Subjects -> ENGINEERING (Total: 2844 journals)
    - CHEMICAL ENGINEERING (259 journals)
    - CIVIL ENGINEERING (255 journals)
    - ELECTRICAL ENGINEERING (182 journals)
    - ENGINEERING (1420 journals)
    - ENGINEERING MECHANICS AND MATERIALS (454 journals)
    - HYDRAULIC ENGINEERING (60 journals)
    - INDUSTRIAL ENGINEERING (101 journals)
    - MECHANICAL ENGINEERING (113 journals)

ELECTRICAL ENGINEERING (182 journals)                     

Showing 1 - 175 of 175 Journals sorted alphabetically
3C TIC     Open Access   (Followers: 4)
Acta Electronica Malaysia     Open Access  
Acta Universitatis Sapientiae Electrical and Mechanical Engineering     Open Access  
Actuators     Open Access   (Followers: 4)
Advanced Electromagnetics     Open Access   (Followers: 20)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 11)
Advances in Electrical Engineering     Open Access   (Followers: 63)
Advances in Microelectronic Engineering     Open Access   (Followers: 13)
Advances in Signal Processing     Open Access   (Followers: 22)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 30)
American Journal of Sensor Technology     Open Access   (Followers: 4)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Archives of Electrical Engineering     Open Access   (Followers: 17)
Atom Indonesia     Open Access   (Followers: 2)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal   (Followers: 1)
Balkan Journal of Electrical and Computer Engineering     Open Access  
Bulletin of Electrical Engineering and Informatics     Open Access   (Followers: 11)
Carpathian Journal of Electronic and Computer Engineering     Open Access  
Case Studies in Mechanical Systems and Signal Processing     Open Access  
CES Transactions on Electrical Machines and Systems     Open Access   (Followers: 1)
Chinese Journal of Electrical Engineering     Open Access   (Followers: 3)
Circuits, Systems, and Signal Processing     Hybrid Journal   (Followers: 17)
Computers & Electrical Engineering     Hybrid Journal   (Followers: 9)
CPSS Transactions on Power Electronics and Applications     Open Access   (Followers: 3)
CSEE Journal of Power and Energy Systems     Open Access   (Followers: 5)
Current Trends in Signal Processing     Full-text available via subscription   (Followers: 10)
ECTI Transactions on Computer and Information Technology (ECTI-CIT)     Open Access  
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 2)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electric Power Components and Systems     Hybrid Journal   (Followers: 11)
Electric Power Systems Research     Partially Free   (Followers: 25)
Electrical and Electronic Engineering     Open Access   (Followers: 72)
Electrical Engineering     Hybrid Journal   (Followers: 26)
Electrical Engineering and Automation     Open Access   (Followers: 11)
Electrical Engineering and Power Engineering     Open Access   (Followers: 4)
Electrical Engineering in Japan     Hybrid Journal   (Followers: 9)
Electrical, Control and Communication Engineering     Open Access   (Followers: 16)
Electrochemical Energy Reviews     Hybrid Journal   (Followers: 3)
Elektron     Open Access  
Elektronika ir Elektortechnika     Open Access   (Followers: 3)
Elkha : Jurnal Teknik Elektro     Open Access  
Emerging and Selected Topics in Circuits and Systems     Hybrid Journal   (Followers: 7)
Emitor : Jurnal Teknik Elektro     Open Access   (Followers: 4)
ETRI Journal     Open Access  
EURASIP Journal on Advances in Signal Processing     Open Access   (Followers: 8)
Ferroelectrics     Hybrid Journal   (Followers: 1)
Ferroelectrics Letters Section     Hybrid Journal   (Followers: 1)
Frontiers of Electrical and Electronic Engineering     Hybrid Journal   (Followers: 9)
Frontiers of Information Technology & Electronic Engineering     Hybrid Journal  
IEEE Access     Open Access   (Followers: 139)
IEEE Electrical Insulation Magazine     Full-text available via subscription   (Followers: 91)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal   (Followers: 1)
IEEE Journal of Photovoltaics     Hybrid Journal   (Followers: 18)
IEEE Journal of Radio Frequency Identification     Hybrid Journal   (Followers: 6)
IEEE Journal of Selected Topics in Signal Processing     Hybrid Journal   (Followers: 43)
IEEE Journal on Miniaturization for Air and Space Systems     Hybrid Journal   (Followers: 2)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 4)
IEEE Networking Letters     Hybrid Journal   (Followers: 3)
IEEE Open Access Journal of Power and Energy     Open Access   (Followers: 4)
IEEE Open Journal of Antennas and Propagation     Open Access   (Followers: 7)
IEEE Open Journal of Circuits and Systems     Open Access   (Followers: 4)
IEEE Open Journal of Intelligent Transportation Systems     Open Access   (Followers: 8)
IEEE Open Journal of Power Electronics     Open Access   (Followers: 12)
IEEE Open Journal of Signal Processing     Open Access   (Followers: 6)
IEEE Sensors Journal     Hybrid Journal   (Followers: 103)
IEEE Sensors Letters     Hybrid Journal   (Followers: 3)
IEEE Signal Processing Magazine     Full-text available via subscription   (Followers: 93)
IEEE Solid-State Circuits Letters     Hybrid Journal   (Followers: 3)
IEEE Transactions on Control of Network Systems     Hybrid Journal   (Followers: 27)
IEEE Transactions on Dielectrics and Electrical Insulation     Hybrid Journal   (Followers: 34)
IEEE Transactions on Green Communications and Networking     Hybrid Journal   (Followers: 5)
IEEE Transactions on Network Science and Engineering     Hybrid Journal   (Followers: 3)
IEEE Transactions on Quantum Engineering     Open Access   (Followers: 3)
IEEE Transactions on Radiation and Plasma Medical Sciences     Hybrid Journal   (Followers: 3)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 14)
IEEE Transactions on Sustainable Energy     Hybrid Journal   (Followers: 17)
IEEJ Transactions on Electrical and Electronic Engineering     Hybrid Journal   (Followers: 21)
IET Control Theory & Applications     Open Access   (Followers: 27)
IET Electric Power Applications     Open Access   (Followers: 55)
IET Electrical Systems in Transportation     Open Access   (Followers: 13)
IET Energy Systems Integration     Open Access   (Followers: 1)
IET Nanodielectrics     Open Access  
IET Smart Grid     Open Access   (Followers: 3)
IETE Journal of Education     Open Access   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
InComTech : Jurnal Telekomunikasi dan Komputer     Open Access   (Followers: 1)
Indonesian Journal of Electrical Engineering and Computer Science     Open Access   (Followers: 18)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
InfoMat     Open Access   (Followers: 1)
Infotekmesin : Media Komunikasi Ilmiah Politeknik Cilacap     Open Access   (Followers: 1)
Ingeniería Electrónica, Automática y Comunicaciones     Open Access  
Integrated Ferroelectrics: An International Journal     Hybrid Journal  
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 14)
International Journal of Electrical and Computer Engineering     Open Access   (Followers: 13)
International Journal of Electrical Engineering Education     Hybrid Journal   (Followers: 8)
International Journal of Electrical Power & Energy Systems     Open Access   (Followers: 36)
International Journal of Microwave Engineering and Technology     Full-text available via subscription   (Followers: 13)
International Journal of Monitoring and Surveillance Technologies Research     Full-text available via subscription   (Followers: 3)
International Journal of Nuclear Security     Open Access  
International Journal of Turbomachinery, Propulsion and Power     Open Access   (Followers: 18)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
International Transactions on Electrical Energy Systems     Hybrid Journal   (Followers: 9)
Iranian Journal of Science and Technology, Transactions of Electrical Engineering     Hybrid Journal  
Izvestiya Vysshikh Uchebnykh Zavedenii. Materialy Elektronnoi Tekhniki : Materials of Electronics Engineering     Full-text available via subscription  
J3eA     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Control, Automation and Electrical Systems     Hybrid Journal   (Followers: 13)
Journal of Electrical and Computer Engineering     Open Access   (Followers: 9)
Journal of Electrical and Computer Engineering Innovations     Open Access   (Followers: 7)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 43)
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering     Open Access   (Followers: 51)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 8)
Journal of Electrical Engineering & Technology     Hybrid Journal   (Followers: 1)
Journal of Electrical Systems and Information Technology     Open Access   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Field Robotics     Hybrid Journal   (Followers: 5)
Journal of International Council on Electrical Engineering     Open Access   (Followers: 1)
Journal of Micro-Bio Robotics     Hybrid Journal   (Followers: 1)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 11)
Journal of Power Technologies     Open Access   (Followers: 7)
Journal of Science and Application Technology     Open Access  
Journal of the Society for Information Display     Hybrid Journal  
Journal of World's Electrical Engineering and Technology     Open Access   (Followers: 2)
Journal on Today's Ideas - Tomorrow's Technologies     Open Access   (Followers: 1)
JPhys Materials     Open Access  
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Ilmiah Mahasiswa SPEKTRUM     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknik Elektro     Open Access   (Followers: 1)
Jurnal Teknik Elektro dan Komputer     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 4)
La Rivista del Nuovo Cimento     Hybrid Journal   (Followers: 1)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Majlesi Journal of Electrical Engineering     Open Access   (Followers: 1)
Material Design & Processing Communications     Hybrid Journal  
Metrology and Instruments / Метрологія та прилади     Open Access   (Followers: 1)
Micro and Nano Systems Letters     Open Access   (Followers: 6)
Nanotechnology Development     Open Access   (Followers: 21)
npj Flexible Electronics     Open Access   (Followers: 1)
npj Materials Degradation     Open Access  
npj Quantum Materials     Open Access   (Followers: 2)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Signal Processing Journal     Open Access   (Followers: 2)
Periodica Polytechnica Electrical Engineering and Computer Science     Open Access  
Presence: Virtual and Augmented Reality     Hybrid Journal   (Followers: 3)
Progress in Additive Manufacturing     Hybrid Journal   (Followers: 7)
Quantum Beam Science     Open Access   (Followers: 1)
Radio Science     Full-text available via subscription   (Followers: 43)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 4)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 12)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Russian Electrical Engineering     Hybrid Journal   (Followers: 5)
SAIEE Africa Research Journal     Open Access  
Sampling Theory, Signal Processing, and Data Analysis     Hybrid Journal  
Scientific Bulletin of Electrical Engineering Faculty     Open Access  
SID Symposium Digest of Technical Papers     Hybrid Journal  
Signal Processing     Hybrid Journal   (Followers: 12)
Signals     Open Access   (Followers: 2)
Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer     Open Access  
Sustainable Energy, Grids and Networks     Hybrid Journal   (Followers: 6)
Synthesis Lectures on Electrical Engineering     Full-text available via subscription   (Followers: 2)
System analysis and applied information science     Open Access  
Telematique     Open Access  
Transactions of the International Society for Music Information Retrieval     Open Access   (Followers: 1)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 1)
Transactions on Environment and Electrical Engineering     Open Access  
Trends in Electrical Engineering     Full-text available via subscription   (Followers: 5)
Tri Dasa Mega : Jurnal Teknologi Reaktor Nuklir     Open Access  
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
Wireless Engineering and Technology     Open Access   (Followers: 5)
Електротехніка і Електромеханіка     Open Access   (Followers: 2)

