cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
ijece@iaesjournal.com
Phone
-
Journal Mail Official
ijece@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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
 645   646   647   648   649 
A hierarchical federated multi-task transfer learning framework for paroxysmal arrhythmia detection, vital sign monitoring, and activity recognition Poomari Durga K.; M. S. Abirami
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.pp2494-2515

Abstract

An internet of medical things (IoMT) enabled remote patient monitoring system needs to accurately detect heterogeneous physiological and activity signals while maintaining patient privacy and data flow constraints on sensitive information. Most previous federated or multi-task learning methods focus on one of the following aspects: one-modality per site, one-task per site, or a distributed model optimization, but they do not consider both the signal processing specific to each modality and the knowledge transfer specific to each task. This study introduces a hierarchical federated multi-task transfer learning approach to solve this problem, which is used to detect paroxysmal arrhythmia, monitor heart rate from electrocardiogram (ECG) signals, and recognize human activities. The framework consists of three computational layers: Edge layer for signal preprocessing and learning local features, Federated layer for privacy preserving model aggregation, Cloud-level multi-task transfer learning layer for sharing transferable representations across related monitoring tasks. Wavelet-based denoising, R-peak detection, QR-interval delineation and convolutional neural network (CNN) dan bidirectional long short-term memory (BiLSTM)-attention based classification are performed on the processed ECG signals. The accelerometer and gyroscope data are not temporally synchronized with the ECG dataset, and all processing and activity recognition is done separately from the other data. With the use of the MIT-BIH Arrhythmia database and human activity recognition dataset, experimental results showed that the arrhythmia classification accuracy was 96.8% and the overall activity-recognition accuracy was 91.0%. The findings show that the proposed architecture is able to enable heterogeneous health-monitoring tasks under a shared decentralized learning architecture without moving raw data away from their source. While the framework offers a basis for scalable and privacy-compliant remote monitoring, there are key points that warrant further exploration such as communication overhead and the inclusion of client data diversity, computational complexity, and clinical validation.
A multi-objective QoS-aware cloud service ranking approach using grey wolf optimization Chamkoori, Alireza
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.pp2709-2719

Abstract

Ranking functionally similar cloud services by their quality of service (QoS) is essential for selecting the best option, yet current ranking methods generally handle only one QoS objective at a time and give up robustness once several conflicting criteria are in play. We propose a multi-objective ranking framework built on grey wolf optimization (GWO) to close this gap, pairing similarity computation via the Kendall Rank Correlation Coefficient (KRCC) with an optimization-driven prioritization step. Testing the resulting MOGWO-based method against several baseline algorithms on a cloud service dataset shows clear gains in both ranking accuracy and efficiency. The framework offers a scalable way to support decision-making in complex cloud environments and a clear structure for prioritizing services across multiple QoS dimensions at once.
Role model identity contract enforcement as a structural determinant of identity drift suppression in autonomous large language model agents Sahana B.; Charithra H. S.; Arshitha S.; Chandrakala Chandrakala; Neethu B. Krishna; Soumya Prasada; Bhanupriya P.; Aryaman Tiwary
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.pp2795-2805

Abstract

Autonomous agents powered by large language models (LLMs) are deployed widely across finance, healthcare, legal review, and customer support. Each deployment assumes that an agent will remain within its assigned role—an assumption that adversarial users routinely challenge through identity drift attacks. This work asks whether placing enforcement logic outside the model, rather than embedding rules in the system prompt, is the primary factor governing drift suppression. We designed RMIC-Guard: a framework where a cryptographically sealed Role-Model Identity Contract (RMIC) is checked by a dual-pass middle ware engine on every planned tool call before execution proceeds. The engine runs a deterministic hard-rule pass followed by a semantic Intent Drift Score (IDS) computed from seven independent embedding-based signals. Experiments across four LLMs (claude-sonnet-4-6, claude-haiku-4-5, llama-3.3-70b-versatile, llama-3.1-8b-instant), four agent roles, and 5,212 API calls show that external enforcement reaches DSR 0.952–1.000, whereas the best prompt-only condition achieves only DSR 0.391. Ablation confirms the dual-pass architecture is necessary: neither pass alone achieves acceptable joint drift-suppression and false-positive performance. All code, contracts, and prompt data are released openly.
An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data Sarra Gourari; Wafa Difallah; Belkacem Draoui
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.pp2652-2663

