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Low-cost real-time campus assistive navigation device for the visually impaired: The University of Ilorin case study

10.11591/ijeecs.v43.i2.pp555-567
Mahmud Hafeez Owolabi , Idajili John Ojochegbe , Ayinla Shehu Lukman , Jimoh-Mahmud Aishat Oladayo , Yusuf Abdulrahman Olalekan , Olaogun Jerry Oluwajomiloju
Navigating dynamic environments remains a significant challenge for visually impaired individuals due to limited spatial awareness, which restricts their mobility, independence, and safety. Traditional aids such as white canes and guide dogs provide physical support but lack contextual feedback. While recent advances in artificial intelligence (AI) and computer vision (CV) have enabled real-time sensing, many assistive systems remain costly, complex, and dependent on continuous network connectivity. This study introduces a low‑cost, portable campus assistive navigation (CAN) device that delivers real‑time perception and offline guidance. The system employs a custom dataset, an optimized YOLOv11 model, and a Pi Camera for continuous object detection, supported by an HC‑SR04 ultrasonic sensor for obstacle avoidance and auditory alerts. Training and validation confirmed robust convergence, with precision ~0.95, recall near 1.0, and mAP@0.5 exceeding 0.9, while mAP@0.5:0.95 remained above 0.8, demonstrating reliable detection and generalization under strict thresholds. Field tests further reported confidence scores of 0.74–0.84, 98% accuracy in distance measurement, and GPS localization within ±1.5 m. Real‑time auditory and haptic feedback via Bluetooth headphones enhanced mobility and safety. The CAN device offers a scalable, affordable solution for autonomous navigation in campus environments.
Volume: 43
Issue: 2
Page: 555-567
Publish at: 2026-08-01

Evaluation of hybrid parallelism for scalable training of DenseNet-121 in diabetic retinopathy classification

10.11591/ijeecs.v43.i2.pp662-671
Indar Sugiarto , Djoni Haryadi Setiabudi , Darrell Cornelius Rivaldo , Taweesak Kijkanjanarat , Resmana Lim
Training large and complex deep learning models is often constrained by GPU memory limitations and prolonged training times. While several parallelism strategies have been proposed, this study specifically evaluates hybrid parallelism—a combination of data parallelism and pipeline parallelism—to address both challenges simultaneously. Using a case study on diabetic retinopathy (DR) classification with the DenseNet-121 architecture, we analyze the trade-off between computational efficiency and memory scalability. Results show that although hybrid parallelism does not yet provide speedup compared to a single-GPU setup—due to communication overhead and pipeline fragmentation—it enables training of large models that exceed the memory capacity of a single GPU. The trained model achieved a validation accuracy of 0.737, a quadratic weighted kappa (QWK) of 0.861, and a weighted F1-score of 0.749. In contrast, pure data parallelism showed a potential speedup of up to 1.9× in scenarios where the model still fits within a single GPU. These findings highlight the critical role of hybrid parallelism in overcoming the memory wall in large-scale model training, though optimization to reduce overhead remains a key challenge.
Volume: 43
Issue: 2
Page: 662-671
Publish at: 2026-08-01

Autonomous trenching robot with intelligent obstacle detection and path optimization for precision cable installation

10.11591/ijeecs.v43.i2.pp425-438
Muhammad Omar , Hamza Ali Nisar , Muhammad Usman , Husnain Siddique , Suffian Zaman , Saad Saleem Khan , Justyna Robinson
Trenching for underground cable and pipeline installation is typically labor intensive, time-consuming, and potentially hazardous, particularly in environments with buried obstacles. This paper presents a low-cost autonomous trenching robot with intelligent obstacle detection and path optimization to improve excavation efficiency, safety, and accuracy. The proposed system integrates ultrasonic and infrared sensors with an embedded controller for real-time obstacle detection and autonomous navigation. A path optimization algorithm automatically adjusts the trenching route whenever an obstacle is detected, allowing continuous operation while reducing unnecessary movement and energy consumption. The robot employs a tracked mobile platform and an automated trenching mechanism capable of maintaining consistent trench depth and width under different terrain conditions. Experimental results demonstrate that the proposed system accurately detects obstacles, successfully replans its path in real time, and performs reliable autonomous trenching with minimal human intervention. Compared with conventional manual trenching methods, the developed robot improves operational efficiency, enhances excavation accuracy, and reduces safety risks for workers. The proposed system provides a practical and scalable solution for underground cable and pipeline installation and has strong potential for future applications in intelligent construction, infrastructure development, and autonomous civil engineering.
Volume: 43
Issue: 2
Page: 425-438
Publish at: 2026-08-01

