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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 88 Documents
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Search results for , issue "Vol 15, No 6: December 2025" : 88 Documents clear
Machine learning-based classification of local muscle fatigue using electromyography signals for enhanced rehabilitation outcomes Baigarayeva, Zhanel; Boltaboyeva, Assiya; Imanbek, Baglan; Ozhikenov, Kassymbek; Karymssakova, Nurgul; Beisembekova, Roza
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.pp5954-5967

Abstract

Muscle fatigue is a key factor affecting rehabilitation progress, safety, and patient engagement. Accurate detection of fatigue during physical activity remains a challenge, particularly in clinical and remote settings. This study presents the development of an Internet of things-based system for classifying local muscle fatigue using surface electromyography (EMG) signals and machine learning. A wearable device was used to collect real-time EMG data and subjective fatigue ratings from 10 healthy participants during sustained isometric grip exercises. Feature extraction was performed on-device, and the data were transmitted wirelessly for analysis. Machine learning models including logistic regression, decision tree (DT), random forest, and extreme gradient boosting (XGBoost) were trained to classify fatigue states. The DT model achieved the highest accuracy of 90.7%, with a precision of 90.7% and a recall of 90.9%. SHAP analysis revealed time under load, smoking, and alcohol use as the most influential factors in fatigue classification. These results show that wearable EMG devices combined with smart algorithms are effective for real-time fatigue monitoring during rehabilitation.
On big data predictive analytics-trends, perspectives, and challenges Benlachmi, Yassine; Yazidi, Abdelaziz El; Rhattoy, Abdallah; Hasnaoui, Moulay Lahcen
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.pp5978-5985

Abstract

The world is experiencing explosive growth in numerous sectors such as healthcare, engineering, scientific studies, business, social networking. This growth is causing an immense surge in data generation too. And with the emergence of technologies like internet of things (IoT), Mobile, and cloud computing, the volume of data being produced is skyrocketing. However, making sense of this colossal amount of data is a daunting challenge. Enter big data computing, a new paradigm that blends large datasets with advanced analytical techniques. Big data is characterized by the three V's: Volume, velocity, and variety, and refers to massive datasets. By processing this data, we can uncover new opportunities and gain valuable insights into market trends. Traditional techniques are simply not equipped to handle the scale of Big Data. The purpose of this article is to gather reviews of various predictive analytics applications related to big data and the advantages of using big data analytics across various decision-making domains.
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.
Neural-network based representation framework for adversary identification in internet of things Narasimhamurthy, Thanuja; Swamy, Gunavathi Hosahalli
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.pp6043-6052

Abstract

Machine learning is one of the potential solutions towards optimizing the security strength towards identifying complex forms of threats in internet of things (IoT). However, a review of existing machine learning-based approaches showcases their sub-optimal performance when exposed to dynamic forms of unseen threats without any a priori information during the training stage. Hence, this manuscript presents a novel machine-learning framework towards potential threat detection capable of identifying the underlying patterns of rapidly evolving threats. The proposed system uses a neural network-based learning model emphasizing representation learning where an explicit masked indexing mechanism is presented for high-level security against unknown and dynamic adversaries. The benchmarked outcome of the study shows to accomplish 11% maximized threat detection accuracy and 33% minimized algorithm processing time.

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