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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 6,486 Documents
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Navigation and steering control of a service robot using Kinectv2 and LiDAR Suci Dwijayanti; Silfani Sandra Miranda; Bhakti Yudho Suprapto; Dlulil Amri; Robi Prasetio
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2454-2472

Abstract

Service robots operating in complex indoor environments with dynamic obstacles and narrow spaces require accurate mapping, localization, navigation, and motion control for safe and efficient operation. However, integrated systems that combine all these processes within a unified framework remain limited. This study proposes an integrated navigation and steering control system using RPLIDAR A3 and Kinect v2 sensors within a ROS2-based PLAN framework. The proposed system combines GMapping SLAM and RTAB-Map for hybrid mapping, extended Kalman filter (EKF) for localization enhancement, bidirectional RRT* for global path planning, artificial potential field (APF) for local obstacle avoidance, and a Type-2 fuzzy logic controller (FLC) for steering control. Experimental results show that the mapping system achieved a mean squared error (MSE), F1-score, recall, and precision of 0.26, 0.1714, 0.0312, and 0.0498, respectively. In steering control evaluation, the proposed Type-2 FLC with seven membership functions outperformed the proportional–integral–derivative (PID) controller under the tested conditions, achieving a rise time of 0.2901 s, settling time of 1.6819 s, peak time of 1.6997 s, overshoot of 0.4167%, and steady-state error of 10 (3.6). These results indicate that the proposed system improves navigation stability, steering performance, and environmental perception, demonstrating strong potential for autonomous service robot applications in complex indoor environments.
Information architecture debt: Why legacy platform schema decisions constrain enterprise AI capability Mihir Shah
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2473-2482

Abstract

Enterprise platforms accumulate a specific category of technical debt this paper terms information architecture debt. Schema decisions optimized for transactional efficiency in earlier computing eras produce structural constraints that limit what artificial intelligence can accomplish on those platforms, largely independent of which models are selected or how they are orchestrated. This paper positions information architecture debt as a category distinct from code debt and infrastructure debt, and it argues that the distinction matters because remediation locality and coordination cost differ sharply. A five-indicator diagnostic framework is proposed, covering duplicate canonical entities, broken semantic chains, provenance gaps, enforcement asymmetry, and consumer assumption divergence. The framework supports a capability ceiling hypothesis: substrate defects impose an upper bound on AI outcomes that no model choice appears able to exceed. A sequenced remediation approach follows, prioritizing entity resolution, semantic alignment, and provenance instrumentation by AI-capability impact rather than ease of fix. Grounded in practitioner experience structuring enterprise information architecture across multi-language, multi-system platforms, the framework offers leaders diagnostic and sequencing tools, and it establishes a conceptual foundation for later quantitative and sector-specific refinement.
Modeling and performance analysis of batteries in PV systems Abdelfettah Boussaid; Hadjer Bounechba; Seif El-Islam Chelli; Salah Hammoudi
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2294-2303

Abstract

This research paper comprehensively examines photovoltaic batteries, essential elements of sustainable solar energy systems. This research includes a pre- cise mathematical model development to represent behavior and performance of these batteries, in order to better understand the storage and use of electri- cal energy generated by solar panels. In addition, a complete simulation was carried out to evaluate the performance of these models under various operating conditions, thus contributing to improve our understanding on various factors that influence batteries efficiency in real situations. In the field of battery man- agement, their performances were examined, such as charging and discharging mode as well as continuous monitoring of the battery status. The objective is to strengthen the use efficiency of solar energy and improve its sustainability, thus contributing to achieving the global sustainability goals and reducing harmful emissions associated with traditional energies.
Quantum-AI cybersecurity threat detection framework: A hybrid architecture for enterprise security operations Muneer Basha Shaik
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2672-2679

Abstract

Enterprise security operations face computational bottlenecks that classical architectures cannot efficiently resolve at scale. This paper presents the quantum-AI cybersecurity threat detection framework (QACTDF), a hybrid architecture that combines quantum-enhanced optimization with AI-driven behavioral analytics for large-enterprise threat detection. The architecture employs a four-layer orchestration model in which an AI-governed routing policy selectively directs security workloads-including threat correlation, cryptographic verification, and anomaly classification-to quantum-assisted processors, while high-throughput tasks such as telemetry ingestion and alert triage are handled by classical infrastructure. Qualitatively evaluated against canonical security framework criteria-detection scope, integration feasibility, scalability, and sustainability-and supported by simulation-derived performance estimates, QACTDF addresses functional capability gaps in conventional security architectures. The framework additionally incorporates post-quantum cryptographic alignment in accordance with NIST FIPS 203 and provides a sequential adoption pathway for enterprise architects integrating quantum capabilities into existing security operations centers.
Heart disease prediction through feature optimization, ensembles, and threshold calibration Mehdi Mostofi; Touraj BaniRostam
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2836-2846

