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31,377 Article Results

An impact of activation functions on CNN-Bi-LSTM SoC estimation for Li-Ion batteries

10.11591/ijece.v16i5.pp2357-2364
Monica K. M. , Abhay A. Deshpande
Estimation of state of charge (SoC) in Li-ion batteries has been accomplished by many methods over couple of years. Every research in this domain majorly focusses on improving accuracy of estimation and some in exploring new algorithms. In this regard, we worked in analyzing the accuracy of estimation of SoC of sophisticatedly accurate techniques. A data-driven SoC estimation using deep neural network architectures (CNN) were collaborated with different activation functions like global pooling, exponential linear unit, Leaky rectified linear unit on convolutional neural network (CNN) and bidirectional-long short-term memory (Bi-LSTM) models. The performance of all these models was evaluated using quantitative error analysis and qualitative signal tracking studies to compare the estimated and actual SoC trajectory. This work consolidates the performances of different models and helps realize how best a fusion model works on the best accuracy in estimation.
Volume: 16
Issue: 5
Page: 2357-2364
Publish at: 2026-10-01

PMGHWO: A particle-guided adaptive grey wolf optimizer for large-scale traveling salesman problems

10.11591/ijece.v16i5.pp2819-2835
Hanadi A. Alshawabkah , Ayman Algafaan
The traveling salesman problem (TSP) remains challenging for metaheuristic algorithms, especially as the number of cities increases. This study proposes the particle-guided adaptive grey wolf optimizer (PMGHWO), which combines grey wolf optimizer (GWO)-based leadership guidance with crossover, particle-guided perturbation, and adaptive local refinement. PMGHWO was evaluated on ten TSPLIB instances and compared with grey wolf optimizer (GWO), genetic algorithms (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), and Harris Hawks optimization (HHO) under the same experimental conditions. The proposed method achieved shorter average tours on all tested instances, with reductions of 18.75% compared with GWO, 19.77% with HHO, 23.30% with GA, 39.08% with WOA, and 57.51% with PSO. Friedman and Wilcoxon tests confirmed significant differences between PMGHWO and the compared methods. The ablation study showed that adaptive refinement had the strongest effect on solution quality, while the influence of particle-guided perturbation and crossover varied across the benchmark instances. Although PMGHWO required more computation than the original GWO, its runtime remained competitive with several of the other methods. Overall, the results show that PMGHWO is a promising approach for TSP optimization and warrants further evaluation on larger and more complex combinatorial problems.
Volume: 16
Issue: 5
Page: 2819-2835
Publish at: 2026-10-01

A comparative study of standard PSO and adaptive fuzzy PSO for optimization

10.11591/ijece.v16i5.pp2720-2730
Paul Olujide Adebayo , Waheed Babatunde Yahya , Rasheed Gbenga Jimoh
In engineering, complex optimization problems often require efficient and reliable solution methods. An example of a metaheuristic algorithm is particle swarm optimization (PSO), which is widely used for its simplicity and adaptability. However, PSO performance is highly sensitive to parameter settings. To tackle this issue, researchers have developed adaptive variants, such as adaptive fuzzy particle swarm optimization (AF-PSO), that adjust parameters in real time during operation. In this study, standard PSO and AF-PSO are compared using several benchmark functions, Sphere, Rosenbrock, Rastrigin, Ackley, Griewank, and Schwefel, along with the multi-objective test problems ZDT1–3 and DTLZ1–2. Standard PSO was run with fixed parameters values (w=0.729, c₁=c₂=1.494) for the computation. The AF-PSO variant of PSO parameters are adaptively tune with a fuzzy inference concept. Each algorithm is evaluated across multiple independent runs to evaluate performance consistency. Across the test set, AF-PSO achieved superior performance in terms of solution quality and convergence. Among the benchmark functions, Griewank (+74.1%), Ackley (+65.4%), and Sphere (+63.0%) each have performances more than the others. Generally, the findings suggest that adapting parameters during the search improves convergence and solution quality, particularly for complex multimodal optimization problems.
Volume: 16
Issue: 5
Page: 2720-2730
Publish at: 2026-10-01

Real-time depth measurement and stability control of AUV using regression approximation and filtered pressure data

