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31,377 Article Results

Design of a portable IoT robot with azure machine learning for monitoring mine workers’ health

10.11591/ijra.v15i3.pp577-588
Shanthi Natarajan , Vijayaraja Loganathan , Dhanasekar Ravikumar , Diwakar Venkat Nalini , Harish Elangovan , Balaji Arikrishnan
The mining environment exposes workers to physical, environmental, and health risks. The lack of effective real-time health monitoring systems leads to delayed medical responses. Hence, this paper discusses developing a portable Internet of Things (IoT) robot with advanced machine learning and cloud computing to monitor mine workers’ health and send out alerts. The system is equipped with IoT sensors that monitor parameters continuously, such as heart rate, body temperature, blood pressure, and environmental factors (gas concentrations, air quality). Data collected in real-time is transmitted to a cloud-based platform for analysis using advanced machine learning algorithms. MQ-135 detects harmful gases, and DHT11 measures humidity and transmits data to the Arduino UNO. The HC-SR04 sensor measures object distances by emitting ultrasonic waves and detecting their echoes, aiding in obstacle detection. The NEO-6M GPS with GSM SIM900 modules transmit location data and emergency alerts via the GSM network, enabling responses to potential dangers. Simulation via Proteus validates the robot’s transceiver connectivity, mobility, and sensing functions. To enhance monitoring precision, the system adopts XGBoost, which classifies mine conditions, and the training model achieves 96.77% accuracy with high precision and recall. The system with Azure Machine Learning improves detection accuracy, raising temperature, CO, NH₄, and NO₂ precision by 7.25%, 15%, 17%, and 18%, respectively. Thus, the system features an intelligent alert mechanism to notify users of emergencies, enhancing worker safety and minimizing health-related risks in mining operations.
Volume: 15
Issue: 3
Page: 577-588
Publish at: 2026-09-01

PIKER-NET: Multi-class retinal disease classification using Pied Kingfisher optimization-based improved residual network

10.11591/ijra.v15i3.pp678-689
Lissy Devasahayam , Ramya Devi Murugadasan , Anandhi Samuel Vijayalakshmi , Chanthiya Puhalenthi , Ramnath Muthusamy , Ahilan Appathurai , Natarajan Mohana Suganthi
Retinal diseases are vision-threatening conditions, including age-related macular degeneration (ARMD), diabetic retinopathy (DR), and glaucoma, that require early and accurate detection to prevent blindness. However, existing methods often struggle with limited feature representation, high inter-class similarity, intra-class variability, and reduced performance in handling noisy and low-quality retinal images. To address these challenges, a novel PIKER-NET framework is proposed for accurate multi-class retinal disease classification. The input fundus images from the RFMiD dataset are pre-processed using a scalable range adaptive bilateral (SCRAB) filter to enhance image clarity by preserving edges while reducing noise. The Improved Residual Network-Rescaled (ImResNet-RS) integrated with Temporal Attention is then employed to extract deep hierarchical features with enhanced discriminative power. Pied Kingfisher Optimization (PKO) algorithm is utilized for feature selection, effectively reducing redundant information while retaining the most relevant features. Residual Multilayer Perceptron (ResMLP) is used to classify retinal diseases into ARMD, branch retinal vein occlusion (BRVO), diabetic neuropathy (DN), DR, healthy, and myopia (MYA). The PIKER-NET achieves an overall accuracy of 98.14% and F1-score of 97.06%. The PIKER-NET approach improves overall accuracy by 3.24%, 4.24%, 6.23%, and 2.00% compared to EyeDeep-Net, IDL-MRDD, DeepDiabetic, and VisionDeep-AI, respectively. The proposed approach has strong clinical relevance by supporting earlier disease screening, reducing misdiagnosis, and enabling faster diagnosis to assist ophthalmologists in improving patient outcomes.
Volume: 15
Issue: 3
Page: 678-689
Publish at: 2026-09-01

Spatial-channel reconstruction for efficient multiscale attention in robotic object detection