Abstract

Early detection of cardiovascular disease is critical for reducing mortality and enabling timely clinical intervention. While machine learning models have been widely applied to heart disease prediction, many studies prioritize overall accuracy rather than sensitivity, which is essential in medical screening tasks. This study proposes a structured multi-stage optimization framework that integrates feature optimization, ensemble model tuning, and recall-oriented decision-threshold calibration within a unified pipeline. Feature selection was performed using RFECV and importance-based methods to identify an optimal subset of 14 predictors, and ensemble classifiers including Random Forest, Extra Trees, and XGBoost were refined through hyperparameter tuning using RandomizedSearchCV. To prioritize sensitivity, the classification threshold was calibrated from 0.5 to 0.3. In the calibrated evaluation split, recall increased to 0.961 while maintaining strong discriminative performance (AUC ≈ 0.93). To assess robustness, stratified cross-validation and bootstrap resampling were conducted; cross-validation demonstrated stable recall performance around 0.88–0.89 across models, and bootstrap analysis yielded a mean recall of 0.902 (95% CI: 0.842–0.959), confirming that the recall-oriented optimization remains statistically reliable. McNemar’s test further indicated no statistically significant performance differences among the ensemble models. These findings demonstrate that integrating feature selection, ensemble learning, and threshold calibration can improve heart disease detection while maintaining predictive stability, providing a clinically aligned machine learning framework for cardiovascular risk assessment.
A systematic review and conceptual framework for cloud-based ontology-driven knowledge management systems in higher education institutions Muhammad Younas; Ahmad Shukri Mohd Noor
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2858-2870

Abstract

Universities nowadays rely on digital platforms for organizing and sharing their institutional courses data. However, institutional information is scattered across different learning management systems, administrative databases, and research repositories, and there is no common system connecting them. Data were extracted from the relevant major academic database Springer Link, Scopus and IEEE for the year ranging from 2015 to 2025. Initially, 248 researches were observed out of which 30 studies review by expert were selected after filtering. The studies were analyzed for the cloud technologies in system design, the ontologies for the structured representation of knowledge and the knowledge graphs for the connected data systems. 22 out of 30 studies reported measurable improvements in knowledge accessibility. However, 78% of the reviewed systems lacked scalable reasoning mechanisms to handle large datasets, and fewer than 30% provided practical implementation guidance. Our research has three contributions: (i) literature synthesis of ontology-driven systems and cloud-based knowledge management in higher education; (ii) a system architecture that integrates cloud service models (IaaS, PaaS, SaaS), SPARQL-based knowledge graphs, and semantic technologies (OWL, RDF ontologies), grounded in a deployment scenario at Jazan University, Saudi Arabia; and (iii) a step-by-step implementation framework.
Integration of micro-grids into Nigeria’s power system: Challenges, policy perspectives and opportunities for sustainable energy access Ismaheel O. Oladejo; Sunday Adeola Ajagbe; Razaaq A. Adetutu; Mathew Timothy Oluwafemi; Matthew Olusegun Adigun
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2321-2334

Abstract

Nigeria’s electricity sector faces persistent challenges, including insufficient generation capacity, weak transmission and distribution networks, frequent outages, and continued reliance on fossil-fuel-based power. These limitations constrain economic growth and highlight the need for more resilient, decentralized, and sustainable electricity solutions. This paper presents a systematic review of the integration of micro-grids (MGs) into Nigeria’s power system, focusing on reliability improvement, control strategies, renewable energy integration, and implementation barriers. The review synthesizes literature from major academic databases to examine the current state of Nigeria’s power sector, the drivers for MG deployment, global development trends, and the policy framework supporting adoption. It evaluates the role of MGs in enhancing reliability, voltage support, resilience, rural electrification, and energy access, particularly through the use of renewable energy sources, battery energy storage systems, and intelligent energy management. Key technical issues considered include islanding capability, protection coordination, reliability indices, hierarchical control structures, and both grid-connected and standalone operation within the Nigerian context. The paper’s main contribution is a comprehensive engineering-based assessment of the reliability, control, and integration requirements of MGs in Nigeria, while also identifying major policy, financial, and infrastructural constraints. Overall, the review concludes that MGs offer a practical pathway toward a more reliable, sustainable, and inclusive electricity supply system in Nigeria.
Auditable patch validity for lightweight language-model software repair Rishabh Singh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2664-2671