Abstract

Efficient water management is essential for sustainable agricultural production, particularly in arid and semi-arid regions where water resources are limited. Machine-learning-based irrigation systems can support automated pump-operation decisions using environmental and soil-related sensor data. However, most previous studies have focused on individual machine-learning models, while the application of stacked ensembles to binary irrigation pump-status prediction remains relatively limited. This study proposes a stacking-based framework for predicting irrigation pump operation in an ON/OFF classification setting. The framework uses environmental and soil-related variables, including soil moisture, temperature, humidity, and a numerical time-related feature. Random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron models were trained as base learners, and their out-of-fold (OOF) predictions were combined using a logistic-regression meta-learner. The models were evaluated on a held-out test set using accuracy, precision, recall, specificity, and F1-score. The individual models achieved accuracies ranging from 97.34% to 99.95%, with XGBoost providing the best individual performance. The proposed stacking ensemble achieved 99.97% accuracy, 99.94% precision, 100.00% recall, 99.94% specificity, and a 99.97% F1-score. Compared with XGBoost, the ensemble further refined predictive performance, improving accuracy by 0.02 percentage points and F1-score by 0.01 percentage points while achieving complete elimination of false negatives (100.00% recall). These results demonstrate the potential of stacked ensemble learning to improve binary pump-operation prediction and support data-driven irrigation management in water-limited environments.
Contour-guided convexity defect geometry for precise ROI extraction in dorsal hand vein recognition Habib Kadem; Merah Mostefa
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.pp2622-2640

Abstract

Dorsal hand vein biometrics depend strongly on accurate region-of-interest (ROI) extraction, yet conventional convexity-defect-based methods remain vulnerable to contour noise, skin texture, and shadow-induced artifacts. This paper introduces contour-guided convexity defect geometry (CG-CDG), a geometry-driven ROI extraction framework in which a candidate convexity defect is accepted as an anatomical valley only if it jointly satisfies two independent criteria: chord-contour topological consistency, ensuring anatomical relevance, and a minimum enclosed-area threshold, ensuring physical plausibility. Unlike prior convexity-defect-based methods, which accept a candidate valley on the basis of a single depth- or angle-based criterion evaluated in isolation, CG-CDG applies this joint plausibility test before defining the region of interest, a square ROI centered on the hand centroid and aligned with the dominant valley orientation. Experiments on a public dorsal hand vein database of 1,024 images from 138 subjects show that, combined with HOG descriptors and City Block distance, CG-CDG achieves an equal error rate (EER) of 0.0266, with a 95% bootstrap confidence interval of [0.0241, 0.0289] over the matching scores (B = 1,000 resamples), the narrowest interval among the four ROI extraction methods compared under the primary evaluation setting. A post-hoc Wilcoxon signed-rank test with Bonferroni correction confirms that this improvement is statistically significant over all competing methods (p < 0.001). On this dataset, geometric validation substantially reduces intra-class variance and yields statistically validated performance without requiring any training data, making CG-CDG a strong candidate both as a standalone, training-free biometric solution and as an anatomically grounded preprocessing stage for hybrid deep-learning pipelines.
BDLock: A blockchain-enabled two tier privacy-aware federated idam service platform using RBAC Muhammad Shakil Pervez; Md. Nasim Adnan; Sarker Tanveer Ahmed Rumee; Moinul Islam Zaber
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.pp2537-2548

Abstract

Centralized identity and access management (IDAM) systems suffer from sin-gle points of failure, lack of authorization transparency, and susceptibility to in-sider threats and privilege abuse. While role-based access control (RBAC) of-fers structured permission management, its enforcement through centralized pol-icy engines introduces auditability gaps unacceptable in modern distributed service delivery environments. This paper presents BDLock, a blockchain-enabled two-tier privacy-aware federated IDAM platform integrating OAuth 2.0, OpenID Connect (OIDC), and Hyperledger Fabric 2.4. The first tier validates JSON Web Tokens (JWT) issued by Keycloak against a Spring Boot resource server, the second tier enforces immutable scope-based RBAC rights on the Hyperledger Fabric ledger, ensuring every access decision is tamper-proof and auditable. Unlike prior approaches, BDLock uniquely bridges OAuth-authenticated off-chain identities to cryptographic on-chain Fabric wallet identities, satisfying all six STRIDE-modelled threats categories across both Web2 and Web3 identity models. Validated with up to 1,800 concurrent users, BDLock achieves a peak throughput of approximately 200 transactions per second using round-robin load balancing. At high concurrency, it outperforms single-peer fallback by up to 25%. Furthermore, it maintains uninterrupted access control during peer failures, eliminating the single point of failure found in all nine compared state-of-the-art systems.
Numerical investigation on the effect of blade count on the performance of a small-scale Savonius wind turbine Dezetty Monika; Muchlishah Muchlishah; Nuha Nadhiroh; Ahmad Fikri; Muhammad Rafi Anwarrahman; Willy Nurhidayat; Mutiar Mutiar
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.pp2335-2346