           

Similar Journals
Journal Cover
Bulletin of Electrical Engineering and Informatics
Number of Followers: 11  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2089-3191 - ISSN (Online) 2302-9285
Published by Universitas Ahmad Dahlan Homepage  [16 journals]
  • A ring monopole quad band antenna loaded with metamaterial and slots for
           wireless applications

    • Authors: Basavalinga Swamy; C. M. Tavade, Kishan Singh
      Abstract: The present wireless applications demand a compact, multi-operated, and stable radiation pattern antenna with good gain and impedance matching performance. To accomplish this requirement. In this paper, we propose a compact metamaterial structure loaded quad band antenna. The structural specifications/layout of the antenna consists of a circular ring monopole fed by a microstrip line. The ground part of the antenna is loaded with a metamaterial rectangular split-ring resonator (RSRR), an L-shaped slot, and two horizontally placed rectangular slots parallel to each other. No external matching circuit is utilized and impedance matching is solely controlled by the placement of slots. The antenna shows operation at 2.1 GHz (2.01-2.24 GHz, a bandwidth of 230 MHz (WLAN)), 4.5 GHz (4.35-4.66 GHz, a bandwidth of 310 MHz (C-band)), 5.5 GHz (5.37-5.77 GHz bandwidth of 400 MHz (WiMAX)), and 7.2 GHz (7.08-7.33 GHz, a bandwidth of 250 MHz (satellite band)). The antenna exhibits good gain and stable radiation pattern in both the plane and thus can be utilized for aforementioned applications.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Improvement of double-layer phosphor structure WLEDS in color homogeneity
           and luminous flux

    • Authors: Phung Ton That; Phung Ton That, Hoang Nam Nguyen
      Abstract: The concept of the analysis is to put a CaAl2O4:Mn2+ green phosphor layer on top of the YAG:Ce3+ yellow phosphor layer. After that, find the added CaAl2O4:Mn2+ concentration appropriate for the highest luminous flux (LF) and color homogeneity (CH). In this analysis, five equivalent WLEDs were applied but with distinct color temperatures, including 5600 K - 8500 K. The findings showed that CaAl2O4:Mn2+ brings great benefits not only to increase the luminous flux but also to increase the color uniformity. Specifically, the higher the CaAl2O4:Mn2+ concentration, the greater the luminous flux released by WLEDs, due to the increased content of green light in WLEDs. Nevertheless, as the CaAl2O4:Mn2+ concentration raised significantly, a small reduction in the color rendering index (CRI) and color quality scale (CQS) occurred. This is supported by simulation and calculation according to the theory of Monte Carlo. The paper results make an important contribution to the manufacture of WLEDs with higher optical performance and color uniformity of remote phosphor structures.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • A Spatial Image Compression Algorithm Based on Run Length Encoding

    • Authors: ABDEL RAHMAN ADNAN ALSOUQI; Inad Aljarrah, Osama Al-Khaleel
      Abstract: Image compression is vital for many areas such as communication and storage of datathat is rapidly growing nowadays. In this paper, a spatial lossy compression algorithmfor gray scale images is presented. It exploits the inter-pixel and the psycho-visualdata redundancies in images. The proposed technique finds paths of connected pixelsthat fluctuate in value within some small threshold. The path is calculated by lookingat the 4-neighbors of a pixel then choosing the best one based on two conditions; thefirst is that the selected pixel must not be included in another path and the second isthat the difference between the first pixel in the path and the selected pixel is within thespecified threshold value. A path starts with a given pixel and consists of the locationsof the subsequently selected pixels. Run-Length encoding scheme is applied on pathsto harvest the inter-pixel redundancy. After applying the proposed algorithm on severaltest images, a promising quality vs. compression ratio results have been achieved.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Outage Performance of Downlink NOMA-aided Small Cell Network with Wireless
           Power Transfer

    • Authors: Anh-Tu Le; Dinh-Thuan Do
      Abstract: This paper considers an downlink for small cell network in heterogeneous network. Due to mobility and anywhere distribution of users, it is necessary to study massive connections and high energy efficiency for such kind of system. To be an enabler of energy harvesting, a power beacon ishelpful to support the base station to send signals to distant users, and we employ wireless power transfer(WPT) from the power beacon to the base station to guarantee the data packets transmission. To provide massive connections, a novel non-orthogonal multiple access (NOMA)scheme is adopted together with WPT is first considered to improveoutage performance and reduce latency. Furthermore, we compute outageprobability (OP) as an important metric to characterize the system performance. Simulation results are verified to match well between theoretical and analytical methods, and main parameters are determined how they affect to the proposed scheme
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Cloud Middleware and Services – A Systematic Mapping Review

    • Authors: Isaac Odun-Ayo; Marion Adebiyi, Olatunji Okesola, Olufunke Vincent
      Abstract: Cloud computing currently plays a crucial role in the delivery of vital information technology services. A unique aspect of cloud computing is the cloud middleware and other related entities that support applications and networks. A specific field of research may be considered, particularly as regards cloud middleware and services at all levels, and thus needs analysis and paper surveys to elucidate possible study limitations. The purpose of this paper is to perform a systematic mapping for studies that capture cloud computing middleware, stacks, tools and services. The methodology adopted for this study is a systematic mapping review. The results showed that more papers on the contribution facet were published with tool, model, method and process having 18.10%, 13.79%, 6.03% and 8.62% respectively. In addition, in terms of tool, evaluation and solution research had the largest number of articles with 14.17% and 26.77% respectively. A striking feature of the systemic map is the high number of articles in solution research with respect to all aspects of the features applied in the studies. This study showed clearly that there are gaps in cloud computing middleware and delivery services that would interest researchers and industry professionals desirous of research in this area.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • System of smart detection and control electrical energy (SiSDeCE) for
           saving of electrical energy consumption

    • Authors: Roslina Roslina; Afritha Amelia, Heru Pranoto, Bakti Viyata Sundawa
      Abstract: This research is further research of Smart Control Electrical Energy (SiSCE) at idle time. In SiSCE, it serves to control the consumption of electrical energy at idle time. This system has been improved in Smart Detection and Control Electrical Energy (SiSDeCE). SiSDeCE is able to detect activities and human in room and control the consumption of electrical energy not only at idle time but at operating hours. The system is combination of PIR sensor, WSN, IoT application and mobile-based monitoring. The results is able to improve saving of electrical energy consumption. at idle time and operating hours. In the past, SiSCE result could saving of electrical energy about 46.8%. SiSDeCE potential saving growth is 24.31%. We accumulate to be 71.11% potential saving growth in the future. 
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • An Optimized RNN-LSTM Approach for Parkinson’s Disease Early
           Detection using Speech Features

    • Authors: Hadeel Ahmed Abd El Aal; Shereen A. Taie, Nashwa El-Bendary
      Abstract: Parkinson's Disease (PD) is the second most common neurodegenerative disorder disease right after Alzheimer's and the most common movement disorder for elderly people. It is characterized as a progressive loss of muscle control, which leads to trembling characterized by uncontrollable shaking, or (tremors) in different parts of the body. In recent years, Deep Learning (DL) models achieved significant progress in automatic speech recognition, however, limited studies addressed the problem of distinguishing people with PD for further clinical diagnosis. In this paper, an approach for the early detection of patients with PD using speech features was proposed, a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) is applied with the Batch Normalization layer and Adaptive Moment Estimation (ADAM) optimization algorithm used after the network hidden layers to improve the classification performance. The proposed approach is applied with 2 benchmark datasets of speech features for patients with PD and healthy control subjects. The proposed approach achieved an accuracy of 95.8% and MCC= 92.04% for the testing dataset. In future work, we aim to increase the voice features that will be worked on and consider using handwriting kinematic features.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Computer Model for Tsunami Vulnerability Using Sentinel 2A and Shuttle
           Radar Tomography Mission Remote Sensing Imagery Optimized by Machine
           Learning

    • Authors: sri yulianto joko prasetyo; Bistok Hasiholan Simanjuntak, Kristoko Dwi Hartomo, Wiwin Sulistyo
      Abstract: This study aims to develop a software framework for the identification of tsunami high vulnerability areas using the DEM (Digital Elevation Model), LULC (Land Use Land Cover), and VI (Vegetation Index) indicators as part of the tsunami mitigation. This study was carried out in five stages, namely: (1) preprocessing data that consists of a collection of Sentinel-2 satellite image data and identification of the research area i.e. the area of Kebumen Regency, Central Java Province, Indonesia which covers 8 districts, namely Ayah, Buayan, Puring, Petanahan, Klirong, Buluspesantren, Ambal, and Mirit Districts. The images were corrected geometrically, radiometrically, and atmospherically; (2) Data Analysis and Classification which is the activities of Sentinel-2 image data extraction using the NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-up Index), NDWI (Normalized Difference Water Index), MSAVI (Modified Soil Adjusted Vegetation Index), and MNDWI (Modified Normalized Difference Water Index) algorithms; (3) Prediction data that was performed using the NDVI, NDBI, NDWI, MSAVI, and MNDWI algorithms, and extracted from Sentinel-2 images using ML (Machine Learning) of Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), and Classification and Regression Tree (CART); (4) Accuracy testing of prediction results with ML, which was performed statistically using the MSE, ME, RMSE, MAE, MPE, and MAPE equations; and (5) Spatial vulnerability prediction which was performed using Ordinary Kriging spatial interpolation. The results show that in 2021 the area was dominated by vegetation density between (-0.1) to (0.3) with moderate to high vulnerability and risk of LULC tsunami as a result of the decreasing of vegetation area. The prediction results for 2021 show a low canopy density of vegetation and a high degree of land surface slope. Based on the prediction results in 2021, the study area mostly shows the existence of built-up lands with a high tsunami vulnerability risk (> 0.1). Vegetation in the study area had decreased to 67% from the original areas in 2017 with an area of 135 km2. Forest vegetation had decreased by 45% from 116 km2 in 2017. Land use for fisheries had increased to the area of 86 km2 from 2017 with an area of 24 km2.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Techno-economic-enviro optimization analysis of diesel/PV/Wind with pumped
           hydro storage for Mentawai island microgrid

