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31,377 Article Results

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

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

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

Analysis of a compact wideband DGS-inspired octagonal patch antenna for sub-6 GHz 5G and IoT wireless systems

10.11591/ijres.v15.i2.pp479-489
Komalavalli Subramanian , Divya Subramani , Sornalatha Ravindran , Muthu Manickam Anbarasu , Mohan Chinnasamy , Anita Daniel
The proposed compact wideband antenna is developed to meet the increasing demand for efficient and miniaturized radiators in sub-6 GHz fifth generation (5G) and internet of things (IoT) wireless systems. The design features an octagonal radiating patch integrated with modified H-shaped slots to enhance the current path and impedance matching, while a graded defected ground structure (DGS) is introduced to improve bandwidth (BW) and suppress unwanted surface wave effects. Fabricated on an FR4 substrate and energised by a simple stripline feed, the antenna maintains a compact size of 18×15 mm² without compromising performance. It achieves a wide fractional BW of 42.81% spanning 3.1–5.8 GHz, with a resonance centered at 4.6 GHz and obtained reflection coefficient of −36 dB, indicating excellent impedance matching. Additionally, the suggested antenna provides the maximum gain of 3.14 dB and an overall radiation efficiency of 80.5%, demonstrating stable radiation characteristics while making it ideal for small, low-profile 5G and IoT communication devices.
Volume: 15
Issue: 2
Page: 479-489
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

A real-time multi-modal deep learning framework for student attentiveness assessment in online learning environments

10.11591/ijres.v15.i2.pp450-460
Rajasekaran Mariswamy , P.V. Praveen Sundar
The rapid growth of online learning platforms has increased the need for intelligent systems capable of monitoring student attentiveness in real time to improve learning effectiveness and adaptive instruction. This paper proposes a multi-modal deep learning framework for attentiveness assessment by integrating visual, behavioral, and temporal information extracted from online classroom interactions. The proposed system consists of four major components, namely data acquisition, preprocessing and normalization, deep feature extraction with temporal learning, and attentiveness evaluation with analytics generation. Visual and spatial characteristics are learned using a convolutional neural network (CNN), while temporal behavioral patterns are captured through a long short-term memory (LSTM) network to model sequential engagement dynamics. The framework is designed to operate in both real-time and offline modes, enabling live monitoring during virtual classes as well as post-session analysis of recorded lectures. The computational pipeline is optimized through fixed-point processing, parallel convolution execution, and latency-aware temporal modeling, making it suitable for field programmable gate array (FPGA)-based and embedded implementations under constrained computational resources. Experimental evaluation conducted on an in-house dataset demonstrates that the proposed framework achieves 92.9% classification accuracy and a 91.9% F1-score, while maintaining strong generalization capability on cross-dataset benchmarks. Furthermore, latency analysis shows an average processing time of 31.6 ms per frame, enabling near real-time inference at approximately 30 frames per second.
Volume: 15
Issue: 2
Page: 450-460
Publish at: 2026-07-01

Developing a water driving cycle tracking device based on GPS and GSM for advancing water vehicle performance

10.11591/ijres.v15.i2.pp524-533
Nur Farazatul Azna Mohd Fadzil , Siti Norbakyah Jabar , Zulkifli Mohd Yusop , Nurru Anida Ibrahim , Arunkumar Subramaniam , Salisa Abdul Rahman
Driving cycles are speed-time profiles used to evaluate vehicle performance, fuel consumption, and exhaust emissions. However, real-world driving-cycle data for water vehicles are still limited, restricting accurate assessment of their energy efficiency and environmental impact. This study developed a low-cost water driving cycle (WDC) tracking device using an Arduino UNO integrated with global positioning system (GPS), global system for mobile communications (GSM), secure digital (SD) card storage, and an liquid crystal display (LCD) display. The device records speed, time, longitude, and latitude during water-vehicle operation. Prototype validation was performed by comparing the recorded speed with a standard GPS speedometer, while field testing was conducted along the Payang Water Taxi (PWT) route in Kuala Terengganu. The collected data were processed to construct a WDC and analysed using the advanced vehicle simulator (ADVISOR). Validation results showed percentage errors of 0.30% and 0.16%, indicating device accuracy within 5%. The ADVISOR analysis estimated fuel consumption of 24.1 L/100 km and emissions of 4.154 g/km HC, 2.851 g/km CO, and 0.08 g/km NOx. The proposed device provides a practical data-acquisition tool for water-vehicle performance evaluation.
Volume: 15
Issue: 2
Page: 524-533
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

