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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 65 Documents
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Search results for , issue "Vol 34, No 3: June 2024" : 65 Documents clear
TQU-HG dataset and comparative study for hand gesture recognition of RGB-based images using deep learning Van-Dinh Do; Van-Hung Le; Huu-Son Do; Van-Nam Phan; Trung-Hieu Te
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1603-1617

Abstract

Hand gesture recognition has great applications in human-computer interaction (HCI), human-robot interaction (HRI), and supporting the deaf and mute. To build a hand gesture recognition model using deep learning (DL) with high results then needs to be trained on many data and in many different conditions and contexts. In this paper, we publish the TQU-HG dataset of large RGB images with low resolution (640×480) pixels, low light conditions, and fast speed (16 fps). TQU-HG dataset includes 60,000 images collected from 20 people (10 male, 10 female) with 15 gestures of both left and right hands. A comparative study with two branches: i) based on Mediapipe TML and ii) Based on convolutional neural networks (CNNs) (you only look once (YOLO); YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-Nas, single shot multiBox detector (SSD) VGG16, residual network (ResNet)18, ResNext50, ResNet152, ResNext50, MobileNet V3 small, and MobileNet V3 large), the architecture and operation of CNNs models are also introduced in detail. We especially fine-tune the model and evaluate it on TQU-HG and HaGRID datasets. The quantitative results of the training and testing are presented (F1-score of YOLOv8, YOLO-Nas, MobileNet V3 small, ResNet50 is 98.99%, 98.98%, 99.27%, 99.36%, respectively on the TQU-HG dataset and is 99.21%, 99.37%, 99.36%, 86.4%, 98.3%, respectively on the HaGRID dataset). The computation time of YOLOv8 is 6.19 fps on the CPU and 18.28 fps on the GPU.
Integrated energy-efficient and location-aware routing in wireless sensor networks Karur Mohammed Saifuddin; Geetha D. Devangavi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1708-1717

Abstract

Sensor nodes in wireless sensor networks are commonly distributed randomly across a given landscape, and their placement may be randomized for specific applications, even extending to national deployments. The energy consumption associated with data transmission and reception by the cluster’s leader is notably higher compared to other nodes. To address this issue, it is recommended that wireless sensor networks adopt a more energy-efficient routing technique. This proposed technique assumes a spatial separation between different node types. Elevating the threshold enhances the likelihood that nodes with ample remaining power will endure as cluster leaders. Ultimately, a hybrid data transfer strategy is formulated, wherein data is directly exchanged between the base station and cluster heads among the super nodes containing advanced nodes. Most nodes employ a combination of single-hop and multi-hop approaches for data transport, aiming to minimize the power required for transmission between the cluster’s control node and the base station. According to simulation results, this proposed method surpasses the stable election protocol (SEP), demonstrating superiority over the improved threshold-sensitive stable election protocol in terms of the operational duration of a wireless sensor network.
Random access memory page caching: a strategy for enhancing shared virtual memory multicomputer systems performance Stepan Vyazigin; Madina Mansurova; Victor Malyshkin; Aygul Shaykhulova
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1879-1892

Abstract

This study examines a modified approach to optimizing the performance of support vector machine (SVM)-type multicomputer systems through a distinct type of caching method that allocates space in the random access memory (RAM) of a computing node for caching pages. The article extensively describes research on enhancing the performance of the SVM system through memory page caching in RAM at the hardware level by implementing the SVM system based on field-programmable gate arrays (FPGA). A systematic comparative evaluation highlights a discernible enhancement in system performance relative to systems not equipped with the revised caching algorithm. These findings could prove instrumental for subsequent studies focused on optimizing the performance of SVM systems, providing empirical data to inform future investigations and potential applications in multicomputer system performance enhancement.
Large file encryption in a reduced-round permutation-based AES file management system Jerico S. Baladhay; Heidilyn V. Gamido; Edjie M. De Los Reyes
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2021-2031

