International Journal of Reconfigurable and Embedded Systems (IJRES)
The International Journal of Reconfigurable and Embedded Systems (IJRES), p-ISSN 2089-4864, e-ISSN 2722-2608, provides a global forum for academics, industry professionals, educators, and policymakers to disseminate innovative research in reconfigurable systems, embedded systems, very large-scale integration (VLSI) design, and the Internet of Things (IoT). As computing shifts from personal devices to embedded intelligence, IJRES showcases frontier developments such as FPGA-based reconfigurable computing, edge AI, low-power architectures, chiplet and 3D IC integration, IoT security, and cyber-physical systems. The journal covers both theoretical advances and practical applications, ranging from algorithms, architectures, and CAD tools to case studies in robotics, automotive, healthcare, smart cities, and intelligent power systems. IJRES particularly welcomes contributions addressing emerging challenges in energy efficiency, scalability, reliability, interoperability, and sustainability. Topics span advanced architectures such as SoC, MPSoC, and NoC, alongside hardware/software co-design, real-time operating systems, and machine learning integration for embedded platforms. Published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU), IJRES is an open access, peer-reviewed journal distributed under the Creative Commons Attribution-ShareAlike 4.0 International License. The IJRES has been indexed in Scopus, with all issues published since 2019 included in the database. The journal is currently ranked with a Source Normalized Impact per Paper (SNIP) of 0.393 and a SCImago Journal Rank (SJR) of 0.202. IJRES is committed to advancing frontier technologies and strengthening the global research community in reconfigurable and embedded systems.
Institute of Advanced Engineering and Science
COPYRIGHT AND LICENSING AGREEMENT This is to certify that the submitted manuscript is an original work, has not been published previously, and is not being considered for publication elsewhere. Upon acceptance, the article will be published by Institute of Advanced Engineering and Science (IAES) under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). The authors retain the copyright to the work. We declare that: This manuscript has not been published previously in the same or substantially similar form. It is not currently under review or consideration for publication by any other journal. All necessary permissions have been obtained for the use of any third-party material included in this manuscript. We agree that upon publication, the article will be distributed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). License Grant We, the authors, retain all copyrights to the submitted article, and grant to IAES the non-exclusive right to reproduce, publish, translate, and distribute the article in any form or medium, including print and electronic formats. The article may be used, shared, or adapted by others, provided that: Proper attribution is given to the original author(s) and source, A link to the license is included, Any changes are indicated, and Any derivative works are licensed under the same terms (ShareAlike). Full license details: https://creativecommons.org/licenses/by-sa/4.0 Author Responsibility The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who has obtained the consent of the co-author(s), if any. After submission of this agreement signed by the corresponding author, changes in authorship or order of authors will not be accepted. Retained Rights Authors retain all proprietary rights in any process, method, or material described in the work. Authors may distribute or reuse the work for personal or institutional use, or include it in other works, with appropriate credit and under the same license. Authors may deposit the published version of the article in institutional or public repositories.
Predicting student academic outcomes from e-learning interaction data using hybrid machine learning models
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.
Copyright (c) 2026 Sajithunisa Hussain, Jayachandran Jeyachidra http://creativecommons.org/licenses/by-sa/4.0
High-performance approximate MAC multiplier using majority logic compressors for CNNs
This research presents an optimized multiple accumulate (MAC) unit multiplier design for efficient convolutional neural network (CNN) operations. This design mainly focuses on making the multiplier systems smaller by using approximate majority compressor methods instead of the usual and traditional approximate methods. The traditional approximate multiplier compressor techniques are leads to increases in logic size, critical path delay, and power consumption; however, the proposed research mitigates these problems and solves them with a novelty-based approach in the Dadda multiplier technique. The novelty of this approach is to reduce the number of stages in the multiplier design using 4:2, 5:2, and 7:2 compressors. This compressor is designed with an approximate method using majority logic; compared to this traditional method, the proposed majority approximate compressor method processed less error differences in multiplication output. The proposed approaches resulted in significant reductions in area, power, and delay relative to traditional multipliers. This research compared seven unique comparisons of MAC-based multiplier architecture, and it will have been developed in Verilog hardware description language (HDL) and synthesized on the Xilinx Vertex-5 FPGA, providing reductions of 58.4% in lookup table (LUT) and 76.2% in occupied slices, and proving less power consumption. This design is a highly suitable approach for real-time CNN and digital signal processing (DSP) applications.
