's construction or during the execution of repair works in shipyards (reconversion works, modification of panels, replacement of equipment, etc.), the strength structure of the ship may be affected and the classification society must certify that it meets the requirements. Replacing old equipment sometimes implies some variations in overall dimensions, which may impose some changes (adjustments) on the layout of structural panel elements. This study applied the Finite Element Method (FEM) considering the von Mises theory in a case study of a beam modification with the support of the ANSYS software. This study used the reverse engineering method to investigate an already implemented solution.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7885
Issue No: Vol. 14, No. 4 (2024)
- Optimization of Wheel Dressing Technological Parameters when Grinding
Hardox 500 Steel
Authors: Tran Huu Danh, Le Hong Ky
Pages: 15854 - 15859
Abstract: This paper presents the research results on the influence of wheel dressing technological parameters on minimum Surface Roughness (SR) and maximum Material Removal Rate (MRR) of Hardox 500 steel. The Box-Behnken planning method with 4 factors was used, including rough wheel dressing depth ar, the number of rough wheel dressing times nr, fine wheel dressing depth af, and the number of fine wheel dressing times nf. The impact of these factors on the objectives of the grinding process was evaluated. Furthermore, an optimal grinding mode was proposed to increase MRR while minimizing SR. The proposed model can be used in industry and further research can investigate the grinding of workpiece materials with high-alloy or high-speed steel.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7986
Issue No: Vol. 14, No. 4 (2024)
- The Flexural Behavior of One-Way Concrete Bubbled Slabs Reinforced by
GFRP-Bars with Embedded Steel I-Sections
Authors: Mohannad Abdulkhaliq, Ali Hussein Al-Ahmed
Pages: 15860 - 15870
Abstract: This study examines the behavior of polymer bubbled deck slab systems, one-way concrete slabs with polymer sphere voids reinforced with Glass Fiber-Reinforced Polymer (GFRP) rebars, and embedded I-shaped steel beams. Six one-way structural concrete slabs (2600 mm long, 600 mm wide, 150 mm deep) were tested and directly supported under two points bending. Five Bubbled Slabs (BS), one of which was un-strengthened, were compared to the reference Solid Slab (SS) without polymer spheres. Each slab had 95 polymer sphere voids of 90 mm diameter and 15.48% self-weight decrease. Several parameters, including specimen type (SS or BS) and internal strengthening, were optimized using steel I-shapes in two distinct forms (2 and 4 pcs. of steel I-sections). Channel Shear Connectors (CSCs) and bent-up steel bars (10 mm in diameter) were implemented to increase shear resistance, with the 4I-section form having a cross-sectional area equivalent to the 2I-section form. In contrast to the SS, the BS exhibited a wider range of deformations during the same loading stage, with ultimate load capacity decreasing by 30% and deflection occurring at a greater ratio of approximately 18% to 85%. Additionally, the embedded steel I-shapes improved specimen performance compared to BS and SS. This occurred by reducing deflection at a service load by 60% and 49%, eliminating cracks, improving ultimate load capacity by 85% and 30%, and enhancing flexural stiffness by 102% and 71%, respectively, at the ultimate loading stage. CSC increased ultimate load by 13% to 22% and deflection by 8% to 15%, compared to specimens without CSC.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7680
Issue No: Vol. 14, No. 4 (2024)
- Mathematical Modeling in Natural Extract Anti-Reflection Coatings using
Green Synthesis Method
Authors: Snehal Marathe, B. P. Patil, Shobha Waghmode
Pages: 15871 - 15875
Abstract: The use of renewable energy sources to replace conventional energy sources like fossil fuels is essential. Solar panels are the most widespread technology for clear energy production. However, is crucial to raise the efficiency of solar panels. A large portion of sunlight is reflected by the front surface of the panel and thus the use of an Anti-Reflecting Coating (ARC) has become significant in raising the efficiency of solar panels, through reducing the reflection losses. The ARCs made of natural extracts were utilized to improve the efficiency of Silicon solar panels. The natural extracts were produced from Kailashpati fruit juice and Badminton ball tree flower powder. In the synthesis of these natural extracts, monometallic gallium chloride nanoparticles were used to check their effect on the efficiency of solar power generation. The novelty of this paper is the attempt to mathematically calculate the absorbance of the ARCs, at a particular wavelength, with the use of the refractive indices and thicknesses of ideal ARCs.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7525
Issue No: Vol. 14, No. 4 (2024)
- The Performance of Stable Zones Protocol for Heterogeneous Wireless Sensor
Networks
Authors: Kamel Khedhiri, Djammal Djabbour, Adnen Cherif
Pages: 15876 - 15881
Abstract: Wireless sensor networks are characterized by significant constraints, with the primary performance parameter being their lifetime. In the context of a wireless sensor network, the distance from the base station emerges as a critical factor that influences the energy consumption of the nodes, thus affecting the overall network lifetime. To address this issue, this study introduces the Stable Zones Protocol for Heterogeneous wireless sensor networks (SZP-H). This protocol strategically divides the network into distinct zones, each differing from the other in terms of its distance from the base station and the initial energy available. This protocol outperformed traditional protocols, effectively mitigating the challenges associated with node energy consumption and improving the overall performance of the wireless sensor network. The simulation results show that SZP-H achieves the highest possible stable period and lifetime and the highest throughput level compared to the FBECS, E-CAFL, and LEACH-FC protocols. Specifically, SZP-H achieves a remarkable extension of the network's lifetime by a ratio of 303%, and 275% compared to FBECS, E-CAFL, and LEACH-FC.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7716