Abstract

The growing demand for clean energy has increased interest in small-scale wind turbines, particularly vertical axis wind turbines (VAWTs), which perform well in turbulent wind conditions and low wind speeds. This study numerically analyzes Savonius rotors with two, three, and four blades to evaluate their aerodynamic performance in uniform inflow. Simulations were performed with a steady-state multiple reference frame (MRF) model at an inflow velocity of 10 m/s. Key parameters used included the torque coefficient (??), power coefficient (??), and tip speed ratio (?). The results show a clear trade-off between configurations. The four-blade rotor produces the highest torque (0.1297 N·m at 950 rpm) and strong self-starting capability, although its peak efficiency (??≈0.60) is likely too high due to model simplification. The two-bladed rotor achieves higher efficiency (??≈0.24) at ?≈0.52 but exhibits significant torque fluctuations. Conversely, the three-bladed rotor produces the lowest efficiency (??≈0.086) but offers smoother torque, beneficial for stable small-scale power generation. Flow analysis confirms this pattern: two blades cause a wide, asymmetrical wake; three blades reduce wake asymmetry with better stability; and four blades produce a compact wake but with higher drag. Overall, the optimal number of blades depends on design priorities—efficiency, torque, or operational smoothness.
A chip level design of a multi-mode compressive sensing image sensor Zahra Sepehri; Sayed Masoud Sayedi; Ehsan Yazdian
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.pp2393-2404

Abstract

This paper presents the full chip-level design of a multi-mode CMOS vision sensor, emphasizing the detailed implementation of its circuit architecture. The proposed chip incorporates our previously developed photodiode sensing array together with the on-chip design of control circuitry. By embedding these building blocks, the chip enables pixel-level compressive sensing and supports dual operation modes, allowing the transmission of image data in both compressed and non-compressed formats. In either mode, the sensor is capable of capturing both scene images and difference images between consecutive video frames, operating at a frame rate of 40 fps. A 64*64 vision chip is implemented using TSMC 0.18um standard CMOS technology. In the normal scene image mode, with compression (N-C) and  without compression (N-nC), the structure consumes 36.99uW and 37.09uW, respectively. Meanwhile, in the difference scene image mode, with compression (D-C) and  without compression (D-nC), it consumes 38.67uW and 38.75uW, respectively.
Functional safety approach for thermal runaway in battery management systems Nouhaila Belmajdoub; Rachid Lajouad; Abdelmounime El Magri; Soukaina Boudoudouh
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.pp2405-2416

Abstract

Forecasting demand for autonomous power storage and supply systems for electric vehicles (EV) has become a key area of research. The rapid popularization of EVs is heavily based on the reliability and safety of rechargeable energy storage systems (ESSs), especially lithium-ion batteries (LIBs). Although these batteries offer high energy density and efficiency, they remain vulnerable to hazardous events such as thermal runaway, leading to severe consequences, including fire and explosion. This paper presents a methodology to fulfill the functional safety of BMS thermal runaway according to ISO 26262 standard. The approach integrates hazard analysis and risk assessment (HARA), the formulation of safety goals (SG), and the specification of functional and technical safety requirements (FSR and TSR). To strengthen assurance, safety contracts based on battery chemistry and specifications are established and linked to structured safety cases using Goal Structuring Notation (GSN). The proposed methodology is validated through simulations in MATLAB Simulink, demonstrating how dynamic safety assurance can detect deviations, trigger control actions, and update safety cases to prevent unsafe states. The results show that the framework effectively minimizes the risks related with over-current, over-voltage, and over-temperature conditions, thus contributing to regulatory compliance and increased confidence in the safety of EVs.
Per-channel margin and nonlinear asymmetry in an eight-channel optical distribution network Mohammed Kareem Al-Swaiedi; Ahmed Ali Skaiky
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.pp2559-2574