10.11591/ijece.v16i5.pp2417-2430
Senanjung Prayoga , Dhaniel Beny Wardhana , Ryan Satria Wijaya
This paper presents the development and experimental validation of a prototype-scale autonomous underwater vehicle (AUV) depth control system using a proportional-integral-derivative (PID) controller with depth feedback from a SEN0257 water-pressure sensor. Raw sensor readings are filtered and calibrated using linear regression, reducing the depth estimation error, as indicated by a decrease in root mean square error (RMSE) from 1.88 to 0.63 cm. The calibrated depth signal is implemented in real time as the feedback source for closed-loop control on the testbed. Controller performance is evaluated by comparing two tuning strategies: Ziegler–Nichols (ZN) closed-loop tuning and manual fine-tuning. Experiments were conducted at depth setpoints of 70 and 100 cm under consistent pool conditions, and additional trials were performed while the AUV executes forward motion to assess robustness under dynamic disturbances. System responses are quantified using rise time, overshoot, settling time, and steady-state error. Results show that calibration significantly improves sensor suitability for feedback, while the fine-tuned PID controller produces a more stable depth response with lower overshoot, smaller steady-state error, and shorter settling time than the ZN controller, despite the faster initial rise achieved by ZN tuning. Overall, combining calibrated pressure-based depth estimation with fine-tuned PID gains enables stable and accurate depth regulation for prototype AUV operation.
Volume: 16
Issue: 5
Page: 2417-2430
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

Stator interturn short circuit fault identification in doubly-fed induction generator and its analysis using artificial neural network

10.11591/ijece.v16i5.pp2379-2392
Vivek Kushwaha , Sanjay Kumar Maurya , Arvind Kumar Yadav
Inter-turn short circuit (ITSC) issues are a common electrical failure mainly caused by the deterioration of winding insulation in the machine over time. Failing to detect such issues early can lead to catastrophic consequences. This article investigates the interturn fault in the stator winding of a doubly-fed induction generator (DFIG) used in wind turbines. A flux linkage difference vector (FLDV) model is introduced in this study for fault detection. Additionally, an artificial neural network (ANN) model is proposed to classify these faults. Specifically, a short-circuit fault is induced in each phase of the stator winding, and the faults are classified by assigning different magnitudes to the respective phases. The ANN is trained to identify which phase contains an interturn fault, with output waveform amplitudes of 1, 2, or 3 corresponding to faults in phases "a," "b," and "c." If no fault is present, the waveform magnitude is designated as "0." This approach enables early fault diagnosis by analyzing waveform patterns, thereby preventing overheating caused by short circuits and avoiding severe, irreversible damage to the windings.
Volume: 16
Issue: 5
Page: 2379-2392
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

Interpretable machine learning framework for congestive heart failure risk prediction

10.11591/ijai.v15.i5.pp4345-4355
Sakchai Tangprasert , Nalinpat Bhumpenpein , Siranee Nuchitprasitchai , Yuenyong Nilsiam
Congestive heart failure (CHF) is a leading cause of morbidity and mortality worldwide and is influenced by multiple clinical risk factors, including age, sex, blood pressure, cholesterol levels, and electrocardiographic characteristics. This study employs publicly available heart disease datasets compiled from populations in Cleveland, Hungary, Switzerland, and Long Beach repositories (2015–2020) and analyzes 11 predictor attributes and one binary outcome using the cross-industry standard process for data mining (CRISP-DM) framework. Four supervised machine learning (ML) models—decision tree, random forest, gradient boosted trees, and naïve Bayes—were developed and evaluated using confusion matrix–based performance metrics, with results showing that the decision tree model offers balanced predictive performance and clear interpretability, making it suitable for heart failure risk prediction. The findings demonstrate the potential of ML approaches to support clinical risk assessment and enhance decision-making in heart failure management.
Volume: 15
Issue: 5
Page: 4345-4355
Publish at: 2026-10-01

Deep reinforcement learning-based credit risk prediction model for multi-attribute data