10.11591/ijra.v15i3.pp544-552
Mohammed Maiza , Chahira Cherif , Samira Chouraqui , Abdelmalik Taleb-Ahmed
Real-time object detection is a core capability for autonomous robots, unmanned aerial vehicles (UAVs), and self-driving systems operating in resource-constrained environments. This paper presents spatial-channel enhanced multiscale attention (SCEMA), a novel lightweight attention module designed to enhance robotic perception while minimizing computational overhead for embedded deployment. SCEMA employs a parallel dual-branch architecture that synergistically combines spatial-channel reconstruction with multiscale attention mechanisms. When integrated into the YOLOv8n framework (3.01M baseline parameters), the proposed YOLO-SCEMA model achieves significant performance gains across multiple challenging benchmarks relevant to robotics automation. Experiments on an NVIDIA RTX 4080 GPU demonstrate that on the ExDark dataset, YOLO-SCEMA improves mAP@50 by 7.37% over the baseline (69.07% to 76.44%) while reducing parameters by 36.88% (3.01M to 1.90M) and computational cost by 8.64% (8.1 to 7.4 GFLOPs). Consistent improvements are also observed on VisDrone2019 (+3.24% mAP@50) and FYP (+1.50% mAP@50) datasets. Comparative analysis demonstrates that YOLOSCEMA achieves superior accuracy-efficiency trade-offs, making it particularly suitable for deployment in low-light conditions, dense scenes, and complex structural environments for autonomous navigation, robotic surveillance, and industrial automation applications.
Volume: 15
Issue: 3
Page: 544-552
Publish at: 2026-09-01

Enhancing grid reliability with solar-wind energy systems

10.11591/ijra.v15i3.pp737-744
Swapna Subudhiray , Smrutiranjan Nayak
Grid-connected hybrid photovoltaic (PV) wind systems are widely recognized as a promising solution for mitigating renewable intermittency; however, most existing studies focus either on component-level modeling or basic integration without detailed analysis of power conditioning, control coordination, and grid-side performance. In particular, the functional role of DC–DC converters and energy management strategies in stabilizing hybrid outputs under variable environmental conditions remains insufficiently explored. This paper proposes a structured grid-connected PV wind hybrid system incorporating coordinated DC–DC conversion with maximum power point tracking (MPPT) and centralized control for voltage stabilization and power smoothing. The proposed architecture explicitly defines the operational role of each subsystem, including PV array, wind turbine generator, DC–DC converters, controller, and grid interface to ensure stable power delivery under fluctuating irradiance and wind speed. A MATLAB-based model is developed to evaluate system behavior under standalone and hybrid operating modes. Simulation results demonstrate improved voltage stability and enhanced power continuity in the hybrid configuration compared to standalone PV operation. The findings confirm that coordinated power conditioning significantly improves grid reliability and supports effective renewable energy integration.
Volume: 15
Issue: 3
Page: 737-744
Publish at: 2026-09-01

Precision enhancement in drone position control based on optimized PID: A comparative study

10.11591/ijra.v15i3.pp529-543
Imam Barket Ghiloubi , Latifa Abdou
This paper presents a comparative study of quadrotor trajectory tracking performance using proportional integral derivative (PID) controllers optimized by two nature-inspired metaheuristic algorithms: the flower pollination algorithm (FPA) and particle swarm optimization (PSO). A nonlinear dynamic model of the quadrotor is first established, capturing both translational and rotational motions. A cascaded PID control architecture is then designed, where the outer loop regulates position and the inner loop controls attitude and altitude. The PID gains are tuned offline by minimizing an objective function combining the integral of time-weighted absolute error (ITAE) and an overshoot penalization term. The optimized controllers are evaluated through three reference scenarios: step response, circular trajectory, and lemniscate trajectory. Quantitative performance metrics, including RMSE, MAE, IAE, and ITAE, are used to assess tracking accuracy. Results show that the PID-FPA controller significantly outperforms PID-PSO in terms of overshoot reduction, faster settling time, smoother responses, and improved tracking precision, especially for complex trajectories. A robustness analysis is finally conducted by introducing a realistic synthetic wind disturbance modeled using sinusoidal components and stochastic turbulence. The FPA-tuned PID demonstrates strong robustness, maintaining accurate trajectory tracking under aerodynamic perturbations.
Volume: 15
Issue: 3
Page: 529-543
Publish at: 2026-09-01

Integrating large language models for context-aware decision making in autonomous mobile robots