    • Authors: Syafii Syafii; Wati Wati, Rahmad Fahreza
      Abstract: This paper presents a techno-economic analysis and environment assessment of hybrid photovoltaic (PV), wind turbine (WT) and diesel genset (DG) with pumped hydro storage (PHS) for a rural microgrid system. The analysis is carried out for a case study with Mentawai community load demand of 165.44 kWh/day at a peak load of 20.46 kW. The Homer simulation results show that there are eight feasible configurations, which the optimal hybrid system configuration to supply community load is the configuration with PV/DG/PHS. An optimal system has been achieved for the lowest NPC of IDR 3,00B consist of 15 kWp PV modules, 1 unit of PHS and a solar inverter with a size 25 kW. The net present cost and payback period are in accordance with criteria for the economic feasibility analysis method of a project. However, the cost of energy is greater than the electrical utility tariff, but this value can be considered for applications in the remote island area. Therefore, the project still feasible to be implemented. Since the renewable fraction of the system is increased hence this proposed system will have the lowest carbon emission.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • An Implementation of Real-Time Detection of Cross-Site Scripting Attacks
           on Cloud-Based Web Applications Using Deep Learning

    • Authors: Isaac Odun-Ayo; Williams Toro-Abasi, Marion Adebiyi, Oladapo Alagbe
      Abstract: Cross-Site Scripting has caused considerable harm to the economy and individual privacy. An effective Cross-Site Scripting attack will amount to strict security breaches for consumers, and the cloud computing environment deployed on the website. As a division of machine learning, Deep Learning consists of three primary learning approaches, which are Supervised, Semi-Supervised, and Unsupervised. It is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. Thus, this study aims to solve this problem by proposing a secure framework that can be used to achieve real-time detection and prevention of Cross-Site Scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases Cross-Site Scripting Payloads and Benign User Inputs Extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious Cross-Site Scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect Cross-Site Scripting attacks using Multi-layer Perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models Deep Belief Network, Ensemble, and Long Short-Term Memory. A Multi-layer Perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting Cross-Site Scripting attacks.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Investigation on the ohmic characteristic of Ni/Ti/4H-SiC

    • Authors: M. I. Idris; Z. A. F. M. Napiah, Marzaini Rashid, M. N. Shah Zainudin, Siti Amaniah Mohd Chachuli, M. A. Azam
      Abstract: Ohmic contact is important for silicon carbide (SiC) devices such as Schottky diode, Junction Field Effect Transistor (JFET) and Metal Oxide Transistor (MOSFET). The effect of post metallization annealing (PMA) on the ohmic characteristics of Ni/Ti/4H-SiC is investigated. The samples were annealed under different ambients of high vacuum, forming gas and N2 gas at 1050˚C for 3 minutes using rapid thermal process (RTP). Current-voltage (I-V) measurements taken for different distances of a transmission line model (TLM) structure have been utilized to extract the contact resistivity. The correlation between surface roughness and resistivity has been investigated. It was found that the involvement of nitrogen during the annealing process at 1050˚C was ineffective to reduce the contact resistivity. The resistivity is improved when the samples were annealed in forming gas (FG), (a mixture of H2 + N2) environment, showing that the incorporation of H2 gas during the annealing process has produced a better result. On the other hand, high vacuum PMA was found to be effective to improve the ohmic characteristic with higher current level at lower voltage. Hence, the enhanced performance observed in high vacuum annealing samples is beneficial to get ohmic contact on Ni/Ti/4H-SiC for PMA process with a low thermal budget.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Feature Selection for Improving Indian Spoken Language Identification in
           Utterance Duration Mismatch Condition

    • Authors: Aarti Bakshi; Sunil Kumar Kopparapu
      Abstract: In spoken language identification (SLID) systems, the test data may be of a sufficiently shorter duration than training data, known as duration mismatch condition. Duration normalized features are used to identify a spoken language for nine Indian languages in duration mismatch conditions. Random forest-based importance vectors of 1582 OpenSMILE features are calculated for each utterance in different duration datasets. The feature importance vectors are normalized across each dataset and later across different duration datasets. The optimal number of duration normalized features is selected to maximize SLID system accuracy. Three classifiers, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), and their fusion, weights optimized using logistic regression, are used. The speech material comprised utterances, each of 30 sec, extracted from the All India Radio dataset with nine Indian languages.Seven new datasets of smaller utterance durations were generated by carefully splitting each utterance. Experimental results showed that 150 most important duration normalized features were optimal with a relative increase in 18-80% accuracy for mismatch conditions. The accuracy decreased with increased duration mismatch.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Brain Tumor Identification with a Hybrid Feature Method of Extraction
           Based on DWT and PCA

    • Authors: Dalia Mohammad Toufiq; Ali Makki Sagheer, Ali Makki Sagheer, Veisi Hadi, Veisi Hadi
      Abstract: The Identification of brain tumors is a critical step that relies on the expertise and abilities of the physician. In order to enable radiologists to spot brain tumors, an automated tumor arrangement is extremely important. This paper presents a technique for MR brain image segmentation and classification to identify images as normal and abnormal. The proposed technique is a hybrid feature extraction submitted to enhance the classification results and basically consists of three stages. The first stage is used a 3-level of discrete wavelet transform (DWT) to extract image characteristics. In the second stage, the Principle Component Analysis (PCA) is applied to reduce the size of characteristics. Finally, a Random Forest classifier (RF) was used with a feature selection for identification. 181 MR brain images are collected (81 normal and 100 abnormal), in distinguishing normal and abnormal tissues, the experimental results obtained an accuracy of 98%, the sensitivity achieved is 99.2%, specificity achieved is 97.8%, and showed the effectiveness of the proposed technique compared with many kinds of literature. The results show that the 3L-DWT+PCA+RF still achieved the best classification results. The proposed model could apply to the brain MRI sphere classification, which will help doctors to diagnose a tumor if it is normal or abnormal in certain degrees. 
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Hajj Pilgrimage Video Analytics Using CNN

    • Authors: Roman Bhuiyan; Junaidi Abdullah, Noramiza Hashim, Fahmid Al Farid, Mohd Ali Samsudin, Norra Abdullah, Jia Uddin
      Abstract: This paper advances video analytics with a focus on crowd analysis for Hajj
      and Umrah pilgrimages. In recent years, there has been an increased interest
      in the advancement of video analytics and visible surveillance to improve the
      safety and security of pilgrims during their stay in Makkah. It is mainly
      because Hajj is an entirely special event that involve hundreds of thousands of
      people being clustered in a small area. This paper proposes a convolutional
      neural network (CNN) system for performing multitude analysis, in particular
      for crowd counting. In addition, it also proposes a new algorithm for
      applications in Hajj and Umrah. We create a new dataset based on the Hajj
      pilgrimage scenario in order to address this challenge. Our algorithm
      outperforms the state-of-the-art approach with a significant reduction of the
      Mean Absolute Error (MAE) result: 240.0 (177.5 improvement) and the Mean
      Square Error (MSE) result: 260.5 (280.1 improvement) when used with the
      latest dataset (HAJJ-Crowd dataset). We present density map and prediction
      of traditional approach in our novel HAJJ-Crowd dataset for the purpose of
      evaluation with our proposed method.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • A new design of stepped antenna loaded metamaterial for RFID applications

    • Authors: Badr NASIRI; Jamal Zbitou
      Abstract: Radio frequency identification is being overloaded with data information, making wideband band antennas very appealing. In this paper, we present a new design of dual band antenna for RFID reader applications operating at 2.45Gz and 5.8GHz with an average gain of 1.16dB at the lower frequency band and 3.2dB at the higher frequency band. The antenna is designed on an FR-4 substrate having a relative dielectric constant of 4.4 and loss tangent of 0.025.  The proposed antenna is simulated, designed and, optimized using CST Microwave Studio and has a small size of 32 mm x 26 mm x 1.6 mm. The antenna consists of a steeped rectangular patch antenna using a partial ground plane loaded a modified split ring resonator. The metamaterial structure was designed and optimized to operate at 2.45GHz and its effective parameters was verified using the Nicolson-Ross Weir method. The performance of the proposed antenna is confirmed by another 3D electromagnetic solver HFSS
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Analysing Admission Control for AODV and DSR Routing Protocol in Mobile
           Ad-Hoc Network

    • Authors: Folayo Aina; Sufian Yousef, Opeyemi Osanaiye
      Abstract: The widespread deployment of mobile ad-hoc network (MANET) in the areas of agriculture, military defence, weather forecasting and disaster control has necessitated the implementation of admission control within a network for a guaranteed quality of service (QoS). Admission control organises traffic flows to ensure the network medium is fairly shared among various nodes in the network. In a wired medium, nodes can monitor the medium to observe the amount of bandwidth used within the network, while on the contrary, mobile nodes in MANET determine this by using the bandwidth of neighbouring nodes. Various admission control algorithms have been proposed in the literature, using different metrics and parameters to achieve different admission control quality. In this work, we propose a measured based admission control algorithm scheme ACMANR (Admission control in mobile ad-hoc network routing) using both bandwidth capacity and resource estimation to achieve a good QoS. Furthermore, we analyse the behaviour of two well-known routing protocols in wireless network, AODV and DSR, in our proposed admission control algorithm. Simulation results obtained for our proposed admission control algorithm using OPNET show that AODV routing protocol had a better throughput while DSR produced a better delay with lower overhead in MANET. Our proposed approach also shows better performance in terms of throughput and delay when compared with the state-of-the-art admission control routing using AODV and DSR
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • An end-fire low profile patch antenna to work on WiMAX frequencies used
           for harvesting power supply