Copyright (c) 2026 Selvarasan Radhakrishnan, Sudhagar Govindhaswamy, Rasadurai Kumaravel http://creativecommons.org/licenses/by-sa/4.0
Intelligent deep learning models for fault diagnosis in sixth generation industrial internet of things environments
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.
Copyright (c) 2026 Hareesha Dandamudi, Chenchu Punnarao Bandi, Simhadri Mallikarjuna Rao, Palacharla SVS Sridhar, Mythili Murugan, Srikanth Kilaru, Rama Krishna Paladugu http://creativecommons.org/licenses/by-sa/4.0
Optimal lift movement based on rest prediction
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.
Copyright (c) 2026 Satish B. Ashwath Narayan, Deekshitha Arasa, Rachana M. Hullamani, Ganesha Ganiga Channabasappa, Rajath Gujjar Raviprakash http://creativecommons.org/licenses/by-sa/4.0
AI-driven co-optimization of ONOFIC circuits and multiband antennas for low-power VLSI
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.
Copyright (c) 2026 Ramavathu Ramesh Naik, Donapati Ramakrishna Reddy, Krishnanaik Vankdoth http://creativecommons.org/licenses/by-sa/4.0
A review of field-programmable gate array-based biomedical signal processing for public health applications
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.
Copyright (c) 2026 Tole Sutikno, Aiman Zakwan Jidin, Lina Handayani http://creativecommons.org/licenses/by-sa/4.0
Configurable embedded solution for multi-mode motor control
Precise robotic systems often require multiple motor types, which increases hardware complexity, cost, and synchronization effort. This study presents an open-source multi-mode motor control platform based on four half-bridge power stages, enabling direct current (DC), brushless direct current (BLDC), and step per motor control on a single hardware architecture. Unlike existing software based multi-mode approaches, the proposed system introduces automatic motor type identification and safe connection verification at the hardware level, re quiring only a microcontroller and a integrated power stage. This represents a key novelty of the platform. Experimental validation was performed using three different motor types. The system achieved correct motor classification over re peated identification tests, with no false detections. Position control experiments confirmed stable operation across DC, BLDC, and stepper motor. The results demonstrate that the proposed platform significantly reduces system complexity while providing reliable multi-motor operation in a compact and low-cost structure.
Copyright (c) 2026 Ufuk Guner http://creativecommons.org/licenses/by-sa/4.0
A low-cost edge-AI smart floor mat using multi-point force sensors for real-time fall detection and elderly safety
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.
Copyright (c) 2026 Sahapong Somwong, Chatree Homkhiew, Thanwit Naemsai, Athirot Mano http://creativecommons.org/licenses/by-sa/4.0
Using OOA-based proportional-integral-derivative controller to enhance the charging and discharging of battery voltage
Today, hybrid energy harvesters are critical in promoting technological advancement by generating sustainable energy and addressing the financial and environmental concerns around batteries. Because of their unexpected input behavior, hybrid energy harvesters present a challenge in producing the necessary stable energy. Thus, this study provides a power conditioning circuit with an optimal controller. Three proportional-integral-derivative (PID) controllers control the charging and discharging of the battery's bidirectional converter. To improve system performance actively and optimally, optimization algorithms are implemented for the optimization of the PID parameters. Osprey optimization algorithm (OOA)-based PID is used, and its performance is compared with five optimization algorithims (Chimp optimization algorithm (ChOA)-based PID, hony badger algorithm (HBA)-based PID, Zebra optimization algorithm (ZOA)-based PID, and cheetah optimization algorithm (COA)-based PID. The comparison between algorithms was done based on the minimum fitness function value, which shows that the OOA is the best one. All results are implemented in MATLAB/Simulink using the 2021a version as follows: (ChOA 3.061%, CO 4.737%, HBA 3.03%, ZOA 3.058%, and OOA 1.52%).
