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Solar photovoltaic power system for Bungin Island

10.11591/ijeecs.v43.i1.pp7-17
Novi Azman , Rudi Naufal Fadhilah , Muhammad Ismail
Bungin Island currently relies on diesel -based electricity power generation, resulting in limited supply reliability. This study evaluates the technical feasibility of an off-grid solar photovoltaic (PV) system integrated with lithium-ion battery energy storage (BESS) using photovoltaic system (PVsyst) simulation. Based on measured load demand of 2,830 kWh/day and NASA solar resource data, the optimized configuration consist of a 0.713 MWp PV array and an 11.54 MWh battery system. Simulation result indicates an annual PV production (E_Array) of 1,258.3 MWh/year. Of this, 1,032.9 MWh/year is delivered to the load, with excess energy of 120.84 MWh/year and a limited unmet load of 13.7 MWh/year (1.33%). The system achieves a solar fraction 98.67%, a performance ratio (PR) of 77.6% and a capacity factor of 17.34%. These results demonstrate that a properly sized PV-battery configuration can reliably replace diesel generation, providing a robust framework for high-renewable electrification in densely populated small islands.
Volume: 43
Issue: 1
Page: 7-17
Publish at: 2026-07-01

MobileNetV2 with transfer learning for brain tumor classification

10.11591/ijeecs.v43.i1.pp281-298
Aziz Srai
This document presents a deep learning-based approach for the automatic classification of brain tumors from magnetic resonance imaging (MRI) images, using the lightweight MobileNetV2 model combined with transfer learning and fine-tuning techniques. The study aims to address the constraints of resource-limited medical environments, where model speed and lightweight design are as important as accuracy. The dataset used is a fusion of three public databases (Figshare, SARTAJ, and BR35H), comprising 4,480 images for training and 1,600 for testing, divided into four classes: glioma, meningioma, pituitary tumor, and healthy brain. The methodology includes several steps: resizing the images to 224×224 pixels, normalizing pixel values between 0 and 1, augmenting the data through random rotations, shifts, and zooms to avoid overfitting, and then extracting features using MobileNetV2 pre-trained on ImageNet. The strategy adopted comprises two phases: first, transfer learning where only the layers added at the top of the model are trained for 10 epochs, then fine-tuning consisting of unfreezing the last 20 layers of the base model and retraining them with a reduced learning rate for 5 epochs. The results obtained show an overall accuracy of 86.56% after fine-tuning, with a macro-mean area under the ROC curve (AUC) of 0.9645, indicating excellent discriminatory power. The confusion matrix reveals that the "no tumor" class achieves perfect performance (400 out of 400), while the "meningioma" class remains the most difficult to classify, often confused with gliomas and pituitary tumors. Compared to more resource-intensive models like VGG16, ResNet50, or EfficientNet, MobileNetV2 offers an optimal balance between performance and lightweight design, with a significantly lower number of parameters, making it particularly well-suited to resource-constrained environments. The authors conclude that this approach provides reliable diagnostic support for radiologists, accelerating tumor detection without replacing medical expertise. Future directions include clinical validation on multi-center data, integration of an automated segmentation step, and exploration of newer architectures such as attention mechanisms.
Volume: 43
Issue: 1
Page: 281-298
Publish at: 2026-07-01

Adaptive vector control of PV-fed induction motor using boost split-source inverter without MPPT

10.11591/ijeecs.v43.i1.pp39-62
Romaissa Hamdi , Yassine Beddiaf , Djamel Sakri , Daoud Rezzak , Hassina Slimani
This paper proposes an adaptive vector control strategy for a photovoltaic (PV)-fed induction motor using a boost split-source inverter (BSSI). Unlike conventional PV conversion systems, the proposed topology combines voltage boosting and DC–AC conversion in a single stage, reducing component count, switching losses, and overall system complexity. In addition, the system operates without a maximum power point tracking (MPPT) algorithm, simplifying the control structure while maintaining stable operation under varying environmental conditions. The proposed control approach integrates sliding mode control (SMC) for robust DC-bus voltage regulation and an adaptive proportional–integral (API) speed controller based on Lie derivative theory for online tuning of controller gains according to the speed tracking error. An active DC-link protection mechanism is also introduced to prevent overvoltage during transient conditions. The main contribution of this paper lies in the combination of the BSSI topology with a hybrid adaptive control framework to improve robustness, dynamic performance, and system reliability under irradiance, temperature, and load variations. Simulation and experimental results demonstrate fast dynamic response, reduced speed and voltage oscillations, accurate speed tracking, and superior performance compared with conventional vector control methods.
Volume: 43
Issue: 1
Page: 39-62
Publish at: 2026-07-01

