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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 62 Documents
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Search results for , issue "Vol 34, No 1: April 2024" : 62 Documents clear
Coherence-based sufficient condition for support recovery using block generalized orthogonal matching pursuit Aravindan Madhavan; Yamuna Govindarajan
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp152-161

Abstract

Challenge is to find the support vectors of the unknown block sparse vector with compressed measurements in an underdetermined system where the number of unknowns is more than that of measurements. To recover unknown block sparse vector, restricted isometry property (RIP) is a sufficient condition need to be satisfied. Finding the restricted isometric constant is a non-polynomial hard problem for large values of n. In this paper coherence-based recovery guarantee has been proposed to recover the support vectors using block generalized orthogonal matching pursuit (BGOMP). It is proved that BGOMP can able to recover the support vectors with lesser number of iteration than block orthogonal matching pursuit (BOMP) by selecting multiple block support elements per iteration. Simulation results show detection performance of BGOMP is better than BOMP, block subspace pursuit (BSP) and block compressive sampling matching pursuit (BCoSaMP) for different block sparsity and block length. In most of the cases for different block sparsity and block length computation time for BGOMP is lesser than BCoSaMP, BSP and BOMP due to the multiple selection of elements in each iteration.
Effects of hammer configurations on pearl millet grinding system with a hammer mill: theory and experiment Moustapha Diop; Mouhamadou Thiam; Abdoulaye Kebe; Ibrahima Gueye
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp658-665

Abstract

In grinding processes using hammer mills, the configuration, number and speed of hammers are some of the main factors that can affect system performance. This paper aims to investigate the effects of hammer configurations in terms of specific energy consumption (SEC), grinding mass efficiency, and productivity. These effects were studied theoretically on the basis of classical grinding laws and experimentally with four different hammer configurations. From theoretical studies, a decreasing power model of SEC versus hammer configurations was developed, which was then validated with a determination coefficient of 0.99 in experiments using a 2 HP-DC hammer mill. The good agreement between theoretical and experimental results confirms that the specific energy consumption and the productivity are directly dependent on hammer configurations, but the effects are not significant for grinding mass efficiency.
Big data clustering based on spark chaotic improved particle swarm optimization Saida Ishak Boushaki; Brahim Hadj Mahammed; Omar Bendjeghaba; Messaoud Mosbah
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp419-429

Abstract

In recent years, the surge in continuously accelerating data generation has given rise to the prominence of big data technology. The MapReduce architecture, situated at the core of this technology, provides a robust parallel environment. Spark, a leading framework in the big data landscape, extends the capabilities of the traditional MapReduce model. Coping with big data, especially in the realm of clustering, requires more efficient techniques. Meta-heuristic-based clustering, known for offering global solutions within reasonable time frames, emerges as a promising approach. This paper introduces a parallel-distributed clustering algorithm for big data within the Spark Framework, named Spark, chaotic improved PSO (S-CIPSO). Centered on particle swarm optimization (PSO), the proposed algorithm is enhanced with a chaotic map and an efficient procedure. Test results, conducted on both real and artificial datasets, establish the superior performance and quality of clustering results achieved by the proposed approach. Additionally, the scalability and robustness of S-CIPSO are validated, demonstrating its effectiveness in handling large-scale datasets.
Evaluating various machine learning methods for predicting students' math performance in the 2019 TIMSS Abdelamine Elouafi; Ilyas Tammouch; Souad Eddarouich; Raja Touahni
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp565-574

