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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6,301 Documents
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Modified differential evolution algorithm to finding optimal solution for AC transmission expansion planning problem Duong, Thanh Long; Bui, Nguyen Duc Huy
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5045-5054

Abstract

The transmission expansion planning (TEP) problem primarily aims to determine the appropriate number and location of additional lines required to meet the increasing power demand at the lowest possible investment cost while meeting the operation constraints. Most of the research in the past solved the TEP problem using the direct current (DC) model instead of the alternating current (AC) model because of its non-linear and non-convex nature. In order to improve the effectiveness of solving the AC transmission expansion planning (ACTEP) problem, a modified version of the differential evolution (DE) is proposed in this paper. The main idea of the modification is to limit the randomness of the mutation process by focusing on the first, second, and third-best individuals. To prove the effectiveness of the suggested method, the ACTEP problem considering fuel costs is solved in the Graver 6 bus system and the IEEE 24 bus system. Moreover, the result of each system is compared to the original DE algorithm and state-of-the-art methods such as the one-to-one-based optimizer (OOBO), the artificial hummingbird algorithm (AHA), the dandelion optimizer (DO), the tuna swarm optimization (TSO), and the chaos game optimization (CGO). The results show that the proposed algorithm is more effective than the original DE algorithm by 1.86% in solving the ACTEP problem.
An ensemble machine learning based model for prediction and diagnosis of diabetes mellitus Sherbiny, Moataz Mohamed El; Rabie, Asmaa Hamdy; Fattah, Mohamed Gamal Abdel; Eldin, Ali Elsherbiny Taki; Mostafa, Hossam El-Din
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5347-5359

Abstract

Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant health risks and global economic burdens. Early prediction and accurate diagnosis are crucial for effective management and treatment. This study presents an ensemble machine learning-based model designed to predict and diagnose Diabetes Mellitus using clinical and demographic data. The proposed approach integrates multiple machine learning algorithms, including random forest (RF), extreme gradient boosting (XGB), and logistic regression (LR), to leverage their individual strengths and enhance the entire performance. The ensemble model was trained and validated on multiple comprehensive datasets. Performance measures demonstrate the robustness of proposed model and its reliability in distinguishing diabetic cases from non-diabetic cases after applying several preprocessing steps. This work ensures the capability of machine learning in advancing healthcare by providing efficient, data-driven tools for diabetes management, aiding clinicians in early diagnosis, and contributing to personalized treatment strategies. Comparative analysis against standalone models highlights the superior predictive capabilities of the ensemble approach. Results had shown that ensemble model achieved an accuracy of 96.88% and precision of 89.85% outperforming individual classifiers.
Enhancing semantic segmentation with a boundary-sensitive loss function: a novel approach Padalkar, Ganesh R.; Khambete, Madhuri B.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5327-5335

Abstract

Semantic segmentation is crucial step in autonomous driving, medical imaging, and scene understanding. Traditional approaches leveraging manually extracted pixel properties and probabilistic models, have achieved reasonable performance but suffer from limited generalization and the need for expert-driven feature selection. The rise of deep learning architectures has significantly improved segmentation accuracy by enabling automatic feature extraction and capturing intricate object details. However, these methods still face challenges, including the need for large datasets, extensive hyperparameter tuning, and careful loss function selection. This paper proposes a novel boundary-sensitive loss function, which combines region loss and boundary loss, to enhance both region consistency and edge delineation in segmentation tasks. Implemented within a modified SegNet framework, the approach proposed in the paper is evaluated with the semantic boundary dataset (SBD) dataset using standard segmentation metrics. Experimental results indicate improved segmentation accuracy, substantiating to proposed method.
Intuitive effectiveness degree of research methodologies for spectrum sensing in cognitive radio network Yellappa, Pushpa; Keshavamurthy, Dr.Keshavamurthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5699-5707