Coral classification in underwater images using a dual-branch deep learning framework

10.11591/ijeecs.v43.i1.pp207-218
Pracharat Sa-ngadsup , Chawan Koopipat
Automated coral classification from underwater imagery is essential for large-scale reef monitoring but remains challenging due to color attenuation, illumination variability, and differences between texture-focused close- range images and morphology-focused colony-level observations. To address these challenges, this study proposes a dual-branch convolutional neural network that integrates information from the CIELAB (LAB) color space with structural descriptors derived from the discrete wavelet transform (DWT). RGB images are first converted to the LAB color space to separate luminance and chromatic components in separate channels, enabling the model to exploit color and lightness information more explicitly. Structural information is extracted from the luminance channel using wavelet decomposition to capture high-frequency morphological patterns. The two representations are processed through parallel convolutional branches and fused at the feature level for classification. Experiments conducted on a unified coral dataset containing texture dominant and morphology-focused imagery across 14 classes show that the proposed method achieves 96.52% accuracy on the texture-focused RSMAS dataset and 88.33% accuracy on the morphology-focused structure RSMAS dataset, reducing the cross-domain performance gap from 22.46% to 8.19% compared with RGB baselines, demonstrating improved cross domain robustness for coral classification under heterogeneous underwater imaging conditions.
Volume: 43
Issue: 1
Page: 207-218
Publish at: 2026-07-01

Layer-wise adaptive structured pruning via genetic algorithms with taylor-based proxy fitness

10.11591/ijeecs.v43.i1.pp314-324
Anh-Truong Vo , Hoang-Loc Tran , Dinh-Duy Phan , Duc-Lung Vu
Deploying deep convolutional neural networks (CNNs) on edge devices requires balancing model accuracy and computational efficiency. While structured pruning limits inference costs by removing redundant filters, most methods apply a rigid, global criterion, ignoring the distinct representational roles of individual layers. This yields suboptimal results, especially under aggressive compression where over-pruning degrades performance. To address this limitation, we propose an adaptive structured pruning framework based on genetic algorithms (GAs) that jointly optimizes layer-wise pruning ratios and strategies. Each layer independently selects between min-importance and median-rank pruning, enabling the exploration of tailored strategy combinations. A training-free taylor based proxy fitness function ensures efficient candidate evaluation without re peated fine-tuning. After fine-tuning the selected architecture, experiments on VGG16 demonstrate that our method achieves 92.78 ± 0.28% accuracy (over 50 independent runs) with a 70.0 ± 3.2% MACsreductiononCIFAR-10, andmaintains 71.82% accuracy on CIFAR-100. These results demonstrate competitive performance compared to existing pruning methods while achieving substantial computational cost reduction.
Volume: 43
Issue: 1
Page: 314-324
Publish at: 2026-07-01

Development of internet of things-based exoskeleton for monitoring elbow rehabilitation therapy

10.11591/ijres.v15.i2.pp553-562
Geevanthran A/L Vegurgama , Mohd Razali Mohamad Sapiee , Khalil Azha Mohd Annuar
The elbow joint is a complex articulation comprising the humeroulnar and humeroradial joints, facilitating flexion-extension movements essential for daily activities. Leveraging advancements in connected systems and paradigms such as the internet of things (IoT), this study proposes an affordable, effective, and IoT-enabled one-degree-of-freedom (1DOF) elbow exoskeleton for home-based rehabilitation. The exoskeleton is designed to provide a natural range of elbow movements (flexion and extension) while enabling real-time monitoring of rehabilitation progress through mobile applications and web servers. The system collects qualitative data on elbow movements, which are critical in rehabilitation therapy, and enables patients to save their rehabilitation status for future reference. This data can be accessed by doctors remotely, ensuring continuity of care. For patients unable to lift their arm independently, a servomotor provides mechanical assistance, enabling them to achieve desired angles for rehabilitation. The IoT platform generates real-time graphs, offering detailed insights into the recovery process through data analysis. This project is a significant advancement in clinical and healthcare settings, as it reduces dependency on human support or physiotherapists. By integrating IoT technology, the proposed exoskeleton ensures effective, autonomous, and data-driven rehabilitation for elbow joint recovery.
Volume: 15
Issue: 2
Page: 553-562
Publish at: 2026-07-01

