IAES International Journal of Robotics and Automation (IJRA)
IAES International Journal of Robotics and Automation (IJRA), ISSN 2089-4856 | e-ISSN 2722-2586, is an open‑access, peer‑reviewed international journal that provides a global platform for researchers, scientists, engineers, and practitioners to publish high‑quality contributions in the broad areas of robotics, automation, manufacturing systems, and sensor technologies. IJRA emphasizes innovations that integrate motion, intelligence, sensing, and control to advance industrial productivity, precision manufacturing, healthcare technologies, environmental monitoring, and human–machine collaboration. Its scope explicitly covers core robotics (kinematics, dynamics, control, modeling, simulation, calibration, trajectory planning, mobile and humanoid robots), autonomous and intelligent systems (AI‑based robotics, machine learning, SLAM, autonomous navigation, swarm robotics), automation and control engineering (industrial automation, cyber‑physical systems, smart factories, PLC/SCADA), human–robot interaction and safety (collaborative robots, ergonomics, ethics, reliability), robotics applications (industrial, medical, agricultural, environmental, service, household, welding, inspection), robotics software and hardware (operating systems, middleware, embedded systems, sensors, actuators, vision systems), and emerging interdisciplinary areas (bio‑inspired robotics, nanorobotics, sustainability). In addition, IJRA covers the science and technology of sensors and their applications, including physical, chemical, biosensors, biomedical sensors, lab‑on‑a‑chip, smart sensors, sensor networks, IoT, MEMS/NEMS, image and vision sensors, 3D sensing, wearable devices, multi‑sensor navigation, and AI‑enabled sensing for robotics and automation. By bridging theory and practice, IJRA pushes the frontier of robotics and automation into new dimensions, ensuring that all published works are freely accessible online for maximum visibility and global impact. The journal is published by Intelektual Pustaka Media Utama (IPMU) in collaboration with the Institute of Advanced Engineering and Science (IAES) and is recognized (accredited) "SINTA 2" by the Ministry of Higher Education, Science, and Technology of the Republic of Indonesia. The journal was accepted for Scopus indexing on November 4, 2023, and it is now listed on the Scopus source page with coverage starting in 2023, carrying a CiteScore of 0.6 and SNIP of 0.063. In addition, the journal has also been indexed in Scimago Journal & Country Rank (ScimagoJR) with an SJR of 0.132.
Institute of Advanced Engineering and Science
This is to certify that the submitted manuscript is an original work, has not been published previously, and is not being considered for publication elsewhere. Upon acceptance, the article will be published by Intelektual Pustaka Media Utama (IPMU) under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). The authors retain the copyright to the work. We declare that: This manuscript has not been published previously in the same or substantially similar form. It is not currently under review or consideration for publication by any other journal. All necessary permissions have been obtained for the use of any third-party material included in this manuscript. We agree that upon publication, the article will be distributed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). License Grant We, the authors, retain all copyrights to the submitted article, and grant to IPMU the non-exclusive right to reproduce, publish, translate, and distribute the article in any form or medium, including print and electronic formats. The article may be used, shared, or adapted by others, provided that: Proper attribution is given to the original author(s) and source, A link to the license is included, Any changes are indicated, and Any derivative works are licensed under the same terms (ShareAlike). Full license details: https://creativecommons.org/licenses/by-sa/4.0 Author Responsibility The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who has obtained the consent of the co-author(s), if any. After submission of this agreement signed by the corresponding author, changes in authorship or order of authors will not be accepted. Retained Rights Authors retain all proprietary rights in any process, method, or material described in the work. Authors may distribute or reuse the work for personal or institutional use, or include it in other works, with appropriate credit and under the same license. Authors may deposit the published version of the article in institutional or public repositories.