Issue No: Vol. 14, No. 4 (2024)
- Improved Quality Parameter Estimation of Photovoltaic System Models based
on SAO Algorithm
Authors: Rim Attafi, Naoufal Zitouni, Masoud Dashtdar, Aymen Flah, Mohamed F. Elnaggar, Mohammad Kanan
Pages: 15882 - 15887
Abstract: Solar energy provides one of the most favorable options regarding the transition to clean energy sources. The parameters of a photovoltaic (PV) system play determine its performance under various scenarios. The PV model parameter estimation is an example of nonlinear planning. This study proposes a novel use of the established Smell Agent Optimizer (SAO) algorithm to anticipate the undefined parameters of the PV model's single-diode and two-diode equivalent circuits. This study aims to create a precise PV model that can accurately characterize its performance under changing operational conditions. The desired objective function is defined as the square of the mean squared error between the model's current-voltage curve and the measured curve.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7919
Issue No: Vol. 14, No. 4 (2024)
- Effects of utilizing Crumb Rubber as Aggregate in Asphalt Mixtures
Authors: Safa I. Oleiwi, Amjad K. Albayati
Pages: 15888 - 15898
Abstract: Experts have given much attention on the use of waste in asphalt paving because of its significance from a sustainability perspective. This paper evaluated the performance properties of asphalt concrete mixes modified with Crumb Rubber (CR) as a partial replacement for two grade sizes of fine aggregate (2.36, and 0.3 mm) at six replacement rates: 0%, 2%, 4%, 6%, 8%, and 10% by weight. Asphalt concrete mixes were prepared at their Optimum Asphalt Content (OAC) and then tested for their engineering properties. Marshall properties, fatigue, rutting, ideal CT index test, Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray (EDX) spectroscopy were deployed to examine the crystalline structure and elemental composition of the CR-modified and unmodified asphalt concrete mixtures. The results showed a difference in Marshall's characteristics. The CT index revealed that the optimum cracking tolerance was achieved with a 2% CR substitution. Wheel track test results indicated that a 4% CR addition improved the rutting resistance of the asphalt mixture. SEM and EDX analyses exhibited significant changes in microstructure and elemental composition with the addition of CR. The main findings reveal that the use of 2% CR as a partial replacement of fine aggregate contributes to the production of more durable asphalt concrete mixtures with better serviceability. However, these results are based on laboratory experiments and require field verification to ensure practical applicability and long-term performance.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7927
Issue No: Vol. 14, No. 4 (2024)
- Comparison of Τwo Modules in Sedimentation Process using Mathematical
Techniques
Authors: Smita R. Pidurkar, Seema Raut, Mangesh Bhorkar
Pages: 15899 - 15902
Abstract: Raw water must be purified before being distributed to any village, town, or city. To purify raw water, various significant processes must be performed in a water treatment plant, with sedimentation being one of the most important. Solid particles in the form of dirt and other contaminants can be found in raw water, especially during the rainy season, which can be removed through sedimentation. Plate settlers or tube settlers are commonly utilized in sedimentation units to treat water quickly, reducing the detention time to 15-20 minutes. This study modified conventional tubes in terms of manufacturing and repositioning to increase turbidity removal in raw water. The experimental work was carried out for one year to calculate the balanced turbidity of the raw water using conventional and modified square-shaped tube settlers. Mathematical analysis was deployed to compare the output from one year's data from both experimental studies. Regression, R2, relative standard deviation, and ANOVA were employed to analyze the experimental models, and the observed and calculated findings were compared. The results show that the modified tube settlers removed more turbidity from the raw water than the conventional ones.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7964
Issue No: Vol. 14, No. 4 (2024)
- Contributory Factors related to the Tensile Strength of Hot Mix Asphalt
Concrete
Authors: Mina M. Oleiwi, Amjad K. Albayati
Pages: 15903 - 15909
Abstract: Tensile strength is a critical property of Hot Mix Asphalt (HMA) pavements and is closely related to distresses such as fatigue cracking. This study aims to evaluate methods for assessing fatigue cracking in Asphalt Concrete (AC) mixes. In order to achieve optimum density at different binder contents, the mixes were compressed using a gyratory compactor. Tensile strength was assessed using the Indirect Tensile (IDT) and Semi-Circular Bend (SCB) tests. The results showed that the tensile strength measured by the SCB test was consistently higher than that measured by the IDT test at 25 °C. In addition, the SCB test showed a stronger correlation between increasing binder content and tensile strength. For binder contents ranging from 4.2% to 5.2%, the IDT test results increased from 541% to 678.7%, while the SCB test results increased from 630.3% to 743.7%. These results suggest that the SCB test provides a more accurate representation of the tensile strength of AC mixes than the IDT test.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7928