Analysis of PM2.5 pollutant sources in Jakarta using deep learning models and back trajectory approach

10.11591/ijeecs.v43.i1.pp325-334
Hendro Pratama Saragih , Imas Sukaesih Sitanggang , Hendra Rahmawan
PM2.5 concentrations in Jakarta frequently exceed World Health Organization (WHO) air quality guidelines, indicating the need for an integrated approach for pollution prediction and source assessment. This study develops a spatiotemporal prediction framework using a long short term memory (LSTM) model integrated with the hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model for backward trajectory analysis. Daily PM2.5 data from five monitoring stations were combined with meteorological variables from ERA5, Visualcrossing, and the global data assimilation system, with spatial context evaluated using Sentinel-2 land cover maps. After hyperparameter tuning, the optimized model demonstrated robust predictive capabilities, achieving a peak coefficient of determination (R2) of 75.87% on the test data. The framework exhibited exceptional relative accuracy, particularly at the Jagakarsa and Kebun Jeruk stations, which recorded mean absolute percentage error (MAPE) values of 13.34% and 17.80%, respectively. Backward trajectory analysis during selected pollution episodes indicates two dominant regional transport pathways that may influence PM2.5 levels in Jakarta. These pathways are associated with air mass transport over industrial and built-up areas in eastern and northern regions surrounding Jakarta. Land cover analysis shows limited vegetation along these pathways. Overall, elevated PM2.5 events are associated with combined local emissions, regional transport, and meteorological conditions that limit pollutant dispersion near the surface.
Volume: 43
Issue: 1
Page: 325-334
Publish at: 2026-07-01

Graph neural network-based biomedical misinformation detection with semantic consistency analysis

10.11591/ijres.v15.i2.pp439-449
Siva Dhievaraj , Agusthiyar Ramu
Conventional misinformation detection approaches primarily rely on textual features and deep learning (DL) classifiers, which often fail to capture complex relationships among biomedical entities and the underlying scientific context of health claims. To address this limitation, this study proposes a graph neural network (GNN)-based biomedical misinformation detection framework that integrates knowledge graph propagation with semantic consistency verification. Initially, key biomedical entities such as diseases, treatments, and biological processes are extracted and mapped into a structured biomedical knowledge graph (BKG) to represent semantic relationships. A graph attention network (GAT) is then employed to model relational dependencies and propagate contextual information across connected entities, enabling the detection of hidden inconsistencies in biomedical claims. The proposed model is evaluated using benchmark biomedical misinformation datasets, including Reliable COVID-19 News Dataset, 2021 (ReCOVery), COVID-19 Healthcare Misinformation Dataset, 2020 (CoAID), and 2018–2020 biomedical health news corpus (HealthStory). Experimental results demonstrate that the proposed framework achieves an average detection accuracy of 96.3%, outperforming conventional long short-term memory (LSTM), convolutional neural networks (CNN), and transformer-based models in terms of precision, recall, and F1-score. The findings highlight that integrating structured biomedical knowledge with graph-based reasoning significantly enhances the reliability and interpretability of misinformation detection systems.
Volume: 15
Issue: 2
Page: 439-449
Publish at: 2026-07-01

Revolutionizing night-time object detection in autonomous vehicles with SCL-YOLOv11 and ROA optimization

10.11591/ijres.v15.i2.pp534-552
Kondapalli Sri Vijaya , Gokula Krishnan Vasudevan , Pinagadi Venkateswara Rao , Therasa Michael , Balasubramanian Lalithambigai , Boddula Prathusha Laxmi
Accurate object detection under low-light conditions is a critical requirement for reliable perception in autonomous driving systems. However, night-time environments often suffer from poor illumination, noise, and reduced feature visibility, which significantly degrade the performance of conventional object detection models. To address this challenge, this paper proposes spatial contrast learning (SCL)-you only look once version 11 (YOLOv11), an enhanced object detection framework designed for night-time scenarios. The proposed approach integrates SCL to improve feature discrimination in dark regions and employs the revolution optimization algorithm (ROA) for effective model parameter optimization. The framework is evaluated on three benchmark night-time datasets, ExDark, LLVIP, and BDD100K, to assess its detection performance. Experimental results demonstrate that the proposed model achieves a mAP@50 of 72.9%, improving the baseline YOLOv11 by 9.5% while also reducing inference latency by 18.3%. Comparative evaluations with existing detectors further confirm that the proposed method provides improved accuracy and efficiency for night-time object detection. These results indicate that the proposed framework can enhance perception reliability for autonomous driving applications operating in low-light environments.
Volume: 15
Issue: 2
Page: 534-552
Publish at: 2026-07-01