Probability density-based quantized spiking neural network for efficient intrusion detection in mobile ad hoc networks

10.11591/ijeecs.v43.i2.pp460-471
Amruth Veerabhadraiah , Devaraj Verma Chitragar
Mobile ad hoc networks (MANETs) enable infrastructure-free wireless communication through decentralized and self-organizing network architectures, making them well suited for dynamic environments such as disaster recovery, military operations, and intelligent transportation systems. However, their distributed topology and continuously changing network structure expose them to sophisticated routing attacks, particularly blackhole attacks (BHA) and wormhole attacks (WHA), which significantly degrade network reliability and challenge conventional intrusion detection techniques. Existing deep learning approaches often struggle to accurately distinguish malicious from legitimate traffic because of high-dimensional feature spaces and limited computational resources in MANET environments. To address these challenges, this paper proposes a probability density-based quantized spiking neural network (PDF-QSNN) framework for efficient intrusion detection in MANETs. The proposed framework first employs adaptive moth flame optimization (AMFO) to identify the most informative features, thereby reducing data redundancy and computational complexity. Subsequently, probability density-based feature encoding generates a discriminative probabilistic representation of network behavior, while the quantized spiking neural network performs lightweight and energy-efficient intrusion classification. Experimental evaluations using simulated BHA and WHA datasets demonstrate that the proposed framework achieves classification accuracies of 92.86% and 91.56%, respectively, outperforming conventional convolutional neural networks (CNN) and stacked recurrent long short-term memory (SRLSTM) models. These results demonstrate the effectiveness of the proposed framework for accurate and computationally efficient intrusion detection in resource constrained MANET environments.
Volume: 43
Issue: 2
Page: 460-471
Publish at: 2026-08-01

FireDetXplainer: an explainable artificial intelligence framework for wildfire detection

10.11591/ijeecs.v43.i2.pp576-585
Janjhyam Venkata Naga Ramesh , Bhargavi Peddi Reddy , Jillellamoodi Naga Madhuri Rajyalakshmi , Rajyalakshmi Uppada , Gaddam Venu Gopal , Rajesh Tulasi
Wildfires pose significant environmental, ecological, and socioeconomic threats, necessitating rapid and reliable detection systems for timely emergency response and disaster mitigation. Recent advances in deep learning have substantially improved wildfire detection accuracy; however, most existing models operate as black-box systems, limiting transparency and reducing user trust in safety-critical applications. This study proposes FireDetXplainer (FDX), an explainable artificial intelligence (XAI) framework designed to enhance the interpretability of deep learning-based wildfire detection while maintaining high predictive performance. The proposed framework integrates convolutional neural network (CNN)-based image classification with explainability techniques to identify the visual regions that contribute most to wildfire detection decisions. By generating intuitive visual explanations, FDX enables users to understand, validate, and trust the model's predictions, thereby supporting transparent and accountable decision-making. Experimental evaluation demonstrates that the proposed framework effectively distinguishes wildfire images from non-fire scenes while providing meaningful visual interpretations that improve model transparency without compromising detection performance. The findings highlight the potential of explainable AI to strengthen the reliability, usability, and practical deployment of intelligent wildfire monitoring systems for environmental surveillance, disaster management, and early warning applications.
Volume: 43
Issue: 2
Page: 576-585
Publish at: 2026-08-01

Transformer-based sentiment modeling for identifying cross country fintech perception gaps

10.11591/ijeecs.v43.i2.pp522-533
Kayla Zhafira Ardinov , Muhardi Saputra , Riska Yanu Fa’rifah
Conventional sentiment analysis lacks granularity for capturing detailed user experiences and cross-country comparative insights in digital finance. This study identifies and maps perception gaps among ShopeePay users in Indonesia and Thailand using a topic-informed sentiment analysis pipeline inspired by aspect-based sentiment analysis (ABSA) principles. Adopting the knowledge discovery in databases (KDD) framework, over 170,000 web scraped reviews were preprocessed, automatically labeled through pseudo labeling, and balanced using random oversampling. To mitigate pseudo-label reinforcement, manual validation on 500 reviews per country achieved agreement rates of 98.80% (Indonesia) and 99.20% (Thailand) with Cohen’s Kappa above 0.97. The fine-tuned DistilBERT model achieved accuracies of 97.65% (Indonesia) and 98.38% (Thailand), though these figures should be interpreted within the pseudo-labeled evaluation context. Significant perception gaps were revealed: Thai users showed lower satisfaction with transactions (67.6% negative) while Indonesian users were more positive (93.0% positive). Process time received negative dominance in both countries, with Indonesia at 78.2% and Thailand at 50.8% negative. These findings demonstrate that user satisfaction is shaped by local infrastructure and cultural contexts, providing strategic insights for regional fintech development.
Volume: 43
Issue: 2
Page: 522-533
Publish at: 2026-08-01