Issue No: Vol. 14, No. 4 (2024)
- Enhancing the Structural Integrity and Performance of an Agricultural
Robot with Caterpillar Tracks: A Comprehensive Deformation Analysis
Authors: Sivayazi Kappagantula, Giriraj Mannayee, Arigela Satya Veerendra, Soham Dutta, Aymen Flah
Pages: 15910 - 15915
Abstract: The robustness and longevity of agricultural robots, specifically those utilizing caterpillar tracks for coconut harvesting, are based on understanding their strain, stress, and load thresholds. This study delves into the deformation characteristics of caterpillar track systems, pinpointing critical structural vulnerabilities and potential points of failure. Through a meticulous analysis of the maximum allowed strain and stress thresholds, this study unravels crucial insights to enhance performance and reliability in coconut field operations. Leveraging the power of ANSYS for structural analysis and simulation under varied constraints, this study aims to fortify the structural integrity of agricultural robots. By offering valuable insights and solutions, this study paves the way for advancements in agricultural robotics technology, ensuring that these machines can endure rigorous tasks while maintaining peak functionality.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7740
Issue No: Vol. 14, No. 4 (2024)
- Comparative Analysis of an Apartment Building using Seismic Codes NBC
105:1994 and NBC 105:2020 (A Case Study)
Authors: Suraj Malla, Mukil Alagirisamy, Purushotam Dangol, Om Prakash Giri
Pages: 15916 - 15922
Abstract: The present study undertakes a comparative analytical examination of seismic analysis standards in Nepal, focusing on NBC 105:1994 and the updated NBC 105:2020, encompassing both the Ultimate Limit State (ULS) and Serviceability Limit State (SLS). Employing a regular Reinforced Concrete (RC) apartment building in Pokhara as a case study, the geometric and sectional configurations of structural elements are intentionally kept consistent for comparison. The analysis involves creating a 3D model using ETABS version 19, encompassing linear static, Equivalent Static (ES), and linear dynamic Response Spectrum (RS) analyses, followed by nonlinear static (pushover) analysis. The results highlight substantial differences between the two codes. Base shear from NBC 105:2020 is notably higher, being 28.59% ULS and 22.74% SLS greater than NBC 105:1994. The scale factor for combined response design values is significantly lower in both X and Y directions for NBC 105:2020. Story shear is extended by 33% ES and 37% RS with NBC 105:2020 compared to NBC 105:1994. Maximum design displacement and Inter-Story Drift (ISD) are markedly higher with NBC 105:2020, indicating its more severe seismic parameters. This study emphasized the enhanced seismic resilience provided by NBC 105:2020, particularly evident in increased base shear, reduced design scale factors, and higher values for story shear, displacement, and ISD. These findings contribute valuable insights into the seismic design improvements introduced in Nepal's seismic codes after the Gorkha Earthquake in 2015.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7858
Issue No: Vol. 14, No. 4 (2024)
- Assessing Wear Coefficient and Predicting Surface Wear of Polymer Gears: A
Practical Approach
Authors: Enis Muratovic, Adil Muminovic, Nedim Pervan, Muamer Delic, Adis Muminovic, Isad Saric
Pages: 15923 - 15930
Abstract: With the ever-increasing number of polymer materials and the current number of commercially available materials, the polymer gear design process, regarding the wear lifetime predictions, is a difficult task given that there are very limited data on wear coefficients that can be deployed to evaluate the wear behavior of polymer gears. This study focuses on the classic steel/polymer engagements that result in a wear-induced failure of polymer gears and proposes a simple methodology based on the employment of optical methods that can be used to assess the necessary wear coefficient. Polymer gear testing, performed on an open-loop test rig, along with VDI 2736 guidelines for polymer gear design, serves as a starting point for the detailed analysis of the wear process putting into service a digital microscope that leads to the evaluation of the wear coefficient. The same wear coefficient, as presented within the scope of this study, can be implemented in a rather simple wear prediction model, based on Archard’s wear formulation. The developed model is established on the iterative numerical procedure that accounts for the changes in tooth flank geometry due to wear and investigates the surface wear impact on the contact pressure distribution to completely describe the behavior of polymer gears in different stages of their lifetime. Although a simple one, the developed wear prediction model is sufficient for most engineering applications, as the model prediction and experimental data agree well with each other, and can be utilized to reduce the need to perform time-consuming testing.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7421
Issue No: Vol. 14, No. 4 (2024)
- Real-Time Monitoring for a Building-Integrated Photovoltaic System based
on the Internet of Things and a Web Application
Authors: Atef Ftirich, Bechir Bouaziz, Faouzi Bacha
Pages: 15931 - 15937