A scalable hybrid deep learning framework for mining actionable knowledge from large-scale and uncertain Twitter data

10.11591/ijres.v15.i2.pp396-405
Abhilash Abhilash , Syed Siraj Ahmed
Existing deep learning approaches often exhibit limitations in contextual comprehension, high computational overhead, and restricted generalization when processing large-scale, tweet-level, and semantically ambiguous text. Moreover, deploying such computationally intensive models in real-time internet of things (IoT)-enabled monitoring systems and embedded platforms introduces additional constraints related to latency, memory footprint, and energy efficiency. To address these challenges, this work proposes a scalable hybrid deep learning framework (SHDLF). The proposed framework effectively captures semantic, syntactic, and temporal dependencies in both short and long social media texts through a novel integration of transformer-based representations and attention-driven feature fusion mechanisms. The architecture is designed with a modular and parallelizable structure to facilitate hardware-aware optimization and potential deployment on embedded and reconfigurable computing platforms, enabling efficient edge-level processing of high-velocity Twitter streams. Extensive experimental evaluations conducted on a large benchmark Twitter dataset demonstrate that SHDLF consistently outperforms state-of-the-art models, including convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and baseline bidirectional encoder representations from transformers (BERT)-based architectures, in terms of accuracy, F1-score, and robustness under noisy conditions. The results confirm that SHDLF offers a robust, scalable, and computationally efficient solution for extracting reliable sentiment insights from noisy and dynamically evolving social media data.
Volume: 15
Issue: 2
Page: 396-405
Publish at: 2026-07-01

Optimal lift movement based on rest prediction

10.11591/ijres.v15.i2.pp291-305
Satish B. Ashwath Narayan , Deekshitha Arasa , Rachana M. Hullamani , Ganesha Ganiga Channabasappa , Rajath Gujjar Raviprakash
Existing elevator control systems in office buildings primarily rely on reactive scheduling strategies that respond only after passenger requests occur, leading to increased waiting times during peak traffic periods. Although reinforcement learning (RL) and deep learning approaches have been explored for intelligent elevator control, many existing methods require high computational complexity and large training datasets, limiting their suitability for embedded elevator controllers and practical smart-building deployment. To address this gap, this paper proposes a lightweight predictive elevator control framework based on the eXtreme gradient boosting (XGBoost) machine learning algorithm for rest-floor prediction. The proposed method uses historical traffic patterns and temporal features to predict future demand floors and proactively reposition idle elevators before passenger requests occur. A comprehensive simulation was conducted for multiple office-building configurations with varying numbers of floors and elevators over one year of operation using realistic traffic patterns. The proposed predictive strategy was compared with a conventional reactive control approach. Results show that the proposed framework reduces cumulative passenger waiting time by approximately 11%–22%, with larger improvements observed in high-rise and high-traffic scenarios, while maintaining comparable energy consumption. The study demonstrates that lightweight supervised machine learning can provide an effective and computationally efficient solution for predictive elevator control in embedded smart-building systems.
Volume: 15
Issue: 2
Page: 291-305
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

Crow search algorithm for efficient IP placement in 2D and 3D network-on-chip architectures

10.11591/ijres.v15.i2.pp373-385
Maamar Bougherara , Amina Guidoum , Rafik Amara
The communication in system-on-chip (SoC) has evolved to meet the increas-ingly complex requirements of modern applications. To address connectivity challenges, the network-on-chip (NoC) has emerged as an efficient solution. While traditional NoCs are primarily based on 2D architectures, the inherent limitations of 2D designs have driven the adoption of 3D architectures, which offer enhanced space utilization and performance optimization. A key step in the design of NoC systems is the placement of cores, also known as the map-ping phase, in which application tasks are assigned to the architecture’s process-ing elements. This phase is considered an nondeterministic polynomial (NP)-complete problem due to its combinatorial complexity. Optimizing this phase is crucial, as it directly impacts the overall performance of the NoC. Various opti-mization algorithms have been employed to maximize the efficiency of 2D and 3D NoCs. In this paper, we adopt the crow search algorithm to find the places both 2D and 3D NoCs with minimal comunication. The goal is to evaluate its performance compared to other optimization algorithms in this crucial step.
Volume: 15
Issue: 2
Page: 373-385
Publish at: 2026-07-01

Design of a flexible modified rectangular dual-band antenna for ISM bands with SAR analysis