    • Authors: Anwer Sabah Mekki; Siba Monther Yousif, Bashar Mudhafar Ahmed, Mustafa Mohammed Jawad
      Abstract: In this paper, an end-fire microstrip patch antenna (MPA) is proposed of 3 GHz as a center frequency, designed, simulated, and measured to work on WiMAX frequencies within standard of 802.16e (WiMAX). A high gain ranged between (12.117-13.324) dB, high front to back ratio (F/B) of (35.770) at the center frequency, a wide band of 1.701GHz, low profile, and semi-ideal voltage standing wave ratio (VSWR) of 1.053 is achieved. The simulation is done using computer simulation technology (CST-MW). The proposed design is based on two Fire-retardant substrates (FR-4) of relative permittivity (ε) 4.3+j0.025 and 1.53 mm thickness for each one, which is considered a high loss material. The measurement results show good agreement with the simulated results. In addition, the design can be used for harvesting power supply from mobile towers. Finally, the proposed design is compared with two other designs in terms of power conversion efficiency and overall size.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Goal Location Prediction based on Deep Learning using RGB-D Camera

    • Authors: Heba Hakim; Zaineb Alhakeem, Salah Al-Darraji
      Abstract: In the navigation system, the desired destination position plays an essential role since the path planning algorithms takes a current location and goal location as inputs as well as the map of the surrounding environment. The generated path from path planning algorithm is used to guide a user to his final destination. This paper presents a proposed algorithm based on  RGB-D camera  to predict the goal coordinates in 2D occupancy grid map for visually impaired people navigation system. In recent years, deep learning methods have been used in many object detection tasks. So, the object detection method based on convolution neural network method is adopted in the proposed algorithm. The measuring distance between the current position of a sensor and the detected object depends on the depth data that is acquired from RGB-D camera. Both of the object detected coordinates and depth data has been integrated to get an accurate goal location in a 2D map. This proposed algorithm has been tested on various real-time scenarios. The experiments results indicate to the effectiveness of the proposed algorithm.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Learning jawi using multi-maker augmented reality

    • Authors: Siti Hasnah Tanalol; Dinna N. Mohd Nizam, Zaidatol Haslinda Abdullah Sani, Aslina Baharum, Asni Tahir, Iznora Aini Zolkifly
      Abstract: This paper discussed the development of multi-maker Augmented Reality for learning Jawi in order to complement the formal study in school. We conducted an experiment with N=10 participants from Pusat Minda Lestari, UMS age 5 and 6 years old, to study the effectiveness of learning Jawi using the developed mobile augmented reality application. We prepared a test environment comprising an EEG system and mobile AR application for analysis and testing.  Results found that the learnability of the students were improved after they used the mobile application to learn basic Jawi. The methodology used was ADDIE Model, which included the Analysis, Design, Development, Implementation and Evaluate phases. This project is an innovation in learning Jawi and hopefully can increase the children’s interest in learning Jawi.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • E-customer loyalty in gamified trusted store platforms: a case study
           analysis in Iran

    • Authors: Ehsan Hosseini; Mohammad Hossein Rezvani
      Abstract: Customer satisfaction, trust, and loyalty are the three most fundamental elements of e-marketing. Previous researchers have noted that satisfaction is a key factor in commanding loyalty. However, the relationship between satisfaction, trust, and loyalty is strongly dependent on the type of platform provided by digital stores. On the other hand, gamification in e-businesses has grown rapidly in recent years. In this context, it is necessary to explore the effects of gamification on e-customer satisfaction and loyalty. In this paper, it is argued that customer satisfaction alone cannot inspire loyalty. Simply speaking, customers’ satisfaction with gamified services can lead to developing trust and, in turn, loyalty. This research also presents a thorough review of the effect of store-related motivational factors, such as gamification, on trust. These factors include moderator and mediator variables. The hypotheses of this study are considered in the context of one of the largest online retail stores in Iran which enjoys a large market share in the Middle East. To this end, Lawshe content validity ratio is utilized and expert opinions are applied to the proposed model. Evaluation results, obtained through the SmartPLS, established the robustness of our modeling in terms of reliability analysis, significance level analysis, discriminant validity analysis, coefficient of determination, model fitting, and cross-validation.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Teaching power system stabilizer and PID impacts on transient condition in
           synchronous generator

    • Authors: Sugiarto Kadiman; Oni Yuliani, Trie Handayani
      Abstract: Understanding the concepts based on problem solving is not an easy methodology in teaching the impact of power systems stabilizer (PSS) on transient synchronous generator using MATLAB capability. Experiments conducted in simulating sessions play an important role in this teaching. This simulation can simulate power system stability behavior with reasonable accuracy in less time. This transient phenomenon of a power system utilizing synchronous generator and modelling by fully three-phase model with changes in stator flux linkages neglected is analyzed by employed single machine infinite bus taken to the power system. Whereas a power system stabilizer which consist of a wash-out circuit, two stages of compensation, a filter unit, and a limiter, is applied to control voltage and frequency of power systems in transient condition. PID (proportional-integral-derivative) controller tuned by Ziegler-Nichols’s method is cascaded to conventional PSS in order to enhance the response time of system while providing a better result in damping for oscillation. This gives the clear idea about PSS and PID controller impacts on transient synchronous generator and its enhancement to the students of Electrical Engineering Program, Institut Teknologi Nasional Yogyakarta.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Starting of Induction Motor Fed with Stand-Alone DFIG

    • Authors: M. Sharawy; Adel A. Shaltout, Naser Abdel-Rahim, Mahmoud A. Al-Ahmar, O. E. M. Youssef
      Abstract: This paper presents dynamic simulation and control of stand-alone doubly fed induction generator (DFIG) loaded with 3-phase induction motors (IMs). The study reveals that direct on-line starting of large IMs causes a large voltage sag across the generator terminals as the starting current drawn reaches up to 8-9 times the rated load current. Traditionally, this problem has tackled by oversizing of the generator or employment of special starters, under the pretext of mitigating voltage sag. This work explores ways that the starting current can be reduced economically by applying constant V/f control side by side with indirect field-oriented control (FOC) applied on the rotor side converter of the DFIG. This methodology enables starting of larger IMs and mitigation of voltage sag that occurs during the start-up period. Two different rating of IMs loaded with speed-squared mechanical torque are mainly considered. Simulation results of the system behavior under study confirm the capability of the proposed control.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Image Processing System using MATLAB-Based Analytics

    • Authors: Gerald K. Ijemaru; Augustine O. Nwajana, Emmanuel U. Oleka, Richard I. Otuka, Isibor K. Ihianle, Solomon H. Ebenuwa, Emenike Raymond Obi
      Abstract: Owing to recent technological advancement, computers and other devices such as phones and digital cameras running several image editing software applications can be further exploited for other operations such as digital image processing operations. This paper attempts to conduct performance evaluation of the various image processing techniques using MATLAB-based analytics. Compared to the conventional techniques and other state-of-the-art applications used for image processing, MATLAB gives several advantages. MATLAB-based technique provides easy implementation and testing of algorithms without recompilation, and provides easy debugging with extensive data analysis and visualization. Besides, MATLAB's computational codes can be enhanced and exploited to process and create simulations of both still and video images. In addition, MATLAB codes are much concise compared to c++, thus making it easier for perusing and troubleshooting. MATLAB can handle errors prior to execution by proposing various ways to make the code faster.  The proposed technique enables advanced image processing operations such as image cropping/resizing, image denoising, blur removal, and image sharpening. The study aims at providing readers with the most recent image processing application-tools running on MATLAB platform. We also provide an empirical-based method of image processing using two-dimensional discrete cosine transform (2D-DCT) derived from its coefficients. With the different and most recent algorithms running on MATLAB toolbox, we provide simulations of several images to evaluate the performance of our proposed technique. The simulation results largely present MATLAB as a veritable approach for image processing operations.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Telehealth care enhancement using the Internet of Things technology

    • Authors: BOUMEHREZ Farouk; A.Hakim Sahour, Noureddine Doghmane
      Abstract: Chronic diseases quickly become broader public health issues because of the difficulty in obtaining appropriate, often long-term health care. So that, it requires the extension of health care for patients with chronic diseases beyond the clinic to include patient’s home and work environment. To reduce costs and provide more appropriate healthcare, we need telehealth care where Internet of Things (IoT) technology plays an important role. The integration of the IoT and medical science offers opportunities to improve healthcare quality, and efficiency and to better coordinate healthcare delivery at home and in the workplace. In this paper, we present the realization of a remote healthcare system based on the IoT technology. The function of this system is the transmission via a gateway of internet collected data using biomedical sensors node based Arduino board (e.g., temperature, electrical activity of the heart, heart rate monitor). These data will be stored automatically in a cloud. The health can then be monitored by the doctor or patient using a web page in real-time from anywhere at any time in the world using laptops or smart phones, etc. This method also reduces the need for direct interaction between doctor and patient.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • RT-RCT: An online tool for real-time retrieval of connected Things

    • Authors: Fatima Zahra Fagroud; El Habib Ben Lahmar, Hicham Toumi, Youssef Baddi, Sanaa El Filali
      Abstract: In recent years, Internet of Things (IoT) represents a giant and a promoter area in innovation and engineering fields. IoT devices are spread in various fields and offer advanced services which assist their users to monitor and control objects remotely. IoT has a set of special characteristics such as dynamic, variety of data and huge scale which introduces a great challenge in the field of retrieval technologies, more precisely real-time retrieval. This paper addresses the issue of real-time retrieval of connected things and tries to propose an innovative solution which allows the retrieval of these things and their descriptive data. The paper proposes an on-line tool for real-time retrieval of connected things and their descriptive data based on network port scanning technique. The performance of this tool proves to be powerful under normal conditions, however more tests must be implemented in the aim to improve the proposed solution. The tool resulted from this work appears to be promising and can be used as a reference by network administrators and IT security managers for the development of new security mechanisms and security reinforcement.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Business process monitoring system in supporting information technology
           governance

