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30,376 Article Results

A hybrid transformer-graph neural networks framework for enhanced physical activity recognition and sedentary behavior analysis

10.11591/ijra.v14i3.pp429-438
Sudarsanam Anandanarayanan , Suvarnalingam Thirumaran
Sedentary behavior has been identified as a major risk factor for chronic diseases such as cardiovascular disorders, obesity, and diabetes. The accurate prediction of sedentary health risks is essential for early intervention and personalized healthcare strategies. This study proposes a novel machine learning-based predictive model that leverages transformer-based architectures and graph neural networks to analyze multidimensional behavioral data. Unlike traditional models, our approach incorporates temporal attention mechanisms to capture long-term dependencies in activity patterns and graph-based learning to model complex relationships between physiological and behavioral factors. The study utilizes real-world datasets, including wearable sensor data and self-reported activity logs, to train and validate the models. Experimental results demonstrate that the proposed framework outperforms conventional machine learning techniques such as random forest and XGBoost, achieving superior predictive accuracy and robustness. The findings highlight the potential of advanced machine learning algorithms in assessing sedentary health risks, enabling proactive health management and intervention strategies.
Volume: 14
Issue: 3
Page: 429-438
Publish at: 2025-12-01

Design and implementation of QUADRESCUE: A ROS-based quadruped robot for disaster response support

10.11591/ijra.v14i3.pp387-398
Sanjay Deshmukh , Ojas Chanakya , Om Gabani , Kashish Patni , Asmita Deshmukh
Search and rescue (SAR) operations in hazardous environments demand robotic systems capable of traversing complex terrains while ensuring responder safety. Traditional wheeled platforms often fail in debris-laden areas, and fully autonomous quadrupeds remain financially out of reach for many rescue agencies. This paper presents the design and development of QUADRESCUE, a modular operator-assisted quadruped robot built to bridge the gap between affordability and capability in disaster response. QUADRESCUE delivers core SAR functionalities including remote visual inspection, real-time terrain mapping via an RGB-D camera, payload transport, and GPS-based survivor localization. Built with a robust three degrees of freedom (3DoF) per leg design, the robot uses inverse kinematics algorithms to precisely control twelve servo motors for stable locomotion across uneven terrain. The system integrates the robot operating system (ROS) for seamless operation, real-time joystick control for easy navigation, an IMU for orientation sensing, and a GPS module with 3-meter accuracy. Field evaluations demonstrate 80–94% success rates on challenging surfaces, substantially outperforming wheeled counterparts 19% to 39% with a 200-meter control range and 45 minutes of runtime. QUADRESCUE offers a lightweight, cost-effective, and repairable solution that combines practical usability with advanced performance, making it well-suited for real-world deployment in emergency rescue situations.
Volume: 14
Issue: 3
Page: 387-398
Publish at: 2025-12-01

Optimal battery sizing using modified spider monkey optimization in grid connected microgrids

10.11591/ijra.v14i3.pp356-365
Meraj Fatima , Manne Rama Subbamma
Microgrids (MGs) must have optimally sized storage and renewable energy sources to operate efficiently, economically, and reliably. MG may benefit from optimization techniques in their scheduling and sizing since they have a variety of energy sources with varying availability conditions and necessary costs. In this research, a novel modified spider monkey-based energy management system (MSM-EMS) has been proposed by increasing the photovoltaic (PV) or battery energy storage system (BESS) module capacity while minimizing grid connectivity dependency. The fundamental idea behind the proposed approach is greater dependability at the lowest feasible cost. By taking into account the BESS utilization factor and PV forced outage rates in a MG, the method becomes more realistic. Despite the absence of renewable energy sources and the grid, the proposed strategy provided critical loads according to schedule while maintaining reserve margins. Experimental findings demonstrate that the modified spider monkey optimization (MSMO)-based algorithm can determine the best BESS size and PV depending on cost. In comparison to particle swarm optimization (PSO) of $2756.1 and ABC of $2912.65, the ideal cost for EMS-MSMO is $2215.77 which is relatively low compared to the existing technique. As a result, the suggested MSMO algorithm and innovative energy management system has been optimized along with PV and battery dimensions.
Volume: 14
Issue: 3
Page: 356-365
Publish at: 2025-12-01

Design and implementation of solar-grid based charging station for electric vehicle with fault detection method using R-Pi and IoT processor

10.11591/ijape.v14.i4.pp794-802
M. Vaigundamoorthi , S. Karthick , V. S. Chandrika , D. Chithra , K. V. Balaramakrishna , K. Lakshmi Khandan , Lakshmana Phaneendra Maguluri , S. Chandrasekar , M. Janarthanan
In this research describes the electrical vehicle (EV) charging station using PV panel with fault detection methods. The PV modules will failure for some time, because of some external factors and internal factors. In direct fault condition the monitor and analyze the external factors such as the life span, high intensity and breakage of the PV panels using Raspberry Pi (R-Pi) processor with internet of things (IoT) system. In power demand/day on the PV panel will be evaluated and analyzed through R-Pi processor and IoT. The efficiency and the range values of the PV panels will be monitored and analyzed through IoT. Proposed work explains, how the fault detection techniques have been improved and adopted in using R-Pi processor through IoT platform. The proposed dataset pre-processing system is incorporated with IoT module. The grid fault clearing time will be compared with the actual values through R-Pi processor. The PV panel faults are detected using thermal image processing, that image parameter values analysis through IoT based internal monitoring system.
Volume: 14
Issue: 4
Page: 794-802
Publish at: 2025-12-01

Forecasting business exceptions in robotic process automation with machine learning

10.11591/ijra.v14i3.pp450-458
Igor Saez , Sara Segura , Mónica Gago
Business exceptions interrupt robotic process automation (RPA) workflows and oblige costly human intervention. This paper explores the application of machine learning (ML) time series forecasting techniques to predict business exceptions in RPA. Using RPA robot logs from a financial service company, we employ ARIMA, SARIMAX, and Prophet statistical models, comparing their performance with ML models such as XGBoost and LightGBM. Furthermore, we explore hybrid approaches that combine the strengths of statistical models with ML techniques, specifically integrating Prophet with XGBoost and LightGBM. Our findings reveal that a hybrid LightGBM model substantially outperforms traditional methods, achieving a 40% reduction in the weighted absolute percentage error (WAPE) when compared to the top-performing statistical model. These results suggest the potential of ML forecasting in optimizing RPA operations through the analysis of log-generated data.
Volume: 14
Issue: 3
Page: 450-458
Publish at: 2025-12-01

Backstepping control in speed loop combined with load torque observer-ESO for IPMSM in electric vehicle

10.11591/ijpeds.v16.i4.pp2271-2279
An Thi Hoai Thu Anh , Tran Hung Cuong , Nguyen Van Hoa
Electric vehicles are gaining popularity due to their environmental friendliness and the need to conserve dwindling fossil fuel resources. In this field, interior permanent magnet (IPM) motors are considered the top choice for propulsion systems due to their high efficiency, high torque-to-current ratio, durability, and low noise. To optimize the speed control performance of IPM motors in the presence of disturbances, a nonlinear speed control algorithm for IPM systems using the backstepping method is developed in this paper. Additionally, a load torque observer using the extended state observer (ESO) method is implemented to enable the system to respond quickly and accurately to load changes while minimizing the effects of disturbances, thereby enhancing the operation and reliability of electric vehicles. The simulation results, conducted in MATLAB/Simulink, demonstrate that the combination of backstepping control and ESO offers good stability for the motor system, while mitigating the impact of disturbances and load variations. This is an important step in optimizing the control system of electric vehicles, contributing to the improvement of performance and reliability in electric vehicle applications.
Volume: 16
Issue: 4
Page: 2271-2279
Publish at: 2025-12-01

FIND-ROUTE: Fourier series integrated deep learning model for energy efficient routing in Internet of Things-wireless sensor network

10.11591/ijra.v14i3.pp468-478
Shobanbabu Ramaswamy Jaganathan , Sathya Rajendran , Karthikeyan Ramamoorthy
The Internet of Things (IoT) relies on wireless sensor networks (WSNs) to transmit data across a wide range of applications. However, the commonly encountered primary challenges in IoT-enabled WSNs are high energy consumption during data transmission, which insists energy optimized routing to prolong the network lifetime. To address these challenges, a novel Fourier series integrated deep learning-based routing (FIND-ROUTE) framework has been proposed for energy-aware communication among IoT nodes in WSN. Initially, a hybrid clustering approach forms an adaptive cluster for efficient data aggregation with reduced energy consumption. After clustering, stable cluster heads (CHs) are elected by a Fourier series-based metaheuristic optimization algorithm for balancing the energy usage with extended network lifetime. Finally, an Intelligent neural network dynamically selects the optimal path and transmits the data efficiently with reduced latency for reliable communication in IoT-WSN. The FIND-ROUTE framework is simulated by using MATLAB, and it is validated by using the WSN-DS dataset. The proposed FIND-ROUTE framework is evaluated based on several parameters, including energy consumption, packet delivery ratio (PDR), network lifetime (NL), time complexity, throughput, number of alive nodes, packet loss ratio (PLR), and space complexity. In comparison, the proposed FIND-ROUTE framework achieves a PDR of 90%, whereas MLBDARP, LQEER, and NBSHO-DRNN achieve 70%, 60%, and 67% respectively.
Volume: 14
Issue: 3
Page: 468-478
Publish at: 2025-12-01

Humanoid robot balance control system during backward walking using linear quadratic regulator

10.11591/ijra.v14i3.pp320-330
Muhammad Arsyi , Andi Dharmawan , Bakhtiar Alldino Ardi Sumbodo , Muhammad Auzan , Jazi Eko Istiyanto , Oskar Natan
Humanoid robots are designed to replicate human activities, including tasks in hazardous environments. However, maintaining balance during backward walking remains a significant challenge due to center of mass (CoM) shifts beyond the support polygon and limited knee joint motion. This study proposes a control strategy that integrates a linear quadratic regulator (LQR) with optimized walking patterns to enhance dynamic stability. The approach combines LQR-based control with CoM trajectory planning to ensure safe and stable backward walking. The methodology includes inverse kinematics for generating walking patterns and the use of Inertial Measurement Unit (IMU) sensors to estimate the CoM trajectory. LQR parameters were tuned through simulation to improve responsiveness to disturbances. Evaluation metrics focused on CoM deviation, rise time, settling time, and overshoot. Experimental results demonstrate that the proposed LQR system effectively maintains the CoM within 5% of the support polygon boundary. The system achieved rise times under one second and settling times below two seconds, while minimizing pitch and roll overshoots. Compared to proportional control, the proposed method significantly improves stability and reduces the risk of falling. This research advances control strategies for humanoid robots, contributing to improved mobility and operational safety. Moreover, it supports Sustainable Development Goal (SDG) 9 by promoting innovation in intelligent robotic systems that can assist in complex or high-risk environments.
Volume: 14
Issue: 3
Page: 320-330
Publish at: 2025-12-01

A comprehensive review of efficient wireless power transfer for electric vehicle charging: advancements, challenges, and future directions

10.11591/ijpeds.v16.i4.pp2156-2169
Md. Ashraf Ali Khan , Kuber Kuber , Yusra Wahab , M. Saad Arif , Shahrin Md. Ayob , Norjulia Mohamad Nordin
Electric vehicles (EVs) have transformed the transportation sector, offering a sustainable alternative to fossil-fuel-powered vehicles. However, their widespread adoption faces challenges such as inadequate charging infrastructure, range anxiety, and concerns about user convenience. Wireless power transfer (WPT) technology provides an efficient, reliable, and user-friendly charging solution that eliminates physical connections, enabling both static and dynamic charging applications. This review explores key components of WPT systems, including wireless charging schemes, compensation circuits, coupling pad structures, and misalignment tolerance, emphasizing their impact on system efficiency and reliability. Findings highlight that WPT can enhance charging convenience, reduce dependence on large battery capacities, and support seamless EV integration into daily life. Additionally, WPT systems improve safety, lower maintenance needs, and create opportunities for autonomous charging. Key advancements in compensation topologies, coupling pad geometries, and misalignment-tolerant capabilities are discussed alongside their role in enhancing power transfer efficiency. By offering insights into the current state-of-the-art and future directions, this paper aims to support the development and deployment of WPT systems, contributing to the global transition toward sustainable transportation.
Volume: 16
Issue: 4
Page: 2156-2169
Publish at: 2025-12-01

Frequency response-based optimization of PID controllers for enhanced fluid control system performance

10.11591/ijape.v14.i4.pp1058-1070
Herri Trisna Frianto , Syahrul Humaidi , Kerista Tarigan , Dadan Ramdan , Doli Bonardo
Temperature and viscosity variations are known to affect the performance of proportional-integral-derivative (PID) controllers in fluid systems. However, there exist gaps in research relative to the thermal effects on the performance of PID based fluid systems. PID controllers are also utilized for fluid control to maintain stability and improve performance. This study aims to explore the influence of temperature and viscosity variations through frequency response analysis for the first time in this regard. Utilizing a controlled experimental setup, gain and phase values were measured across different temperature points. Bode and Nyquist plots were generated to observe system behavior, stability, and response to changes in temperature and fluid viscosity. The results show a clear inverse relationship between temperature and gain, with a notable phase lag increase as temperature rises. At 25 °C, the gain was measured at 15.83 dB with a phase of -52.63°, which gradually reduced to a gain of 13 dB and a phase of -61.53° at 80 °C. The Nyquist analysis revealed stable operation within this temperature range, but the shift in response indicates increased system vulnerability as viscosity decreases with rising temperature. The derived linear equations effectively model the gain-phase relationship, with an R² of 0.9985, suggesting a highly accurate fit. Overall, the study concludes that temperature-induced viscosity changes significantly impact PID-controlled fluid systems, emphasizing the need for adaptive control strategies in fluctuating environments.
Volume: 14
Issue: 4
Page: 1058-1070
Publish at: 2025-12-01

Analysis and implementation of computation offloading in fog architecture

10.11591/ijra.v14i3.pp479-492
Prince Gupta , Rajeev Sharma , Sachi Gupta , Adesh Kumar
The fast expansion of connected devices has led to an unparalleled increase in data across sectors like industrial automation, social media, environmental monitoring, and life sciences. The processing of this data presents difficulties owing to its magnitude, temporal urgency, and security stipulations. Computation offloading has arisen as a viable alternative, allowing resource-constrained devices to assign demanding work to more robust platforms, thus improving responsiveness and efficiency. This paper examines decision-making strategies for computing offloading by assessing various algorithms, including a deep neural network with deep reinforcement learning (DNN-DRL), coordinate descent (baseline), AdaBoost, and K-nearest neighbor (KNN). The performance evaluation centers on three primary metrics: system accuracy, training duration, and latency. The computation offloading mitigates these issues by transferring intricate workloads from resource-limited devices to more proficient platforms, thus enhancing efficiency and responsiveness. The evaluation examines accuracy, training duration, and latency as key parameters. The results indicate that KNN attains maximum accuracy and minimal latency, AdaBoost provides a robust balance despite increased training costs, and the baseline underperforms in both efficiency and responsiveness. These findings underscore the trade-offs between computational expense, precision, and real-time application, providing insights for forthcoming IoT and edge-computing systems.
Volume: 14
Issue: 3
Page: 479-492
Publish at: 2025-12-01

A method integral sliding mode control to minimize chattering in sliding mode control of robot manipulator

10.11591/ijra.v14i3.pp345-355
Mai Hoang Nguyen , Truc Thi Kim Nguyen
This paper presents an improved sliding mode control (SMC) strategy for robotic manipulators by introducing a novel exponential integral-based adaptive gain law, referred to as integral sliding mode control (ISMC). The proposed approach dynamically adjusts the switching gain KKK in real-time, based on the accumulated system error, thereby effectively reducing chattering while preserving system robustness. Unlike many existing methods, the ISMC strategy eliminates the need for state observers or complex estimation techniques, simplifying implementation. Theoretical analysis is provided using Lyapunov stability theory, ensuring global convergence. Simulation results on 2-DOF and 3-DOF robotic arms demonstrate superior tracking accuracy and smoother control signals compared to conventional SMC approaches. This work contributes a lightweight yet effective SMC enhancement with practical benefits for real-world robotic applications.
Volume: 14
Issue: 3
Page: 345-355
Publish at: 2025-12-01

Mandailing smoked fish cuisine: cultural, nutritional, and local wisdom insights

10.11591/ijaas.v14.i4.pp1166-1180
Esi Emilia , Nila Reswari Haryana , Risti Rosmiati , Erli Mutiara , Laili Fitria , Rachmat Mulyana , Wisnu Prayogo
This study explores the uniqueness of Mandailing traditional cuisine, focusing on the cultural and nutritional significance of its iconic smoked fish dishes, such as smoked fish rendang, smoked fish curry, smoked fish with chili sauce, and smoked fish with vegetables. These dishes showcase the traditional fish smoking practices developed as a preservation method, allowing the Mandailing community to adapt to the abundance of rivers and natural resources in their highland environment. Smoking fish not only extended its shelf life but also became a cornerstone of Mandailing culinary identity, reflecting the community’s ingenuity and resourcefulness. Mandailing cuisine is deeply influenced by neighboring culinary traditions from West Sumatra and North Tapanuli, resulting in a rich fusion of bold flavors, often characterized using coconut milk and fresh spices. The preparation of smoked fish combines traditional high-heat cooking techniques with unique flavor profiles that distinguish Mandailing dishes from other Indonesian cuisines. This research highlights the importance of Mandailing smoked fish practices in sustaining local food systems and preserving cultural heritage. By emphasizing both cultural and nutritional aspects, it underlines the relevance of these traditional practices in promoting food diversity, environmental sustainability, and the recognition of Indonesia’s rich culinary landscape.
Volume: 14
Issue: 4
Page: 1166-1180
Publish at: 2025-12-01

ADC-LIO: A direct LiDAR-inertial odometry method based on adaptive distortion covariance

10.11591/ijra.v14i3.pp399-408
Lixiao Yang , Youbing Feng
Focusing on the localization challenges for robots in dynamic navigation environments, this study proposes a direct LiDAR-inertial odometry (LIO) system named ADC-LIO, which achieves robust pose estimation and accurate map reconstruction using adaptive distortion covariance. ADC-LIO is engineered to address uncertain motion patterns in autonomous mobile robots, effectively integrating LiDAR scan undistortion within the Kalman filtering update process by embedding an iterative smoothing process and a backpropagation strategy. The ADC-LIO architecture enhances point cloud accuracy, improving the system's overall performance and robustness. In addition, an adaptive covariance processing method is developed to resolve motion-induced sensing uncertainties, which calculates different covariances according to the error characteristics of the point cloud. This method enhances the constraints of high-quality point clouds, reduces the limitations on low-quality point clouds, and utilizes information more effectively. Experiments on the publicly available NTU-VIRAL dataset validate the effectiveness of ADC-LIO, which improves pose estimation accuracy and reduces absolute position errors compared to other state-of-the-art methods, including FAST-LIO, Faster-LIO, FR-LIO, and Point-LIO. The proposed ADC-LIO is an appealing odometry method that delivers accurate, real-time, and reliable tracking and map-building results, posing a practical solution for robotic applications in structured indoor and GPS-denied outdoor environments.
Volume: 14
Issue: 3
Page: 399-408
Publish at: 2025-12-01

Determinants of integrated teaching capacity among teachers in ethnic minority primary schools in northern Vietnam

10.11591/ijere.v14i6.30087
Hang Nguyen Thi Thu , Chuyen T. H. Nguyen
This study explores factors affecting the integrated teaching capacity of primary school teachers in ethnic minority schools in the Northern mountainous regions of Vietnam. Given the challenges of linguistic and cultural diversity in this context, the research aims to address gaps in current practices and propose measures for improvement. A quantitative approach was adopted, surveying 280 teachers and administrators using exploratory factor analysis (EFA) and multivariate regression. The results identify four primary factors influencing teaching capacity: i) language, culture, and parent coordination; ii) teacher capacity and community participation; iii) teaching materials, equipment, and teacher attitudes; and iv) policies and support from management agencies. Among these, language, culture, and parent coordination are the most impactful. The study underscores the need for targeted teacher training programs and improved collaboration with local communities to enhance teaching outcomes. These findings provide actionable insights for policymakers and educators to improve integrated teaching in ethnically diverse and economically challenged regions.
Volume: 14
Issue: 6
Page: 4295-4306
Publish at: 2025-12-01
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