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31,291 Article Results

Numerical investigation on the effect of blade count on the performance of a small-scale Savonius wind turbine

10.11591/ijece.v16i5.pp2335-2346
Dezetty Monika , Muchlishah Muchlishah , Nuha Nadhiroh , Ahmad Fikri , Muhammad Rafi Anwarrahman , Willy Nurhidayat , Mutiar Mutiar
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 (𝐶𝑇), power coefficient (𝐶𝑝), and tip speed ratio (𝜆). 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 (𝐶𝑝≈0.60) is likely too high due to model simplification. The two-bladed rotor achieves higher efficiency (𝐶𝑝≈0.24) at 𝜆≈0.52 but exhibits significant torque fluctuations. Conversely, the three-bladed rotor produces the lowest efficiency (𝐶𝑝≈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.
Volume: 16
Issue: 5
Page: 2335-2346
Publish at: 2026-10-01

AI-driven PUE optimization in hyperscale data centers using real-time telemetry

10.11591/ijece.v16i5.pp2688-2697
Hussein Yassin Al-Adawi
The rapid growth of hyperscale data centers has significantly increased energy consumption, making power usage effectiveness (PUE) one of the most critical metrics for evaluating operational efficiency and sustainability. This study presents a real-world case study of a hyperscale data center environment and investigates the potential of artificial intelligence (AI)-driven optimization using real-time telemetry data. Operational reports, rack-level power measurements, and facility energy consumption records were analyzed to evaluate current performance and identify opportunities for efficiency improvement. Historical operational analysis revealed a substantial reduction in PUE from approximately 2.70 under legacy operating conditions to 2.01 following infrastructure modernization and operational optimization initiatives. To support future efficiency improvements, a telemetry-driven AI optimization framework was developed as a decision-support architecture for adaptive cooling and energy management. The framework integrates real-time operational telemetry, including IT load, facility power consumption, environmental conditions, cooling demand, and historical PUE behavior, to support intelligent operational decision-making. A synthetic six-month time-series dataset with 5-minute intervals was generated based on observed operational patterns to enable long-term analytical evaluation. The primary contribution of this research is the development of a telemetry-driven AI optimization framework that combines operational data analysis, sustainability-oriented decision support, and adaptive cooling optimization within a hyperscale data center environment. Analytical evaluation and benchmark comparison indicate that AI-assisted optimization could potentially reduce PUE to a projected range between 1.4 and 1.6. These results represent optimization scenarios rather than experimentally validated production outcomes. The findings highlight the potential of AI-enabled infrastructure management to improve operational efficiency, sustainability performance, and strategic decision-making within hyperscale data center environments.
Volume: 16
Issue: 5
Page: 2688-2697
Publish at: 2026-10-01

A hierarchical federated multi-task transfer learning framework for paroxysmal arrhythmia detection, vital sign monitoring, and activity recognition

10.11591/ijece.v16i5.pp2494-2515
Poomari Durga K. , M. S. Abirami
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.
Volume: 16
Issue: 5
Page: 2494-2515
Publish at: 2026-10-01

Electromagnetic interference challenges and mitigation strategies in wide-bandgap power converters: A critical review

10.11591/ijece.v16i5.pp2431-2453
Arsalan Muhammad Soomar , Shoaib Shaikh , Ni Jiahua , Lyu Guanghua , Piotr Musznicki , Syed Hadi Hussain Shah
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.
Volume: 16
Issue: 5
Page: 2431-2453
Publish at: 2026-10-01

Detection and classification of thyroid diseases using ultrasound images through deep learning techniques

10.11591/ijece.v16i5.pp2806-2818
Kamal Subedi , Akash Kumar Yadav , Lochan Paudel
Thyroid diseases remain one of the significant public health issues around the world, with thyroid nodules estimated to be prevalent among approximately 30 to 50% of the adult population through ultrasound imaging screening processes. Determination of the nature of the thyroid nodules in a patient's body is vital for ensuring effective treatment options since early detection of any malignant nodule will result in positive treatment results. The problem with such an analysis is the fact that the visual appearance of both benign and malignant nodules resembles each other in ultrasonic images. Therefore, a two-stage deep learning-based model for automatic detection and classification of thyroid nodules in ultrasound images was developed in this paper. At the first stage, the proposed framework applied Attention U-Net segmentation to identify the region of interest (ROI) for thyroid nodules in ultrasound images. Expert-drawn pixel-level annotations of thyroid nodules were not available; hence, pseudo-labels were used in training. They were obtained by applying CLAHE, Otsu thresholding, and morphological operations. In the second stage, a customized CNN network was applied to classify the segmented thyroid region into either benign or malignant classes. Data augmentation using the Mixup and CutMix techniques helped reduce the risk of overfitting, enhancing the model's generalizability. This experiment was tested on 480 ultrasound images gathered from two teaching hospitals in Nepal. The proposed framework shows that segmentation with weak supervision along with classification using deep learning can provide efficient diagnosis of thyroid nodules even with minimal medical data annotation. Through 3-fold stratified cross-validation, the Attention U-Net achieved a Dice Score of 0.8772 and IoU of 0.7873 for segmentation, while the classification stage obtained an overall accuracy of 96.73%, a specificity rate of 97.89%, and a mean ROC-AUC of 94.60%.
Volume: 16
Issue: 5
Page: 2806-2818
Publish at: 2026-10-01