    • Authors: Lanto Ningrayati Amali; Muhammad Rifai Katili, Sitti Suhada, Tri Alfandra Labuga
      Abstract: Information technology (IT) is essential in supporting an organization's business sustainability and growth, making it critically dependent on IT. Therefore, a focus on IT Governance, consisting of leadership, organizational structure, and process ensuring that IT organization supports and expands the organizational strategies and goals is required. When the business supports the strategic significance of IT investment, the implementation of an IT strategy will lead to the adoption of an IT Governance model. It will support and help the description of the benefit roles and responsibilities from IT systems and infrastructure. This paper aims to develop a business process monitoring system to support IT Governance in improving user service and measuring organizational performance. The research method was the system development method with the Waterfall model. To measure the performance of the business process, the self-assessment method with performance matrix tools was applied. The study resulted in a business process monitoring system that can enhance the organization’s primary business process in services, supporting the said organization’s performance.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • A Novel Imbalanced Data Classification Approach Using Both Under and Over
           Sampling

    • Authors: SeyyedMohammad JavadiMoghaddam; Asadollah Noroozi
      Abstract: The performance of the data classification has encountered a problem when the data distribution is imbalanced. This fact results in the classifiers tend to the majority class which has the most of the instances. One of the popular approaches is to balance the dataset using over and under sampling methods. This paper presents a novel pre-processing technique that performs both over and under sampling algorithms for an imbalanced dataset. The proposed method uses the SMOTE algorithm to increase the minority class. Moreover, a cluster-based approach is performed to decrease the majority class which takes into consideration the new size of the minority class. The experimental results on 10 imbalanced datasets show the suggested algorithm has better performance in comparison to previous approaches.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • DCT based feature extraction and SVM classification for musical
           instruments tone recognition

    • Authors: Linggo Sumarno; Rifai Chai
      Abstract: The conducted research proposes a feature extraction and classification combination method that is used in a tone recognition system for musical instruments. It is expected that by implementing this combination, the tone recognition system will require fewer feature extraction coefficients than those previously investigated. The proposed combination comprises of feature extraction using DCT (Discrete Cosine Transform) and classification using SVM (Support Vector Machine). Bellyra, clarinet, and pianica tones were used in the experiment, with each indicating a tone with one, several, or many major local peaks in the transform domain. Based on the results of the tests, the proposed combination is efficient enough to be used in a tone recognition system for musical instruments. This is indicated in recognizing a tone, it only needs at least eight feature extraction coefficients.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Analysis of thermal models to determine the loss of life of mineral oil
           immersed transformers

    • Authors: Mohammad Tolou Askari; Mohammad Javad Mohammadi, Jagadeesh Pasupuleti, Mehrdad Tahmasebi, Shangari K. Raveendran, Mohd Zainal Abdin Ab Kadir
      Abstract: Hot spot as well as top oil temperatures have played the most effective parameters on the insulation life of the electrical transformers. The prediction of these factors is very important for determining the loss of life of the electrical transformers in the transmission and distribution systems. Thus, an accurate mathematical method is required to determine hot spot and top oil temperature based on the different types thermal models. In this study calculates an accurate hot spot temperature and then calculate the loss of life of electrical transformer according to the numerical analysis method. An alternative solution for calculating the thermal model is suggested in this work. Also, results are validated with actual temperatures. Finally, this method is implemented on 2500 KVA electrical transformer.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Social impact of renewable energy systems: solar energy system in
           vulnerable community case study

    • Authors: Yeison Alberto Garcés-Gómez; Vladimir Henao-Céspedes, Diana Marcela Gómez Sánchez, Ángel Andrés López Trujillo, Nicolás Toro García
      Abstract: Photovoltaic lighting systems are unable to reach people with low purchasing power due to high installation costs, so they have traditionally been concentrated in families with high purchasing power and currently do not take into account the social power that this type of system represents. This article analyzes through bibliometric review the effect that lighting can have on human development and how a good lighting system can positively affect a community environment. It is proposed the social design of a photovoltaic lighting system which will be installed in a vulnerable community with resources obtained by the community itself and the whole process of accompaniment achieving a satisfactory impact on the community and achieving integration between the same from community participation. The development of workshops with the children of the community has also been proposed, leading to the training and recognition of alternative energy systems as a strategy of social appropriation.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Optimal distributed generation placement using artificial intelligence for
           improving active radial distribution system

    • Authors: Omar Muhammed Neda; Omar Muhammed Neda, Mustafa A. Mhawesh
      Abstract: There are several economic, technical, and environmental profits of Distributed Generator (DG) units which are believed for improving the safety and reliability of the distribution power grids. However, these benefits can be maximized by ensuring optimum sizing and positioning of DG units because an arbitrary location of DG units may adversely affect and jeopardize power grids which could contribute to maximising of power loss, degradation of the voltage profile and decrease of reliability. Therefore, several approaches were suggested to ensure optimum position and size of DGs. The primary aim of this article is for establishing technique for optimum scheduling and operating of DG units to lessen power loss, revamp voltage profile and overall network reliability. Artificial intelligence method called Particle Swarm Optimization (PSO) is utilized in this work as a tool for finding the best site and size of DG units in single step to lessen power loss in addition to boost the voltage profile. In this paper, IEEE 33 node distribution system is utilized as a test system to show applicability of PSO algorithm and it was performed on a MATLAB software. The results of the PSO algorithm are compared with the results attained by other artificial intelligence methods in the literature. Finally, the results prove that the PSO method outperform the other methods with precise and fast results, and that it is highly efficient in obtaining a global solution and capable of overcoming the disadvantages of traditional algorithm.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Nonlinear control for an optimized grid connection system of renewable
           energy resources

    • Authors: M. El Malah; A. Ba-Razzouk, E. Abdelmounim, M. Madark
      Abstract: This paper proposes an integral backstepping based nonlinear control strategy for a grid connected wind-photovoltaic hybrid system. The proposed control strategy aims at extracting the maximum power available while respecting the grid connection standards. The proposed system has a reduced number of power electronic converters, thereby ensuring lower costs and reduced energy losses, which improves the profitability and efficiency of the hybrid system. The effectiveness of the proposed topology and control methodology is validated using the MATLAB/Simulink software environment. The satisfactory results achieved under various atmospheric conditions and in different operating modes of the hybrid system, confirm the high efficiency of the proposed control strategy.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Optimal Tuning of PI controllers using Adaptive Particle Swarm
           Optimization for Doubly-Fed Induction Generator connected to the grid
           during a Voltage Dip

    • Authors: Elmostafa Chetouani; Youssef Errami, Abdellatif Obbadi, Smail Sahnoun
      Abstract: This paper proposes the Adaptive Particle Swarm Optimization technique (APSO) to control the active and reactive power produced by a variable wind energy conversion system and the exchanged power between the electric grid and the system during a Voltage Dip (VD). Besides, to get the variable speed wind energy maximum power, a Maximum Power Point (MPP) methodology is utilized. The system under study is a 5 MW wind turbine connected via a gearbox to a Doubly-Fed Induction Generator (DFIG). The DFIG stator is branched directly to the electrical network, while the Back-to-Back converters couple the rotor to the grid. The decoupled vector control of the rotor side converter and the grid side converter is established primarily by a conventional Proportional-Integral (PI) and a second level by an intelligent PI whose gains are tuned using the proposed control. The performances and results obtained by APSO tuned PI controllers are analyzed and compared with those attained by classical PI controllers through the Matlab/Simulink. The superiority of the advised technique is examined during a two-phase short-circuit fault condition and confirmed by the reduced oscillations.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Implementation of Active Filter with PI controller for Harmonic Mitigation
           of PV Grid connected system

    • Authors: Achala Khandelwal; Pragya Nema
      Abstract: The recent trends show the interconnection of PV system with electric grid. With this configuration the issue of harmonics comes into existence. SAPF (Shunt Active Power Filter) has emerged as a good substitute for passive filters to reduce the harmonics to great extent. The SAPF’s most vital part is the applied control strategies. Several researches are being under process to advance the functioning of SAPF. One of the important control requirements of SAPF is the regulation of DC link up capacitor voltage. Here the voltage supervision of capacitor is being done using PI controller. The paper show current harmonics compensation of PV grid connected system using PI controller based SAPF.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Harmonics resonance elimination technique using active static compensation
           circuit

    • Authors: Rakan Khalil Antar; Mohammed Y. Suliman, Asef A. Saleh
      Abstract: The existence of nonlinear loads produces high distortion and low power factor in the power system that leads to get poor power quality. Resonance problem is occurred due to the power system inductances and the compensation capacitors which increases the harmonic distortion. Therefore, it is necessary to prevent the action of resonance even if conventional or modern methods are built to improve the power system quality. In this paper, active static compensation circuit is proposed and designed to have the features of improving power factor, reducing THD, and eliminating the harmonics resonance effect at the same time with different linear and nonlinear load conditions. These features have been performed based on a modified pulse width modulation technique to drive and control the proposed circuit.  The originality designed point of this technique is to have ability to operate the active static compensation circuit as harmonics injector, power factor corrector and resonance eliminator at the same time. Simulation model results illustrate that the proposed circuit is effective for both steady-state and transient operations conditions. The THD of the supply voltage and current at firing angle (α=300) is reduced by 99% and 98.8% respectively. While the power factor is improved to stay around unity.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Automatic Voltage Regulator Performance Inhancement Using a Fractional
           Order Model Predictive Controller

    • Authors: Imen Deghboudj; Samir Ladaci
      Abstract: In this paper we propose a new design method for fractional model predictive control (FO-MPC) and apply it for the control of an Automatic Voltage Regulator (AVR). The proposed FO-MPC is synthesized for the class of linear time invariant system. The main contribution is to use a fractional order system as prediction model, whereas the plant model is considered as an integer order one. The fractional order model is implemented using the singularity function approach. A comparative study is given with the classical MPC scheme. Numerical simulation results on the controlled AVR performances show the efficiency and the superiority of the fractional order MPC.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Smart Aerosonde UAV Longitudinal Flight Control System based on Genetic
           Algorithm

