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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 70 Documents
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Search results for , issue "Vol 15, No 4: August 2025" : 70 Documents clear
Assessing the knowledge and practices of internet of things security and privacy among higher education students Adamova, Aigul; Zhukabayeva, Tamara; Zhartybayeva, Makpal; Zhumabayeva, Laula
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4074-4086

Abstract

When multiple internet of things (IoT) devices interact, there are risks of privacy breaches, personal data leaks, various attacks, and device manipulation. Security is one of the most important technological research problems that currently exist for the IoT. The main purpose of the present paper is to determine the level of awareness of university students about existing security issues when using IoT devices. The paper presented the methodology of the survey. A questionnaire was developed covering four areas, such as fact-finding about general concepts of the IoT, security measures when using IoT devices, security threats and the presence of vulnerabilities of IoT devices, general policies, practices and shared responsibilities. A methodology for calculating the Awareness Level Index is proposed. This study has potential limitations. The effect estimates in the model are based on a survey of undergraduate and master’s degree students in “Computer Science” and “Software Engineering” within several universities. A total of 370 undergraduate and master’s students participated in the survey. The data processing resulted in the development of recommendations and suggested measures. This study will be useful for both stakeholders and researchers to develop effective strategies and make informed decisions.
Rapid and efficient maximum power point tracking in photovoltaic systems with modified fuzzy logic approach Said, El-bot; Moujahid, Yassine El; Mohamed, Chafik El Idrissi; Benlafkih, Abdessamad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3621-3631

Abstract

Photovoltaic systems (PVs) often face difficulties in maximizing their output power and maintaining a stable DC-DC connection voltage, especially under variable weather conditions (VWC). The power produced by photovoltaic panels is very sensitive to changes in sunlight and temperature, which vary throughout the day. This paper presents the design of an intelligent controller approach based on modified fuzzy logic (MFLC), adapted to enable the most effective maximum power point tracking (MPPT) of a photovoltaic solar module. The technique reduces delays in MPPT and sustains efficiency despite changing environmental conditions. A DC-DC boost converter is connected to the photovoltaic solar module, which in turn is linked to a load, and computer simulations using MATLAB/Simulink were used to validate the method's effectiveness. Results reveal that the MFLC controller significantly enhances the efficiency of the PVs, achieving improvements of up to 97.05%, with a rapid settling time of less than 10 milliseconds across all test scenarios.
An analysis between the Welsh-Powell and DSatur algorithms for coloring of sparse graphs Kraleva, Radoslava; Kralev, Velin; Katsarski, Toma
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3867-3875

Abstract

In this research an analysis between the Welsh-Powell and DSatur algorithms for the graph vertex coloring problem was presented. Both algorithms were implemented and analyzed as well. The method of the experiment was discussed and the 46 test graphs, which were divided into two sets, were presented. The results show that for sparse graphs with a smaller number of vertices and edges, both algorithms can be used for solving the problem. The results show that in 50% of the cases the Welsh-Powell algorithm found better solutions (23 in total). So, the DSatur algorithm found better solutions in only 19.6% of cases (9 in total). In the remaining 30.4% of cases, both algorithms found identical solutions. For graphs with a larger number of vertices, the usage of the Welsh-Powell algorithm is recommended as it finds better solutions. The execution time of the DSatur algorithm is greater than the execution time of the Welsh-Powell algorithm, reaching up to a minute for graphs with a larger number of vertices. For graphs with fewer vertices and edges, the execution times of both algorithms are shorter, but the time is still greater for the DSatur algorithm.
Revolutionizing autism diagnosis using hybrid model for autism spectrum disorder phenotyping Rathod, Vijayalaxmi N.; Goudar, Rayangouda H.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3904-3912

Abstract

The growing prevalence of autism spectrum disorder (ASD) necessitates efficient data-driven screening solutions to complement traditional diagnostic methods, which often suffer from subjectivity and limited scalability. This study introduces a hybrid ensemble model combining logistic regression (LR) and naive Bayes (NB) for ASD classification across four age groups (toddlers, children, adolescents, and adults) using behavioral screening datasets. By integrating statistical learning and probabilistic inference, the proposed model effectively captured behavioral markers, ensuring a higher classification accuracy and improved generalization. The experimental evaluation demonstrated its superior performance, achieving 94.24% accuracy and 99.40% area under the receiver operating characteristic curve (AUROC), surpassing those of individual classifiers and existing approaches. This artificial intelligence (AI)-driven framework offers a scalable, cost-effective, and accessible solution for both clinical and telemedicine-based ASD screening, facilitating early intervention and risk assessment. This study underscores the transformative potential of AI in neurodevelopmental diagnostics, paving the way for more efficient and widely deployable autistic screening technologies.
Secure clustering and routing – based adaptive – bald eagle search for wireless sensor networks Ranganathasharma, Roopashree Hejjaji; Chandrashekaraiah, Yogeesh Ambalagere
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3824-3832