Abstract

In the rapid evolving digital landscape, the imperative to ensure data security has never been more crucial. This paper addresses the pressing challenges in data security by introducing a file encryption management system, leveraging a modified advanced encryption standard (AES) algorithm with reduced round iterations and bit permutation. This system aims to comprehensively secure various file types, providing a dependable solution for file exchange. Our findings reveal substantial improvements in both encryption and decryption processes using the reduced-round permutation-based AES (RRPBA). The adapted algorithm demonstrates a significant 38.8% acceleration in encryption time and a remarkable 44.86% improvement in decryption time, positioning it as a pivotal component for efficient file operations within the management system. Moreover, the throughput assessments showcase a remarkable 33.73% improvement in encryption and 23.72% in decryption, outperforming the original AES, emphasizing the algorithm's superior computational effectiveness, signaling positive implications for future high-performance applications. In conclusion, the study not only addresses critical security challenges but also presents a viable solution with tangible speed advantages for file encryption and decryption processes within digital file management systems.
Malaria cell identification using improved machine learning and modified deep learning architecture S., Shashikiran; H. D., Sunitha
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2078-2086

Abstract

Malaria continues to be a serious problem for public health because of its occurrence in tropical and subtropical areas with inadequate healthcare systems and few resources. For prompt intervention and treatment of malaria, effective and precise diagnosis is essential. Professional pathologists examine blood smear films by hand to get a microscopic diagnosis and another way they will do a rapid antigen malaria test which produces the result of 50% accuracy. Convolutional neural network (CNN) is a type of deep learning (DL) model that has been effectively used for a variety of image recognition applications. Our suggested approach uses, improved machine learning (IML) methods like support vector machine (SVM)+principal component analysis (PCA) fit, SVM+t-distributed stochastic neighbor embedding (t-SNE) fit, and CNN architecture with an accuracy of 86.23%, 88.27%, and 97.16% accuracy respectively, to combine feature extraction, data augmentation, and modify the layers by including the SVM algorithm in the final layer of the CNN architecture. The proposed method will significantly reduce pathologists' burden by automating the identification of malaria and improving diagnosis accuracy in resourceconstrained contexts.
Integrated electronic system for FET biosensor assessment based on current-voltage curve tracing Achmad Arif Bryantono; Leonardo Kamajaya; Fitri Fitri; Sungkono Sungkono; Herwandi Herwandi; Agwin Fahmi Fahanani
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1463-1471

Abstract

Field-effect transistor (FET) biosensors are pivotal in diverse applications, from environmental monitoring to healthcare diagnostics. Current-voltage (I-V) curve tracing is a powerful method for evaluating FET biosensor behavior, enabling comprehensive analysis of their FET biosensor characteristics. Traditional I-V curve tracing methods often require complex and expensive equipment, limiting their accessibility and practicality for routine sensor assessment. This study aims to develop and demonstrate an integrated electronic system for assessing FET biosensors using I-V curve tracing. The integrated electronic system uses readily available components, including microcontrollers, analog circuitry, and user-friendly software. We developed a compact, low-cost device that generates I-V curves for the FET biosensor. The integrated electronic system successfully generated I-V curves for various FET biosensors. The system demonstrated consistent, reliable performance, portability, and ease of use, making it a practical solution for routine sensor assessment. The average error in measurements using bipolar junction transistors (BJT) and metal-oxide-semiconductor field-effect transistors (MOSFETs) results in 2.62%, and measurements at different pH levels have a sensitivity of 21.6 mV/pH and a linearity of 0.9892. This innovation contributes to the advancement of FET biosensor technology. In the future, the developments should focus on ensuring their accuracy and reliability in various sensor fields.
Improved vigenere using affine functions surrounded by two genetic crossovers for image encryption Hamid El Bourakkadi; Abdelhakim Chemlal; Hassan Tabti; Mourad Kattass; Abdellatif Jarjar; Abdelhamid Benazzi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1787-1799