    • Authors: Ahmed Elbatal; Ahmed Medhat Youssef, Mohamed M. Elkhatib
      Abstract: Synthesis of a flight control system for such an aircraft for achieving robust stability and acceptable performance across specified flight envelope in the presence of uncertainties represents an attractive and challenging design problem. This paper is devoted to the design of an intelligent flight control system for the Aerosonde UAV model using the genetic self-tuning PID algorithm. In this method, the gains of PID controller are optimally tuned via genetic algorithm to improve the transient responses of the system. The proposed system is modeled and simulated using Simulink/MATLAB software. Simulation results of the proposed controller are compared with those of the classical PID controller to prove the robustness of the proposed controller in the presence of external disturbances.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Freshness assessment of tilapia fish in traditional market based on an
           electronic nose

    • Authors: Radi Radi; Eka Wahyudi, Muhammad Danu Adhityamurti, Joko Purwo Leksono Yuroto Putro, Barokah Barokah, Dwi Noor Rohmah
      Abstract: Since aroma still becomes the main quality parameters in food products, the use of aroma meters such as an electronic nose (e-nose) will be increase in the future, including in determining the quality of fish products. This study evaluates an e-nose based on a series of gas sensors to measure the freshness of fish products. The e-nose was designed based on semiconductor sensors as a detector, a combination of valve-vial-oxygen gas as carrier, a microcontroller as an interface and controller, and a computer for data recording and processing. Firstly, the e-nose was used to classify fresh and non-fresh tilapia. A total of 48 samples of fresh tilapia and 50 samples of non-fresh tilapia were prepared with the same size of 7 g per sample. Measurement with the e-nose was performed for each sample in three stages, namely flushing, collecting, and purging. Sensor response were recorded with a sampling period of a second, processed to form aroma patterns, and then continued with pattern classification software by using two algorithms, namely: Principal Component Analysis (PCA) and neural network (NN). There were four methods for forming aroma patterns, namely: (a) absolute data, (b) normalized absolute data, (c) relative data, (d) normalized relative data. The results of PCA and NN analysis showed that the normalized absolute data method provides the best classification level. The NN can classify the fresh and non-fresh samples with the level of accuracy of 93.88%. With this method, the trained NN was used to predict the freshness of 15 samples of tilapia obtained randomly from a traditional market. The results show that 60.0% of the samples are in the fresh category, 33.3% are in the non-fresh category, and 6.7% are not included in both categories. Besides being measured with the e-nose, these samples were also measured with an ammonia test kit.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • A FPGA Threshold-based Fall Detection Algorithm for Elderly Fall
           Monitoring with Verilog

    • Authors: Pui Mun Lo; Azniza Abd Aziz
      Abstract: Fall is one of the leading causes of accidental or unintentional injury deaths worldwide due to serious injuries such as head traumas and hip fractures. As life expectancy improved, the rapid increase in aging population implied the need for the development of vital sign detector such as fall detector to help elderly in seeking for medical attention. Immediate rescue could prevent victims from the risk of suspension trauma and reduce the mortality rate among elderly population due to fall accident effectively. This paper presents the development of FPGA-based fall detection algorithm using a threshold-based analytical method. The proposed algorithm is to minimize the rate of false positive fall detection proposed from other researchers by including the non-fall events in the data analysis. Based on the performance evaluation, the proposed algorithm successfully achieved a sensitivity of 97.45% and specificity of 97.38%. The proposed algorithm was able to differentiate fall events and non-fall events effectively, except for fast lying and fall that ending with sitting position. The proposed algorithm shows a good result and the performance of the proposed algorithm can be further improved by using an additional gyroscope to detect the posture of the lower body part.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Wide-band metamaterial perfect absorber (MPA) through double arrow shape
           printed on a thin dielectric

    • Authors: Siti Adlina Md Ali; Maisarah Abu, Siti Normi Zabri, Shipun Anuar Hamzah
      Abstract: A wide-band metamaterial perfect absorber was introduced. The dual arrow shapes and the ground plane were in between the 0.0035λ TLY-3. Lump element technique was applied to enhance the absorption bandwidth, which was connected between both of the arrow structures. The limitation during fabrication process in using lump element, had seriously restricted its practical applications for microwave absorption. Then, a very thin line was connected between both arrow structures to represent the resistance by lump element which was expected to ease the fabrication process and practical applications as well. Four cases were analyzed: double arrow, double arrow with lump connected, double arrow with lump connected and 9 mm air gap, and thin line connected with 6 mm air gap. The fourth case achieved the highest operational absorbency frequency, which developed about 7.38 GHz     (3.87 GHz to 11.25 GHz) approximately to 7.38 GHz. Three resonant frequencies were achieved; 4.17 GHz, 6.09 GHz and 10.30 GHz with perfect absorbency. These properties are expected to be used in practical applications such as satellite and radar communications transmission. These properties of the metamaterial absorber could increase the functionality of the metamaterial absorber to be used in any application especially in reducing radar cross section for stealth application.  
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • The novel noise classification techniques found on quadruple threshold
           statistical detection filter under fix intensity impulse outlier
           environment

    • Authors: Vorapoj Patanavijit; Kornkamol Thakulsukanant
      Abstract: Because of the enormous necessity of contemporary advance noise suppressing algorithms, this article proposes the novel noise classification technique found on QTSD (quadruple threshold statistical detection) filter, which is ultimately improved from the outstanding TTSD (Triple Threshold Statistical Detection) filter. The four additional parameters (or thresholds) for each auxiliary situations are incorporated into the proposed QTSD framework for dealing with the limitation of the earlier noise classification technique. The mathematical pattern is modeled by each photograph elements and is investigated in contradiction to the 1st auxiliary threshold for analyzing whether it is non-noise or noise photograph elements. Subsequently, the calculated photograph element is analyzed with the contradiction between the 2nd threshold, which is modeled by using the normal distribution (mean and variance), and is analyzed with the contradiction between the 3rd threshold, which is modeled by using the quartile distribution (median). Finally, the calculated photograph element is investigated in contradiction to the 4th threshold, which is modeled from maximum or minimum value for analyzing whether it is non-noise or noise photograph elements FIIN (fix intensity impulse outlier). For performance evaluation, extensive noisy photographs are made up of nine photographs under FIIN environment distribution, which are synthesized for investigating the proposed noise classification techniques found on QTSD filter in the objective indicators (noise classification correctness, non-noise classification correctness and overall classification correctness). From these extensive simulated results, the proposed noise classification technique can outstandingly produce the higher correctness than the earlier noise classification techniques. 
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Orchid types classification using supervised learning algorithm based on
           feature and color extraction

    • Authors: Pulung Nurtantio Andono; Eko Hari Rachmawanto, Nanna Suryana Herman, Kunio Kondo
      Abstract: Orchid flower as ornamental plants with a variety of types where one type of orchid has various characteristics in the form of different shapes and colors. Here, we chosen Support Vector Machine, Naïve Bayes, and K-Nearest Neighbor algorithm which generates text input. This system aims to assist the community in recognizing orchid plants based on their type. We used more than 2250 and 1500 images for training and testing respectively which consists of 15 types. Testing result shown impact analysis of comparison of three supervised algorithm using extraction or not and several variety distance. Here, we used SVM in Linear, Polynomial, and Gaussian kernel while K-Nearest Neighbor operated in distance starting from K1 until K11. Based on experimental results provide Linear kernel as best classifier and extraction process had been increase accuracy. Compared with Naïve Bayes in 66%, and a highest KNN in K=1 and d=1 is 98%, SVM had a better accuracy. SVM-GLCM-HSV better than SVM-HSV only that achieved 98.13% and 93.06% respectively both in Linear kernel. On the other side, a combination of SVM-KNN yield highest accuracy better than selected algorithm here.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Detection of acute stress caused by cognitive tasks based on physiological
           signals

    • Authors: Valentina Markova; Todor Ganchev, Kalin Kalinkov, Miroslav Markov
      Abstract: We report on the development of an automated detector of acute stress based on physiological signals. Our detector discriminates between high and low levels of acute stress accumulated by students when performing cognitive tasks on a computer. The proposed detector builds on well-known physiological signal processing principles combined with the state-of-art Support Vector Machine (SVM) classifier. The novelty aspects here come from the design and implementation of the signal pre-processing and the feature extraction stages, which were purposely designed and fine-tuned for the specific needs of acute stress detection and from applying existing algorithms to a new problem. The proposed acute stress detector was evaluated in person-specific and person-independent experimental setups using the publicly available CLAS dataset. Each setup involved three cognitive tasks with a dissimilar crux of the matter and different complexity. The experimental results indicated a very high detection accuracy when discriminating between acute stress conditions due to significant cognitive load and conditions elicited by two typical emotion elicitation tasks. Such a functionality would also contribute towards obtaining a multi-faceted analysis on the dependence of work efficiency from personal treats, cognitive load and acute stress level.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Levenberg-Marquardt Backpropagation Neural Network with Techebycheve
           Moments for Face Detection

    • Authors: Ali Nadhim Razzaq; Rozaida Ghazali, Nidhal Khdhair El Abbadi, Hussein Ali Hussein Al Naffakh
      Abstract: Face detection is an intelligent approach which used in a variety of applications that identifies human faces in digital images. This work presents a new method which composes of a neural network and Techebycheve transforms for face detection. For feature extraction, Tchebychev transform was applied, in which a discrete Tchebychev transform is given for different sampling patterns and a number of samples here were performed on color images. A Levenberg-Marquardt backpropagation neural network was applied to the transformed image to find faces in the face detection dataset and FDDB benchmarked database. Model performance was measured based on its accuracy and the best result from the new proposed method was 98.9%. Simulation results showed that the proposed method handles face detection efficiently.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Constructed Model for Micro-Content Recognition in lip reading Based Deep
           Learning