Abstract: Building-Integrated Photovoltaic (BIPV) systems have become the most attractive clean solution for generating sustainable energy in building structures. Thus, the challenge of improving their efficiency is of extreme importance. The deployment of remote monitoring systems based on the Internet of Things (IoT) presents an opportunity to reduce the overall costs associated with BIPV systems. However, the performance of these monitoring systems varies depending on different parameters and environmental conditions. This paper presents a low-cost IoT-based prototype for monitoring of a solar photovoltaic panel connected to a battery. The current, voltage, and temperature values of the panel and battery, along with environmental parameters such as humidity, temperature, and solar irradiance were measured using low-cost relevant sensors. The data were sent and stored in PostgreSQL database servers through the Raspberry Pi4 Wi-Fi microcontroller board. Real-time visualization of data was facilitated through Web monitoring interfaces. The intelligent monitoring system was implemented in a facility located in Gabes, Tunisia.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7531
Issue No: Vol. 14, No. 4 (2024)
- Machine Learning Baseline Energy Model (MLBEM) to Evaluate Prediction
Performances in Building Energy Consumption
Authors: Rijalul Fahmi Mustapa, Muhammad Asraf Hairuddin, Atiqah Hamizah Mohd Nordin, Nofri Yenita Dahlan, Ihsan Mohd Yassin, Nur Dalila Khirul Ashar
Pages: 15938 - 15946
Abstract: Electric Energy Consumption (EEC) prediction for building operations can be performed using a Baseline Energy Model (BEM), which is vital to ensure the efficiency of the EEC estimates with its respective independent variables. However, developing the BEM to represent the relationship between independent variables can be a complex task due to the EEC variability in an educational building that differs during its operation period. The best-suited BEM must be continuously improvised to achieve good modeling with accurate and reliable predictions that capture the building operations’ current dynamics. This study aims to conduct a comparative performance assessment between deep learning, machine learning, and statistical models to develop the BEM and, therefore, predict the EEC of the building for 24, 48, 72, and 96 hours, while considering the operation of the lecture weeks and the associated number of students and staff. The hours and temperature are considered as independent variables to be tested with residual error evaluations, whilst the correlation coefficient, coefficient of determination, and training time are also taken into account. Three models with different categories involving Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and AutoRegressive Integrated Moving Average with Exogenous inputs (ARIMAX) were compared, concluding that SVR was the best and can be used as a universal model in the Machine Learning Baseline Energy Model (MLBEM) studies. Accurate EEC prediction will offer a huge advantage for building operators to properly monitor, plan, and manage the EEC, hence avoiding excessive utility bills.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7683
Issue No: Vol. 14, No. 4 (2024)
- Enhancing Arabic Fake News Detection: Evaluating Data Balancing Techniques
Across Multiple Machine Learning Models
Authors: Eman Aljohani
Pages: 15947 - 15956
Abstract: The spread of fake news has become a serious concern in the era of rapid information dissemination through social networks, especially when it comes to Arabic-language content, where automated detection systems are not as advanced as those for English-language content. This study evaluates the effectiveness of various data balancing techniques, such as class weights, random under-sampling, SMOTE, and SMOTEENN, across multiple machine learning models, namely XGBoost, Random Forest, CNN, BIGRU, BILSTM, CNN-LSTM, and CNN-BIGRU, to address the critical challenge of dataset imbalance in Arabic fake news detection. Accuracy, AUC, precision, recall, and F1-score were used to evaluate the performance of these models on balanced and imbalanced datasets. The results show that SMOTEENN greatly improves model performance, especially the F1-score, precision, and recall. In addition to advancing the larger objective of preserving information credibility on social networks, this study emphasizes the need for advanced data balancing strategies to improve Arabic fake news detection systems.
PubDate: 2024-08-02
DOI: 10.48084/etasr.8019
Issue No: Vol. 14, No. 4 (2024)
- An Efficient Optimization System for Early Breast Cancer Diagnosis based
on Internet of Medical Things and Deep Learning
Authors: Amna Naz, Hamayun Khan, Irfan Ud Din, Arshad Ali, Mohammad Husain
Pages: 15957 - 15962
Abstract: Improving patient outcomes and treatment efficacy requires effective early detection of breast cancer. Recently, medical diagnostics has been transformed by merging the Internet of Things (IoT) technology with AI and ML methods. Better and faster diagnoses have been made possible by this revolutionary synergy, which allows the study of both real-time and historical data. Unfortunately, many people still die from breast cancer because modern diagnostics are not good enough to catch the disease in its early stages, even though medical science has come a long way. To overcome this obstacle, this study introduces a new medical diagnostic system that utilizes IoT to accurately distinguish between patients with and without tumors. The proposed model achieved 95% classification accuracy in differentiating between non-tumor and tumor instances by utilizing a Convolutional Neural Network (CNN) with hyperparameter adjustment. This approach can improve the accuracy and efficiency of breast cancer diagnosis by integrating medical devices with AI applications and healthcare infrastructure. In the long run, this study could help reduce breast cancer deaths by increasing early detection rates. This study can revolutionize healthcare delivery and improve patient outcomes on a global scale through continued innovation and collaboration with medical IoT technology.