10.11591/ijres.v15.i2.pp468-478
Mohan Chinnasamy , Uma Mariappan , Charulatha Gopinathan , Ashokkumar Mani , Anita Daniel , Sree Devi Baskaran
Regarding dual industrial, scientific, and medical (ISM) band utilization, a planar, high-gain, dual-band modified antenna has been implemented application. This modified antenna features a rectangular patch with combined slots. This antenna has a profile of approximately 0.25 λ0×0.18 λ0. The combination of slots and modified rectangular patch allows for multiple-band performance. The designed antenna operates in two bands: 5.81 GHz and 2.42 GHz wireless body area network (WBAN). The antenna offered maximum radiation efficiencies of 76.4% and 82.8% in the two operating bands, with peak gains of 3.43 dB and 3.81 dB. The suggested antenna has reflection coefficients of -28.3 dB at 2.43 GHz and -23.9 dB at 5.81 GHz, respectively. The antenna's safety features were additionally evaluated employing a threelayer human body phantom initiated of fat, muscle, and skin tissues. The specific absorption rate (SAR) of the proposed antenna was evaluated using a three-layer human tissue model representing skin, fat, and muscle. The calculated SAR values were analysed according to the IEEE C95.1-1999 and IEEE C95.1-2005 safety guidelines, and the results confirm that the antenna operates within the permissible exposure limits. The measured results closely match simulations, demonstrating its reliability. Owing to its compact size, improved efficiency, strong impedance performance, and validated safety compliance, the proposed antenna is much impressed for effective ISM band communications.
Volume: 15
Issue: 2
Page: 468-478
Publish at: 2026-07-01

Machine learning for energy conversion prediction and photovoltaic-on grid protection system using IoT

10.11591/ijres.v15.i2.pp416-425
Habib Satria , Muhammad Fadlan Siregar , Indri Dayana , Dadan Ramdan , Hermansyah Hermansyah , Muhammad Irwanto , Syafii Syafii
The advancement of photovoltaic (PV) systems in tropical regions faces significant efficiency challenges due to fluctuating panel surface temperatures. This study addresses these issues by implementing machine learning (ML) models, specifically k-nearest neighbors (KNN) and extreme gradient boosting (XGBoost), to classify and monitor panel temperatures. To enhance system resilience, an internet of things (IoT) based on-grid protection system was developed, featuring a dual-relay redundancy mechanism that triggers an automated trip when the current exceeds 1.30 A. This integration ensures the protection of both the PV infrastructure and household electrical loads. Experimental results demonstrate that the KNN model exhibits superior reliability with a testing accuracy of 93% and a baseline performance of 96.67%, successfully identifying both normal (25 °C to 35 °C) and high-temperature (36 °C to 48 °C) states. In contrast, while the XGBoost model reached a maximum validation accuracy of 94.44% during training, it only achieved a testing accuracy of 84% and showed significant limitations in detecting normal temperature patterns. Beyond classification, the IoT framework proved highly precise in real-time energy monitoring, with sensor error rates below 2%. This research offers a strategic solution for optimizing energy conversion and system reliability, providing a robust framework for sustainable clean energy management in tropical climates.
Volume: 15
Issue: 2
Page: 416-425
Publish at: 2026-07-01

A low-cost edge-AI smart floor mat using multi-point force sensors for real-time fall detection and elderly safety

10.11591/ijres.v15.i2.pp350-363
Sahapong Somwong , Chatree Homkhiew , Thanwit Naemsai , Athirot Mano
This study describes the creation of a smart floor mat (SFM) that integrates edge-based artificial intelligence (AI) processing on an embedded system to identify movements such as standing, sitting, and falling to improve the safety of the elderly. The design incorporates nine force sensitive resistor (FSR) sensors, an ESP32 microcontroller, and a multi-class support vector machine (SVM) algorithm to analyze the sensor data in real time or long-time immobility detection, the device will automatically switch on and activate alarms to alert tele-caregivers and helpers via Telegram Bot notifications, indicator lights, and speakers for immediate responses. Experimental results demonstrated that the classification accuracy was 93.33% in model evaluation and 88.33% on the embedded platform, respectively, with an F1-score of 0.82-0.83 and an utterly perfect fall event detection (100%). Data are automatically logged in Google Sheets through Wi-Fi for trend analysis and health monitoring. The proposed SFM is low-cost, foldable, portable, and capable of supporting real-time monitoring and proactive safety management in the elderly. This innovation contributes to the development of smart home healthcare systems and is in line with the goal of achieving a better quality of life.
Volume: 15
Issue: 2
Page: 350-363
Publish at: 2026-07-01
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