Abstract

Terminal resolve rate obscured where repository-level language-model patches lost utility between generation and execution. This study addressed that measurement gap by proposing an auditable patch-validity framework whose conceptual novelty was to represent each artifact as monotonic survival through six evidence-linked checkpoints: non-empty generation, syntactic validity, repository applicability, harness completion, behavioral resolution, and review readiness. The first five were measured from archived generations, normalization records, and Docker-harness outcomes; review readiness remained a proposed deployment check. In a 300-artifact diagnostic slice, 64 failed syntax, 77 failed applicability, 151 reached execution but remained unresolved, and 8 resolved. Across 1,600 larger-run artifacts, 776 (48.5%) were invalid or inapplicable and 22 resolved. Staged prompting improved completion in some configurations, but its strongest diagnostic resolution advantage, 4/50 versus 1/50, was exploratory (p = 0.25) and reversed at larger scale. By converting terminal outcomes into syntactic, repository-state, and semantic failure tiers, the framework enabled low-cost pre-execution filtering, targeted remediation, and auditable CI resource allocation. It was a measurement and triage instrument, not a new repair algorithm.
Label-free acoustic monitoring of honeybee swarming: An unsupervised online learning approach Abdelmadjid Guessoum Graba; Djoher Dalila Graba
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2483-2493

Abstract

Colony losses caused by honeybee swarming remain a major operational chal lenge because departure occurs within minutes, although acoustic changes be gin tens of minutes earlier and could allow timely intervention if detected reli ably. Current detection systems miss this window because the circadian rhythm of individual hive acoustics is not modelled, making it impossible to separate genuine pre-swarming drift from normal day-to-night spectral variation. Re cursive least squares is used to estimate a colony-specific circadian baseline, Mahalanobis distance scoring is applied to quantify deviations, and BOCPD ac cumulates Bayesian evidence of a regime shift, with a threshold derived from warmup data guaranteeing a controlled false alarm rate without labelled record ings and regardless of bee race, season, or microphone placement. A controlled simulation was used for evaluation, yielding 3.5× higher precision than the best label-free baseline, swarm anticipation exceeding 25 minutes before departure, and a false alarm rate held at the nominal 5% target. The low per-frame cost and memory footprint make this algorithm deployable on resource-constrained embedded devices, enabling continuous autonomous hive monitoring without expert supervision.
Electromagnetic interference challenges and mitigation strategies in wide-bandgap power converters: A critical review Arsalan Muhammad Soomar; Shoaib Shaikh; Ni Jiahua; Lyu Guanghua; Piotr Musznicki; Syed Hadi Hussain Shah
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2431-2453

Abstract

The widespread adoption of high-frequency power electronic converters, particularly those utilizing wide-bandgap (WBG) semiconductors like silicon carbide (SiC) and gallium nitride (GaN), has significantly escalated electromagnetic interference (EMI) challenges. Driven by ultra-fast switching transitions and parasitic coupling, these EMI disturbances now span wider frequency ranges. This review highlights that conventional, filter-only mitigation strategies are increasingly inadequate for modern, high-power-density converters. Instead, effective EMI suppression requires a coordinated, multilayer approach incorporating source-level suppression, propagation-path control, layout-aware engineering, and soft-switching strategies. Additionally, the analysis shows that hybrid modeling frameworks—integrating time and frequency-domain methods—offer superior EMI characterization compared to standalone approaches. Looking forward, AI-assisted EMI prediction and compact integrated filters represent critical research directions. Unlike traditional surveys that focus narrowly on conducted EMI or filter-based fixes, this review introduces a unified "source–path–system" framework. By integrating conducted and radiated EMI mechanisms, addressing WBG-specific challenges, and evaluating system-level mitigation trade-offs, this paper provides a comprehensive guide for designing next-generation, electromagnetic compatibility (EMC) -compliant power electronic platforms.

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