10.11591/ijra.v15i3.pp607-620
Vishnu Kumar Mishra , Megha Mishra , Talasila Ram Kumar , Yenna Geetha Reddy , Gundla Rajesh , Battula Phijik , Bandla Srinivasa Rao
The dynamic evolution of industrial automation has created an imperative need to transition from inflexible, rule-based systems to flexible, intelligent agents that can facilitate human-robot collaboration. The purpose of this research was to integrate large language models (LLMs) with autonomous mobile robots (AMRs) to improve context-aware decision-making. The inflexibility of traditional systems has often hindered performance in dynamic environments, as systems often rely on predefined algorithms and sensor configurations. To improve this, a modular framework was created, consisting of a central processing unit and an LLM API to interpret natural language and process environmental information. Quantitative results have been clearly specified in the abstract, which states that the success rate in resolving navigation exceptions by the proposed framework was 89% with a 5% localization error rate. Moreover, substantial savings were observed in token usage and computational resources. This study provided an imperative framework for smarter industrial automation, filling the gap between mechanical precision and artificial intelligence. The practical experimentation of an AMR model gives an outcome of a successful navigation exception resolution rate of 89% by means of the proposed framework, with an error rate of 5% localized to each exception. Additionally, significant reductions in the number of tokens used and the time taken to process tokens provide a scalable means for developing contextually-based, robust, autonomous mobile platforms’ decisions.
Volume: 15
Issue: 3
Page: 607-620
Publish at: 2026-09-01

Test rig development for overshot water wheels under low-head, low-flow conditions in pico-hydropower applications

10.11591/ijaas.v15.i3.pp1227-1238
Izzatie Akmal Zulkarnain , Irma Wani Jamaludin , Mohd Farriz Basar , Kamaruzzaman Sopian
This study presents the development and validation of a controllable experimental test rig for overshot water wheels operating under low-head and low-flow conditions commonly found in pico-hydropower resources. Many potential pico-hydro sites operate at heads below 1.5 m with minimal flow, where conventional turbines often experience reduced efficiency and suitable laboratory-scale testing facilities remain limited. The developed system consists of two high-density polyethylene (HDPE) water tanks, an adjustable-inclination water channel, a circulation pump, and an overshot water wheel equipped with recyclable polyethylene terephthalate (PET) bottle blades. The test rig operates within hydraulic range of 0.525 - 1.169 m head and 0.01 - 0.02 m³/s flow rate, enabling the simulation of realistic pico-hydropower conditions. Water velocity was regulated using the water trajectory method by varying the channel inclination angle between 10° and 50°, producing velocities ranging from 1.494 m/s to 4.519 m/s. Experimental results show stable and repeatable hydraulic conditions with measurement uncertainty of approximately ± 0.03 m/s. These findings demonstrate that the developed test rig provides a reliable platform for future investigations of overshot water wheel performance parameters, including rotational speed, angular velocity, force, torque, mechanical power, and efficiency in pico-hydropower applications.
Volume: 15
Issue: 3
Page: 1227-1238
Publish at: 2026-09-01

Flying ad hoc network applications, mobility models, routing protocols, and challenges: a review

10.11591/ijaas.v15.i3.pp1316-1333
Taher M. Ghazal , Nabil El Kadhi , Munir Ahmad
Applications of unmanned aerial vehicles (UAVs) are rapidly increasing in surveillance, disaster management, precision agriculture, logistics, environmental monitoring, and intelligent transportation systems. UAVs/drones have made their way towards the application of ad hoc networks. Multiple interconnected UAVs flying together have presented us with a new type of ad hoc network known as flying ad hoc networks (FANETs). While multiple small UAVs acting as a FANET offer a lot of benefits, there are also several challenges concerning this type of network. This study presents a systematic literature review (SLR) of FANET technologies by reviewing recent literature published between 2017 and 2025 from IEEE Xplore, Scopus, ScienceDirect, and Google Scholar databases. A structured review methodology based on inclusion and exclusion criteria was adopted to analyze more than 150 research articles. The study presents a comprehensive taxonomy of FANET mobility models and routing protocols, along with a comparative analysis of communication architectures, trajectory optimization methods, and implementation challenges. The findings reveal that routing efficiency, adaptive mobility models, secure communication, energy optimization, and scalable architectures are the major determinants of FANET performance. This article concludes that good connectivity, improved security, effective routing, carefully selected mobility models, and the architecture of FANET nodes are key parameters for obtaining optimal results.
Volume: 15
Issue: 3
Page: 1316-1333
Publish at: 2026-09-01

Consensus-based path planning for UAV swarms under multiple constraints: A review