Auto-generated unit testing using PCA-aDynaMOSA

10.11591/ijeecs.v43.i1.pp233-249
Made Raja Adi Surya Saputra , Maria Seraphina Astriani
Automated test case generation is essential for improving software quality; however, many-objective search-based testing approaches often experience scalability issues when the number of test objectives increases. This condition leads to slower convergence, higher computational effort, and reduced ability to cover complex program structures. To address this gap, this study proposes an enhanced version of the aDynaMOSA algorithm by incorporating principal component analysis (PCA) to reduce redundant objectives during the search process. The proposed method preserves essential objective information while eliminating dependency noise that typically slows the evolutionary search. Experiments were conducted using the SF110 benchmark dataset through EvoSuite, and the approach was compared with standard many-objective search strategies. The findings demonstrate that PCA-based objective reduction can improve performance, achieving up to 3.87%, 5.50%, and 3.75% for coverage of line, branch, and mutation respectively. These results indicate that dimensionality reduction can significantly enhance scalability and efficiency in automated evolutionary test generation, providing a foundation for future adaptive and hybrid optimization strategies.
Volume: 43
Issue: 1
Page: 233-249
Publish at: 2026-07-01

A review of field-programmable gate array-based biomedical signal processing for public health applications

10.11591/ijres.v15.i2.pp320-338
Tole Sutikno , Aiman Zakwan Jidin , Lina Handayani
Biomedical signal processing is essential for modern diagnostics, monitoring, and preventive healthcare in public health and mobile health (mHealth) systems. Signals such as electroencephalography (EEG), electromyography (EMG), and heart rate variability (HRV) offer vital insights into brain, muscle, and cardiovascular health. However, achieving real-time, energy-efficient, and scalable processing remains challenging for conventional hardware such as central-processing units (CPUs), graphics-processing units (GPUs), and application-specific integrated circuits (ASICs). Field-programmable gate arrays (FPGAs) provide a promising alternative through their reconfigurability, parallelism, and adaptability to dynamic biomedical workloads. This review examines FPGA-based implementations for EEG, EMG, and HRV processing, focusing on key metrics including latency, throughput, and power efficiency. It also discusses design strategies such as low-power optimization, hardware–software co-design, and FPGA-based machine learning acceleration, with attention to data integrity and security in medical contexts. Integration with wearable, portable, and telemedicine platforms is explored, alongside comparative analyses with traditional computing architectures. The paper identifies challenges in power–performance trade-offs, design complexity, and clinical validation, and highlights emerging directions such as artificial intelligence (AI)-driven FPGA platforms, neuromorphic design, and sustainable low-cost solutions for large-scale health monitoring. Overall, FPGA-based biomedical signal processing emerges as a foundation for intelligent, efficient, and accessible next-generation public-health technologies.
Volume: 15
Issue: 2
Page: 320-338
Publish at: 2026-07-01

Predicting student academic outcomes from e-learning interaction data using hybrid machine learning models

10.11591/ijres.v15.i2.pp259-268
Sajithunisa Hussain , Jayachandran Jeyachidra
The rapid growth of digital learning platforms has generated large volumes of student interaction data, providing opportunities for intelligent prediction of academic outcomes. Beyond educational analytics, such prediction tasks are relevant for reconfigurable systems, embedded platforms, very large scale integration (VLSI) accelerators, and internet of things (IoT)-enabled edge devices in smart learning environments. This study proposes a hybrid machine learning framework for predicting student performance using the e-learning student reactions dataset, which captures engagement patterns, behavioral responses, and interaction dynamics. Eight classifiers— eXtreme gradient boosting (XGBoost), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), radial basis function (RBF), and deep neural network (DNN)—are evaluated using both an 80–20 train–test split and K-fold cross-validation to assess accuracy and generalization. Results show the RBF model achieves the highest accuracy of 1.00, demonstrating its ability to capture complex, nonlinear behavior. From a systems perspective, the framework can be mapped onto field programmable gate arrays (FPGAs) or embedded devices, leveraging parallel computation for low-latency inference, and integrated with IoT-enabled smart classrooms for real-time edge analytics. These findings confirm that hybrid machine learning models not only improve student performance prediction but also serve as practical workloads for reconfigurable, embedded, and VLSI-based intelligent systems in digital education.
Volume: 15
Issue: 2
Page: 259-268
Publish at: 2026-07-01