10.11591/ijai.v15.i5.pp4624-4634
Al-Khowarizmi Al-Khowarizmi , Ferry Fachrizal , Romi Fadillah Rahmat , Arif Ridho Lubis , Hariyadi Hariyadi
This paper aims to develop a multi-attribute data-based credit risk prediction model using the deep reinforcement learning (DRL) approach. In the context of creditworthiness evaluation, a model is needed that is not only accurate but also adaptive to complex data dynamics and risk patterns. This study uses a dataset of 5,000 entries that have gone through a preprocessing process, with features such as credit score, income, age, loan amount, and employment history. Initial correlation analysis through heatmap visualization shows that credit score has a strong negative relationship with credit risk, while other features show weak to moderate correlations. The evaluation of model performance is shown through the area under the curve (AUC) value on the receiver operating characteristic (ROC) curve of 0.88, which indicates excellent discrimination ability. The final evaluation results show that the DRL model has an accuracy of 91%, precision of 89%, recall of 90%, and F1-score of 89.5%, indicating that the model not only has high accuracy but is also balanced in its sensitivity and specificity. Thus, the application of DRL has proven effective in predicting credit risk adaptively and accurately, and can be implemented as an intelligent solution in an automated credit decision-making system based on multi-attribute data.
Volume: 15
Issue: 5
Page: 4624-4634
Publish at: 2026-10-01

Virtual laboratories and online learning in engineering education: a review

10.11591/ijere.v15i5.39538
Hamza Abu Owida , Areen Arabiat
The rapid expansion of virtual laboratories and online learning in engineering education, accelerated by the COVID-19 pandemic, has created an urgent need for clearer pedagogical guidance on how digital laboratory modalities should be used. This review addresses the unresolved educational problem of ensuring that virtual and remote laboratory environments support not only conceptual understanding but also practical laboratory skills and broader professional competencies. Using a structured review of recent empirical studies, systematic reviews, and meta-analyses, the paper synthesizes evidence on simulation-based laboratories, remote laboratories, immersive environments, and the wider online learning conditions that shape their effectiveness. The review finds that virtual laboratories are consistently effective for conceptual learning, visualization, flexibility, and access, while remote and immersive environments can strengthen authenticity and practical skill development when combined with active learning, multimodal assessment, and strong instructional support. However, persistent limitations remain in psychomotor skill development, assessment validity, accessibility, and institutional readiness. The review concludes that no single modality is sufficient on its own; rather, a hybrid model that pedagogically integrates virtual, remote, immersive, and physical laboratory experiences offers the most defensible pathway for meaningful and sustainable engineering laboratory education.
Volume: 15
Issue: 5
Page: 3857-3869
Publish at: 2026-10-01

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

A chip level design of a multi-mode compressive sensing image sensor

10.11591/ijece.v16i5.pp2393-2404
Zahra Sepehri , Sayed Masoud Sayedi , Ehsan Yazdian
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.
Volume: 16
Issue: 5
Page: 2393-2404
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

Information architecture debt: Why legacy platform schema decisions constrain enterprise AI capability

10.11591/ijece.v16i5.pp2473-2482
Mihir Shah
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.
Volume: 16
Issue: 5
Page: 2473-2482
Publish at: 2026-10-01

A compact tri-band THz patch antenna using slot-loaded radiator and defected ground structure

10.11591/ijece.v16i5.pp2549-2558
Tran-Thi Bich Ngoc , Truong Thi Phuong Nhi
Terahertz (THz)- based antennas are essential for future 6G sensing and short range communications, especially when multi-band flexibility and compact di mensions are required. This paper presents a compact microstrip patch an tenna designed for THz applications with tri-band operation. The antenna is formed out of a rectangular patch filled with slots, a defective ground structure (DGS), and a microstrip feedline. Slot loading and the DGS generate various current paths, exciting several resonant modes and enhancing impedance match ing across the resonant frequency range. The proposed design is implemented on a 30 µm thick RO3003 substrate with a relative permittivity of 3 and loss tan gent of 0.001. The antenna has physical dimensions of 0.27×0.28×0.03 mm3. Simulation results show the antenna operates at three frequencies with a remark ably low return loss of up to-61.35 dB over the range of 1.59-3.2 THz. It offers tri-band gains of 6.01 dBi, 7.30 dBi, and 7.31 dBi, with a voltage standing wave ratio (VSWR) close to the ideal value of 1 for most bands. Furthermore, field distributions and power breakdown calculations are performed to better under stand the radiation mechanisms and efficiency characteristics at THz frequen cies. The antenna radiated power across the band, achieving up to 86.5% of the power accepted. These results indicate that the suggested design provides tri-band capabilities while being a simple, compact and efficient candidate for the demand for improved antennas in next-generation wireless communication systems.
Volume: 16
Issue: 5
Page: 2549-2558
Publish at: 2026-10-01
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