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IAES International Journal of Robotics and Automation (IJRA)
ISSN : 20894856     EISSN : 27222586     DOI : -
Core Subject : Engineering,
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 533 Documents
Textile industry innovation: systematic review of key trends and particularities Sebastián Cardona-Acevedo; Alejandro Arango-Correa; Diana Carolina Rios Echeverri; Alejandro Valencia-Arias; Jhon Edward Aguirre Cuervo
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp720-736

Abstract

Innovation in the textile industry is a key strategic factor, influenced by geographic disparities, structural challenges, and rapid technological change. However, fragmented knowledge makes it difficult to fully understand the phenomenon. This study aimed to analyse how various types of innovation appear and interact in the global textile sector. A systematic literature review was carried out following PRISMA 2020 guidelines, using Scopus and Web of Science databases. From an initial pool of 94 articles, 19 met the inclusion criteria. Findings reveal that beyond specific advancements like automation or smart textiles, structural tensions hinder the integrated adoption of technological, organisational, and sustainability innovations. The diversity of analytical approaches shows there is no single, unified path to innovation in this sector. Instead, multiple innovation trajectories coexist, influenced by local conditions and unequal institutional capacities. In addition, knowledge gaps between developed and emerging regions, as well as the lack of focus on early stages of the supply chain, highlight the need to rethink research priorities. Ultimately, innovation in the textile industry must be understood as a comprehensive process that brings together technology, organisational change, and sustainability, requiring a holistic approach to improve competitiveness and ensure long-term transformation across the sector.
Graph-guided contrastive transformer architecture for robust and explainable network intrusion detection Archana Jayapal; Kamalakkannan Somasundaram; Arun Kumar Ramamoorthy
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp698-708

Abstract

Intrusion detection systems (IDS) are very instrumental in protecting contemporary network infrastructures against the ever-advancing cyberattacks. Conventional signature-based and machine learning-enabled IDS solutions frequently have difficulty when it comes to high false-positive rates, inability to flexibly adapt to novel attacks, and the lack of support for complex traffic dynamics. New deep learning architectures have better detection properties, yet are limited by feature overlap, temporality, and lack of extensiveness to generalization in changing network conditions. To overcome these issues, this paper presents a new graph-guided contrastive transformer-based intrusion detection system (GCT-IDS) which aims at improving detection accuracy and robustness and preserving real-time feasibility. The framework combines feature interaction by graph modeling, contrastive representation learning, and a sparse self-attention transformer to effectively learn global traffic relationships and behavioral variations. The CSE-CIC-IDS2018 data is used to test the proposed method in real network conditions.
AI driven automated library assistance using pick-to-light system Archana S. Ubale; Vaishali Baste; Harshada Bhushan Magar; Nilakshee R. Rajule
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp639-646

Abstract

This paper presents the design and implementation of a library assistance system that uses a pick-to-light mechanism and integrates AI-based book recommendations. Book retrieval done manually in libraries is frequently time-consuming, prone to errors, and inefficient. As a solution, we will suggest a hybrid library assistance system, which would be based on an ATmega328 microcontroller, user identification by RFID, pick-to-light, and an AI-based collaborative filtering recommender. The user is directed to the preferred books through the LEDs placed on the shelves, and the AI module gives the user personalized suggestions. The experimental outcomes of 100 users show significant advances over the old traditional manual processes. The time spent searching for a book dropped by 96.4 seconds to 34.8 seconds, the error rate dropped to 3.2%, user satisfaction went up to 4.6, and the success rate of the entire book search process went up to 98.1%. The 500 retrieval cycles of the stress testing showed stability, reliability, and uniformity of the system, and the performance of the LED-RFID. These findings suggest that the proposed system is highly efficient for the library, minimizes human error, and positively impacts the user experience, making it applicable to real-life library environments.

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