 Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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- Predicting the Height of Individuals with Machine Learning Methods by
Considering Non-Genetic Factors
Authors: Tugba CELİKTEN; Hüseyin Yasin DÖNMEZ, Tuba AKBAS, Osman ALTAY
Abstract: As many parents want to know how many centimeters their child will be in the future, many people in their developmental years want to know how many centimeters their future height will be. In addition, the development of children in terms of height and weight is medically controlled from the moment they are born. As a result, height development is important for both individuals and medical professionals. In this study, it is aimed to predict the height of individuals using personal and family information and factors affecting height. In the study, the 10 most known characteristics among the factors affecting height were selected. These attributes, mother's height, father's height, economic status, jumping and weight sports status, gender, information about the child's age, history of chronic illness in the individual, the longest living region, and the individual's height were taken as input values in machine learning methods. Using these input values, the length of the individual was predicted using Linear Regression (LR) and Artificial Neural Network (ANN) from machine learning methods. In addition, three error measurement methods were used to evaluate the success of the model: mean absolute error (MAE), mean square error (MSE) and R-Square (R^2). In the R^2 evaluation metric, the method was 84.48% in LR and 81.74% in ANN.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Deep Transfer Learning-Based Broken Rotor Fault Diagnosis For Induction
Motors
Authors: Fırat DİŞLİ; Mehmet GEDİKPINAR, Abdulkadir SENGUR
Abstract: Due to their starting and running torque needs as well as their four-quadrant operation, modern industrial drives utilise induction motors (IM). Failures in the rotor bars of the motor can be found using the voltages and currents of each of the three phases as well as the acceleration and velocity signals. For the diagnosis of the quantity of broken rotor bars for a failed IM, conventional signal processing-based feature extraction techniques and machine learning algorithms have been applied in the past. The number of broken rotor bars is determined in this study by looking into a novel technique. For the aforementioned aims, specifically, the deep learning methodologies are studied. In order to do this, convolutional neural network (CNN) transfer learning algorithms are described. Initially, a bandpass filter is used for denoising, and then the signals are transformed using the continuous wavelet transform to create time-frequency pictures (CWT). The collected images are used for deep feature extraction and classification using the support vector machine (SVM) classifier, as well as for fine-tuning the pre-trained ResNet18 model. Metrics for performance evaluation employ categorization accuracy. Additionally, the results demonstrate that the deep features that are recovered from the mechanical vibration signal and current signal yield the greatest accuracy score of 100%. Nonetheless, a performance comparison with the publicly available techniques is also done. The comparisons also demonstrate that the proposed strategy outperforms the compared methods in terms of accuracy scores.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Threats Detection in IoT Network
Authors: Hanan ABU KWAİDER; Erdinç AVAROĞLU
Abstract: The recent growth in Internet of Things (IoT) deployment has increased the rapidness of integration and extended the reach of the internet from computers, tablets, and phones to countless devices in our physical world. This growth makes our life more convenient and industries more efficient. However, at the same time, it brought numerous challenges in terms of security and expanded the area of cyber-attacks, especially the DoS and DDoS attacks. Moreover, since many IoT devices run custom or outdated operating systems, and most do not have enough resources to run typical intrusion detection systems, it was necessary to search for alternative solutions. Therefore, many researchers have joined the race to develop new lightweight intrusion detection methods. In this study, we have investigated the detection of different DoS attacks on the IoT network using machine learning techniques. The studied attacks are TCP Syn-Flood Attack, UDP Flood Attack, HTTP Slowloris GET Attack, Apache Range Header DoS, and Port Scan attack. We have proposed a new dataset, namely HEIoT21, which was generated in a real smart home environment using a collective of IoT devices and non-IoT devices connected to a wireless network. The proposed dataset included normal and anomaly data, and using the CiCflowmeter application, we extracted 82 network features from the proposed dataset. The dataset was labeled and categorized into binary-class and multi-class. Our dataset underwent multiple feature selection methods to keep only enough features to produce a good detection accuracy; for that, we have used Anova F-value Feature Selection, Random