Abstract

The growth of a country strongly depends on the quality of its educational system. All over the world, the education sectors are experiencing a fundamental evolution of their mode of operation. The greatest challenge for education today is the low success rate of learners and the abandonment of education in institutions at a premature age. Early prediction of student failure can help administrators provide timely guidance and supervision to enhance student success and retention. We propose a performance prediction model based on students' social and academic integration using several classification algorithms. This study involves a comparative analysis of five algorithms: logistics regression, k-nearest neighbors (K-NN), support vector machine (SVM), decision tree, and random forest. They were applied to a set of data from TIMSS 2019 in Morocco, to determine their effectiveness in predicting student performance using prediction models such as logistics regression, KNN, SVM, decision-tree, and random forest, decision-makers can make data-driven decisions to enhance educational strategies and improve outcomes in mathematics education.
Chicken tracking for location mapping of lameness chickens using YOLOv8 and deep learning-based tracking algorithm Wiwit Agus Triyanto; Kusworo Adi; Jatmiko Endro Suseno
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp407-418

Abstract

The chicken farming industry is one of the biggest food industries that supports the achievement of food security internationally. Farmers need an independent tool that can monitor the welfare conditions of chickens in cages. Using their tools, farmers can ideally detect the condition of chickens. Lameness chickens, can be known for activity and dredging of their location in the cage. Occlusion, and background in the cage are interesting challenges. By observing behavior, image handling practices can be used to identify tainted chicks and provide an early warning of sickness in chickens. In this study, you only look once, version 8 (YOLOv8) which is a convolutional neural network (CNN) network model was chosen to perform the detection, tracking, and mapping of chicken locations. YOLOv8 was combined with various algorithm optimizers to improve training performance, such as root mean square (RMS) Prop, stochastic gradient descent (SGD), ADAM, and ADAMW. Multi-object tracking algorithms such as BOT-sort and ByteTrack are also used to improve tracking performance. Based on the results, YOLOv8 with combinations of optimizer algorithms ADAMW has the best mAP, support, precision and F1-score values compared to the others, with 0.936, 0.993, 0.990, 0.991. Meanwhile, for multi object tracking, ByteTrack is faster in inference time(s) values compared to the others, with 0.2.
Formal validation of authentication scheme in 5G-enabled vehicular networks using AVISPA Mays A. Hamdan; Amel Meddeb Maklouf; Hassene Mnif
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp396-406

Abstract

Smart transportation may come from 5G-enabled cars. Traffic reports include congestion, roads, and driving. Urbanisation and population growth increase traffic accidents and travel time. Traffic accidents kill and injure most people worldwide. Intelligent transportation systems (ITS) improves driver and pedestrian safety. This study connects the VANET to 5G to create a 5G-enabled vehicle network because the road-side unit (RSU) is expensive and unsecure. This study connects numerous automobiles to TA for 5G-BS D2D communication. Data transmissions between autos are risky. Several scholars suggest authentication techniques for safe vehicle-to-vehicle communications. Overhead may enable side-channel attacks with these tactics. A secure and effective efficient and secure authentication-privacy-preserving (ES-APP) system connected TA, 5G-BS, and on-border unit (OBU) was presented. Initialization, vehicle registration, parameter renewal, message signing, single and batch verification are ES-APP steps. The formal evaluation automated verification of internet security protocols and applications (AVISPA) tool with on-the-fly model-checker (OFMC) and attack searcher (ATSE) back-ends secures the suggested ES-APP technique. ES-APP appears impervious to active and passive AVISPA assaults.
SWT-PCA-CNN: hyperspectral image classification with multi-stage feature extraction and parameter tuning Tilottama Goswami; Kandi Navya Shruthi; Sindhu Chokkarapu; Raghavendra Kune; Mukesh Kumar Tripathi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp59-68

Abstract

Hyperspectral imaging is an increasingly popular technique in remote sensing, offering a wealth of spectral information for a range of applications. This paper presents a comparative study of hyperspectral image classification techniques using three different datasets: Indian Pines, Salinas, and Pavia University. The study employs a combination of three methods, namely stationary wavelet transforms (SWT), principal component analysis (PCA), and convolutional neural network (CNN), to develop a model for hyperspectral image classification. The proposed approach combines SWT and PCA for spatial feature extraction and dimensionality reduction, followed by classification using CNN. Furthermore, the study performs parameter tuning by changing the optimizer, activation function, and filter size of the CNN model on the Indian Pines dataset. The results demonstrate that the proposed SWT-PCA-CNN approach outperforms the conventional DWT-PCA and PCA-KNN algorithms, achieving an overall classification accuracy of 98.2%, 99.86%, 99.80% on the Indian Pines, Salinas and Pavia University datasets respectively. The study highlights the effectiveness of the proposed approaches for hyperspectral image classification and their potential for applications in remote sensing and other fields.
Available medical imaging modalities for melanoma screening Hamza Abu Owida; Muhammad Saleh Al-Ayyad; Jamal Al-Nabulsi; Nidal Turab
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp245-253