Copyright (c) 2026 Hassanin Falah Abdul Hassan, Issa Ahmed Abed http://creativecommons.org/licenses/by-sa/4.0
Crow search algorithm for efficient IP placement in 2D and 3D network-on-chip architectures
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.
Copyright (c) 2026 Maamar Bougherara, Amina Guidoum, Rafik Amara http://creativecommons.org/licenses/by-sa/4.0
An IoT-enabled vision-aid for the blind integrating ultrasonic obstacle detection and GPS-based location tracking
Visual impairment significantly affects independent mobility and personal safety, creating a need for affordable and reliable assistive navigation technologies. This paper presents the design and implementation of a low-cost wearable Vision-Aid system to support visually impaired individuals during outdoor navigation. The primary objective of the study is to enhance obstacle awareness, location tracking, and emergency communication using accessible embedded technologies. The proposed system integrates ultrasonic sensors for real-time obstacle detection, an Arduino microcontroller for data processing, a global positioning system (GPS) module for location tracking, and a global system for mobile communication (GSM) module for emergency alert transmission. Audio feedback is provided through a voice module to guide the user safely. Experimental evaluations were conducted under various environmental conditions to assess obstacle detection accuracy, response time, and location reliability. The results demonstrate accurate obstacle detection, timely audio alerts, and reliable real-time location sharing with caregivers. The proposed system improves user confidence, mobility, and safety while maintaining low implementation cost. This work highlights the potential of embedded and internet of things (IoT)–based assistive devices to enhance autonomy for visually impaired individuals and provides a foundation for future integration of artificial intelligence (AI)-based object recognition.
Copyright (c) 2026 Varuna Kumara, Akshatha Naik, Ashwini, Navilgone Krishna Vaishnavi, Ruchitha Kamath Subhashchandra, Trapthi http://creativecommons.org/licenses/by-sa/4.0
A scalable hybrid deep learning framework for mining actionable knowledge from large-scale and uncertain Twitter data
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.
Copyright (c) 2026 Abhilash, Syed Siraj Ahmed http://creativecommons.org/licenses/by-sa/4.0
Matter protocol-enabled device onboarding for cross-platform internet of things systems
The Matter protocol, created by the connectivity standards alliance (CSA), comes in with a single standard to make sure these devices can connect and be controlled across platforms like Google Home, Apple HomeKit, Amazon Alexa, and Samsung SmartThings. The rapid expansion of the internet of things (IoT) is driving the urgent need for secure and efficient onboarding processes for a wide range of connected devices. It necessitates a robust framework to seamlessly integrate new additions into existing systems while upholding security standards. This initiative focuses on implementing the Matter protocol on ESP32 devices, employing a Raspberry Pi hub as the central communication point to facilitate smooth device-to-hub interactions. This work presents the onboarding devices for interconnected IoT systems using the Matter protocol. The Matter device is configured and tested within the Amazon ecosystem using an Alexa Echo Dot, as well as with the smart home assistant ecosystem along with a smartphone application. By configuring the Raspberry Pi hub as a designated Matter hub and exploring interactions within the home assistant ecosystem supporting diverse platforms like Apple HomeKit and Google Home, the work enhanced interoperability and broadened the utility of IoT devices within an interconnected network. This initiative forges a foundation for an adaptable and cohesive IoT environment.
Copyright (c) 2026 Geetishree Mishra, Hemavathi, Harish V Mekali http://creativecommons.org/licenses/by-sa/4.0
Machine learning for energy conversion prediction and photovoltaic-on grid protection system using IoT
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.
Copyright (c) 2026 Habib Satria, Muhammad Fadlan Siregar, Indri Dayana, Dadan Ramdan, Hermansyah, Muhammad Irwanto, Syafii http://creativecommons.org/licenses/by-sa/4.0
ESP-NOW based multi-node internet of things system for agricultural solar dryers with hybrid offline-online monitoring
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.
Copyright (c) 2026 Rahmat Siswanto, Putri Dewintari, Sapar http://creativecommons.org/licenses/by-sa/4.0
Graph neural network-based biomedical misinformation detection with semantic consistency analysis
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.