Forest importance feature selection, and Sequential Forward Feature Selection. The feature selection techniques produced three new sub-datasets, which were evaluated using multiple machine learning algorithms like Logistic Regression (LR), J48 Decision Tree (DT), Naïve Bayes, and Artificial Neural Network (ANN). A comparison study was conducted on the result obtained from applying the different machine learning algorithms on the derived sub-datasets, which led to the finding that the most suitable feature selection technique for the proposed dataset was Anova F-value and the best-fit machine learning algorithm for the proposed dataset was The Decision Tree which produced an accuracy result of 99.92% for binary classification and 99.94% for multi-class classification. In the end, our study was compared with other studies in the field of IoT intrusion detection, and we found that the result obtained through this study was higher than most others. Therefore, the proposed dataset could be of great use to those who want to work on the analysis and detection of the existing network security threats. Also, this study can be considered a cornerstone for a proper lightweight intrusion detection system, where the datasets can be expanded to include other types of attacks, new detection rules can be added, and an alert mechanism can be integrated to become a complete detection system.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional
Binary Pattern
Authors: Arif Metehan YILDIZ; Tuğçe KELEŞ, Kübra YILDIRIM, Sengul DOGAN, Türker TUNCER
Abstract: Audio violence detection (AVD) is a hot-topic research area for sound forensics but there are limited AVD researches in the literature. Our primary objective is to contribute to sound forensics. Therefore, we collected a new audio dataset and proposed a binary pattern-based classification algorithm. Materials and method: In the first stage, a new AVD dataset was collected. This dataset contains 301 sounds with two classes and these classes are violence and nonviolence. We have used this dataset as a test-bed. A feature engineering model has been presented in this research. One-dimensional binary pattern (BP) has been considered to extract features. Moreover, we have applied tunable q-factor wavelet transform (TQWT) to generate features at both frequency and space domains. In the feature selection phase, we have applied to iterative neighborhood component analysis (INCA) and the selected features have been classified by deploying the optimized support vector machine (SVM) classifier. Results: Our model achieved 97.01% classification accuracy on the used dataset with 10-fold cross-validation.Conclusions: The calculated results clearly demonstrated that feature engineering is the success solution for violence detection using audios..
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- CDIEA: Chaos and DNA Based Image Encryption Algorithm
Authors: Ali ARI
Abstract: A proposal for an image encryption algorithm called Chaos and DNA Based Image Encryption Algorithm (CDIEA) has been put forward. CDIEA is a combination of block cipher algorithms, permutations, chaotic keys, and DNA operations. It leverages the strong structures of modern cryptography and the properties of chaotic systems, rather than relying solely on chaos. The permutations used in CDIEA are constructed using a strategy called the wide trail design, which makes it resilient to many forms of cryptanalysis. CDIEA operates as a byte-oriented SP-network and has been confirmed to have high security for practical image encryption through both theoretical analysis and computer experiments.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Effect on Thermal and Structural Properties of Element Content in CuAlBe
Shape Memory Alloys Irradiated with a Constant Gamma Radiation Dose
Authors: Şahide Nevin BALO; Abdulvahap ORHAN
Abstract: Since gamma radiation is a type of radiation that can change the structural properties of materials, CuAlBe shape memory alloy with two different weight percentages was used in this study. CuAlBe shape memory alloys were irradiated with a constant gamma radiation dose of 40 kGy, and the resulting thermal and structural changes in the alloys were investigated. Changes in enthalpy and in the transformation temperature of the alloys were determined by differential scanning calorimetry (DSC), and thermodynamic parameters of alloy samples were calculated. Microstructural changes were determined by X-ray analysis. Microstructural changes were verified by metallographic observations, and microhardness measurements were taken. The study investigated to what extent the physical parameters of CuAlBe shape memory alloys changed depending on the alloying elements when subjected to a constant irradiation dose.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- The Effect of The Number of Reference Points and Distribution on
Coordinate Transformation in Underground Mining Measurements
Authors: Levent TASCI; Hacı Sait ARSLAN