Adaptive feature selection for credit scoring models

10.11591/ijeecs.v43.i2.pp507-521
Mostafa Mohamed SeifElnasr , Yasser Omar , Saleh Mesbah
Accurate credit scoring and delinquency prediction are critical for financial institutions, particularly when evaluating both banked and unbanked clients. Traditional feature selection approaches such as wrapper, filter, and embedded methods often require repeated retraining and may not efficiently explore optimal feature subsets in multi-class credit risk settings. This study proposes a reinforcement learning (RL)–based adaptive feature selection framework using tabular Q-learning to dynamically identify informative feature subsets for credit bucket classification and delinquency prediction. The framework was evaluated on two datasets, including proprietary credit datasets (24,533 records with 18–19 features). Using stratified 10-fold cross-validation, the proposed approach achieved 62.84% accuracy (F1=0.63) for 8-class credit bucket prediction, outperforming traditional wrapper-based methods by up to 4.2% while reducing computational cost compared to exhaustive subset evaluation. For delinquency prediction, the model achieved approximately 70% F1-score, demonstrating improved minority-class sensitivity. Compared to filter and embedded methods, the RL-based framework produced more compact feature subsets while maintaining competitive computational efficiency. These findings demonstrate that adaptive RL driven feature selection provides a practical and scalable mechanism for enhancing predictive performance in credit risk modelling, supporting automated and consistent decision making in financial institutions.
Volume: 43
Issue: 2
Page: 507-521
Publish at: 2026-08-01

Metaheuristic optimization for atrial fibrillation detection: feature extraction, selection, and hyperparameter tuning

10.11591/ijai.v15.i4.pp3878-3887
Zaid Nouna , Hamid Bouyghf , Mohammed Nahid , Issa Sabiri
Atrial fibrillation (AF) detection from electrocardiogram (ECG) signals is crucial for early diagnosis and intervention. This study presents a multi-objective optimization approach for AF detection, focusing on feature extraction, selection, and neural network hyperparameter tuning. The methodology uses cross-validation during the training of the two concatenated ECG dataset features and simultaneously minimizes the error rate on the separate validation folds of each dataset and reduces the number of selected features, enhancing model generalization and efficiency. Particle swarm optimization (PSO), grey wolf optimization (GWO), and differential evolution (DE) algorithms were implemented to navigate this multi-objective space. While all three algorithms were explored, the final solution, demonstrating a superior trade-off between accuracy and feature reduction, was obtained using DE. This approach effectively identifies optimal feature subsets and neural network configurations, yielding a robust and compact AF detection model. The proposed approach has shown promising results, with the model achieving accuracies of 96.38% and 90.69%, and corresponding area under the curve (AUC) values of 0.99 and 0.96, for the first and second datasets, respectively, using 10 optimally selected features.
Volume: 15
Issue: 4
Page: 3878-3887
Publish at: 2026-08-01

Strategic optimization of artificial intelligence digital learning in informatics engineering

10.11591/ijai.v15.i4.pp2999-3008
Aan Ansori , Birru Muqdamien , Ahmad Tabrani , Reza Syafrizal , Sutanto Sutanto , Eko Wahyu Wibowo , Syifa Amara Dhestiyani
This study examines the strategic optimization of artificial intelligence (AI)-based digital learning in the Department of Informatics Engineering by analyzing the interaction between internal and external factors that influence successful implementation. Employing a qualitative approach based on strengths, weaknesses, opportunities, and threats (SWOT) analysis, this research utilizes the internal factor analysis summary (IFAS) and external factor analysis summary (EFAS) to assess institutional readiness, constraints, and strategic opportunities for AI integration in higher education. The results indicate that external factors exert a stronger strategic influence than internal factors, as reflected by a higher EFAS score (1.50) compared to the IFAS score (1.25). Key external opportunities include the increasing demand for AI competencies, supportive national policies, and opportunities for industry collaboration, while the main internal limitations are limited faculty expertise in AI and insufficient AI-specific learning resources. Based on these findings, this study formulates a SWOT-based strategic framework that converts analytical outcomes into practical recommendations for curriculum development, faculty capacity building, and the adoption of adaptive AI learning technologies. This research contributes a context-specific and empirically grounded strategic model that advances AI integration beyond descriptive analysis, supporting more effective, personalized, and sustainable digital learning, particularly within Informatics Engineering programs in Indonesian Islamic higher education institutions.
Volume: 15
Issue: 4
Page: 2999-3008
Publish at: 2026-08-01