Real-time low-drift global optimization for dynamic scene LiDAR SLAM localization
To address challenges like global drift, unstable matching, and high computational cost in light detection and ranging simultaneous localization and mapping (LiDAR SLAM) under complex conditions, this paper proposes an improved algorithm based on the LeGO-LOAM framework. A Newton-optimized normal distributions transform (NDT) is integrated to improve point cloud registration by constructing a negative log-likelihood objective and optimizing pose estimation. Using initial pose information from LeGO-LOAM accelerates convergence and enhances system robustness. This work addresses the problem of insufficient adaptability of existing algorithms in real scenarios. By deploying an independently designed four-wheel omnidirectional mobile robot platform, a hybrid LiDAR SLAM framework is used for precise positioning and map construction in complex campus environments, successfully reducing the positioning error to the centimeter level. Experiments on the KITTI dataset show a 43.51% reduction in maximum localization error and a 30.83% decrease in average error. Field tests in real-world campus environments with pedestrians, bicycles, and vehicles demonstrate strong reliability, adaptability, and resistance to interference. Horizontal error was reduced by about 58.26%, lowering the average error from 4.60 m to 1.92 m. Although computational load increases, it is offset by using high-performance LiDAR and processors. The enhanced accuracy and drift reduction significantly outperform traditional methods. At critical time points such as 50 seconds and 100 seconds, the system achieved high-precision pose estimation and accurate environmental reconstruction.
Copyright (c) 2026 Peiyan Yang, Jiuyang Yu, Pan Liu, Wenfeng Xia, Yaonan Dai http://creativecommons.org/licenses/by-sa/4.0
Hybrid force/position approach for flexible-joint robot with fuzzy-super twisting sliding mode control
This paper presents a novel hybrid force/position control strategy for rigid-link flexible-joint robots (RLFJR) operating in constrained environments. The proposed approach integrates fuzzy logic with the super-twisting sliding mode control (FSTSMC) algorithm to enhance robustness and reduce the chattering phenomenon typically associated with sliding mode controllers. A two-loop control structure is adopted: an inner loop dedicated to position control using the FSTSMC, and an outer loop for force regulation employing a classical PI controller. To address the challenge of limited joint state measurements in industrial robots, a high-gain nonlinear observer is designed for accurate joint state estimation. The effectiveness of the proposed method is validated through simulations on a PUMA 560 robot model, performing a circular trajectory while applying a constant contact force. Results demonstrate high tracking precision in both joint and Cartesian spaces, rapid convergence of errors, and significant mitigation of chattering effects, confirming the feasibility and efficiency of the proposed control scheme for interaction tasks involving flexible-joint manipulators.
Copyright (c) 2026 Rafik Amaini, Farid Ferguene http://creativecommons.org/licenses/by-sa/4.0
Experimental validation of a trajectory tracking controller for a two-wheeled mobile robot
One of the most important and challenging problems of any kind of autonomous mobile robot is the ability to accurately control its onboard actuators, enabling it to fulfill a specified task. In the case of a two-wheeled mobile robot, this can only be achieved through a pair of adequate steering control signals. The main goal of this paper is to design a nonlinear multivariable controller allowing a self-made mobile robot prototype to track a prescribed trajectory. The basic principle of this control approach uses the Lyapunov theory as a primary tool to derive two steering control laws, making a three-state error vector converge to zero. Tuning the proposed controller parameters is carried out using an equivalent dynamic simulated model. This controller is then applied to generate the resulting command signals to the actual robot. This is achieved through a real-time high-speed serial communication between a stationary personal computer (PC), on which a MATLAB/Simulink version of this controller is performing, and an onboard Microchip 16 bits dsPIC33FJ64MC802 microcontroller running a firmware that takes care of all the data exchange with the connected PC and a set of two proportional integral derivative (PID) controllers ensuring that the rotational speeds of the robot wheels are kept very close to those required by the main controller, running on this PC. The performance of the proposed controller is evaluated using two different shaped trajectories. These tests show that the robot is able to gradually follow the required path with minimal lateral error. The robustness of this controller is demonstrated through its capability to reject external disturbances triggered during these experimental tests.
Copyright (c) 2026 Boualem Kazed, Abderrezak Guessoum http://creativecommons.org/licenses/by-sa/4.0
Real-time control signal rectification and actuation mapping for robot joint control
This paper presents the control signal rectification and actuation mapping (CSRAM) framework, developed to improve the reliability and precision of real-time robot joint control. The framework integrates three modules, namely the drive signal rectifier (DSR), the signal pole detector (SPD), and the rising/downstream detector (RDD), which ensure signal compatibility, dynamic mapping consistency, and directional stability during actuation. Unlike conventional control converters, CSRAM effectively compensates for nonlinearities, latency, and synchronization issues in closed-loop systems. Experimental validation using a hexapod-to-quadruped (Hexaquad) robot showed that the proposed method, when combined with an anti-windup PI controller, reduced steady-state error from 14% to below 1%, improved transient and settling times by 0.3 to 0.4 seconds, and decreased three-dimensional trajectory RMSE by 63.7%. These results confirm that CSRAM provides a low-complexity and computationally efficient preprocessing layer for improving real-time performance in multi-joint and legged robotic systems, with strong potential for adaptive and industrial robotic platforms.