Abstract: This article focuses on examining the effect of the number and distribution of reference points on coordinate transformations in underground mining measurements using a handheld laser scanner. The points inside the mine were measured as reference using a Total Station. The same point cloud data was subjected to coordinate transformation with different numbers and elevations of reference points. It was observed that the homogeneous distribution of reference points used in the transformation increases precision in the horizontal and vertical directions. Both homogeneous and excessive use of transformation points result in the same good results in the horizontal, but in the vertical, it improves further. Using a large number of transformation points and using them at different elevations results in the same good results in the horizontal, but in the vertical, it increases precision even more. The article concludes that a sufficient number of homogeneously and heterogeneously distributed reference points are necessary for accurate coordinate transformation.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Molecular Docking Study on Interaction of Polyvinyl Alcohol (PVA) with
Group IA Bacteriocin
Authors: Nihan ÜNLÜ; Arzu ÖZGEN, Canan AKSU CANBAY
Abstract: PVA with the molecular formula (C2H4O)n is a polymer prepared from polyvinyl acetates by replacing acetate groups with hydroxyl groups. It is a synthetic polymer with low surface tension, flexible and soft, water-soluble and cross-linkable thanks to the hydroxyl groups in its structure, biodegradable and non-toxic due to the carbon bonds in its chain. Bacteriocins are compounds of a protein nature that are ribosomally synthesized by bacteria and suitable for use as a filler in polymer matrices, especially in food packaging systems, and drug design because they are natural antimicrobial compounds sensitive to various enzymes and do not disrupt the physicochemical structure of foods while inhibiting pathogenic microorganisms. Considering their biochemical properties, they are generally divided into 4 different classes. The fact that Nisin and PVA have a structure that can serve a common purpose and have superior properties made us wonder about the interaction and bonding modes between these two. Molecular docking work is important because it prevents time, energy, and economic consumption and prepares the ground for the synthesis of new and advanced materials that are likely to be obtained in the laboratory environment. Therefore, in this study, Nisin bacteriocin (in Group IA) was chosen as the target, and a single monomer of the PVA polymer was chosen as the ligand, and the interaction between them was simulated by molecular docking method. A rational depiction of ligand-protein binding interactions was made.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- A Hierarchical Reinforcement Learning Framework for UAV Path Planning in
Tactical Environments
Authors: Mahmut Nedim ALPDEMİR
Abstract: Tactical UAV path planning under radar threat using reinforcement learning involves particular challenges ranging from modeling related difficulties to sparse feedback problem. Learning goal-directed behavior with sparse feedback from complex environments is a fundamental challenge for reinforcement learning algorithms. In this paper we extend our previous work in this area to provide a solution to the problem setting stated above, using Hierarchical Reinforcement Learning (HRL) in a novel way that involves a meta controller for higher level goal assignment and a controller that determines the lower-level actions of the agent. Our meta controller is based on a regression model trained using a state transition scheme that defines the evolution of goal designation, whereas our lower-level controller is based on a Deep Q Network (DQN) and is trained via reinforcement learning iterations. This two-layer framework ensures that an optimal plan for a complex path, organized as multiple goals, is achieved gradually, through piecewise assignment of sub-goals, and thus as a result of a staged, efficient and rigorous procedure.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- 3-D Printed Dual-Band Frequency Selective Surfaces for Radome Applications
Authors: Mete BAKIR
Abstract: In this study, dual-band frequency selective surface (FSS) structures are designed by using 3-D printing technology for antenna radome applications. Four different configurations are studied to find the best alternative for FSS substrate not only for electromagnetic (EM) responses but also for its mechanical properties suitable for radomes. To ease the manufacturing process, a conductive paint is also studied instead of copper microstrip lines. In addition, graphite is also used for the comparison. Different 3-D printed configurations, various thickness values and three different materials for conductive part are examined and compared to find the most efficient radome structure.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- An NCA-based Hybrid CNN Model for Classification of Alzheimer’s Disease
on Grad-CAM-enhanced Brain MRI Images
Authors: Feyza ALTUNBEY ÖZBAY; Erdal ÖZBAY