Abstract

Wireless sensor networks (WSNs) are self-regulating networks consisting of several tiny sensor nodes for monitoring and tracking applications over extensive areas. Energy consumption and security are the two significant challenges in these networks due to their limited resources and open nature. To address these challenges and optimize energy consumption while ensuring security, this research proposes an adaptive – bald eagle search (A-BES) optimization algorithm enabled secure clustering and routing for WSNs. The A-BES algorithm selects secure cluster heads (SCHs) through several fitness functions, thereby reducing energy consumption across the nodes. Next, secure and optimal routes are chosen using A-BES to prevent malicious nodes from interfering with the communication paths and to enhance the overall network lifetime. The proposed algorithm shows significantly lower energy consumption, with values of 0.27, 0.81, 1.38, 2.27, and 3.01 J as the number of nodes increases from 100 to 300. This demonstrates a clear improvement over the existing residual energy-based data availability approach (REDAA).
Breast cancer identification using a hybrid machine learning system Arifin, Toni; Agung, Ignatius Wiseto Prasetyo; Junianto, Erfian; Agustin, Dari Dianata; Wibowo, Ilham Rachmat; Rachman, Rizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3928-3937

Abstract

Breast cancer remains one of the most prevalent malignancies among women and is frequently diagnosed at an advanced stage. Early detection is critical to improving patient prognosis and survival rates. Messenger ribonucleic acid (mRNA) gene expression data, which captures the molecular alterations in cancer cells, offers a promising avenue for enhancing diagnostic accuracy. The objective of this study is to develop a machine learning-based model for breast cancer detection using mRNA gene expression profiles. To achieve this, we implemented a hybrid machine learning system (HMLS) that integrates classification algorithms with feature selection and extraction techniques. This approach enables the effective handling of heterogeneous and high-dimensional genomic data, such as mRNA expression datasets, while simultaneously reducing dimensionality without sacrificing critical information. The classification algorithms applied in this study include support vector machine (SVM), random forest (RF), naïve Bayes (NB), k-nearest neighbors (KNN), extra trees classifier (ETC), and logistic regression (LR). Feature selection was conducted using analysis of variance (ANOVA), mutual information (MI), ETC, LR, whereas principal component analysis (PCA) was employed for feature extraction. The performance of the proposed model was evaluated using standard metrics, including recall, F1-score, and accuracy. Experimental results demonstrate that the combination of the SVM classifier with MI feature selection outperformed other configurations and conventional machine learning approaches, achieving a classification accuracy of 99.4%.
A comparative study of deep learning-based network intrusion detection system with explainable artificial intelligence Kai, Tan Juan; Ong, Lee-Yeng; Leow, Meng-Chew
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4109-4119

Abstract

In the rapidly evolving landscape of cybersecurity, robust network intrusion detection systems (NIDS) are crucial to countering increasingly sophisticated cyber threats, including zero-day attacks. Deep learning approaches in NIDS offer promising improvements in intrusion detection rates and reduction of false positives. However, the inherent opacity of deep learning models presents significant challenges, hindering the understanding and trust in their decision-making processes. This study explores the efficacy of explainable artificial intelligence (XAI) techniques, specifically Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), in enhancing the transparency and trustworthiness of NIDS systems. With the implementation of TabNet architecture on the AWID3 dataset, it is able to achieve a remarkable accuracy of 99.99%. Despite this high performance, concerns regarding the interpretability of the TabNet model's decisions persist. By employing SHAP and LIME, this study aims to elucidate the intricacies of model interpretability, focusing on both global and local aspects of the TabNet model's decision-making processes. Ultimately, this study underscores the pivotal role of XAI in improving understanding and fostering trust in deep learning -based NIDS systems. The robustness of the model is also being tested by adding the signal-to-noise ratio (SNR) to the datasets.
Energy-efficient secure software-defined networking with reinforcement learning and Weierstrass cryptography Andanaiah, Nagaraju Tumakuru; Rao, Malode Vishwanatha Panduranga
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4227-4238