Copyright (c) 2026 Addie Irawan, Akhtar Razul Razali, Aliza Che Amran, Hamzah Ahmad http://creativecommons.org/licenses/by-sa/4.0
ISTD-LIOM: Direct LiDAR-inertial odometry and mapping with intensity-enhanced stable triangle descriptor
To address the cumulative drift problem of light detection and ranging (LiDAR)-inertial odometry (LIO) in long-duration localization and mapping tasks, this paper proposes a LiDAR-inertial odometry and mapping system, intensity-enhanced stable triangle descriptor-LiDAR-inertial odometry and mapping (ISTD-LIOM), based on the intensity-enhanced stable triangle descriptor (ISTD). This system, built on the FAST-LIO2 front-end architecture, achieves global consistency localization through loop closure detection and global optimization. First, we design the ISTD descriptor by combining geometric descriptors of triangles (including vertex plane normal vectors and edge lengths) with local intensity distribution descriptors to form a compact, rotation-invariant feature representation. Next, an adaptive keyframe management mechanism is constructed, which filters keyframes based on inter-frame relative poses and generates a descriptor database. A hybrid retrieval strategy is then proposed, which combines descriptor similarity matching and spatial distance filtering, forming an efficient loop closure candidate recognition mechanism. After applying plane iterative closest point (ICP) refinement and geometric-intensity consistency validation, the loop closure constraints are integrated into a pose graph optimization framework, correcting odometry drift. Experiments on the KITTI dataset demonstrate that the ISTD-LIOM system significantly enhances map global consistency while maintaining real-time computational performance.
Copyright (c) 2026 Lixiao Yang, Sheng Hua, Youbing Feng, Shangzong Yang, Jie Wang http://creativecommons.org/licenses/by-sa/4.0
Development of autonomous quadcopter unmanned aerial vehicle using APM 2.8 flight controller
This paper presents the development of a quadcopter unmanned aerial vehicle (UAV) using the APM 2.8 flight controller as the core of its navigation and control system. The project aims to design, assemble, and evaluate a stable and cost-effective quadcopter platform suitable for basic autonomous flight tasks such as waypoint navigation and altitude hold. The system incorporates essential components, including brushless DC motors, ESCs, a GPS module, a telemetry radio, and a power distribution system, integrated with the APM 2.8 running on the ArduPilot firmware. Waypoints are planned via Mission Planner software, with a flight control system embedded in the firmware. Real-world flight tests were conducted to evaluate the UAV’s performance in executing autonomously predefined survey grid and zigzag waypoints trajectories over open terrain. The root mean square error (RMSE) was calculated to assess the performance of waypoint tracking accuracy. The results show that the quadcopter UAV achieved an RMSE of 1.78 meters during zigzag waypoint tracking and 1.56 meters during survey grid, demonstrating reliable flight control performance offered by the APM 2.8 for basic autonomous mission tasks. This work highlights the feasibility of using APM 2.8 for cost-effective UAV development in research, education, and prototyping purposes.
Copyright (c) 2026 Mohd Yusuf Amran, Mohd Ariffanan Mohd Basri, Aminurrashid Noordin http://creativecommons.org/licenses/by-sa/4.0
Semantic segmentation for data validation in unmanned robotic vehicles
Semantic segmentation is a vital aspect of computer vision, widely used in fields such as autonomous driving, medical imaging, and industrial automation. Maintaining high-quality datasets is crucial for enhancing model accuracy and minimizing real-world errors. This paper focuses on developing a comprehensive data validation pipeline for semantic segmentation using OpenCV. The proposed framework integrates automated integrity checks, preprocessing techniques, and consistency verification to manage large-scale datasets effectively. Key validation processes include image quality assessment (detection of blurriness and noise), verification of annotation accuracy, class distribution analysis, and identification of anomalies. Additionally, OpenCV-powered preprocessing steps, such as image resizing, normalization, contrast optimization, and data augmentation, are applied to refine dataset quality for segmentation models. This paper also addresses scalability concerns associated with processing extensive datasets, introducing optimized batch handling and parallel validation techniques. By implementing a structured validation workflow, this research enhances the reliability, robustness, and overall effectiveness of semantic segmentation models, ensuring high-quality training data for deep learning applications.