10.11591/ijra.v15i3.pp621-638
Yana Lu , Lianpeng Li , Hui Zhao , Xu Zhao
UAV swarms are essential for emergency response, logistics, reconnaissance, and environmental monitoring, yet achieving safe and scalable path planning under dynamic conditions and complex constraints remains challenging. Unlike existing surveys that categorize algorithms by theoretical foundations, this paper systematically reviews UAV swarm path planning through the lens of spatial, temporal, and task-level consistency constraints. We classify recent advances into classical path search, intelligent optimization, and deep reinforcement learning, emphasizing how each addresses geometric continuity, behavioral coordination, and full-chain perception–decision–planning consistency under multi-constraint coupling. We further identify critical limitations in scalability, dynamic adaptability, and heterogeneous swarm cooperation, and outline future directions, including distributed control, multi-source perception fusion, cross‑platform collaboration, and robust autonomous decision-making. This review provides a unique, application‑centric taxonomy based on consensus constraints, offering actionable insights for developing consistency-aware UAV swarm path planning technologies.
Volume: 15
Issue: 3
Page: 621-638
Publish at: 2026-09-01

Remote controlled agricultural robot for spraying liquid type pesticides

10.11591/ijra.v15i3.pp553-560
Kalagotla Chenchireddy , Vadthya Jagan , M. Aruna Bharathi , Malaji Sushama , Varghese Jegathesan , Shabbier Ahmed Sydu
In modern agriculture, the use of automation and robotics is becoming increasingly essential to enhance efficiency, reduce labor, and ensure the safe handling of hazardous materials. This paper presents the design and development of a remote-controlled agricultural robot specifically intended for spraying liquid-type pesticides using radio frequency communication. The system comprises a mobile robot platform equipped with a pesticide tank, a spraying mechanism powered by a DC pump, and a set of drive motors for navigation. The robot is remotely controlled through an RF transmitter and receiver pair, allowing the operator to manually guide the robot across agricultural fields without direct contact with pesticides. The RF module transmits commands such as movement directions and spray activation, which are interpreted by an Arduino microcontroller onboard the robot. The system aims to reduce human exposure to harmful chemicals, minimize labor efforts, and increase precision in pesticide application. This solution is cost-effective, user-friendly, and adaptable for small to medium-sized farms, offering a practical step toward smart farming.
Volume: 15
Issue: 3
Page: 553-560
Publish at: 2026-09-01

Optimization of tensile strength and modulus elasticity from recycled mask polymer blends

10.11591/ijaas.v15.i3.pp894-901
Indah Widiastuti , Slamet Jatmiko , Budi Harjanto , Arif Bagus Panuntun
This research aims to analyze the effect of differences in the proportion of recycled polypropylene (r-PP), compatibilizer, and type of mask on the tensile strength and modulus of elasticity of recycled mask product materials. The research design used in this research uses the Taguchi orthogonal L4 experimental method, which uses a quantitative approach, namely by manipulating certain conditions and providing control over the test samples. This research focused on the effect of varying the proportion of r-PP composition, maleic anhydride polypropylene (MAPP) compatibilizer, as well as varying the type of mask waste used. In this research, the composition of r-PP used was 10% and 30%. Meanwhile, the MAPP composition used is 0% and 6%. In this study, tensile testing was carried out in accordance with the American Society for Testing and Materials (ASTM) D 638 standard type V in the form of a dogbone. The results of the research show that differences in the proportion of r-PP in the composition of the polymer mixture have an effect on the tensile strength and modulus of elasticity of the recycled mask product material.
Volume: 15
Issue: 3
Page: 894-901
Publish at: 2026-09-01

Ethical issues in digital mental health delivery: a comprehensive scoping review of global challenges

10.11591/ijaas.v15.i3.pp911-920
Nurul Ain Ain Mohd Mohd Daud , Hapsah Md Yusof , Norazani Ahmad , Fauziah Mohd Sa’ad , Nurul Hasyimah Mat Rani , Pau Kee , Nur Shuhana Mohd Sansuddin
Digital technologies are becoming increasingly integrated into mental health counselling. While expanding the boundaries of service provision, these technologies have also raised numerous ethical challenges that urgently need clarification. This study conducts a scoping review following the standardized methodology developed by Arksey and O’Malley. It identifies five core ethical challenges facing practitioners: privacy and data security; informed consent and autonomy; service accessibility and equality; algorithmic fairness and bias mitigation; and inclusiveness and equity. Existing comparative analyses show that Western countries, including the United States (US) and the United Kingdom (UK), have mature digital mental health regulatory frameworks. In contrast, non-Western countries generally face gaps in legal protections, which have created a global ethical divide. Artificial intelligence (AI)-related risks, such as algorithmic bias and lack of transparency, further exacerbate these problems. The study calls on policymakers to improve relevant regulatory frameworks and requires practitioners to receive specialized ethical training.
Volume: 15
Issue: 3
Page: 911-920
Publish at: 2026-09-01

Drivers of salesperson performance: value transfer, personality congruence, and selling ambiance

10.11591/ijaas.v15.i3.pp1155-1165
Evi Dian Kartikasari , Sulastri Sulastri , Isnurhadi Isnurhadi , Muchsin Saggaf Sihab
This study examines preference-based transfer of value (PTV) as a mediating mechanism linking adaptive selling (AS), salesperson-brand personality congruence (SPC), and positive selling ambiance (PSA) to salesperson performance (SP) in a retail banking context. Using a quantitative cross-sectional survey, questionnaires were collected from 262 frontline salespeople of PT Bank BNI (Persero) Tbk in Indonesia. The proposed model was tested using structural equation modeling (SEM). The findings show that AS, SPC, and PSA significantly enhance PTV, and PTV positively predicts SP. Mediation analysis indicates that PTV transmits the effects of these antecedents on performance, highlighting the importance of converting selling behaviors and interaction climates into customer-internalized value. The study contributes by introducing and empirically validating PTV as a novel psychological channel in sales performance research and offers managerial implications for recruitment, training, and performance assessment in business-to-consumer financial services. Future research may extend this model across industries and examine boundary conditions using multi-group designs.
Volume: 15
Issue: 3
Page: 1155-1165
Publish at: 2026-09-01

Students' perspectives on the effectiveness of ChatGPT in virtual learning

10.11591/ijaas.v15.i3.pp921-931
Isaac Asampana , Henry Akwetey Matey , Ben T. Ocra , Jones Yeboah Nyame
This research investigates how students perceive chat generative pre-trained transformer (ChatGPT) as a learning aid, examining its effects on engagement, usability, and educational achievement. Using the technology acceptance model (TAM) as the conceptual framework, a 24-item survey was designed and administered through simple random sampling. Results reveal that students largely view ChatGPT positively, describing it as an accessible and beneficial tool that promotes flexible and independent learning. The findings confirm TAM predictions, showing that perceived ease of use and favorable attitudes significantly influence adoption. By combining insights from both distance and on-campus learners, the study enriches understanding of how generative artificial intelligence enhances cognitive development and learning adaptability. Overall, the research reflects cautious optimism about ChatGPT’s pedagogical value, acknowledging both its educational benefits and challenges while underscoring the essential role of usability and learner attitudes in fostering sustained artificial intelligence utilization in higher education.
Volume: 15
Issue: 3
Page: 921-931
Publish at: 2026-09-01

Recent advances in computer vision segmentation for urban tree detection using street view imagery: a literature review

10.11591/ijaas.v15.i3.pp975-989
Muhammad Fareez Rashdan , Nurbaity Sabri , Shafaf Ibrahim , Nor Masri Sahri
Segmentation of trees in urban settings is important for tracking vegetation and managing green infrastructure in urban areas. However, there are numerous factors that make it difficult to obtain precise results, such as differences in tree species, season, and environmental variables. Street-view imagery can provide a valuable database to be used for the detection and identification of trees. Unfortunately, the process of labelling images manually is time-consuming and inconsistent. In recent years, deep learning models have provided better automation and precision. However, difficulties in obtaining results persist due to factors such as occlusion, background, and changes in lighting conditions. Therefore, this paper presents an overview of the current trends in the use of deep learning for the detection and segmentation of trees. Specifically, the following aspects will be discussed in this work: preprocessing of images, semantic and instance segmentation, models based on deep learning, and transformers. This literature review also analyzes current datasets, evaluation measures, and applications, offering knowledge about existing challenges and future directions for further studies. The article is designed to be a source of reference for scientists and practitioners working on problems related to urban vegetation classification and computer vision.
Volume: 15
Issue: 3
Page: 975-989
Publish at: 2026-09-01
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