    • Authors: Nada Hussain Alsamaraie; Matheel Emad Abdulmunem, Akbas Ezaldeen Ali
      Abstract: Communication between human beings has several ways, one of the most known and used is speech, both visual and acoustic perceptions sensory are involved, because of that, the speech is considered as a multi-sensory process. Micro contents are a small pieces of information that can be used to boost the learning process. Deep learning is an approach that dives into deep texture layers to learn fine grained details. The convolution neural network (CNN) is a deep learning technique that can be employed as a complementary model with micro learning to hold micro contents to achieve special process. In This paper a proposed model for lip reading system is presented with proposed video dataset. The proposed model receives micro contents (the English alphabet) in video as input and recognize them, the role of CNN deep learning is clearly appeared to perform two tasks, the first one is feature extraction and the second one is the recognition process. The implementation results show an efficient accuracy recognition rate for various video dataset that contains variety lip reader for many persons with age range from 11 to 63 years old, the proposed model gives high recognition rate reach to 98%.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Switchable Bandstop to Allpass Filter using Cascaded Transmission Line SIW
           Resonators in K-Band

    • Authors: Amirul Aizat Zolkefli; Noor Azwan Shairi, Badrul Hisham Ahmad, Adib Othman, Nurulhalim Hassim, Zahriladha Zakaria, Imran Mohd Ibrahim, Huda A Majid
      Abstract: In this paper, a switchable bandstop to allpass filter using cascaded transmission line SIW resonators is proposed. The switchable filter is performed by the switchable cascaded transmission line SIW resonators using discrete PIN diodes. Therefore, it can be used for rejecting any unwanted frequencies in the communication systems. The proposed filter design is operated in K-band and targeted for millimeter wave front end system for 5G telecommunication. Two filter designs with different orientation (Design A and B) are investigated for the best performance and compact size. As results, Design B is the best by giving a maximum attenuation of 39.5 dB at 26.4 GHz with the layout size of 33 × 30 mm.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • A review on various security attacks in vehicular ad hoc networks

    • Authors: Mustafa Maad Hamdi; Lukman Audah, Mohammed Salah Abood, Sami Abduljabbar Rashid, Hussain Mahdi, Ahmed Shamil Mustafa, Ahmed Shakir Al-Hiti
      Abstract: Ad hoc vehicle networks (VANET) are being established as a primary form of Mobile Ad hoc networks (MANET) and a critical infrastructure to provide vehicle passengers with a wide range of safety applications. VANETs are increasingly common nowadays because it is connecting to a wide range of invisible services. The security of VANETs is paramount as their future use must not jeopardize their users' safety and privacy. The security of these VANETs is essential for the benefit of secure and effective security solutions and facilities, and uncertainty remains, and research in this field remains fast increasing. We discussed the challenges in VANET in this survey. Were vehicles and communication in VANET are efficient to ensure communication between Vehicles to Vehicles (V2V), Vehicles to Infrastructures(V2I). Clarified security concerns have been discussed, including confidentiality, authentication, integrity, availableness, and non-repudiation. We have also discussed the potential attacks on security services. According to analysis and performance evaluations, this paper shows that the ACPN is both feasible and appropriate for effective authentication in the VANET. Finally, the article found that in VANETs, encryption and authentication are critical.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Development of Split Ring Resonator for Pineapple Moisture Detection

    • Authors: NOORSALIZA ABDULLAH; Anis Afrina Mohd Amirudin, Ezri Mohd
      Abstract: Microwave resonant is one of the sensors used in characterizing the material and is also one of the most sensitive sensors for measuring dielectric properties. This project proposed the resonant method due to its accuracy and sensitivity. The split ring resonators were mounted on the fruit sample surface to observe the resonant frequency behavior,and to measure the fruit freshness. The resonator was set at 6 GHz using the FR4 lossy substrate. The findings show that the coupling distanceand the ring radius have the greatest impact on preserving the resonant behavior. The fundamental of the obtained resonant frequencies was observed based on the different moisture content of the test material. The moisture level was observed at 38.2%, 54%, 69%, and 86.7%. At 86.7%, the resonant frequency has the highest shifting by shifting to the left. This shows that the larger resonant frequency changeoccurs when the water content is higher.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Investigation of Energy Efficient Protocols Based on Stable Clustering for
           Enhancing Lifetime in Heterogeneous WSNs

    • Authors: Adnan Hussein Ali; Jaber H. Majeed, Waleed Khalid Al-Azzawi, Adnan Hussein Ali
      Abstract: There are certain challenges faced with Wireless Sensor Networks (WSNs) performances,consumption can be seen amongst all these challenges as a serious area of research. Data from sensor nodes are transmitted by most WSN energy either among many nodes or to the Base Station (BS), and due this connection, several routing protocols were developed for supporting in data transmission in the WSNs. Extending network lifetime in an operational environment is the major objective of the wireless sensor network. Charging or exchanging sensor node batteries is almost impossible. Energy balancing and energy efficiency are significant research scopes as per designing of routing protocols aimed at self-organized WSNs. A heterogeneous WSN is one where every node has different amount of energy linked to it before it is deployed in a network. Therefore, different energy efficient routing protocols have been proposed which enables lesser consumption of energy, longer stability period which leads to the network lifetime increasing. In this study, the average energy of a WSN is computed after every logical round of operation for our protocol - HPEEA and compare it with two well-known heterogeneous protocols namely- SEP and CCS. At the end of the considered number of logical operations, MATLAB with simulation results confirm that HPEEA protocol have a reduction in the energy consumption compared to other protocols.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Efficient Incremental Data Backup of Unison Synchronize Approach

    • Authors: Prakai Nadee; Preecha Somwang
      Abstract: Data communication and computer networks have growing explosive in every aspect of business. There are using computer networks to offer instantaneous access to information in online libraries around the world. The popularity and essential of data communication has produced a strong demand in all job for people with more computer networking expertise. Companies need workers to plan, use and manage the database system that aspects of security. The security policy must apply to data stored in a computer system as well as to information transferring a network. This paper aim to define a computer data backup policies of the Incremental backup by using Unison synchronize for a file-synchronization tool and Load Balancing File Synchronization Management (LFSM) for traffic management. The policy is able to the full backup only first as one time from obtaining a copy of the data. The easiest aspect of value to assess is replacement for restoring the data from only change and performing the correct information. As a result, the new synchronize is able to improve performance of data backup and computer security system.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Low resource deep learning to detect waste intensity in the river flow

    • Authors: Ferdinandus Fidel Putra; Yulius Denny Prabowo
      Abstract: Waste has become a significant problem in Indonesia, especially in the capital city of Jakarta due to many disasters caused by it. The one cause of flooding is the blockage of river flow by waste. The monitoring of litter is essential to find out the waste intensity in the river. The research was formed which aims to produce an application that can detect, track, and calculate river waste using YOLO v3 algorithm. This research was done in order to simplify the process of monitoring waste in the river and can calculate waste using video. This research uses 340 images directly from photos and videos, captured by researchers—detection of waste processed frame by frame by changing video into several structures. From the acquired result from the experiment, it's proven that YOLO v3 can be used for detection and counting waste recorded on video. The result of this research is an application that can detect waste and it is able to detect said objects with 98,74% of confidence from case video.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Boosting with crossover for improving imbalanced medical datasets
           classification

    • Authors: Abeer S. Desuky; Asmaa Hekal Omar, Naglaa M. Mostafa
      Abstract: Due to the common use of electronic health databases in many healthcare services, healthcare data are available for researchers in the classification field to make diseases’ diagnosis more efficient. However, healthcare-medical data classification is most challenging because it is often imbalanced data. Most proposed algorithms are susceptible to classify the samples into the majority class, resulting in the insufficient prediction of the minority class. In this paper, a novel preprocessing method is proposed, using boosting and crossover to optimize the ratio of the two classes by progressively rebuilding the training dataset. This approach is shown to give better performance than other state-of-the-art ensemble methods, which is demonstrated by experiments on seven real-world medical datasets with different imbalance ratios and various distributions.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Comparative Analysis of the Essential CPU Scheduling Algorithms

    • Authors: Farhad Hussein hussein; Hoger K. Omar
      Abstract: CPU scheduling algorithms have a significant function in multiprogramming operating systems. When the CPU scheduling is effective a high rate of computation could be done correctly and also the system will maintain a stable state. As well as, CPU scheduling algorithms are the main service in the operating systems that fulfill the maximum utilization of the CPU. This paper aims to compare the researches on the CPU scheduling algorithms towards which one is the best algorithm for gaining a higher CPU utilization. The comparison has been done between ten scheduling algorithms with presenting different parameters, such as performance, Algorithm’s complexity, Algorithm’s problem, average waiting times, Algorithm’s advantages-disadvantages, allocation way, etc. The main purpose of the article is to analyze the CPU scheduler in such a way that suits the scheduling goals. However, knowing the algorithm type which is most suitable for a particular situation by showing its full properties.               
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Modeling Cache Performance for Embedded Systems

    • Authors: Ogechukwu Kingsley Ugwueze, Chijindu C. V; Udeze C. C, Ahaneku A. M, Eneh N. J, OBINNA M EZEJA, EDWARD C ANOLIEFO
      Abstract: This paper presents a cache performance model for embedded systems. The need for efficient cache design in embedded systems has led to the exploration of various methods of design for optimal cache configurations for embedded processor. This requirement is arising from the need for better users’ experiences which will be realized by improving performance parameter like processing speed of embedded systems. Also power consumptions which are mainly on the cache needs to be reduced to increase efficiency. This work presents a cache hit rate estimation model for embedded systems that can be use to explore optimal cache configurations using Bourneli’s binomial cumulative probability based on application of reuse distance profiles. The model presented was evaluated using three mibench benchmarks which are bitcount, basicmath and FFT for 4kb, 8kb, 16kb, 32kb and 64kb sizes of cache under 2-way, 4-ways, 8-ways and 16-ways set associative configurations, all using least recently-used (LRU) replacement policy. The results were compared with the results obtained using sim-cheetah from simplescalar simulators suite. The mean errors for bitcount, basicmath, and FFT benchmarks are 0.0263%, 2.4476%, and 1.9000% respectively.  Therefore, the mean error for the three benchmarks is equal to 1.4579%. The margin of errors in the results was below 5% and within the acceptable limits showing that the model can be used to estimate hit rates of cache and to explore cache design options.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Naive Bayes Modification For Intrusion Detection System Classification
           with Zero Probability