Copyright (c) 2026 Ivan Sunit Rout, P Pal Pandian, Anil Raj, Anil Melwyn Rego, Sajna Parimita Panigrahi http://creativecommons.org/licenses/by-sa/4.0
A review of recent activities in Human Swarm Interaction (HSI) research is presented in this paper. The paper begins with providing a short description of swarming. It then discusses HSI and explains why it is beneficial to enable human operators to supervise swarms of robots. Then, a wide range of papers, which present novel methods for interacting with swarms of robots, are reviewed. Four control methods that can be used to transmit an operator’s intent to a swarm are also discussed. Levels of autonomy and flexible autonomy in HSI are furthermore described. At the end of the paper, a discussion of the gaps in knowledge that still must be filled to enable swarms of robots to operate in the real world is presented. It is suggested that more research into techniques for remote interaction with robotic swarms be conducted. This includes methods that enable remote interaction with swarms of swarms. More work on HSI in degraded communications environments is also required. Additional research into swarm autonomy is furthermore needed to facilitate efficient supervisory control. Lastly, there is room for more work on trust in HSI, as robotic swarms can only be used by humans if they can be trusted.
Copyright (c) 2026 Jan Carlo Barca http://creativecommons.org/licenses/by-sa/4.0
Remote procedure call communication and control of autonomous mobile robot for indoor smart waste monitoring
The integration of autonomous mobile robots (AMRs) and Internet of Things (IoT) technology has revolutionized various industries, including smart waste management (SWM). In this paper, the implementation of a customized remote procedure call (RPC) methodology was successfully demonstrated. This methodology facilitated control and monitoring of AMRs for smart indoor waste management to collect and dispose waste, monitor bin threshold levels and report relevant parameters to a cloud-based platform. Key operational parameters from the AMR and the smart bins via assembled user smart dashboard ensures seamless user monitoring for indoor waste management. Our findings underscore the relevance of RPC in advancing smart waste management technologies, contributing to operational efficiency and sustainability.
Copyright (c) 2026 Ashaari Yusof, Abdullah Man, Azmi Ibrahim, Mohamed Ashraf Husni Zai, Md. Jakir Hossen http://creativecommons.org/licenses/by-sa/4.0
A study on motivated consumer innovativeness in robotic golf caddies
The current study examined the antecedents and consequences of image in the field of robotic golf caddy. Data were collected from 393 golfers in Korea. The data analysis revealed that functionally, hedonically, and cognitively motivated consumer innovativeness are the key factors that affect image. It was also found that image helps in regard to enhancing desire, and then it positively affects intentions to use and WOM intention. Perceived price unfairness of caddy fees additionally moderated the relationship between functionally motivated consumer innovativeness and image. This study is significant from a theoretical perspective as it is the first to identify consumer motivations in the field of robotic golf caddies. From a practical standpoint, the findings offer important implications for the development of marketing strategies for robotic golf caddies, which are currently at the commercialization stage.
Copyright (c) 2026 Jinsoo Hwang, Sujin Song, Sungbeen Park http://creativecommons.org/licenses/by-sa/4.0
Autonomous reconstruction of strip-shredded documents via self-supervised deep learning and global optimization
Autonomous reconstruction of mechanically shredded documents is a labor-intensive challenge in forensic and archival workflows, particularly for scripts with complex structures such as Simplified Chinese. While traditional manual reassembly is tedious, existing digital tools typically rely on extensive human intervention. This paper presents an automated reassembly framework that integrates a lightweight convolutional feature extractor with global combinatorial optimization. By adapting the established SqueezeNet v1.1 backbone, we employ a task-specific self-supervised learning strategy trained on synthetically shredded samples, enabling the adapted model to capture local stroke continuity and edge-geometry cues without manual annotation. The framework infers pairwise relationships from calibrated edge-region inputs, organizing compatibility scores into an asymmetric traveling salesman problem (ATSP) formulation. The optimal fragment sequence is solved deterministically using the Concorde TSP solver, yielding a globally consistent reconstruction. Experimental results on physically shredded documents demonstrate reconstruction accuracies of 86.5% for Simplified Chinese and 94.8% for Western scripts. These results indicate that the proposed pipeline effectively generalizes from synthetic training data to real-world scenarios, providing a practical, high-throughput foundation for automated document recovery under computational constraints typical of robotic or embedded systems.