PubDate: 2024-08-02
DOI: 10.48084/etasr.8080
Issue No: Vol. 14, No. 4 (2024)
- PSI-SAW and PSI-MARCOS Hybrid MCDM Methods
Authors: Tran Van Dua
Pages: 15963 - 15968
Abstract: This paper presents a study on the hybridization of Multi-Criteria Decision-Making (MCDM) methods: Preference Selection Index (PSI), Simple Additive Weighting (SAW), and Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS). The hybridization was conducted between the PSI and the other two methods, resulting in new methods, namely PSI-SAW and PSI-MARCOS. For each specific problem, applying these two hybrid methods to rank alternatives among the available options produces three different sets of rankings: one created by PSI, one by the hybrid PSI-SAW, and one by the hybrid PSI-MARCOS. The accuracy of the proposed models was tested in three different cases. The test results show that both proposed models exhibit high accuracy. This study provides users with highly accurate and useful methods for MCDM.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7992
Issue No: Vol. 14, No. 4 (2024)
- The Projection-Based Data Transformation Approach for Privacy Preservation
in Data Mining
Authors: Diana Judith Irudaya Raj, Vijay Sai Radhakrishnan, Manyam Rajasekhar Reddy, Natarajan Senthil Selvan, Balasubramanian Elangovan, Manikandan Ganesan
Pages: 15969 - 15974
Abstract: Data mining is vital in analyzing large volumes of data to extract functional patterns and knowledge hidden within the data. Data mining has practical applications in various scientific areas, such as social networks, healthcare, and finance. It is important to note that data mining also raises ethical concerns and privacy considerations. Organizations must handle data responsibly, ensuring compliance with legal and ethical guidelines. Privacy-Preserving Data Mining (PPDM) refers to conducting data mining tasks while protecting the privacy of sensitive data. PPDM techniques aim to strike a balance between privacy protection and data utility. By employing PPDM techniques, organizations can perform safe and private data analysis, protecting sensitive information while deriving valuable insights from the data. The current paper uses geometric transformation-based projection techniques such as perspective projection, isometric projection, cabinet projection, and cavalier projection to protect data privacy and improve data utility. The suggested technique's performance was assessed with the K-means clustering technique. The UCI repository's Bank Marketing dataset was used to verify the error rate of the proposed projection techniques.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7969
Issue No: Vol. 14, No. 4 (2024)
- A Study on the Influence of FDM Parameters on the Tensile Behavior of
Samples made of ASA
Authors: Dragos Gabriel Zisopol, Mihail Minescu, Dragos Valentin Iacob
Pages: 15975 - 15980
Abstract: This paper presents the results of the study on the influence of FDM printing parameters on the tensile behavior of samples made of ASA. To perform the study, 27 tensile samples were made of ASA on Anycubic 4 Max 2.0 3D printer using as the height of the single-pass layer Lh = 0.10/0.15/0.20 mm and as filling percentage Id = 50/75/100%. The results obtained from tensile testing of the samples on the universal testing machine demonstrate the way in which the FDM parameters influence the tensile strength, the percentage elongation at break, and the elastic modulus. The parameter that significantly influences the tensile characteristics of ASA samples made using FDM is the filling percentage (Id).
PubDate: 2024-08-02
DOI: 10.48084/etasr.8023
Issue No: Vol. 14, No. 4 (2024)
- Developing an Automatic 3D Solid Reconstruction System from only Two 2D
Views
Authors: Long Hoang, Thanh Tuan Nguyen, Hoang Anh Tran, Duc Huy Nguyen
Pages: 15981 - 15985
Abstract: Three-dimensional (3D) solid models of mechanical machine parts are widely used in modern mechanical engineering. One expected approach in creating 3D models is automatically reconstructing them from 2D engineering drawings. This work expands the previous automatic reconstruction methods by adding principles, algorithms, and coding to reconstruct oblique planes on the part. The proposed method uses only two views as the input to reconstruct the 3D solid part, including oblique planes. The proposed method has been implemented by a program written in the ARX 2018 language running on the AutoCAD 2021 platform to reconstruct multiple 3D parts from their two views. Experimental test results on many samples confirmed that the proposed method is reliable, absolutely accurate, and achieves a high reconstruction speed. The output 3D model has also been tested and confirmed for compatibility with CAD/CAM software such as Solid Works, Inventor, and PTC Creo.
PubDate: 2024-08-02
DOI: 10.48084/etasr.8141
Issue No: Vol. 14, No. 4 (2024)
- Application of Unmanned Aerial Vehicle and Ground Control Point for
Mapping and Road Geometric Review
Authors: Tampanatu P. F. Sompie, Ralgie E. Makangiras, Josef A. J. Sumajouw, Chris Hombokau
Pages: 15986 - 15992
Abstract: Technology implementation, particularly the use of Unmanned Aerial Vehicles (UAVs) and photogrammetry, is being employed in road works for regional and road planning. The current study deploys aerial photographs and data processing along with software, like Agisoft Metashape, PCI Geomatica, Global Mapper, and Autocad Civil 3D as an efficient and effective way to generate digital maps and perform geometric road reviews. The accuracy test of CE90 performed for horizontal accuracy was 0.003 m and the LE90 carried out for vertical accuracy was 0.006 m. This accuracy level is valuable for road planning, ensuring that the data utilized for decision-making are reliable and precise. The study focused on Wori Street spanning from Pandu to Kima Atas Street Manado, covering the section from Sta. 0+000 to Sta. 5+225, which is a collector road with a designated speed of 50 km/h. Among the 16 bends analyzed, 11 met highways’ standards for the collector road class, certifying compliance with safety guidelines. Furthermore, the existing road slope conforms to standard requirements, remaining below 8%. This adherence to safety criteria is vital for the design and operation of safe roads.