AI-driven co-optimization of ONOFIC circuits and multiband antennas for low-power VLSI

10.11591/ijres.v15.i2.pp306-319
Ramavathu Ramesh Naik , Donapati Ramakrishna Reddy , Krishnanaik Vankdoth
This paper presents an artificial intelligence (AI)-assisted optimization framework for on-off current feedback controlled (ONOFIC)-enhanced domino circuits implemented in advanced fin field-effect transistor (FinFET) and carbon nanotube field-effect transistor (CNTFET) technologies. The framework integrates artificial neural network (ANN) surrogate modeling with evolutionary optimization (genetic algorithm (GA), particle swarm optimization (PSO), and NSGA-II) to reduce leakage, improve energy efficiency, and enhance robustness under process voltage temperature (PVT) variations, aging effects (bias temperature instability (BTI)/hot carrier injection (HCI)), and antenna-induced parasitic coupling. By replacing repeated HSPICE simulations with fast ANN predictions, the proposed methodology reduces computational cost by more than 90% while achieving up to 30–35% gains in leakage and power-delay product (PDP)/energy-delay product (EDP) performance. The results demonstrate that ANN-assisted evolutionary optimization provides a scalable and technology-agnostic workflow suitable for next-generation internet of thing (IoT), radio frequency (RF)-integrated, and low-power very large scale integration (VLSI) platforms.
Volume: 15
Issue: 2
Page: 306-319
Publish at: 2026-07-01

ESP-NOW based multi-node internet of things system for agricultural solar dryers with hybrid offline-online monitoring

10.11591/ijres.v15.i2.pp426-438
Rahmat Siswanto , Putri Dewintari , Sapar Sapar
Existing internet of things (IoT) systems for agricultural solar dryers rely on continuous internet connectivity, limiting deployment in remote rural areas with unreliable infrastructure. This study develops and validates a multi-node IoT architecture using ESP-NOW peer-to-peer communication that enables infrastructure-independent operation with optional ThingSpeak cloud synchronization. The system integrates ESP32 nodes, SHT41 sensors, relay-controlled actuators, and a hybrid solar-battery-grid power supply, deployed at a cocoa processing facility in South Sulawesi, Indonesia. Field evaluation confirmed: >95% packet delivery at 50 m, <10 ms latency, 94.3% cloud synchronization reliability, and 96% upload timing precision within ±2 s (σ=0.96 s). The dual-mode architecture sustained continuous local monitoring and actuator control during all network outages, with autonomous cloud reconnection requiring no manual intervention. Drying trials showed a -40 57% reduction in cocoa drying duration (3–4 days vs. 5–7 days baseline) through automated chamber control (40–60 °C; RH <60%). Energy analysis yielded an intensity of 0.12–0.17 kWh/kg dried output, with the IoT subsystem consuming less than 1% of total drying energy. The validated architecture provides a deployable, offline-capable, and energy-efficient solution for post-harvest monitoring in infrastructure-constrained environments, with applicability to diverse crop drying and storage scenarios.
Volume: 15
Issue: 2
Page: 426-438
Publish at: 2026-07-01

Intelligent deep learning models for fault diagnosis in sixth generation industrial internet of things environments

10.11591/ijres.v15.i2.pp281-290
Hareesha Dandamudi , Chenchu Punnarao Bandi , Simhadri Mallikarjuna Rao , Palacharla SVS Sridhar , Mythili Murugan , Srikanth Kilaru , Rama Krishna Paladugu
The integration of sixth-generation (6G) communication and Industry 4.0 technologies has transformed industrial automation, connectivity, and intelligent data analysis. However, the increasing volume and diversity of data generated from multiple industrial sources create significant challenges for accurate and real-time fault detection. This study presents a deep learning-based framework designed to improve fault identification in 6G-enabled Industry 4.0 environments. The proposed system processes heterogeneous data collected from internet of things (IoT) devices, monitoring sensors, and automated industrial equipment to ensure reliable and scalable fault analysis. A hybrid model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is implemented to capture spatial features and temporal relationships within industrial datasets. The framework also focuses on optimizing computational resources while maintaining high detection performance. Simulation-based evaluations demonstrate that the proposed approach enhances fault detection accuracy and system reliability, making it suitable for advanced smart manufacturing and industrial monitoring applications.
Volume: 15
Issue: 2
Page: 281-290
Publish at: 2026-07-01
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