Copyright (c) 2026 Siva Dhievaraj, Agusthiyar Ramu http://creativecommons.org/licenses/by-sa/4.0
A real-time multi-modal deep learning framework for student attentiveness assessment in online learning environments
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.
Copyright (c) 2026 Rajasekaran Mariswamy, P.V. Praveen Sundar http://creativecommons.org/licenses/by-sa/4.0
An efficient hybrid genetic–cuckoo search algorithm for the quadratic assignment problem
Quadratic assignment problem (QAP) is one of the most difficult NPhard combinatorial optimization problems with applications ranging from facility layout design, scheduling and manufacturing systems to software optimization. This paper introduces a hybrid metaheuristic algorithm based on genetic algorithm (GA) and cuckoo search (CS); an improved solution quality and convergence speed is observed for QAP instances where GA itself performs poorly as well. This approach leverages the exploration capabilities of GA with computationally intensive exploitation that is easy for CS, to create a balanced yet robust searching mechanism across complex optimization landscapes. We tested the algorithm on benchmark instances taken from quadratic assignment problem library (QAPLIB) and compared it with many classical heuristics such as standard GA, particle swarm optimization (PSO) method and original CS algorithm. Experimental findings showcase that the presented hybrid GA-CS algorithm outperforms traditional standalone GAs regarding solution quality and computational time with significance by promptly converging toward high-quality solutions, especially for medium- to large-scale test instances. In addition, performance improvements over competing methods are shown as statistically significant using the Wilcoxon signed-rank test. The results show that the proposed hybrid framework is an efficient and accurate optimization technique for solving challenging QAP.
Copyright (c) 2026 Firas Abdullah Attia, Iraq Tareq Abbas http://creativecommons.org/licenses/by-sa/4.0
Design of a flexible modified rectangular dual-band antenna for ISM bands with SAR analysis
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.
Copyright (c) 2026 Mohan Chinnasamy, Uma Mariappan, Charulatha Gopinathan, Ashokkumar Mani, Anita Daniel, Sree Devi Baskaran http://creativecommons.org/licenses/by-sa/4.0
Analysis of a compact wideband DGS-inspired octagonal patch antenna for sub-6 GHz 5G and IoT wireless systems
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.
Copyright (c) 2026 Komalavalli Subramanian, Divya Subramani, Sornalatha Ravindran, Muthu Manickam Anbarasu, Mohan Chinnasamy, Anita Daniel http://creativecommons.org/licenses/by-sa/4.0
Fire prediction monitoring system based on a spatial interpolation algorithm
Fires endanger not only the environment's wealth but also the entire fauna and flora, drastically disrupting a region's biodiversity and ecology. This article monitors the spread of a fire in a specific area, determines its approximate direction, attempts to extinguish it in an organized manner, and identifies the safest solutions. A new system has been developed, consisting of two parts: embedded and reconfigurable. This system comprises four accurate flame sensors, a buzzer, an Arduino, and a field-programmable gate array (FPGA) DEV board. The Arduino and FPGA collect data from these sensors and send it to MATLAB, which processes and displays the results. This paper also uses a two-stage prediction based on a spatial interpolation algorithm. The results showed that the speed and direction of fire spread could be predicted quickly and accurately using a spatial interpolation algorithm, achieving the lowest predictive error (mean absolute error (MAE) ≈0.33 and root mean square error (RMSE) ≈0.48) at approximately epoch 70. Moreover, the proposed method achieved a 92% success rate in detecting flames and fire flashes, indicating that the sensors respond to fires within under 1 minute of occurrence.