PubDate: 2024-08-02
DOI: 10.48084/etasr.8040
Issue No: Vol. 14, No. 4 (2024)
- The Influence of Capillary Geometry on the Stiffness of Hydrostatic
Bearings in Medium-Sized Cylindrical Grinding Machines: A Simulation
Analysis
Authors: Manh-Toan Nguyen, The-Hung Tran, Duc-Do Le, Duc-Toan Tran, Duy-Thinh Bui, Bui Tuan Anh
Pages: 15993 - 15999
Abstract: One of the most crucial parameters in the field of machine tool engineering is the stiffness of the machine tool spindle, as it has a direct impact on the precision and accuracy of machining. This paper presents a research on the effects of geometric parameters of capillary tubes, including diameter (dc) and length (lc), on the stiffness of hydrostatic bearings in machine tools. The oil pump pressure in this study was set at a range from 3 MPa to 5 MPa. The simulation results demonstrate the relationship between the capillary geometric parameters, pressure, and the stiffness of the hydrostatic spindle unit. These findings indicate a direct relationship between stiffness and pump pressure, with increases in pressure also raising lubricant temperature, which may impair cooling. The shape of the capillary directly affects the ratio of oil chamber pressure to pump pressure, which in turn affects hardness. The results of the study indicate that for spindles in machine tools, a dc ranging from 0.3 mm to 0.6 mm and a (lc/dc) ratio of between 20 and 100 are feasible based on the given stiffness requirement. It is possible to select an appropriate set of capillary parameters to achieve the best stiffness with specific requirements under the working conditions of hydrostatic bearings based on the results of the simulation analysis.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7976
Issue No: Vol. 14, No. 4 (2024)
- Design and Implementation of an IoT-Integrated Smart Locker System
utilizing Facial Recognition Technology
Authors: Abdulrahman A. Alzhrani, Mohammed Balfaqih, Fadi Alsenani, Mohemmed Alharthi, Ali Alshehri, Zain Balfagih
Pages: 16000 - 16010
Abstract: The Internet of Things (IoT) has been widely employed in the development of smart locker systems over the last decade. However, some of these systems are based on authentication methods which lack flexibility. Such systems did not consider the possibility that an authentication method could be unavailable for different reasons, namely access card loss, camera or mice break, etc. Moreover, such systems do not consider dual-authentication methods that enhance security. This paper aims to develop a smart locker system that considers several authentication methods including dual authentication (phone number and One Time Password (OTP)), fingerprint, face recognition, and emergency code utilizing IoT technology. Dual authentication method is the considered base authentication method. The system has been fabricated and evaluated taking into account different scenarios including monitoring door status, ensuring access for authorized users, and denying access to unauthorized users.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7737
Issue No: Vol. 14, No. 4 (2024)
- Multi-Objective Load-balancing Strategy for Fog-driven Patient-Centric
Smart Healthcare System in a Smart City
Authors: Gaurav Goel, Amit Kr Chaturvedi
Pages: 16011 - 16019
Abstract: The spatially concentrated architecture of the cloud environment causes excessive latency and network congestion in traditional smart healthcare systems designed for smart cities. Fog computing underpins IoT-enabled smart city solutions for latency sensitivity by putting computing power closer to the network boundary. However, resource management issues degrade service quality and accelerate energy depletion in real-time smart healthcare systems, as the fog node workload has increased exponentially. This paper offers a fog-driven patient-centric smart healthcare system for an e-healthcare environment to maintain Quality of Service (QoS) during severe traffic load on a fog platform. The multi-objective EQLS (Energy-efficient QoS-aware Load balancing Strategy), is proposed to stabilize workload among processing nodes to increase real-time sensitivity of critical tasks within optimal response time and energy usage. Using the iFogSim simulator to present the significance of research work, the proposed technique is compared to existing load-balancing policies (Round Robin (RR) and Fog Node Placement Algorithm (FNPA)) regarding energy usage, response time, and cost. The simulation results reveal that EQLS saves 8.7% and 14.9% more energy and 6.2% and 13.4% greater response time over FNPA and RR, respectively. The results signify that the proposed approach can efficiently support real-time applications of smart cities.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7749
Issue No: Vol. 14, No. 4 (2024)
- Design of a Compact Circular Microstrip Patch Antenna for 5G Applications
Authors: Awatef Djouimaa, Karima Bencherif
Pages: 16020 - 16024
Abstract: This paper presents the design and analysis of a compact circular microstrip patch antenna for 5G millimeter wave technology applications. The dimensions of the proposed patch are 5.959 mm × 5.959 mm × 1.400 mm. The antenna exhibits a resonant frequency of 28 GHz, a return loss of -45 dB, a bandwidth of 1.7638 GHz, and a gain of 0.1573. In order to meet the requirements of 5G applications at 28 GHz, a compact, high-gain, and large bandwidth antenna is necessary. The principal objective of this study is to enhance the performance of the antenna parameters, thereby achieving an optimal balance between size, gain, and bandwidth. A notable improvement in bandwidth and gain is achieved. The design, analysis, and optimization processes were conducted using the High Frequency Surface Structure (HFSS) software, which employs the Finite Element Method (FEM) numerical method.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7961
Issue No: Vol. 14, No. 4 (2024)
- Assessing Institutional Performance using Machine Learning on Arabic
Facebook Comments
Authors: Zainab Alwan Anwer, Ahmad Shaker Abdalrada
Pages: 16025 - 16031
Abstract: Social networks have become increasingly influential in shaping political and governmental decisions in Middle Eastern countries and worldwide. Facebook is considered one of the most popular social media platforms in Iraq. Exploiting such a platform to assess the performance of institutions remains underutilized. This study proposes a model to help institutions, such as the Iraqi Ministry of Justice, evaluate their performance based on sentiment analysis on Facebook. Different machine learning algorithms were used, such as Support Vector Machine (SVM), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Naive Bayes (NB), and Random Forest (RF). Extensive experimental analysis was performed using a large dataset extracted from Facebook pages belonging to the Iraqi Ministry of Justice. The results showed that SVM achieved the highest accuracy of 97.774% after retaining certain stop words, which proved to have a significant impact on the accuracy of the algorithms, ensuring the correct classification of comments while preserving the sentence's meaning.