Abstract: Alzheimer’s, one of the most prevalent varieties of dementia, is a fatal neurological disease for which there is presently no known cure. Early diagnosis of such diseases and classification with computer-aided systems are of great importance in determining the most appropriate treatment. Imaging the soft tissue of the brain with Magnetic Resonance Imaging (MRI) and revealing specific findings is the most effective method of Alzheimer’s diagnosis. A few recent studies using Deep Learning (DL) to diagnose Alzheimer’s Disease (AD) with brain MRI scans have shown promising results. However, the fundamental issue with DL architectures like CNN is the amount of training data that is required. In this study, a hybrid CNN method based on Neighborhood Component Analysis (NCA) is proposed, which aims to classify AD over brain MRI with Machine Learning (ML) algorithms. According to the classification results, DenseNet201, EfficientNet-B0, and AlexNet pre-trained CNN architectures, which are 3 architectures that give the best results as feature extractors, were used as hybrids among 10 different DL architectures. By means of these CNN architectures, the features trained on the dataset and the features obtained by Gradient-weighted Class Activation Mapping (Grad-CAM) are concatenated. The NCA method has been used to optimize all concatenated features. After the stage, the optimized features have been classified with KNN, Ensemble, and SVM algorithms. The proposed hybrid model achieved 99.83% accuracy, 99.88% sensitivity, 99.92% specificity, 99.83% precision, 99.85% F1-measure, and 99.78% Matthews Correlation Coefficient (MCC) results using the Ensemble classifier for the 4-class classification of AD.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Variation of Electrical Resistivity During Magnetic Field-Induced
Martensitic Transformation in Vanadium added NiMnSnB alloys
Authors: Gökhan KIRAT
Abstract: In this study, the structural and electrical properties of Ni49-xVxMn37Sn12B2 (x = 0, 1, 2, and 3) ferromagnetic shape memory alloys were investigated. According to XRD analyzes at room temperature, the x=0 sample was in the martensite phase, the x=1 and 2 samples were in the mixture phase, and the x=3 sample was in the austenite phase. The resistivity analyses depend on temperature showed that all samples exhibited martensitic transformation and the phase transformation temperature decreased with V doping. Magnetoresistance (MR) values were calculated using ρ-T curves performed under 0 T and 1 T magnetic fields. The observed negative MR is consistent with Kataoka's s-d model. As-Af interval was determined and M-H measurements were made at constant temperatures determined in this interval. The results were attributed to the magnetic field-induced phase transformation (MFIPT). In order to examine the effects of MFIPT on the electrical resistivity, the resistivity depend on magnetic field was measured using the same thermal process. The overlapping of the curves in the high magnetic field revealed that the resistivity decreased due to the MFIPT as well as the MR.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Novel Quaternary CuAlZnMg High Temperature Shape Memory Alloy (HTSMA)
Fabricated by Minor Batch of Zn and Mg Additions
Authors: Güneş BAŞBAĞ; Oktay KARADUMAN, İskender ÖZKUL, Canan AKSU CANBAY, Mustafa BOYRAZLI
Abstract: Shape memory alloys (SMAs) constitute the second largest commercial smart material class after piezoelectric materials. Different SMA alloy systems or SMAs with miscellaneous functionalities and characteristic properties have been designed for using in different applications until today. High temperature shape memory alloys (HTSMAs) are also widely desired to be used in various smart materials applications. HTSMAs with different functional and characteristic properties are muchly demanded for different tasks to be done by these alloys or devices designed by these alloys. A common and practical way to fabricate SMAs or HTSMAs with different shape memory effect (SME) and other properties is to fabricate them with different alloying compositions and add different additive elements. In this work, a quaternary CuAlZnMg HTSMA with an unprecedented composition consisting minor amount of zinc and magnesium additives was produced by arc melting method. As a result of applying post-homogenization in high β–phase temperature region and immediate quenching, the microstructural mechanism of a SME property was formed in the produced alloy. After then, to examine SME characteristics of the CuAlZnMg alloy some differential thermal analysis (DTA), microstructural (XRD) and magnetization (VSM) characterization tests were carried out. The DTA results showed that the alloy is a HTSMA exhibiting reverse martensitic transformations at temperature range between 167 °C and 489 °C. The XRD pattern obtained at room temperature revealed the martensite phases formed in the alloy, which phases are the base mechanism of the reversible martensitic transformation (the SME property) of the alloy. The VSM test showed that the alloy exhibit a diamagnetic property with a weak ferromagnetic coercivity contribution.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Analysis of Views on Digitalization of Design Studios
Authors: Nihal Arda AKYILDIZ; Betül BEKTAŞ EKİCİ, Songül KARABATAK, Müslim ALANOĞLU
