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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

High-performance approximate MAC multiplier using majority logic compressors for CNNs

10.11591/ijres.v15.i2.pp269-280
Selvarasan Radhakrishnan , Sudhagar Govindhaswamy , Rasadurai Kumaravel
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.
Volume: 15
Issue: 2
Page: 269-280
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

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

Fire prediction monitoring system based on a spatial interpolation algorithm

10.11591/ijres.v15.i2.pp490-503
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
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.
Volume: 15
Issue: 2
Page: 490-503
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

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

Meta-learning contrastive fusion intelligence for domain-adaptive content categorization using adaptive DNN

10.11591/ijres.v15.i2.pp514-523
Janani Sivapriya Venkatakrishnan , Saravana Moorthy Raja Moorthy , Angel Shanmugam
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.
Volume: 15
Issue: 2
Page: 514-523
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

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

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

An efficient hybrid genetic–cuckoo search algorithm for the quadratic assignment problem

10.11591/ijres.v15.i2.pp461-467
Firas Abdullah Attia , Iraq Tareq Abbas
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.
Volume: 15
Issue: 2
Page: 461-467
Publish at: 2026-07-01

Binary hybrid pathfinder algorithm for efficient feature selection in resource-constrained embedded systems

10.11591/ijres.v15.i2.pp504-513
Rahul Mirajkar , Premanand Ghadekar , Vijay Dasharath Chougule , Renuka Bhandari , Hridaynath Khandagale , Mahavir A. Devmane , Mangesh Hajare , Kuldeep B. Vayadande
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.
Volume: 15
Issue: 2
Page: 504-513
Publish at: 2026-07-01

Using OOA-based proportional-integral-derivative controller to enhance the charging and discharging of battery voltage

10.11591/ijres.v15.i2.pp364-372
Hassanin Falah Abdul Hassan , Issa Ahmed Abed
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%).
Volume: 15
Issue: 2
Page: 364-372
Publish at: 2026-07-01

Matter protocol-enabled device onboarding for cross-platform internet of things systems

10.11591/ijres.v15.i2.pp406-415
Geetishree Mishra , Hemavathi Hemavathi , Harish V Mekali
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.
Volume: 15
Issue: 2
Page: 406-415
Publish at: 2026-07-01
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