Label-free acoustic monitoring of honeybee swarming: An unsupervised online learning approach

10.11591/ijece.v16i5.pp2483-2493
Abdelmadjid Guessoum Graba , Djoher Dalila Graba
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.
Volume: 16
Issue: 5
Page: 2483-2493
Publish at: 2026-10-01

A multi-objective QoS-aware cloud service ranking approach using grey wolf optimization

10.11591/ijece.v16i5.pp2709-2719
Alireza Chamkoori
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.
Volume: 16
Issue: 5
Page: 2709-2719
Publish at: 2026-10-01

Functional safety approach for thermal runaway in battery management systems

10.11591/ijece.v16i5.pp2405-2416
Nouhaila Belmajdoub , Rachid Lajouad , Abdelmounime El Magri , Soukaina Boudoudouh
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.
Volume: 16
Issue: 5
Page: 2405-2416
Publish at: 2026-10-01

BDLock: A blockchain-enabled two tier privacy-aware federated idam service platform using RBAC

10.11591/ijece.v16i5.pp2537-2548
Muhammad Shakil Pervez , Md. Nasim Adnan , Sarker Tanveer Ahmed Rumee , Moinul Islam Zaber
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.
Volume: 16
Issue: 5
Page: 2537-2548
Publish at: 2026-10-01

Design science research in developing a religious chatbot based on Bulugh al-Maram

10.11591/ijece.v16i5.pp2782-2794
Aris Tjahyanto , Irmasari Hafidz , Faizal Johan Atletiko
Chatbots have recently gained significant popularity. For instance, ChatGPT has become a preferred tool for many individuals seeking instant answers without relying on human responses. This immediacy sets chatbots apart from books, which require users to search for information manually. This time-consuming process does not align with millennials' preference for convenience and efficiency. Studying hadith independently using the Bulugh al-Maram book demands considerable time and effort. The limited use of natural language processing technologies in religious chatbots restricts their ability to handle complex inquiries effectively. A chatbot capable of answering hadith-related questions could greatly assist the public in studying hadith texts by providing direct responses without extensive searching. This chatbot was designed for web browsers, utilizing deep learning as its core technology. This research led to the development of a chatbot prototype for learning hadith from Bulugh al-Maram. Built using the design science research (DSR) methodology, the prototype achieves an intent recognition rate (IRR) of 86.82%. However, its capabilities are below the BERT model, demonstrating a strong ability to accurately interpret user questions and statements.
Volume: 16
Issue: 5
Page: 2782-2794
Publish at: 2026-10-01

Calibration-guided score fusion for robust multimodal traffic anomaly detection

10.11591/ijece.v16i5.pp2516-2525
Quang Hiep Do , Thien Tan Nguyen
Multimodal traffic anomaly detection is affected by differences in visual and audio score ranges, temporal fluctuations, and unstable decision thresholds. This paper proposes a calibration-guided score fusion (CGSF) framework that processes video frames and audio spectrograms through separate reconstruction-based models. The resulting anomaly scores are temporally smoothed, normalized using validation data, and combined at the score level. A percentile estimated from normal validation samples is then used as the decision threshold. The framework was evaluated on the MAVD and DADA2000 datasets. On MAVD, CGSF achiev,,,,,,ed a ROC-AUC of 0.553, a PR-AUC of 0.082, and an F1-score of 0.129. It outperformed direct fusion in precision, recall, and F1-score, although the gain in ROC-AUC was small. Analysis on DADA2000 showed smoother temporal score behaviour after calibration and smoothing. The results indicate that CGSF mainly improves score comparability and threshold consistency rather than producing a large increase in detection accuracy. Its modular design also allows the visual and audio branches to be trained and updated independently.
Volume: 16
Issue: 5
Page: 2516-2525
Publish at: 2026-10-01

Gamification and MOOC persistence: an integrated UTAUT2–SDT–engagement model

10.11591/ijere.v15i5.37547
Waraporn Jirapanthong , Siwakorn Banluesapy
Massive open online courses (MOOC) completion rates remain low despite the continued growth of online learning platforms. This study examined the factors influencing learning persistence (PERS) in gamified MOOCs using an integrated unified theory of acceptance and use of technology 2-self-determination theory (UTAUT2–SDT)–engagement (ENG) model. A cross-sectional survey was conducted with 200 MOOC learners in Thailand, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results showed that student ENG significantly predicted behavioral intention (BI) (β=0.352,p
Volume: 15
Issue: 5
Page: 4657-4666
Publish at: 2026-10-01