Abstract

The phenomenon of spectrum sensing plays an essential role in cognitive radio network (CRN) that is performed in real-time for better adaptability to dynamic usage of spectrum. However, efficient decision-making is often noted to be affected by dynamic environmental condition, interference, and noise leading to declination in performance. In recent times, there are proposals for various methodologies addressing such issues targeting towards improving spectrum sensing along with machine learning and energy detection approach, which is gaining its pace for technical research implementation. Irrespective of this advancement, ambiguity shrouds regarding the contrast effectiveness associated with these methods and their appropriateness in different situation. Hence, this manuscript presents a comprehensive and yet crisp review work to offer concise assessment of latest methodologies towards spectrum sensing used in CRN ecosystem. The paper has an inclusion of existing techniques, presents their potentials and shortcomings, exhibited evolving trends of research, extracts key gaps and challenges. The prime intention of this review work is towards guiding the future researchers and scholars by facilitating deeper insight towards the recent state of technologies in spectrum sensing.
Implementation of a network intrusion detection system for man-in-the-middle attacks Okokpujie, Kennedy; Abdulateef-Adoga, William A.; Owivri, Oghenetega C.; Ijeh, Adaora P.; Okokpujie, Imhade P.; Awomoy, Morayo E.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp6027-6042

Abstract

Intrusion detection systems (IDS) are critical tools designed to detect and prevent unauthorized access and potential network threats. While IDS is well-established in traditional wired networks, deploying them in wireless environments presents distinct challenges, including limited computational resources and complex infrastructure configurations. Packet sniffing and man-in-the-middle (MitM) attacks also pose significant threats, potentially compromising sensitive data and disrupting communication. Traditional security measures like firewalls may not be sufficient to detect these sophisticated attacks. This paper implements a network intrusion detection system that monitors a computer network to detect Address Resolution Protocol spoofing attacks in real-time. The system comprises three host machines forming the network. Using Kali Linux, a bash script is deployed to monitor the network for signs of address resolution protocol (ARP) poisoning. An email alert system is integrated into the bash script, running in the background as a service for the network administrator. Various ARP spoofing attack scenarios are performed on the network to evaluate the efficiency of the network IDS. Results indicate that deploying IDS as a background service ensures continuous protection against ARP spoofing and poisoning. This is crucial in dynamic network environments where threats may arise unexpectedly.
AI SWLM: artificial intelligence-based system for wildlife monitoring Krishnan, Arun Govindan; Bhuvana, Jayaraman; Thai, Mirnalinee Thanga Nadar Thanga; Ramanujam, Bharathkumar Azhagiya Manavala
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp216-229

Abstract

Detection and recognition of wild animals are essential for animal surveillance, behavior monitoring and species counting. Intrusion of animals and the disaster to be caused can be averted by the timely recognition of intruding animals. An artificial intelligence-based system for wildlife monitoring (AI SWLM) is designed and implemented on the camera trap images. The challenges such as detecting and recognizing animals of different sizes, shape, angles and scale, recognizing the animals of same and different species, detecting them under various illumination conditions, with pose variants and occlusion are addressed by identifying the optimal weights of the deep learning architecture, AI SWLM. Models were trained using Gold Standard Snapshot Serengeti dataset with random weights and the best weights of model were used as initial weights for training the augmented data. This has doubled the performance in terms of mean average precision, which can be interpreted.
Machine learning-based predictive maintenance framework for seismometers: is it possible? Putra, Arifrahman Yustika; Lestari, Titik; Saputro, Adhi Harmoko
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp187-205

Abstract

Seismometers are crucial in earthquake and tsunami early warning systems, since they record ground vibrations due to significant seismic events. The health condition of a seismometer is strongly related to the measurement of seismic data quality, making seismometer health condition maintenance critical. Predictive maintenance is the most advanced control or measurement system maintenance method, since it informs about the faults that have occurred in the system and the remaining lifetime of the system. However, no research has proposed a seismometer predictive maintenance framework. Thus, this article reviews general predictive maintenance methods and seismic data quality analysis methods to find the feasibility of developing a predictive maintenance framework for seismometers in seismic stations. Based on the review, it is found that such a framework can be built under particular challenges and requirements. Finally, machine learning is the best approach to build the classification and regression models in the predictive maintenance framework due to its robustness and high prediction accuracy.
An integrated FSM-BABER-SROA framework for secure and energy-efficient internet of things networks using blockchain consensus Yaragal, Achyut; Bendigeri, Kirankumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp518-534