PubDate: 2024-08-02
DOI: 10.48084/etasr.8079
Issue No: Vol. 14, No. 4 (2024)
- Exploring Advance Approaches for Drowning Detection: A Review
Authors: Nouf Alharbi
Pages: 16032 - 16039
Abstract: This research mainly explores the existing drowning detection methodologies, focusing primarily on the roles carried out by Machine Learning (ML) and Deep Learning (DL) algorithms. It directly emphasizes the dominance of ML in the analysis of raw sensor data along with the contribution of DL to computer vision, which also reveals the present gap between advanced vision along detection models. The holistic approaches are mainly advocated, potentially integrating wearable devices, vision-based systems, as well as sensors while also balancing their performance, regional applicability, and cost-effectiveness. The challenges aligned to enabling real-time detection and reduced latency are important for the time-sensitive realm of incidents related to drowning. Future directions necessarily include the exploration of advanced forms of vision models and segmentation techniques for innovative detection algorithms. Integration of wearable devices and sensors with the inclusion of vision-based systems is important for the required adaptability. The upcoming proposal aims to integrate robotics into rescue operations bringing revolution to response times. The study also covers the requirement for a compact combination of ML and DL algorithms and a generalized solution for the equilibrium maintenance between cost-effectiveness, sophistication, and regional applicability.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7804
Issue No: Vol. 14, No. 4 (2024)
- INDCOMP: A Shiny App for Open Data Repository of the Performance of an
Indonesian Company Listed at the Indonesia Stock Exchange
Authors: Prana Ugiana Gio, Herman Mawengkang, Muhammad Zarlis, Saib Suwilo
Pages: 16040 - 16048
Abstract: Investors, practitioners, and stock researchers highly need data related to financial performance to predict a company's financial health condition, which is used as a basis to consider investing in it. The Indonesia Stock Exchange (IDX) website provides reports on the company's financial performance. Unfortunately, the company’s financial data found on the IDX website are in PDF format, and researchers must download them one by one, which takes a long time. This study presents a website-based application, named Indonesia Company Performance (INDCOMP), built using the R programming language and involving various R packages and frameworks to assist investors, practitioners, and stock researchers in studying the financial performance of companies. This application can help users quickly access the financial performance data of various companies, present financial performance data in data tables, and perform data visualizations as well as statistical analyses.
PubDate: 2024-08-02
DOI: 10.48084/etasr.8131
Issue No: Vol. 14, No. 4 (2024)
- Miscibility Analysis of Ethanol-Diesel Blends with Additives: A
Comprehensive Investigation
Authors: Amit M. Patel, Ronakkumar R. Shah, Vijay K. Patel, Chandrakant Sonawane
Pages: 16049 - 16053
Abstract: Diesel-Ethanol (DE) blends have gained attention as an alternative fuel due to their potential to reduce emissions and improve the performance of diesel engines. However, a significant challenge when dealing with DE blends is that of phase separation. Achieving optimal miscibility between ethanol and diesel often requires the addition of additives. This research article thoroughly examines the miscibility, blend stability, and phase separation of DE mixtures with various additives. To prepare blends for the miscibility analysis, DE blends with 10%, 15%, and 20% ethanol were mixed with selected additives (n-pentanol, n-butanol, ethyl acetate, and diethyl ether) at a 2% concentration in each blend. Prior to the miscibility analysis, all blends were mixed using a magnetic stirrer and a probe ultrasonicator. The results indicated that DE10 with 2% n-propanol, DE10 with 2% n-butanol, DE15 with 2% n-butanol, and DE10 with 2% diethyl ether exhibited good miscibility without phase separation or sedimentation after four weeks. However, it was noted that all the other blends with higher ethanol content were eventually phase-separated after four weeks.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7914
Issue No: Vol. 14, No. 4 (2024)
- Enhancing the Capacity of Large LEO Satellites with Internetworked Small
Piggybacks for Low Latency Payload Data Transmission
Authors: V; Ramalakshmi, Venkata Narayana Madhavareddy, Govardhani Immadi, V. V. Srinivasan
Pages: 16054 - 16060
Abstract: In most cases, the utilization of the costly payload onboard Low Earth Orbit (LEO) satellites is restricted by the limited throughput of the payload data downlink to the ground station during the visibility window. The usefulness of these data in critical applications reduces due to the large latency of the process. Different techniques involving efficient modulation schemes, increased power within the allowed level and frequency band, and capacity enhancement using close-by satellites have been studied and implemented with their relative merits and limitations in an attempt to reduce the latency of the data to the user. The present study proposes a constellation of low-cost data relay satellites placed in the same orbital plane along with the main satellite, increasing directly the effective visibility window. As a result, the utility of the main satellite is also increased by the same factor. A detailed analysis of the constellation and configuration of the relay satellites is presented in this paper.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7449
Issue No: Vol. 14, No. 4 (2024)
- Optimizing Edge AI for Tomato Leaf Disease Identification
Authors: Anitha Gatla, S. R. V. Prasad Reddy, Deenababu Mandru, Swapna Thouti, J. Kavitha, Ahmed Saad Eddine Souissi, A. S. Veerendra, R. Srividya, Aymen Flah
Pages: 16061 - 16068