Copyright (c) 2026 Karrar Shakir Muttair, Ali Zuhair Ghazi Zahid, Rana Jawad Azeez, Oras Ahmed Shareef Al-Ani, Ahmed Mahmood Farhan, Raed Hameed Chyad Alfilh, Raed Hasan Hussain, Muthana H. Al-Saidi, Abbas Ali Diwan, Zeshan Ahmed, Hazeem Baqir Taher http://creativecommons.org/licenses/by-sa/4.0
Binary hybrid pathfinder algorithm for efficient feature selection in resource-constrained embedded systems
Feature selection is critical for embedded machine learning systems where computational resources and memory are severely constrained. This paper presents the binary quadratically interpolated hybrid pathfinder algorithm (BQIHPFA), a novel metaheuristic optimization method designed for efficient feature subset selection in resource-limited classification tasks. BQIHPFA adapts the continuous QIHPFA to binary search spaces through sigmoid transfer functions and employs a hybrid two-group enhancement strategy combining pathfinder dynamics with salp swarm algorithm-inspired exploration. We evaluate BQIHPFA against three established binary optimization algorithms (binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization (BWO)) on three benchmark datasets with varying dimensionalities: Língua Brasileira de Sinais (Brazilian Sign Language) movement (90 features), Parkinson's disease detection (22 features), and Sonar Rock vs. Mine (60 features). Experimental results demonstrate that BQIHPFA achieves competitive classification accuracy (average 83.57%) with substantial feature reduction (average 64.1%) while executing 5.2 times faster than complex baselines and consuming minimal memory (peak: 45-58 MB). Ablation experiments demonstrate that every algorithmic part makes a 8-24% contribution to the total performance. BQIHPFA offers an easy-to-use, non-specific feature selection method to automated resource-constrained embedded classification systems, applicable to be deployed to low-power computing environments, and internet of things (IoT) edge systems.
Copyright (c) 2026 Rahul Mirajkar, Premanand Ghadekar, Vijay Dasharath Chougule, Renuka Bhandari, Hridaynath Khandagale, Mahavir A. Devmane, Mangesh Hajare, Kuldeep B. Vayadande http://creativecommons.org/licenses/by-sa/4.0
Meta-learning contrastive fusion intelligence for domain-adaptive content categorization using adaptive DNN
The rapid growth of heterogeneous digital text sources, including social media, news streams, and scientific literature, poses significant challenges to traditional content categorization models due to domain shift, vocabulary drift, and semantic inconsistencies. Existing deep learning approaches often rely on domain-specific patterns and static representations, resulting in degraded performance when applied to unseen or cross-domain data. Moreover, these methods lack scalability in real-world scenarios characterized by domain drift and limited labeled data. To address these challenges, this study proposes a meta-learning contrastive fusion intelligence (MCFI) framework for domain-adaptive content categorization. The framework integrates domain context-aware normalization (DCAN) for robust preprocessing, binary particle swarm optimization (BPSO) for selecting domain-invariant features, and a hybrid architecture combining contrastive learning-enhanced bidirectional encoder representations from transformers (CL-BERT) with capsule network-enhanced transformer (CapsTrans). A meta-learning strategy is employed to learn domain-invariant representations and enable rapid adaptation to new domains, while contrastive learning enhances inter-domain separability. A domain-adaptive decision layer further refines feature contributions dynamically. Experimental results on multiple benchmark datasets demonstrate that the proposed MCFI framework consistently outperforms state-of-the-art methods in terms of accuracy, F1-score, and generalization, providing a scalable and effective solution for cross-domain text classification.
Copyright (c) 2026 Janani Sivapriya Venkatakrishnan, Saravana Moorthy Raja Moorthy, Angel Shanmugam http://creativecommons.org/licenses/by-sa/4.0
Developing a water driving cycle tracking device based on GPS and GSM for advancing water vehicle performance
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.
Copyright (c) 2026 Nur Farazatul Azna Mohd Fadzil, Siti Norbakyah Jabar, Zulkifli Mohd Yusop, Nurru Anida Ibrahim, Arunkumar Subramaniam, Salisa Abdul Rahman http://creativecommons.org/licenses/by-sa/4.0
Revolutionizing night-time object detection in autonomous vehicles with SCL-YOLOv11 and ROA optimization
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.
Copyright (c) 2026 Kondapalli Sri Vijaya, Gokula Krishnan Vasudevan, Pinagadi Venkateswara Rao, Therasa Michael, Balasubramanian Lalithambigai, Boddula Prathusha Laxmi http://creativecommons.org/licenses/by-sa/4.0
Development of internet of things-based exoskeleton for monitoring elbow rehabilitation therapy
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.
Copyright (c) 2026 Geevanthran A/L Vegurgama, Mohd Razali Mohamad Sapiee, Khalil Azha Mohd Annuar http://creativecommons.org/licenses/by-sa/4.0