Abstract

The prevalence of melanoma of the skin has seen a significant rise in recent decades, constituting approximately one-third of all diagnosed cancer cases. Melanoma, the most fatal variant among cutaneous malignancies, exhibits a 4% probability of occurrence over an individual’s lifetime. The increasing incidence and mortality rates of skin cancer impose a substantial burden on healthcare resources and the economy. In recent years, several optical modalities, including dermoscopy, reflectance confocal microscopy (RCM), optical coherence tomography, multiphoton excited fluorescence imaging, and dermatofluorescence, have been extensively studied and utilized to improve the non-invasive diagnosis of skin cancer. This review article provides an analysis of the approach employed in the recently developed optical non-invasive diagnostic technologies. It explores the clinical uses of these techniques, while also examining their respective advantages and disadvantages. Furthermore, the paper explores the possibility for additional advancements in these technologies in the future.
Revolutionizing healthcare image analysis in pandemic-based fog-cloud computing architectures Alzahraa Elsayed; Khalil Mohamed; Hany Harb
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp441-454

Abstract

Healthcare data analysis has become essential after epidemic outbreaks. The manual examination of medical images such as X-rays and computed tomography (CT) scans became one of these challenges. This paper introduces a healthcare architecture that tackles the analysis efficiency and accuracy challenges by harnessing artificial intelligence (AI) capabilities. This architecture utilizes fog computing and presents a modified convolutional neural network (CNN) designed specifically for image analysis. Different architectures of CNN layers are thoroughly explored and evaluated to optimize overall performance. To demonstrate the effectiveness of the proposed approach, a dataset of X-ray images is utilized for analysis and evaluation. Comparative assessments are conducted against recent models such as VGG16, VGG19, MobileNet, and related research papers. Notably, the proposed approach achieves an exceptional accuracy rate of 99.88% in classifying normal cases, accompanied by a validation rate of 96.5%, precision and recall rates of 100%, and an F1 score of 100%. These results highlight the immense potential of fog computing and modified CNNs in revolutionizing healthcare image analysis and diagnosis, not only during pandemics but also in the future. By leveraging these technologies, healthcare professionals can improve the efficacy and accuracy of medical image analysis, leading to improved patient care and outcomes.
Electrical discharge reproduction in rod-barrier-plane system Benharat Samira; Belgacem Leila; Doufene Dyhia; Bouazabia Slimane; Haddad Abderrahmane; Sakmeche Mounir
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp1-10

Abstract

The present paper deals with new modeling to reproduce the electric discharge in the rod-plane air gap system with rubber insulating barrier under AC and impulse voltage. This model considers the randomness character of discharge evolution which is governed by the electric field. The discharges shape obtained by this model are compared with ones given by experimental tests. The established model reproduces correctly the forms of discharges obtained by experimental tests under AC voltage. It is found that the behavior of the electrical discharge depends not only on the dimension (thickness and width) of the insulating barriers but on its positions in the air gap as well. It is to highlight that the mode of applied voltage is of key importance barrier. Experimental investigation shows that the developed arc can evolve on 1 to 4 channels. The generated discharges in AC voltage distinguish by the formation of a multiple-channel arc. Whereas, the discharge under lightning impulse voltage found to progress in a single channel whatever the barrier position and dimensions. The model confirms that electric field is the most important factor in the behavior of the rod-insulating barrier-plane system submitted to high voltage.

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