Hybrid deep learning model for enhanced short-term gold price forecasting

10.11591/ijai.v15.i4.pp3228-3239
Hoang Ha Nguyen , Minh Duc Nguyen , Cuong H. Nguyen-Dinh
Accurate short-term gold price forecasting is crucial for the financial decision-making. This paper introduces a short-term gold prediction network (STGP-Net), a novel hybrid deep learning model designed to enhance prediction accuracy by integrating one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM). STGP-Net leverages the 1D-CNN's ability to extract local temporal features and the LSTM capacity to model long-range dependencies within gold price time series. Various sliding window configurations are employed to generate input sequences for multi-step ahead prediction. Comprehensive experiments were conducted comparing STGP-Net against 1D-CNN + recurrent neural network (RNN) and 1D-CNN + bidirectional long short-term memory (BiLSTM) baseline models across three configurations using metrics like mean absolute error (MAE), root mean square error (RMSE), and determination (R²). The results demonstrated that STGP-Net consistently provided better performance and robustness, proving more effective for short-term gold price forecasting than the alternative hybrid models tested.
Volume: 15
Issue: 4
Page: 3228-3239
Publish at: 2026-08-01

OCTModNet: a deep learning-based framework for optical coherence tomography image multi-class classification

10.11591/ijeecs.v43.i2.pp495-506
Saja Ataallah Muhammed , Abbas M. Ali , Dler Salih Hasan
Sight is one of the most significant senses in human beings. Losing sight can change a human’s life dramatically. Early diagnosis and detection of retinal diseases will prevent sight loss. Optical coherence tomography (OCT) imaging is helpful in ocular imaging with its high resolution and non invasive imaging for diagnosing retinal diseases. This study proposes OCTModNet, a novel multi-class hybrid classification model that is capable of classifying seven different retinal diseases using a combination of publicly available datasets, Kaggle OCT-C8, and OCTDL datasets. For effective extraction of the significant features, the collected data is resized and normalized. Augmentation techniques are used to improve the training and learning process. For model development, we applied transfer learning and fine-tuned several state-of-the-art deep learning models, ResNet50, InceptionV3, DenseNet121, VGG16, and EfficientNetV2S. Two models that performed best, VGG16 and EfficientNetV2S, were selected as the model backbones with integration of a squeeze and excitation block. The results showed that the OCTModNet achieved an outstanding performance with 99% test accuracy, 99% precision, 99% recall, 99% F1-score, and an area under the curve (AUC) as 99% across the unseen data. These results point out the robustness and reliability of the proposed model to classify OCT images and have the potential to enhance clinical decision-making and assist ophthalmologists in the early detection of diseases.
Volume: 43
Issue: 2
Page: 495-506
Publish at: 2026-08-01

Low-power high-speed FinFET DRAM array using sleep transistors

10.11591/ijeecs.v43.i2.pp413-424
N Praveena , N Shylashree
Dynamic random-access memory (DRAM) is a fundamental memory technology widely employed in modern digital systems because of its high storage density and simple cell structure. However, conventional DRAM cells suffer from considerable power dissipation and propagation delay, which limit their suitability for high-speed and low-power applications. This paper presents three novel FinFET-based DRAM architectures incorporating sleep transistor techniques to reduce power consumption while improving operating speed. The proposed designs include a 2T-DRAM with sleep transistors and two configurations of 3T-DRAM with sleep transistors. The FinFET technology enhances switching performance and reduces propagation delay, whereas the sleep transistor technique effectively suppresses leakage, dynamic, and short-circuit power during memory operations. The proposed DRAM cells are designed and evaluated using the cadence virtuoso analog design environment. Simulation results demonstrate significant improvements over conventional DRAM architectures. The proposed 2T-DRAM achieves a 66% reduction in write delay, while the proposed 3T-DRAM achieves up to a 98% reduction in read delay. Furthermore, write power consumption is reduced by 56.8%, 63.02%, and 99.6% for the 2T, 3T-B, and 3T-C configurations, respectively. During read operations, power consumption is reduced by 99.8%, 99.5%, and 99.8%, respectively. These results demonstrate that the proposed FinFET DRAM architectures provide an effective solution for high-speed, low-power embedded memory applications.
Volume: 43
Issue: 2
Page: 413-424
Publish at: 2026-08-01