Copyright (c) 2026 Yi-Chang Wu, Pei-Shan Chiang, Yao-Cheng Liu http://creativecommons.org/licenses/by-sa/4.0
Modeling and control of a 3D under-actuated bipedal robot using partial feedback linearization
This article presents a dynamic modeling and control framework for a 3D underactuated five-link bipedal robot with 14 degrees of freedom (DoF) and eight actuators. The robot exhibits highly nonlinear, strongly coupled, and hybrid dynamics, posing challenges for conventional control approaches. To address these issues and introduce our research contribution, a partial feedback linearization (PFL)-based tracking framework is proposed, which analytically decouples the system into actuated and unactuated subsystems, enabling efficient real-time control. Unlike hybrid zero dynamics (HZD) methods that enforce virtual constraints online and require offline gait optimization, or model predictive control (MPC) schemes that are online optimization based dependent and computationally demanding, the proposed PFL approach achieves computational simplicity and fast implementation through closed-form control laws. In contrast to zero-moment point (ZMP)-based controllers, PFL enables dynamic underactuated walking with PD feedback for accurate trajectory tracking and disturbance attenuation, though robustness to large uncertainties and disturbances may require additional mechanisms, such as adaptive control, sliding-mode, or fuzzy logic. Simulation results of the applied control method demonstrate the periodic nature and stability of generated walking gaits, which proves the effectiveness and reliability of the proposed control approach.
Copyright (c) 2026 Ali Guessam, Foudil Abdessemed, Abdelmadjid Chehhat http://creativecommons.org/licenses/by-sa/4.0
Multi-modal transformer and convolutional attention architectures for melanoma detection in dermoscopic images
The deadliest type of skin cancer, melanoma, requires early and accurate detection for a successful course of treatment. Traditional diagnostic techniques, which rely on visual inspection and dermoscopy, are frequently arbitrary and prone to human error. Automated melanoma detection exemplifies the integration of multimedia, a truly interdisciplinary field that melds visual data processing, human-computer interaction, and digital technologies. This study presents a multi-modal architecture: a multi-modal transformer network (MMTN) and a convolutional attention mechanism multi-modal (CAMM) that combines clinical data and dermoscopy images to enhance melanoma detection. The models achieve higher performance compared to other approaches by utilizing the strengths of architecture based on transformers, an encoder for image processing, dense layers for clinical data also Spatial Attention for the second architecture proposed. We evaluate the models on the entire set of ISIC 2019 data, showing significant improvements in accuracy and AUC. The models achieve high accuracy and AUC using CPU in both architectures. Our findings highlight the potential of a multi-modal learning architecture to enhance clinical decision-making and diagnostic accuracy in dermatology. To our knowledge, this is the first implementation combining MobileNet, transformer encoder attention, and clinical data fusion for the ISIC 2019 dataset, providing a significant advancement in the automated categorization of skin malignancies.
Copyright (c) 2026 Guidoum Amina, Maamar Bougherara, Amara Rafik http://creativecommons.org/licenses/by-sa/4.0
Cascading automata to improve efficiency of large language models agents with GraphRAG for error analysis
Robotic process automation (RPA) has been deployed in a plethora of industries, including the banking and insurance sectors. However, the key challenge of handling unexpected situations manifests either as an inadequacy of programming (since all situations cannot possibly be foreseen) or incongruous inputs. In parallel, deep learning models, including large language models (LLMs) and visual language models (VLMs), have shown human-like cognitive capabilities in real-world tasks, germinating the field of agentic LLMs. However, their computational expense, slow inference times, and massive energy consumption impede large-scale usage. We propose a framework that combines the two approaches to enable expedient invocation of LLMs for handling exceptions and supervising RPA bots. It aims to minimize the need for human supervision by “meta” automation, while also reducing energy usage and processing time. The automation workflow is presented as a graph, and our pipeline uses the GraphRAG framework to analyze and fix errors. We demonstrate the potential of our pipeline through two real-world examples in the banking and insurance sectors, provide our GitHub repository for reproducibility, and conclude with future research directions.
Copyright (c) 2026 Hrishikesh K. Haritas, Vineet H. Sadarangani, Ganeshayya Ishwarayya Shidaganti, Darshan Bankapure, Rahul K. Vishal, Shreya Vijayasimha http://creativecommons.org/licenses/by-sa/4.0
A comparative look at how emerging technologies evolve to managing otitis media
Otitis media (OM) is an epidemic of middle ear infection in tens of millions of patients across the globe, most vulnerable of whom are children, with hearing loss and other negative consequences unless treated. Conventional diagnosis and treatment are marred by failure to diagnose, service shortage, and delayed diagnosis. This present paper is directed towards a comparative outlook of the newly emerging technologies, such as artificial intelligence (AI), machine learning, telemedicine, and wearable biosensors, that are revolutionizing the management of OM. We emphasize the way such devices enhance diagnostic accuracy, facilitate remote and real-time monitoring, and provide tailored treatment schemes. Our approach is more sophisticated compared to the currently available state-of-the-art methods reported in the literature based on real-time telemedicine systems, multimodal data fusion, and interpretable AI. Privacy issues of information, model generalizability issues, and technological adoption barriers are also discussed. The results also substantiate that adoption of these advanced devices can effectively reduce OM's burden globally and improve patient outcomes.
Copyright (c) 2026 Divya Pandey, Monisha Awasthi, Dharmendra Kumar, Deepak Kumar Pant, Ankur Goel http://creativecommons.org/licenses/by-sa/4.0
Vibration control of semi-active suspension system using super-twisting sliding mode controller
The development of suspension systems arises from the impact of vehicle vibrations caused by road irregularities on passengers. Among various suspension systems, semi-active suspension (SAS) is favored for its cost-effectiveness and power efficiency. Magnetorheological (MR) dampers are commonly used in SAS to enhance vibration control by adjusting the magnetic field. However, the traditional sliding mode control (SMC) method often causes chattering, which affects performance. This study proposes the application of a super-twisting sliding mode controller (STSMC) to improve vibration control in SAS and overcome the chattering problem. Simulations and experimental evaluations were conducted on a quarter-car test bench with different road excitations. The results show that the STSMC-based system outperforms the traditional controller in vibration suppression. Specifically, the suppression effect on the root mean square value of body acceleration on a sinusoidal road surface can reach up to 38.2%. Therefore, the STSMC controller demonstrates superior vibration control in SAS systems equipped with MR dampers, providing a valuable reference for future research on SAS vibration control.
Copyright (c) 2026 Liuding Sun, Siti Azfanizam Ahmad, Jun Kit Ong, Suhadiyana Hanapi, Azizan As’arry http://creativecommons.org/licenses/by-sa/4.0
Design and drag force analysis of an autonomous underwater remotely operated vehicles for coral reef health assessment
This research presents the conception and building of an inexpensive remotely operated vehicle (ROV) system to ease the tasks of underwater inspection and environmental monitoring in areas where the global positioning system (GPS) signal is not available. A Raspberry Pi-based control unit, an inertial measurement unit (IMU), and depth sensors are merged in the system so that simple data acquisition and remote operation can be carried out. ROV hydrodynamic drag and stability for a state of ideal balance and maneuverability were assessed through tests based on preliminary simulations in Fusion 360 and empirical calculations. The ROV is confirmed to be behaving as expected in terms of stability, imaging capabilities, and responsiveness to operator control in the testing that was done in controlled water environments. This paper, the work, and the testing, in fact, present the initial design, but it is a significant step towards the consideration of the possible further embedding of autonomous features “simultaneous localization and mapping (SLAM)-based navigation, doppler velocity log (DVL), light detection and ranging (LiDAR) systems” for completely autonomous underwater guided missions.
Copyright (c) 2026 Pandiyarajan Rajendran, Srinivasan Alavandar http://creativecommons.org/licenses/by-sa/4.0
Emotion recognition and classification using Inception EfficientNet based on electroencephalography signals
Emotions are intricate psychological phenomena arising from the interaction of internal cognitive states and external environmental inputs. The manual extraction of electroencephalography (EEG) signals results in less optimal performance of learning models. To overcome this, a novel EEG-based emotion recognition and classification (EEG-EMRE) model has been proposed for the detection and classification of emotions. Initially, the input EEG-Signals are pre-processed using quantum signal processing (QSP) to enhance the quality by removing the noise from the signal. The enhanced signals are fed into an improved Inception EfficientNet for extracting the relevant features. The Penta types of emotions, such as happy, sad, anger, scared, and anxiety, are classified using a bidirectional-k nearest neighbors (KNN) classification network. The performance of the proposed EEG-EMRE approach is evaluated using the F1-Score, recall, specificity, accuracy, and precision. The proposed Inception EfficientNet for feature extraction network improves the overall accuracy by 0.41%, 1.52%, 0.63%, 1.55% better than ResNet, AlexNet, GoogleNet, and DenseNet. The proposed EEG-EMRE method achieves an overall accuracy by 0.68%, 1.77%, and 0.52% better than the linear formulation of differential entropy (LF-DfE), extreme learning machine wavelet auto encoder (ELM-W-AE), and attention-based convolutional transformer neural network (ACTNN), respectively.
Copyright (c) 2026 Jananee J, Emerson Solomon F, Sundar Raj M http://creativecommons.org/licenses/by-sa/4.0
Sentiment aware interactive Chatbot AI using multi agent processing model
Understanding user sentiment has become more important for organizations and consumers due to the rapid growth of social media platforms such as marketplaces, platforms for connecting brands and consumers, and public discussion platforms. Emotions that are based on aspects, nuanced within context, and multifaceted often require complex sentiment analysis algorithms to interpret properly. Furthermore, these systems do not provide real-time information to help companies make better decisions and enhance consumer satisfaction. To tackle these challenges, a novel Interactive Chatbot artificial intelligence (IChat-AI) approach has been proposed in this paper for sentiment-aware chatbot interaction. The word to vector (W2V), term frequency-inverse document frequency (TF-IDF), and bag of words (BoW) are utilized to effectively extract essential features. The deep Kronecker neural network (DKNN) is utilized to predict and classify the emotions into five classes, such as sad, happy, neutral, angry, and fearful. Python has been used to simulate the suggested model. The efficacy of the suggested system is examined employing parameters including recall, execution time, F1-score, complexity, precision, scalability, accuracy, and response time. The developed IChat-AI strategy performs better regarding accuracy than the existing methods, including RoBERTa, TLSA, and multimodal transformers fusion for desire, emotion, and SA (MMTF-DES) approaches, by 5.33%, 4.73%, and 14.39%.
Copyright (c) 2026 Vinod Kumar Shukla, Sumithra Alagarsamy, Vijaylakshmi Nagarajan, Gavaskar Shanmugam http://creativecommons.org/licenses/by-sa/4.0
Modeling and simulation of an active quarter-car suspension system using a synergetic controller
This paper presents the modeling and simulation of an active quarter-car suspension system (AQCSS) designed to enhance operational performance and ride comfort across various road conditions. First, a dynamic quarter-car model was developed, incorporating all the components of AQCSS and road-induced stimuli, based on the Euler–Lagrange method. Subsequently, a synergetic controller is designed by selecting a manifold that meets the system’s technical requirements. The proposed controller ensures a balance between ride comfort and road-holding performance by leveraging this manifold design. This control framework enables flexible adjustment of the damping force in real time according to the system states and external excitations. The stability of the closed-loop system is rigorously established through Lyapunov analysis. Numerical simulations are carried out in MATLAB to assess the proposed control law by benchmarking it against a passive suspension configuration and a sliding mode control approach, thereby demonstrating its effectiveness.
Copyright (c) 2026 Dao Trong Dung, Trong Nghia Le, Alexandr D. Lukyanov, Nguyen Xuan Chiem http://creativecommons.org/licenses/by-sa/4.0
System design for hydrogen gas detection with intelligent embedded communication and Internet of Things integration
The design and analysis of an embedded hydrogen gas detection system embody a complex engineering challenge that integrates sensor technology, embedded system design, and safety engineering. The fast advancement of microcontrollers, energy-efficient electronics, and sophisticated sensing algorithms is facilitating the creation of compact, efficient, and dependable hydrogen detection solutions. The research case article focuses on the hardware system design of Hydrogen gas detection. The hydrogen gas detection system includes an MQ8 sensor for gas sensing, a Raspberry Pi-4 as the main controller, Zigbee for wireless communication, a 16×2 liquid crystal display (LCD) for display, a light emitting diode (LED), and a buzzer for alerts, along with supporting circuitry for signal processing. The gas concentration is monitored and verified through the Thingspeak.com Internet of Things platform, which enables wireless data transmission. The designed system is verified based on environmental factors such as temperature and humidity for comprehensive analysis. The system response was analyzed and tested under different threshold conditions, including 300 ppm and 1000 ppm.
Copyright (c) 2026 Shyam Kumar Menon, Adesh Kumar, Surajit Mondal http://creativecommons.org/licenses/by-sa/4.0
EdgeRetina: Hybrid multimedia architecture for diabetic retinopathy screening on low-cost mobiles
Diabetic retinopathy (DR) is a major cause of preventable blindness, particularly in areas with limited medical resources where access to ophthalmologists is critical. Existing automated solutions struggle to balance clinical performance, cost-effectiveness, and robustness in the face of fundus image variability—including lighting differences, artifacts, and uneven capture quality. To address this challenge, we propose EdgeRetina, an integrated solution for diabetic retinopathy screening on low-cost mobiles. Our approach combines lightweight preprocessing (128×128 resizing, intensity normalization, and targeted augmentations simulating real-world conditions) with a hybrid SqueezeNet-MobileViT architecture (1.4 million parameters), optimized by dynamic threshold calibration (median: 0.3), maximizing clinical utility. Clinically calibrated INT8 quantization reduces the model to 8.27 MB (-92%) without altering diagnostic performance (sensitivity of 90.7% for referable diabetic retinopathies), while preserving compatibility with floating point 32 (FP32)-based gradient-weighted class activation mapping (Grad-CAM) visualizations. Evaluated on the APTOS 2019 dataset, this solution achieves an AUC of 0.96 with a latency (inference time) of 15.43 ms, reducing CPU consumption by 43% compared to FP32. The dynamic threshold/INT8 coupling decreases false positives by 71.4%. This pipeline thus enables accurate, accessible, and early screening of diabetic retinopathy on low-cost mobile devices, combining operational efficiency and diagnostic reliability in constrained environments, which is crucial to prevent avoidable blindness.
Copyright (c) 2026 Guidoum Amina, Achour Soltana, Maamar Bougherara, Amara Rafik, Mhamed Tayeb http://creativecommons.org/licenses/by-sa/4.0
An improved black-winged kite algorithm optimized back-propagation neural network for biceps curl classification
Accurately identifying and classifying biceps curl types is of vital importance for sports training and upper limb joint rehabilitation training. It can improve the effect and reduce the risk of injury caused by incorrect training. In this study, a dataset of biceps curl training was obtained by measuring wearable sensors. After data preprocessing, 340 samples of 35-dimensional feature data were obtained. The classification labels of the dataset were marked as 1-5 according to the five types of biceps curl. This study proposed a black-winged kite algorithm (IBKA) that uses the good point set (GPS) method and the adaptive spiral search rule, a multi-strategy. IBKA optimized the initial weights, biases, and hidden layer numbers and provided them to the back-propagation neural network (BPNN) to establish the IBKA-BPNN model. The constructed IBKA-BPNN model improved the classification accuracy of the training set from 79.83% to 94.54%, and the accuracy of the test set from 69.61% to 88.33%. The IBKA-BPNN model proposed in this study provides a reliable decision-making basis for real-time coaching, athlete performance analysis, and upper limb rehabilitation. Future work will expand the dataset, integrate more bio signals, and explore lightweight deployment on wearable hardware.
Copyright (c) 2026 Chunqing Liu, Kim Geok Soh, Hazizi Abu Saad, Haohao Ma http://creativecommons.org/licenses/by-sa/4.0