Abstract

This paper presents an improved method for encrypting color images, surpassing the effectiveness of genetic crossover and substitution operations. The technique incorporates dynamic random functions to enhance the integrity of the resulting vector, increasing temporal complexity to thwart potential attacks. The improvement involves integrating genetic crossover and utilizing two extensive pseudorandom replacement tables derived from established chaotic maps in cryptography. Following the controlled vectorization of the original image, our approach initiates with a first genetic crossover inspired by deoxyribonucleic acid (DNA) behavior at the pixel level. This genetic crossover is succeeded by a confusion-diffusion lap, reinforcing the connection between encrypted pixels and their neighboring counterparts. The confusion-diffusion process employs dynamic pseudorandom affine functions at the pixel level. Then a second genetic crossover operator is applied. Simulations conducted on a diverse set of images with varying sizes and formats showcase the robustness of our method against statistical, brute-force, and differential attacks.
Microstrip antenna system for communication capabilities applications Fredelino A. Galleto Jr.; Aaron Don M. Africa; Alyssa Joie F. Tablada; John Ernesto G. Amadora Jr.; Ira Third L. Burgos; Alliyah Mae K. Borebor; Rocelle Andrea S. Belandres; Rafael Dominic L. Montaño
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1643-1653

Abstract

In this comparative study, seven different microstrip antenna shapes, including rectangular, elliptical, triangular, inset fed, H-notch, and E-notch, were observed and analyzed, focusing on their suitability for global positioning system (GPS) application in microsatellites. To enable meaningful comparison, the study utilized the optimal resonant frequency in GPS applications, which is 1.57542 GHz. All the antenna designs have been generated using MATLAB’s Antenna Toolbox and are 100% efficient under ideal conditions with zero polarization loss, which is assumed in the link budget analysis. The results show that each antenna shape has been found to offer distinct advantages and limitations. Along with this, the circular and elliptical patch antenna presented a well-balanced performance, which is suitable for GPS applications. However, the elliptical shape falls behind the circular shape, which was determined to be the most optimal choice for GPS application, providing excellent isotropic antenna gain, return loss, voltage standing wave ratio (VSWR), and strong link budget analysis results.
Enhancing Hadoop distributed storage efficiency using multi-agent systems Rabie Mahdaoui; Manar Sais; Jaafar Abouchabaka; Najat Rafalia
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1814-1822

Abstract

Distributed storage systems play a pivotal role in modern data-intensive applications, with Hadoop distributed file system (HDFS) being a prominent example. However, optimizing the efficiency of such systems remains a complex challenge. This research paper presents a novel approach to enhance the efficiency of distributed storage by leveraging multi-agent systems (MAS). Our research is centered on enhancing the efficiency of the HDFS by incorporating intelligent agents that can dynamically assign storage tasks to nodes based on their performance characteristics. Utilizing a decentralized decision-making framework, the suggested approach based on MAS considers the real-time performance of nodes and allocates storage tasks adaptively. This strategy aims to alleviate performance bottlenecks and minimize data transfer latency. Through extensive experimental evaluation, we demonstrate the effectiveness of our approach in improving HDFS performance in terms of data storage, retrieval, and overall system efficiency. The results reveal significant reductions in job execution times and enhanced resource utilization, there by offering a promising avenue for enhancing the efficiency of distributed storage systems.
Plant pathology identification using local-global feature level based on transformer Manh-Hung Ha; Duc-Chinh Nguyen; Manh-Tuan Do; Dinh-Thai Kim; Xuan-Hai Le; Ngoc-Thanh Pham
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp1582-1592

Abstract

Deep learning plays a crucial role in addressing the challenge of plant disease identification in the field of agriculture. Detecting diseases in plants requires extensive effort, along with a comprehensive understanding of various plant diseases and increased processing time. Balancing both speed and accuracy in predicting leaf diseases in plants can significantly improve crop production and reduce environmental damage. In this paper, we examined deseases on popular plants in agriculture. We proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation. Paticular, we use refined feature of different layer to correlate semantics from high-level feature and low-level feature. Besides, to capture the extended temporal scale across the entire image, we employ a transformer to discern long-range dependencies among frames. Subsequently, the enhanced features incorporating these dependencies are inputted into a classifier for preliminary crop pathology prediction. The plant village dataset and VietNam strawberry disease (VNStr) dataset were utilized for training and disease classification in the experiments. Extensive experiments show that the proposed method outperforms by 99.18% and 94.05% accuracy in plant village and VNStr, respectivly. The model after being judged was applied on Android devices and therefore is easy to use.

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