A deep learning-based system for coral reef image segmentation using YOLOv8 with EfficientNet-B0 in Indonesian waters

https://ijeecs.iaescore.com/index.php/IJEECS/article/view/44980
Raden Sutiadi , Sparisoma Viridi , Giyanto Giyanto , Andarta F. Khoir , Rizkie S. Utama , Elsa D. Aulia , Tri A. Hadi , Ludi Parwadani Aji
Manual coral point count with excel extension (CPCe) analysis requires approximately 4–6 hours to process one 50-image station, limiting the scale of coral reef monitoring. This study presents an artificial intelligence-based workflow using YOLOv8 with an EfficientNet-B0 backbone to automate benthic cover estimation. A total of 8,147 underwater images from 151 transects across 39 Indonesian coral reef stations were annotated into eleven benthic categories for model training, while evaluation was conducted using the 2021 Derawan Islands dataset. During validation, YOLOv8 achieved an average mAP@0.5 of 0.58, recall of 0.79, and an average absolute percentage cover error of 9.8% compared with CPCe. The model processed each image in 3.13 seconds, equivalent to 156.47 seconds per 50-image station, representing a 92–138× speedup over manual CPCe analysis. These results show that the proposed workflow can support scalable and near-real-time coral reef monitoring across Indonesia.
Volume: 43
Issue: 2
Page: 628-639
Publish at: 2026-08-01

PID-based performance optimization of ultra-wideband microstrip patch antennas for indoor positioning systems

10.11591/ijeecs.v43.i2.pp450-459
Fredelino A. Galleto Jr. , Aaron Don M. Africa , Bettina Gaille H. Dayrit , Gia Kyla S. Guevarra , Chrismon Elijah Q. Mansilungan , Michael Angelo M. Obciana , Mariah Venice A. Rodriguez , Keane Dwight A. Sulit
Ultra-wideband (UWB) technology has become a key enabler for high accuracy indoor positioning systems (IPSs), where antenna performance directly influences localization accuracy, signal quality, and communication reliability. However, designing compact UWB microstrip patch antennas with wide bandwidth, low reflection loss, and stable radiation characteristics remains a significant challenge. This paper presents a PID-based performance optimization approach for UWB microstrip patch antennas to improve antenna characteristics for indoor positioning applications. The proposed methodology integrates MATLAB-based electromagnetic simulation with parameter optimization to refine antenna geometry while incorporating optimization concepts inspired by rough set theory and fuzzy logic to support efficient design parameter selection. Using MATLAB and the Parallel Computing Toolbox, the proposed approach significantly reduces computational complexity while accelerating the optimization process. Experimental results demonstrate substantial improvements in reflection coefficient (S11), voltage standing wave ratio (VSWR), radiation pattern, and antenna directivity, particularly around the target operating frequency of 10 GHz. Among the evaluated configurations, the optimized rectangular microstrip patch antenna consistently outperformed the triangular design in terms of impedance matching and radiation performance. The proposed optimization framework provides an effective and computationally efficient solution for enhancing UWB antenna performance, making it well suited for high-precision indoor positioning and next-generation wireless communication systems.
Volume: 43
Issue: 2
Page: 450-459
Publish at: 2026-08-01

Extended-state observer control with online payload identification for HRI in series elastic actuators

10.12928/telkomnika.v24i4.27787
Edwin; Fundación Universitaria Los Libertadores Villarreal-López , Horacio; Universidad de San Buenaventura Bogotá Coral-Enriquez , Luini L.; Universidad Distrital Francisco José de Caldas Hurtado-Cortés
Safe human–robot interaction requires actuators that combine compliance with accurate force inference. Series elastic actuators (SEAs) are particularly suitable for this purpose; however, separating payload-induced torques from voluntary human interaction forces without dedicated sensors remains a critical challenge. This paper proposes a unified observer-based control framework for SEAs that integrates three key components: a linear parameter-varying extended state ob server (LPV-ESO), an online algebraic payload estimator, and a disturbance driven motion intention identification (MII) mechanism. The payload estimator continuously updates the observer gains to adapt to load variations, while the LPV-ESO decouples payload dynamics from human interaction forces. The dis turbance estimate is then processed through the MII block to infer operator intent in real time without additional sensing hardware. Numerical simulations under nominal conditions and ±10% parametric uncertainty confirm bounded trajec tory tracking error, rapid convergence of payload estimation, and effective sepa ration of payload-induced torques from voluntary interaction forces. The results demonstrate that the proposed framework achieves sensorless force decompo sition and reliable motion intention inference, offering a practical solution for SEA-based collaborative robots in rehabilitation, assistance, and industrial ap plications.
Volume: 24
Issue: 4
Page: 1409-1418
Publish at: 2026-08-01
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