Abstract: This study addresses the critical challenge of real-time identification of tomato leaf diseases using edge computing. Traditional plant disease detection methods rely on centralized cloud-based solutions that suffer from latency issues and require substantial bandwidth, making them less viable for real-time applications in remote or bandwidth-constrained environments. In response to these limitations, this study proposes an on-the-edge processing framework employing Convolutional Neural Networks (CNNs) to identify tomato diseases. This approach brings computation closer to the data source, reducing latency and conserving bandwidth. This study evaluates various pre-trained models, including MobileNetV2, InceptionV3, ResNet50, and VGG19 against a custom CNN, training and validating them on a comprehensive dataset of tomato leaf images. MobileNetV2 demonstrated exceptional performance, achieving an accuracy of 98.99%. The results highlight the potential of edge AI to revolutionize disease detection in agricultural settings, offering a scalable, efficient, and responsive solution that can be integrated into broader smart farming systems. This approach not only improves disease detection accuracy but can also provide actionable insights and timely alerts to farmers, ultimately contributing to increased crop yields and food security.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7802
Issue No: Vol. 14, No. 4 (2024)
- Federated Learning for Privacy-Preserving Air Quality Forecasting using
IoT Sensors
Authors: Abdullah Alwabli
Pages: 16069 - 16076
Abstract: Air quality forecasting is crucial for public health and urban planning. However, traditional machine learning models face challenges with centralized data collection, raising privacy and security concerns. Federated learning (FL) offers a promising solution by enabling model training across decentralized data sources while preserving data privacy. This study presents an FL framework for predicting the Air Quality Index (AQI) using data from many Internet of Things (IoT) sensors deployed in urban areas. The proposed FL framework facilitates model training using diverse sensor data while maintaining data privacy at each source. Local computational resources at the sensor level are used for initial data processing and model training, with only model updates shared centrally, reducing data transmission requirements. The FL model achieved comparable accuracy to centralized approaches while enhancing data privacy. This work represents a significant advancement for smart city initiatives and environmental monitoring, offering a scalable, real-time, and privacy-aware framework for air quality monitoring systems that leverage IoT technology.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7820
Issue No: Vol. 14, No. 4 (2024)
- Air Quality Decentralized Forecasting: Integrating IoT and Federated
Learning for Enhanced Urban Environmental Monitoring
Authors: Vibha Kulkarni, Adepu Sree Lakshmi, Chaganti B. N. Lakshmi, Sivaraj Panneerselvam, Mohammad Kanan, Aymen Flah, Mohamed F. Elnaggar
Pages: 16077 - 16082
Abstract: Air quality forecasting is a critical environmental challenge with significant implications for public health and urban planning. Conventional machine learning models, although quite effective, require data collection, which can be hampered by issues relating to privacy and data security. Federated Learning (FL) overcomes these limitations by enabling model training across decentralized data sources without compromising data privacy. This study describes a federated learning approach to predict the Air Quality Index (AQI) based on data from several Internet of Things (IoT) sensors located in different urban locations. The proposed approach trains a model using data from different sensors while preserving the privacy of each data source. The model uses local computational resources at the sensor level during the initial data processing and training, sharing only the model updates to the central location. The results show that the performance of the proposed FL model is comparable to a centralized model and ensures better data privacy with reduced data transmission requirements. This study opens new doors to real-time, scalable, and efficient air quality monitoring systems. The proposed method is quite significant for smart city initiatives and environmental monitoring, as it provides a solid framework for using IoT technology while preserving privacy.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7869
Issue No: Vol. 14, No. 4 (2024)
- The Effect of Construction Joints on the Behavior of Reinforced Concrete
Deep Beams
Authors: Saba Basim Kadhum, Alaa Hussein Al-Zuhairi
Pages: 16083 - 16089
Abstract: The main objective of the present research is to conduct a thorough investigation into the impact of construction joints on the structural performance of reinforced concrete deep beams. This study involves a series of experimental tests and the use of advanced numerical analysis techniques to gain a deeper understanding of the behavior of these beams in the presence of construction joints. The experimental component incorporates analysis findings from both previous and current research. Specifically, six reinforced concrete deep beam specimens featuring horizontal and inclined construction joints were utilized as simply being supported with two-point loading. The test findings indicate that the presence of a horizontal construction joint located below, at, or above the mid-height of the beam can lead to reductions in the ultimate load capacity by 9%, 11%, and 1%, respectively. The numerical part of the study focused on creating detailed models of the deep beam specimens with construction joints using the ABAQUS software. The proposed model showed a good agreement with the experimental tests, with estimations not exceeding 7% for the load-carrying capacity. This reduction becomes more significant when the concrete compressive strength is high, necessitating the use of bonding agents and additional reinforcement techniques to mitigate the impact of construction joints on the structural integrity.
PubDate: 2024-08-02
DOI: 10.48084/etasr.7896
Issue No: Vol. 14, No. 4 (2024)
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