The impact of digital comics as a teaching and learning tool in economics education on future-ready talent development: the mediating role of attitude

10.11591/ijere.v15i5.37622
Hainnuraqma Rahim , Abd Hadi Mustaffa , Norashida Othman , Putri Aliah Mohd Hidzir
Economics education at the tertiary level dominated by text-heavy materials that often disengage students. At the same time, the potential of digital comics as an innovative pedagogical alternative are insufficiently examine. Hence, this study examines the impact of digital comics (IMP) on students’ learning in economic subjects at Universiti Teknologi MARA (UiTM) Melaka, with particular focus on the mediating role of attitude (ATT). A quantitative design was employed using structured questionnaires distributed to 146 diploma and degree students to measure the determinants of perceived IMP. Data were analyzed using the partial least squares– structural equation modeling (PLS-SEM). The findings show that ATT and subjective norms (SN) significantly influence the IMP, while digital literacy (DL) and perceived behavioral control (PBC) exhibit no direct effects. In addition, ATT significantly mediates the relationships with SN, PBC and the IMP. Practically, the findings suggest that lecturers can embed digital comics into case-based class activities to enhance engagement and conceptual understanding. The novelty of the study indicated that this is the first studies applying theory of planned behavior (TPB) to digital comics in economic education. This study also offers future research which can be extend the studies into experimental, longitudinal and cross-sectional.
Volume: 15
Issue: 5
Page: 4192-4206
Publish at: 2026-10-01

Temperature impact analysis on photovoltaic output power using an IoT-based wiper cooling system

10.11591/ijece.v16i5.pp2272-2281
Ajeng Bening Kusumaningtyas , Ikhsan Kamil , Adnan Afriadi
Solar photovoltaic (PV) performance is significantly affected by surface temperature and panel cleanliness, both of which influence energy conversion efficiency. Elevated temperatures reduce power output, while dust accumulation decreases effective solar irradiance. This study proposes an IoT-based integrated cooling and cleaning system as its main contribution, enabling real-time monitoring, intelligent control, and remote operation within a unified framework. The system utilizes an NTC 10K temperature sensor for continuous monitoring, while an ESP8266 microcontroller functions as the central controller for data processing, wireless communication, and web-based interfacing. When the panel temperature exceeds a predefined threshold (40–42 °C), The IoT-enabled system automatically activates a water pump and wiper mechanism to simultaneously reduce temperature and remove surface contaminants. Experimental results show a significant temperature reduction from approximately 41–43 °C to 30–33 °C. This improvement leads to an increase in voltage output of 0.5%–1.2% and current output of 3–6%, depending on load conditions. The cleaning mechanism further enhances irradiance absorption by maintaining panel surface clarity. These findings demonstrate that IoT integration enables adaptive and data-driven optimization of PV performance. The proposed system offers a practical and scalable engineering solution to improve efficiency, reliability, and long-term operation of PV installations in real-world applications.
Volume: 16
Issue: 5
Page: 2272-2281
Publish at: 2026-10-01

Robust resource allocation in multi-cell UE-specific RIS-assisted D2D relay networks under imperfect CSI

10.11591/ijece.v16i5.pp2575-2594
Kayode Popoola , Ayodeji Ajani , Stuart Nicholson , Muheeb Ahmed , Srilatha Narayangari Pamuri , Ibrahim Bala Alhassan
Device-to-device (D2D) communication enhances spectral efficiency but remains constrained by limited transmission range, underlay interference, and the half-duplex overhead of conventional relays. User equipment-specific reconfigurable intelligent surfaces (UE-RIS) offer a promising alternative by enabling passive beamforming to strengthen D2D links without additional spectrum consumption. However, existing studies typically assume perfect channel state information (CSI) and single-cell operation, limiting their applicability to practical deployments. This paper proposes a robust multi-cell resource allocation (RMRA) framework for UE-RIS-assisted D2D relay networks under imperfect CSI. A hybrid uncertainty model is adopted, combining statistical Gauss-Markov CSI errors for intra-cell links with bounded norm-ball uncertainty for inter-cell links. The joint optimisation of resource reuse, transmit power allocation, and RIS phase configuration is formulated as a stochastic mixed-integer nonlinear program that maximises network spectral efficiency while satisfying outage and quality-of-service constraints. To efficiently solve the problem, a three-stage algorithm is proposed comprising distance-pruned Hungarian assignment, robust power control using Bernstein-type inequality and S-procedure based semidefinite programming, and soft actor-critic (SAC) based passive beamforming. Simulation results show that RMRA achieves a 94% D2D access rate at light load and over 75% at full load, improves sum spectral efficiency by 34.7% and 70.2% over AF relaying and direct D2D, respectively, attains 118.5 bits/s/Hz/W energy efficiency, and maintains 30.2 bits/s/Hz under severe CSI uncertainty.
Volume: 16
Issue: 5
Page: 2575-2594
Publish at: 2026-10-01
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