Abstract

The physical layer of a wavelength-division-multiplexed optical distribution network for smart-city Internet-of-Things access is almost always reported from a single simulation run, which says nothing about its stability. This paper characterized an eight-channel C-band link over 25 km of standard single-mode fibre, modelled in OptiSystem, across thirty independent replications yielding 240 per-channel Q-factor observations. Every channel cleared the Q ≥ 6 threshold in every replication, with a system mean of 8.66, a per-channel 95% confidence interval never wider than 0.46, and a worst realization of 6.64 on the weakest channel, so the link held a stable margin rather than crossing the boundary between runs. The per-channel Q-factors followed a U-shape across the wavelength grid with its minimum at the centre, and a short-reach reference at 1 km showed that two thirds of the edge-to-interior asymmetry was established within the first kilometre. A launch-power sweep placed the usable ceiling between 10 and 13 dBm. A control experiment with the fibre nonlinearity disabled collapsed the edge-to-interior ratio from 1.354 to 1.038, establishing that 89% of the asymmetry was nonlinear in origin, while a four-wave-mixing coherence length of 0.74 km against the 25 km span identified cross-phase modulation as the mechanism.

Page 648 of 649 | Total Record : 6486

 645   646   647   648   649 

Filter by Year

2011 2026


Reset
Filter By Issues
All Issue Vol 16, No 5: October 2026 Vol 16, No 4: August 2026 Vol 16, No 3: June 2026 Vol 16, No 2: April 2026 Vol 16, No 1: February 2026 Vol 15, No 6: December 2025 Vol 15, No 5: October 2025 Vol 15, No 4: August 2025 Vol 15, No 3: June 2025 Vol 15, No 2: April 2025 Vol 15, No 1: February 2025 Vol 14, No 6: December 2024 Vol 14, No 5: October 2024 Vol 14, No 4: August 2024 Vol 14, No 3: June 2024 Vol 14, No 2: April 2024 Vol 14, No 1: February 2024 Vol 13, No 6: December 2023 Vol 13, No 5: October 2023 Vol 13, No 4: August 2023 Vol 13, No 3: June 2023 Vol 13, No 2: April 2023 Vol 13, No 1: February 2023 Vol 12, No 6: December 2022 Vol 12, No 5: October 2022 Vol 12, No 4: August 2022 Vol 12, No 3: June 2022 Vol 12, No 2: April 2022 Vol 12, No 1: February 2022 Vol 11, No 6: December 2021 Vol 11, No 5: October 2021 Vol 11, No 4: August 2021 Vol 11, No 3: June 2021 Vol 11, No 2: April 2021 Vol 11, No 1: February 2021 Vol 10, No 6: December 2020 Vol 10, No 5: October 2020 Vol 10, No 4: August 2020 Vol 10, No 3: June 2020 Vol 10, No 2: April 2020 Vol 10, No 1: February 2020 Vol 9, No 6: December 2019 Vol 9, No 5: October 2019 Vol 9, No 4: August 2019 Vol 9, No 3: June 2019 Vol 9, No 2: April 2019 Vol 9, No 1: February 2019 Vol 8, No 6: December 2018 Vol 8, No 5: October 2018 Vol 8, No 4: August 2018 Vol 8, No 3: June 2018 Vol 8, No 2: April 2018 Vol 8, No 1: February 2018 Vol 7, No 6: December 2017 Vol 7, No 5: October 2017 Vol 7, No 4: August 2017 Vol 7, No 3: June 2017 Vol 7, No 2: April 2017 Vol 7, No 1: February 2017 Vol 6, No 6: December 2016 Vol 6, No 5: October 2016 Vol 6, No 4: August 2016 Vol 6, No 3: June 2016 Vol 6, No 2: April 2016 Vol 6, No 1: February 2016 Vol 5, No 6: December 2015 Vol 5, No 5: October 2015 Vol 5, No 4: August 2015 Vol 5, No 3: June 2015 Vol 5, No 2: April 2015 Vol 5, No 1: February 2015 Vol 4, No 6: December 2014 Vol 4, No 5: October 2014 Vol 4, No 4: August 2014 Vol 4, No 3: June 2014 Vol 4, No 2: April 2014 Vol 4, No 1: February 2014 Vol 3, No 6: December 2013 Vol 3, No 5: October 2013 Vol 3, No 4: August 2013 Vol 3, No 3: June 2013 Vol 3, No 2: April 2013 Vol 3, No 1: February 2013 Vol 2, No 6: December 2012 Vol 2, No 5: October 2012 Vol 2, No 4: August 2012 Vol 2, No 3: June 2012 Vol 2, No 2: April 2012 Vol 2, No 1: February 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue