IAES International Journal of Robotics and Automation (IJRA)
Robots are becoming part of people's everyday social lives and will increasingly become so. In future years, robots may become caretaker assistants for the elderly, or academic tutors for our children, or medical assistants, day care assistants, or psychological counselors. Robots may become our co-workers in factories and offices, or maids in our homes. The IAES International Journal of Robotics and Automation (IJRA) is providing a platform to researchers, scientists, engineers and practitioners throughout the world to publish the latest achievement, future challenges and exciting applications of intelligent and autonomous robots. IJRA is aiming to push the frontier of robotics into a new dimension, in which motion and intelligence play equally important roles. Its scope includes (but not limited) to the following: automation control, automation engineering, autonomous robots, biotechnology and robotics, emergence of the thinking machine, forward kinematics, household robots and automation, inverse kinematics, Jacobian and singularities, methods for teaching robots, nanotechnology and robotics (nanobots), orientation matrices, robot controller, robot structure and workspace, robotic and automation software development, robotic exploration, robotic surgery, robotic surgical procedures, robotic welding, robotics applications, robotics programming, robotics technologies, robots society and ethics, software and hardware designing for robots, spatial transformations, trajectory generation, unmanned (robotic) vehicles, etc.
Articles
470 Documents
<![CDATA[A hybrid transformer-graph neural networks framework for enhanced physical activity recognition and sedentary behavior analysis ]]>
<![CDATA[Anandanarayanan, Sudarsanam]]>;
<![CDATA[Thirumaran, Suvarnalingam]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp429-438
<![CDATA[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.]]>
<![CDATA[Design of low-power, high-speed approximate 4:2 compressors for efficient partial product reduction in multipliers ]]>
<![CDATA[Michael, Jabez Daniel Vincent David]]>;
<![CDATA[Gorantla, Anusha]]>;
<![CDATA[Appathurai, Ahilan]]>;
<![CDATA[Ramachandran, Dinesh]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp459-467
<![CDATA[Partial product reduction becomes the main task in the multiplication process. Therefore, the partial product stages of multipliers are reduced with the usage of compressors, by using compressors in the multiplier. Using compressors in the multiplier circuit significantly impacts multiplier performance. Approximate compressors are crucial for achieving better design metrics in parallel multipliers. This paper proposes to create various new approximate 4:2 compressor circuits. A trade-off is made between the performance and accuracy of this approximate circuit design approach. The proposed designs have been implemented using XOR-XNOR gates with a 2-to-1 multiplexer, and also XOR-XNOR gates with transmission gates. All these circuits have been simulated using Cadence in different technological nodes. Compared with the existing technique, the proposed 4:2 approximation compressor provides 51.4% power reduction and 26.45% delay reduction for 45 nm equipment.]]>
<![CDATA[A survey on convolutional neural network hardware acceleration through approximate computing multiple and accumulates unit ]]>
<![CDATA[Sudhakaran, Suvitha Pathiyadan]]>;
<![CDATA[Thangakalai, Aathmanesan]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp366-375
<![CDATA[Convolutional neural networks (CNNs) are applied to a different range of real-world complex tasks to provide effective solutions with high accuracy. Based on the application's complexity, CNN demands a lot of processing units and memory spaces for its effective implementation. Bringing this computational task to hardware for processing the data to enhance the acceleration helps in achieving real-time performance improvement. Recent studies focused on approximation methodology to overcome this problem. This proposed survey analyzes various recent methods involved in implementing approximating computing-based processing elements and their usage in CNNs. Primarily, the survey focuses on multiple and accumulates (MAC) unit and their various approximation methods, which acts as a fundamental block as a processing element in the CNN layers. Secondly, it focuses on various CNN hardware acceleration architectures and their layers designed using different methods and their wide range of applications. Some of the recent design methods applied to various ranges of applications are also analyzed in the proposed survey. This detailed analysis gives an outlook on effective approximation blocks and the CNN architecture to be effectively used in various designs, with a scope of area in which future improvement can be made.]]>
<![CDATA[Forecasting business exceptions in robotic process automation with machine learning ]]>
<![CDATA[Saez, Igor]]>;
<![CDATA[Segura, Sara]]>;
<![CDATA[Gago, Mónica]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp450-458
<![CDATA[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.]]>
<![CDATA[Enhancing health status prediction and data security using transformer-based deep learning architectures ]]>
<![CDATA[Senthamarai, Subramaniyan]]>;
<![CDATA[Mala, Raja Manickam]]>;
<![CDATA[Palanisamy, Vellaiyan]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp418-428
<![CDATA[This paper proposes a privacy-preserving transformer-based federated learning (PPTFL) framework designed to enhance privacy, accuracy, and computational efficiency in healthcare data analysis. Federated learning (FL) has emerged as a promising solution for distributed machine learning while preserving data privacy, especially in sensitive sectors like healthcare. However, challenges such as maintaining high accuracy and managing communication overhead remain. The proposed PPTFL framework leverages the power of transformer models to improve the performance of federated learning while integrating privacy-preserving techniques. The model demonstrates superior performance with an accuracy of 92.87%, an F1 score of 92.37%, and a privacy budget (ϵ) of 1.6, outperforming existing approaches in terms of both privacy and accuracy. The model also exhibits computational efficiency, with lower communication cost and reasonable training time. Comparative evaluations with four relevant literature models further validate the effectiveness of the proposed PPTFL framework. This work highlights the potential of PPTFL to revolutionize healthcare informatics by providing secure, accurate, and efficient solutions for federated learning applications.]]>
<![CDATA[FIND-ROUTE: Fourier series integrated deep learning model for energy efficient routing in Internet of Things-wireless sensor network ]]>
<![CDATA[Jaganathan, Shobanbabu Ramaswamy]]>;
<![CDATA[Rajendran, Sathya]]>;
<![CDATA[Ramamoorthy, Karthikeyan]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp468-478
<![CDATA[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.]]>
<![CDATA[Analysis and implementation of computation offloading in fog architecture ]]>
<![CDATA[Gupta, Prince]]>;
<![CDATA[Sharma, Rajeev]]>;
<![CDATA[Gupta, Sachi]]>;
<![CDATA[Kumar, Adesh]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp479-492
<![CDATA[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.]]>
<![CDATA[Design and development of a modular magnetic wheeled robot for out-pipe inspection ]]>
<![CDATA[Rajendran, Sugin Elankavi]]>;
<![CDATA[Ramanathan, Kuppan Chetty]]>;
<![CDATA[Guasekaran, Harish Kumar]]>;
<![CDATA[Pinagapani, Arun Kumar]]>;
<![CDATA[Devaraj, Dinakaran]]>;
<![CDATA[Mathanagopal, Ramya]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp331-344
<![CDATA[This paper presents the design of a modular mobile robot capable of climbing and inspecting vertical ferromagnetic pipes using magnetic wheels. Mobile robots used for climbing ferromagnetic surfaces employ magnetic tracks, wheels, and magnets attached to the robot’s body. When it comes to ferromagnetic pipes, magnetic wheels and magnets attached to the body can be used. Among them, magnetic wheels are commonly used for inspecting ferromagnetic pipes. While current robots are suitable for large pipes, they are not practical for smaller ones. To address this gap, a small-sized robot equipped with a magnetic wheel system that ensures both strong attachment and smooth movement along vertical ferromagnetic surfaces is developed. The robot’s magnetic adhesion performance was analyzed through simulations using finite element method magnetics and validated through laboratory experiments. The results show an average error of only 8.25% between simulation and real-world tests, confirming the system’s reliability for external pipe inspection.]]>
<![CDATA[SHIELD: Security based hybrid autonomous deep learning network for load balancing in cloud ]]>
<![CDATA[Kathirmalaiyan, Loga Priyadarshini]]>;
<![CDATA[Muthu, Nithya]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp439-449
<![CDATA[Load balancing in the Internet of Things (IoT) enhances the efficiency of the system by dynamically allocating tasks across devices and cloud resources. However, task scheduling struggles with unpredictable tasks, scalability, security risks, and unauthorized access control. To overcome these limitations, a novel security-based hybrid autonomous deep learning network for load balancing in cloud (SHIELD) framework has been proposed for secure task scheduling in cloud resources. Initially, the data received from the IoT devices is passed under certain security constraints to ensure the authenticity of the data. These privacy-preserved data are fed to the task scheduling module, which is employed by the dual DL Network to generate a schedule for resource management. Finally, cloud resources employ optimal allocation of tasks based on the generated schedule to ensure secure load balancing. The proposed framework is simulated by using Cloud Simulator 7G (CloudSim7G). The SHIELD framework is assessed by such metrics, including accuracy, recall, precision, F1-score, and specificity. In comparison, the proposed SHIELD framework achieves a privacy overhead of 14% outperforms the existing QODA-LB, Best-KFF, SPSO-TCS, and VMMISD techniques by achieving 10%, 11%, 12%, and 13% respectively.]]>
<![CDATA[Mobile robot replacement in multi-robot fault-tolerant formation ]]>
<![CDATA[Elsayed, Ahmed M.]]>;
<![CDATA[Elshalakani, Mohamed]]>;
<![CDATA[Hammad, Sherif Ali]]>;
<![CDATA[Maged, Shady Ahmed]]>
<![CDATA[ IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijra.v14i3.pp311-319
<![CDATA[Formation control in multi-robot systems (MRS) is essential for collaborative transport, environmental surveillance, material handling, and distributed monitoring. A major challenge in MRS is maintaining predefined formations or cooperative task execution when individual robots experience operational faults, potentially isolating them from the group. In mission-critical scenarios, preserving the number of operational robots is crucial for task success. To address this, we propose a Robot Replacement approach framework for differential wheeled mobile robots. This approach isolates faulty robots and dynamically replaces them with pre-deployed spares, ensuring uninterrupted formation tasks. A graph theory-based framework models inter-robot communication and formation topology, enabling decentralized coordination. The proposed techniques were implemented in a MATLAB/Simulink simulation environment. The simulated robots are equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders for navigation. Simulation results demonstrate that the framework successfully maintains the target formation and task continuity during robot failures by dynamically integrating replacements with minimal disruption.]]>