Abstract: Design studios, one of the most important components of architectural education had to move to digital environments urgently and independent of space with the Covid-19 pandemic. This study aims to evaluate whether design studios can be digitized according to the views of students and instructors in two different architecture schools (Firat and Balikesir University) by considering this compulsory experience. The views of 71 students and 5 academicians were taken with a purposeful sampling method. Content and descriptive analyses were performed on the views collected from the participants. As a result, the biggest problem in moving design studios to a digital environment is the disappearance of physical environments in which collaboration, idea sharing, and discussions will occur. In addition, the advantages of online education continued their attraction for some students in the digitalization of design studios. In terms of instructors, games, simulations, modeling, and virtual reality can be used for successful digital studio applications and the infrastructures of these applications can be improved with support from mobile devices.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Investigation of electronic and thermal properties of CoCrFe and CoCrFeNi
high entropy alloys via extended tight-binding DFT computational method
Authors: Fatih Ahmet ÇELİK; Sefa KAZANÇ
Abstract: In this study, CoCrFe and CoCrFeNi transition high entropy alloys (HEAs) are modelled by extended tight-binding density functional theory (DFT) method. Also, the geometric optimizations, band structures, density of states (DOS), thermodynamic properties and phonon dispersion curves of alloys are investigated to give a detailed information. The results show that the covalent d–d bonding between Fe-Cr is occurred because of strong metallic Cr–Fe interactions. The entropy (S) value increases gradually with the addition of Ni element to the CoCrFe alloy. The heat capacity (Cv) increases due to the harmonic effect of the phonons in the range of 0-400 K and then, close to the classic limit at high temperatures with 0.82 J/mol.K and 0.94 J/mol.K for the CoCrFe and the CoCrFeNi. The alloy systems exhibit metallic properties because the DOS of the metals have a nonzero value at the Fermi energy level. Also, the addition of element Ni to the CoCrFe alloy system causes a decrease in phonon frequencies.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Assessment of Elenolic Acid Incorporation on Physical Properties of
Chitosan Films to be Used as Active Packaging Material
Authors: Ayça AYDOĞDU; Osman UCKUN
Abstract: There is growing interest in biodegradable active packaging materials to extend shelf lives of food by retarding deteriorative reactions. The objective of this study was to fabricate active packaging films made from elenolic acid and chitosan. Elenolic acid is one of the phenolic compounds of olive leaves. Different amount of elenolic acid (%2.5 and %5 w/v) was incorporated into chitosan films (%1 and 2% w/v). The physical properties (density, moisture content, solubility, water vapor permeability, opacity, and color), total phenolic content and antioxidant activity were investigated. While elenolic acid addition did not affect the moisture content of chitosan films and the density, opacity, a* and b* values increased significantly (p ≤ 0.05). Elenolic acid incorporation reduced the water vapor permeability of chitosan films by 25%. Correlated to total phenolic content of the films, antioxidant activity of films reached up to % 85. Elenolic acid added chitosan films exhibited good water vapor barrier properties, opacity and antioxidant activity indicating that they could be developed as biodegradable active food packaging material for the food industry.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Evaluation of the Effects of Earthquakes on Radon and Total Electron
Content Values and Meteorological Changes on the North Anatolian Fault
Zone, Türkiye
Authors: Dawar Hama Khalid MOHAMMED; Fatih KÜLAHCI, Ahmet SAİT ALALI
Abstract: A cross-correlation analysis is proposed to analyse the relationships of soil Radon-222 gas, Ionospheric Total Electron Content (TEC), and some meteorological variables with earthquakes from the North Anatolian Fault Zone, Türkiye, one of the most active fault lines in the World. Statistically important results are obtained for Earthquake-Rn gas changes and Seismo-Ionospheric Coupling. In addition, we think that this study will be an important step for further studies on earthquake precursors.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- The Effects of Hydroxyapatite on the Corrosion Behaviour of AZ Series Mg
Alloys
Authors: Yakup SAY
Abstract: Metallic biomaterials are widely used in the orthopedic and dental applications owing to their advanced biocompatibility and sophisticated mechanical properties. Many studies are carried out to develop new alloys with high specific strength, high corrosion resistance and high biocompatibility as an alternative to present metallic biomaterials. Mg alloys are potential alloys as a biomaterial, especially because they have low density and high biocompatibility. However, especially the corrosion properties of Mg alloys need to be improved. In this study, the surfaces of AZ31, AZ61 and AZ91 alloys, which are promising as biomaterials, were coated with hydroxyapatite with high biocompatibility, and the effects of the bioceramics coatings on corrosion resistance were comprehensively investigated. Crack-free and porous surface morphologies were obtained in all bioceramic coatings and the presence of the coatings on the surfaces was supported by EDS analysis. As a result of the corrosion tests performed in SBF, it was determined that the AZ91 alloy had the highest corrosion resistance among the uncoated samples. The hydroxyapatite bioceramic coatings also improved the corrosion properties of all samples. However, among all samples, the highest corrosion resistance was obtained in the hydroxyapatite coated AZ91 alloy.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Anomaly Detection in Yarn Tension Signal Using Independent Component
Analysis
Authors: Canan TAŞTİMUR; Mehmet AĞRİKLİ, Erhan AKIN
Abstract: Finding patterns in data that defy expected behavior is what anomaly detection entails. In many application fields, these incorrect patterns are referred to as contaminants, abnormalities, exceptions, or outliers. The significance of anomaly detection is that it helps to identify irregularities in data across a range of application domains and turns them into valuable information. When the yarn tension signals are inspected, anomaly states in the signals are seen in situations where it defect for whatever reason. This distinction makes it possible to predict whether the twister is malfunctioning. So, a bigger issue is avoided. The employment of Cluster-Based Algorithms, Statistical Method Algorithms, and other techniques to identify anomalies is common in the literature. The yarn tension signals in the twisting machines have been analyzed in this work using independent component analysis, and the problematic signal locations have been identified. The proposed method has been contrasted with other ways, and it has produced the highest success rate.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- FRF Based Structural Modification of a Mechanical System by Adding Masses
and Utilizing the Grey Wolf Optimization Technique
Authors: Murat ŞEN; Osman YİĞİD, Orhan ÇAKAR
Abstract: Resonance and anti-resonance frequencies are important parameters that determine the dynamic behavior of mechanical systems. Changes in these parameters, which depend on the system's physical properties such as mass and stiffness, also affect the system's dynamic behavior. Finding the necessary structural modifications to adjust the resonance and anti-resonance frequencies of a system to the desired values is a study area of inverse structural modification. In this study, an inverse structural modification method for one and multi-rank modifications is presented. With the presented method some resonance or anti-resonance frequencies of mechanical systems can be shifted to prescribed values by calculating the necessary modifications. The presented method is based on Sherman-Morrison (SM) formula and uses the frequency response functions (FRF) of the original system directly. For one modification an exact solution is obtained on the other hand for two or more modifications some nonlinear set of equations has to be solved. A meta-heuristic optimization technique known as Grey Wolf Optimizer (GWO) is applied for the solution of the nonlinear equations. The method is applied to a six-degrees-of-freedom mass-spring system. Some resonance and anti-resonance frequencies in the frequency bandwidth of the system are selected as target frequencies. The necessary modification masses are calculated to match these frequencies. After applying the calculated masses to the system the target frequencies are obtained successfully.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Computational Relationship of The Surface Area and Stiffness of the Spring
Constant on Fractional Bagley-Torvik Equation
Authors: Falade KAZEEM IYANDA; Abd'gafar TİAMİYU, Adesina ADİO, Huzaifa Muhammad TAHİR, Umar Muhammad ABUBAKAR, Sahura BADAMASİ
Abstract: In this paper, we formulate an efficient algorithm based on a new iterative method for the numerical solution of the Bagley-Torvik equation. The fractional differential equation arises in many areas of applied mathematics including viscoelasticity problems and applied mechanics of the oscillation process. We construct the fractional derivatives via the Caputo-type fractional operator to formulate a three-step algorithm using the MAPLE 18 software package. We further investigate the relationships between the surface area and stiffness of the spring constants of the Bagley-Torvik equation on three case problems and numerical results are presented to demonstrate the efficiency of the proposed algorithm.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- A Hybrid Model Based on Deep Features and Ensemble Learning for the
Diagnosis of COVID-19: DeepFeat-E
Authors: Berivan ÖZAYDIN; Ramazan TEKİN