Abstract

The rapid expansion of the internet of things (IoT) and wireless sensor networks (WSNs) has intensified the demand for energy-efficient, reliable, and secure data transmission. Traditional clustering and static sleep scheduling approaches often fail to ensure long-term sustainability and tamper-resistant communication. This paper presents BABER-SROAChain, a hybrid optimization and security framework that integrates four core modules: i) Fuzzy similarity matrix (FSM)-based clustering for spatial-energy-aware node grouping, ii) Binary Al-Biruni earth radius (BABER) optimization for intelligent cluster head (CH) selection, iii) ship rescue optimization algorithm (SROA) for adaptive sleep scheduling, and iv) a lightweight blockchain protocol with modified practical byzantine fault tolerance (PBFT) consensus for secure inter-cluster communication. The unified objective function incorporates cluster efficiency, redundancy minimization, latency reduction, and packet delivery ratio maximization. Simulation experiments on large-scale WSNs (100–300 nodes) demonstrate that BABER-SROAChain achieves up to 20% improvement in network lifetime, 18% lower energy consumption, and 15% higher packet delivery ratio compared to state-of-the-art models. Additionally, it minimizes blockchain consensus latency while ensuring high data integrity. The proposed framework offers a scalable, secure, and energy-aware solution suitable for real-time IoT applications, including smart cities, healthcare monitoring, and industrial automation, while addressing the dual challenges of performance optimization and blockchain-based security.
Design of a thermionic electron gun of 6 MeV linac by using neural network based surrogate model Nuraini, Elin; Sihana, Sihana; Taufik, Taufik; Darsono, Darsono; Saefurrochman, Saefurrochman; Utama, Rajendra Satriya
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp477-487

Abstract

High performance electron guns are fundamental components in linear accelerators (linacs), directly influencing beam quality and downstream system efficiency. However, designing electron guns for applications such as a 6 MeV linac presents complex trade-offs between current, perveance, and beam emittance. Traditional simulation-driven optimization methods are computationally expensive and limit rapid prototyping. In this study, we develop a neural network-based surrogate model trained on CST Studio Suite simulation data to predict the electron gun's performance metrics. Our approach significantly accelerates the optimization process by providing real-time predictions of beam current and perveance across a wide design parameter space. The surrogate model achieves high prediction accuracy, with training and validation losses on the order of 10⁻⁷. Results demonstrate that neural network models can serve as reliable and efficient tools for electron gun design, offering considerable computational savings while maintaining accuracy. Future extensions include expanding the surrogate model to multi-objective optimization and incorporating thermal and mechanical effects into the design process.
From YOLO V1 to YOLO V11: comparative analysis of YOLO algorithm (review) Beqqali Hassani, Imane; Benhida, Soufia; Lamii, Nabil; Oqaidi, Khalid; Ouiddad, Ahmed; Ghiadi, Soukaina
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp450-462

Abstract

Object detection in images or videos faces several challenges because the detection must be accurate, efficient and fast. The you only look once (YOLO) algorithm was invented to meet these criteria. But with the creation of several versions of this algorithm (from V1 to V11), it becomes difficult for researchers to choose the best one. The main objective of this review is to present and compare the eleven versions of the yolo algorithm in order to know when using the appropriate one for the study. The methodology used for this work is aligned with preferred reporting items for systematic reviews and meta-analyses (PRISMA) principles and the results demonstrate that the choice of the best version mainly depends on the priorities of the study. If the study prioritizes accuracy and detection of small objects, it should use YOLO V4, YOLO V5, YOLO V6, YOLO V7, YOLO V8, YOLO V9, YOLO V10 or YOLO V11. While studies that prioritize detection speed should use YOLO V5, YOLO V6, YOLO V7, YOLO V8, YOLO V10 or YOLO V11. In complex environment, researchers should avoid using YOLO V1, YOLO V2, YOLO V3, YOLO V5, YOLO V7 and YOLO V9. And researchers who are looking for a good accuracy and speed and a reduced number of parameters should use YOLO V10 or YOLO V11.

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