    • Authors: Yogiek Indra Kurniawan; Fakhrur Fakhrur Razi, Nofiyati Nofiyati, Bangun Wijayanto, Muhammad Luthfi Hidayat
      Abstract: One of the methods used in detecting the Intrusion Detection System is by implementing Naïve Bayes algorithm. However, Naïve Bayes has a problem when one of the probabilities is 0, it will cause inaccurate prediction, or even no prediction was found. This paper proposed two modifications for Naïve Bayes algorithm. The first modification eliminated the variable that has 0 probability and the second modification changed the multiplication operations to addition operations. This modification is only applied when the Naïve Bayes algorithm does not find any prediction results caused by zero probabilities. The results of this research show that the value of precision, recall, and accuracy in the modification made tends to increase and better than the original Naïve Bayes algorithm. The highest Precision, Recall, and Accuracy are obtained from modification by changing the multiplication operation to the addition. Increasing precision can reach 4%, increasing recall reaches 2% and increasing accuracy reaches 2%.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Proposal for a framework of adaptive mobile intelligent agents

    • Authors: Elena Fabiola Ruiz-Ledesma; Rosaura Palma-Orozco, Elizabeth Acosta-Gonzaga
      Abstract: Intelligent agents are computational entities which have elements that provide them with the ability to perceive and manipulate their environment: sensors and actuators. These are characterized by displaying various properties that allow them to adapt in order to achieve their objective. Autonomy, learning, collaboration and reasoning are examples of these properties, which together make them intelligent artificial entities. This article shows the development of a framework that has made it possible to speed up the construction of a system of adaptive mobile intelligent agents, called SySAge. The system agents have integrated search and learning techniques for the execution of automated processes focused on solving search, classification and optimization problems. It has been found that through learning, the agents were able to estimate input parameters and apply them in estimating the shortest route in a graph, considering cost and penalty aspects. To determine the choice of search technique, a probabilistic selection was used. The autonomous behavior of each agent was appreciated through the various attempts to solve the search problem and not to focus the information acquired individually on a single agent. The agents displayed a conditional behavior, depending on the experience acquired in previous executions, with which they were able to decide which search technique should be used to avoid incurring any penalty. The contribution of the study was mobility, which constituted an additional characteristic, incorporating in the agents the ability to move around and use resources from various nodes in a distributed environment. It has been concluded that if collaboration mechanisms are integrated, the agents could be able to share the acquired experience. If the automated learning mechanisms are used, it will be possible to remember the values acquired in past experiences (previous iterations) with which it will be possible to try to solve function optimization problemswell-prepared abstract enables the reader to identify the basic content
      of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety.
      .
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Analyzing the Relationship between Information Technology Jobs Advertised
           On-line and Skills Requirements using Association Rules

    • Authors: Frederick Flores Patacsil; Michael Acosta
      Abstract: This study proposed a methodology for identifying and analyzing skill – job relationship using frequency word occurrences of skills as a requirement of the job. It collected published job vacancy data to IT job and skills requirement from various job hunting websites. They were manually assigned job title for as implied by its job skill based on the published job and employed association rule mining using FP-growth. The study revealed that skill words are highly related in a certain job requirement. The results of the study could provide insights on the gap between the school acquired skills and actual IT industry skill needs and as basis for curriculum enhancement and policy making interventions by the Philippine government.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Performance comparison of TF-IDF and Word2Vec models for emotion text
           classification

    • Authors: Denis Eka Cahyani; Irene Patasik
      Abstract: Emotion is the human feeling when communicating with other humans or reaction to everyday events. Emotion classification is needed to recognize human emotions from text.  This study compare the performance of the TF-IDF and Word2Vec models to represent features in the emotional text classification. We use the Support Vector Machine (SVM) and Multinomial Naïve Bayes (MNB) methods for classification of emotional text on commuter line and transjakarta tweet data. The emotion classification in this study has two steps. The first step classifies data that contain emotion or no emotion. The second step classifies data that contain emotions into five types of emotions i.e. happy, angry, sad, scared, and surprised. This study used three scenarios, namely SVM with TF-IDF, SVM with Word2Vec, and MNB with TF-IDF. The SVM with TF-IDF method generate the highest accuracy compared to other methods in the first dan second steps classification, then followed by the MNB with TF-IDF, and the last is SVM with Word2Vec. Then, the evaluation using Precision, Recall, and F1-Measure results that the SVM with TF-IDF provides the best overall method. This study shows TF-IDF modeling has better performance than Word2Vec modeling and this study improves classification performance results compared to previous studies.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Assessment of automatic univariate time series forecasting using nonlinear
           autoregressive exogenous (NARX) model

    • Authors: Hermansah Hermansah; Dedi Rosadi, Abdurakhman Abdurakhman, Herni Utami
      Abstract: In this paper, we propose an automatic forecasting method of univariate time series using nonlinear autoregressive exogenous (NARX) model. In this automatic setting, users only need to supply the input of time series. Then, an automatic forecasting algorithm will be able automatically without user intervention to set up the the appropriate setting parameters, estimate the parameters in the model and calculate forecasts. The proposed method is applied to real data and its performance is compared with several automatic methods available in the literature. The forecasting accuracy was measured by meaning of mean squared error (MSE) and mean absolute percent error (MAPE) and the results showed the proposed method outperforms the other automatic methods.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Twitter sentimental analysis from time series facts: the implementaion of
           enhanced support vector machine

    • Authors: Abhishek Kumar; Vishal Dutt, Vicente García-Díaz, Sushil Kumar Narang
      Abstract: Sentiment Analysis through textual data mining is an indispensable system used to extract the contextual social information from the texts submitted by the intended users. Now days, world wide web is playing a vital source of textual content being shared in different communities by the people sharing their own sentiments through the websites or web blogs. Sentiment analysis has become vital since based on the extracted expressions, individuals or the businesses can access or update their reviews. Sentimental mining is typically used to classify these reviews depending on its assessment as whether these reviews come out to be neutral, positive or negative. At this point, we have worked out am approach to analyze ans classify the sentiments; All the unstructured opinions are preprocessed into a well-structured form. Subsequently lexicon-oriented method converts the regulated analysis into statistical rank value. In this approach, we have exploited feature normalization and enhanced feature selection technique for ore efficient classification. Elimination of stop word, punctuation halting, POS labeling, computing opinion marks have been performed using lexicon based approach. Finally, the classifier reports the sentiment as either negative, positive or neutral. Afterwards, Support vector machine (SVM) classifier was employed to categorize reviews in which radial basis kernel is adjusted by its hyper plane parameters with marginal constant and Gamma. This enhanced Support vector machine furnishes improved outcomes as compared to logical regression and naive bayes classifiers. The output classification execution of this proposed system provides the optimal results when compared to other state of art classification methods also.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Classifying lymphoma and tuberculosis case reports using machine learning
           algorithms

    • Authors: Moanda Diana Pholo; Yskandar Hamam, Abdelbaset A. Khalaf, Chunling Du
      Abstract: Available literature reports several lymphoma cases misdiagnosed as tuberculosis, especially
      in countries with a heavy TB burden. This frequent misdiagnosis is due to the
      fact that the two diseases can present with similar symptoms. The present study therefore
      aims to analyse and explore TB as well as lymphoma case reports using Natural
      Language Processing tools and evaluate the use of machine learning to differentiate
      between the two diseases.
      To conduct this study, case reports were collected for each disease using web scraping.
      Natural language processing tools and text clustering were then used to gain insight
      into the created dataset. Finally, five machine learning algorithms were applied to the
      collected data, which was comprised of 765 lymphoma and 546 tuberculosis case reports.
      Each method was evaluated based on accuracy, precision and recall.
      The results showed that the multi-layer perceptron model achieved the best accuracy
      (93.1%), recall (91.9%) and precision score (93.7%), thus outperforming other algorithms
      in terms of correctly classifying the different case reports.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Business Process Re-enginering of Tourism E-marketplace By Engaging
           Government, Small Medium Enterprises and Tourists

    • Authors: Kadek Cahya Dewi; Ni Wayan Dewinta Ayuni
      Abstract: Not all tourism actors in Indonesia had utilize the e-marketplace. Therefore, one of the Indonesian government's focus is to improve the tourism business process model through e-marketplace based system. The research purpose was to re-engineer the business process of tourism e-marketplace by engaging government, Small Medium Enterprises (SMEs) and tourists. The research used the mixed method approach that conducted by modifying The McKinsey BPR Methodology. As the result, this research adding two novel aspects to the previous research which are "role" and "activities". The new Tourism E-marketplace Business Model proposed three kinds of role, namely: (1) Government, (2) SMEs, and (3) Tourists. This model also introduced activities including catalogue, finance, inventory management, collaboration, order fulfilment and customization. The proposed model was implemented and can be found in http://gonusadua.com. TELOS feasibility study was conducted to evaluate the model and found the final score of 8.3. It can be concluded that this model was feasible to develop and provide benefits for the government, SMEs, as well as the tourist. Beside had a contribution in built a new model of tourism e-marketplace, the research had also constructed a new tourism e-marketplace system with some improvements on the business model.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
  • Experience moderator effect on the variables that influence intention to
           use mobile learning

    • Authors: Ayad Shihan Izkair; Muhammad Modi Lakulu
      Abstract: The study has two objectives, first is exploring the variables that affect the intention to use mobile learning and second is investigating the experience moderator effect on the variables that influence intention to use mobile learning in higher education institutions (HEI) in Iraq. Then formulate a model for intention to use mobile learning. A questionnaire has been conducted in this research for collecting the feedback from the participants. The findings confirmed that “Social Influence” (SI), “Performance Expectancy” (PE), “Facilitating Conditions” (FCs), “Effort Expectancy” (EE) and “Satisfaction”  (SA) have an important influence on the intention to use mobile learning. But, this study has rejected the “Personal Innovativeness” (PINN) factor as it was found not important. Furthermore, The study has confirmed that the experience moderator variable has an influence of “Effort Expectancy” (EE), “Social Influence” (SI), and “Performance Expectancy” (PE) on the intention to use mobile learning. This study is significant to the field of discipline as it will provide a roadmap for HEI to recognize the factors that affect the intention to use mobile learning.
      PubDate: Fri, 01 Oct 2021 00:00:00 +000
       
 
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