Abstract: COVID-19, which has been declared a pandemic disease, has affected the lives of millions of people and caused a major epidemic. Despite the development of vaccines and vaccination to prevent the transmission of the disease, COVID-19 case rates fluctuate worldwide. Therefore, rapid and reliable diagnosis of COVID-19 disease is of critical importance. For this purpose, a hybrid model based on transfer learning methods and ensemble classifiers is proposed in this study. In this hybrid approach, called DeepFeat-E, the diagnosis process is performed by using deep features obtained from transfer learning models and ensemble classifiers consisting of classical machine learning methods. To test the proposed approach, a dataset of 21,165 X-ray images including 10,192 Normal, 6012 Lung Opacity, 1345 Viral Pneumonia and 3616 COVID-19 were used. With the proposed approach, the highest accuracy was achieved with the deep features of the DenseNet201 transfer learning model and the Stacking ensemble learning method. Accordingly, the test accuracy was 90.17%, 94.99% and 94.93% for four, three and two class applications, respectively. According to the results obtained in this study, it is seen that the proposed hybrid system can be used quickly and reliably in the diagnosis of COVID-19 and lower respiratory tract infections.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Electroencephalogram-Based Major Depressive Disorder Classification Using
Convolutional Neural Network and Transfer Learning
Authors: Şuheda KAYA; Burak TASCİ
Abstract: Major Depressive Disorder (MDD) is a worldwide common disease with a high risk of becoming chronic, suicidal, and recurrence, with serious consequences such as loss of workforce. Objective tests such as EEG, EKG, brain MRI, and Doppler USG are used to aid diagnosis in MDD detection. With advances in artificial intelligence and sample data from objective testing for depression, an early depression detection system can be developed as a way to reduce the number of individuals affected by MDD. In this study, MDD was tried to be diagnosed automatically with a deep learning-based approach using EEG signals. In the study, 3-channel modma dataset was used as a dataset. Modma dataset consists of EEG signals of 29 controls and 26 MDD patients. ResNet18 convolutional neural network was used for feature extraction. The ReliefF algorithm is used for feature selection. In the classification phase, kNN was preferred. The accuracy was yielded 95.65% for Channel 1, 87.00% for Channel 2, and 86.94% for Channel 3.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Optimization Studies on the Changeable Components of Hydroelectric Power
Plants
Authors: M. Cihat TUNA; Alp Buğra AYDIN
Abstract: The design flow rate, the dimensions of the transmission structure and the penstock size have a large impact on the cost of run-of-river type hydroelectric power plants. Equipment costs constitute a large part of the total budget of the plant. Optimum sizing, which maximizes the use of hydraulic potential, does not fit together with optimum sizing, which is necessary to obtain economic benefit from its investment. The main design parameters can be selected with the help of an optimization study in terms of both economic benefit and hydraulic potential. In this study, an easy to implement model, aimed at determining the costs associated with the different components in the structural organization of a hydroelectric power plant, is developed by a feasibility study to overcome the difficulties in practice. Gokcekoy HEPP, built in Turkey, was selected as the system. Annual energy production values were calculated by taking into account the current energy market conditions in Turkey. In addition, real situation studies were carried out regarding design flow rate selection, forced pipe diameter optimization and transmission channel sizing.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
- Performance Comparison of Biology based Metaheuristics Optimization
Algorithms using Unimodal and Multimodal Benchmark Functions
Authors: Fatma BELLİ; Harun BİNGÖL
Abstract: Optimization is used in almost every aspect of our lives today and makes our lives easier. Optimization is generally studied as classical and heuristic optimization techniques. Classical optimization methods are not effective in real-world engineering problems. These methods, by their nature, require a mathematical model. Metaheuristic optimization methods have started to be used frequently today in the solution of these problems when a mathematical model cannot be created or a solution cannot be produced in an effective time even if it is created. These methods, by their nature, cannot produce effective results in all engineering problems. Therefore, new metaheuristic optimization methods are constantly being researched. In this study, quality test functions have been used to compare the performances of five algorithms that have been developed in recent years and produce effective results. The results obtained from these functions are shared in this study. It has been observed that the Artificial Hummingbird Optimization Algorithm (AHA) gives better results than other metaheuristic algorithms.
PubDate: Wed, 29 Mar 2023 00:00:00 +030
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