Abstract

In the age of rapidly advancing 5G connectivity, artificial intelligence (AI), and the internet of things (IoT), network data has grown enormously, demanding more efficient and secure management solutions. Traditional networking systems, limited by manual controls and static environments, are unable to fulfill the dynamic demands of modern internet services. This paper proposes an innovative software-defined networking (SDN) framework that utilizes exponential spline regression reinforcement learning (ESR-RL) with genus Weierstrass curve cryptography (GWCC) to boost energy efficiency and data security. The ESR-RL algorithm reliably anticipates network traffic patterns, optimizing path selection to enhance routing efficiency while minimizing consumption of energy. GWCC also enables strong encryption and decryption, considerably increasing data security without impacting system performance. To further improve network reliability, the Skellam distributed Siberian TIGER optimization algorithm (SDSTOA) is used to dynamically acquire features and balance loads, resulting in optimal network performance. Extensive simulations show that the proposed framework performs better than existing models in terms of accuracy, precision, recall, F-measure, sensitivity, and specificity. Improvements in latency, turnaround time, and network throughput demonstrate the framework's success. This scalable and adaptive technology establishes a new standard for SDN systems by providing a safe, energy-efficient, and performance-optimized strategy for future network infrastructures.
Optimized reactive power management system for smart grid architecture Raghvin, Manju Jayakumar; Bharamagoudra, Manjula R.; Dash, Ritesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3707-3716

Abstract

The Indian power grid is an extensive and mature power system that transfers large amounts of electricity between two regions linked by a power corridor. The increased reliance on decentralized renewable energy sources (RESs), such as solar power, has led to power system instability and voltage variations. Power quality and dependability in a smart grid (SG) setting can be enhanced by the careful tracking and administration of solar energy generated by panels. This study proposes a number of reactive power regulation algorithms that take smart grids into account. When developing a kernel, debugging is a must in optimal reactive power management. In this research, a debugging primitive called physical memory protection (PMP), a security feature, is considered. Debugging in the kernel domain requires specialized tools, in contrast to the user space where we have kernel assistance. This research proposes an optimal reactive power management in smart grid using kernel debugging model (ORPM-SG-KDM) for managing the reactive power efficiently. This research achieved 98.5% accuracy in kernel debugging and 99.2% accuracy in optimal reactive power management. Kernel debugging accuracy is increased by 1.8% and 3% of reactive power management accuracy is increased.
Multi-layer convolutional autoencoder for recognizing three-dimensional patterns in attention deficit hyperactivity disorder using resting-state functional magnetic resonance imaging Begum, Zarina; Shaik, Kareemulla
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3965-3976

Abstract

Attention deficit hyperactivity disorder (ADHD) is a neurological disorder that develops over time and is typified by impulsivity, hyperactivity, and attention deficiency. There have been noticeable changes in the patterns of brain activity in recent studies using functional magnetic resonance imaging (fMRI). Particularly in the prefrontal cortex. Machine learning algorithms show promise in distinguishing ADHD subtypes based on these neurobiological signatures. However, the inherent heterogeneity of ADHD complicates consistent classification, while small sample sizes limit the generalizability of findings. Additionally, methodological variability across studies contributes to inconsistent results, and the opaque nature of machine learning models hinders the understanding of underlying mechanisms. We suggest a novel deep learning architecture to overcome these issues by combining spatio-temporal feature extraction and classification through a hierarchical residual convolutional noise reduction autoencoder (HRCNRAE) and a 3D convolutional gated memory unit (GMU). This framework effectively reduces spatial dimensions, captures key temporal and spatial features, and utilizes a sigmoid classifier for robust binary classification. Our methodology was rigorously validated on the ADHD-200 dataset across five sites, demonstrating enhancements in diagnostic accuracy ranging from 1.26% to 9.6% compared to existing models. Importantly, this research represents the first application of a 3D Convolutional GMU for diagnosing ADHD with fMRI data. The improvements highlight the efficacy of our architecture in capturing complex spatio-temporal features, paving the way for more accurate and reliable ADHD diagnoses.

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