https://isjr.co.in/index.php/ITAI <p><strong>International Transactions in Artificial Intelligence</strong></p> <p><strong>Journal Scope:</strong></p> <p><em>International Transactions in Artificial Intelligence</em> is a premier peer-reviewed journal dedicated to advancing the field of artificial intelligence (AI) through high-quality research and contributions from scientists, researchers, and practitioners across the globe. The journal aims to provide a platform for the dissemination of cutting-edge research, innovation, and knowledge in the diverse and rapidly evolving field of AI.</p> <p><strong>Key Focus Areas:</strong></p> <p><em>International Transactions in Artificial Intelligence</em> covers a wide range of topics and research areas within the field of AI. The journal's scope includes, but is not limited to, the following key focus areas:</p> <ol> <li> <p><strong>Machine Learning</strong>: Theoretical foundations, algorithms, and applications of machine learning, including deep learning, reinforcement learning, and other related methodologies.</p> </li> <li> <p><strong>Natural Language Processing</strong>: Research on understanding, generating, and processing human language, including sentiment analysis, language modeling, and machine translation.</p> </li> <li> <p><strong>Computer Vision</strong>: Advancements in computer vision, image and video analysis, object recognition, and scene understanding.</p> </li> <li> <p><strong>AI Ethics and Governance</strong>: Exploration of ethical, legal, and societal implications of AI, as well as strategies for responsible AI development and deployment.</p> </li> <li> <p><strong>AI Applications</strong>: Practical applications of AI in various domains, including healthcare, finance, education, and industry, highlighting real-world use cases and case studies.</p> </li> <li> <p><strong>Reinforcement Learning</strong>: Research on reinforcement learning algorithms and their applications in robotics, game playing, and autonomous systems.</p> </li> <li> <p><strong>AI in Robotics</strong>: Integration of AI techniques with robotics, including robot perception, motion planning, and human-robot interaction.</p> </li> <li> <p><strong>AI for Problem Solving</strong>: Techniques for problem-solving, reasoning, and decision-making using AI, including knowledge representation and expert systems.</p> </li> <li> <p><strong>AI and Healthcare</strong>: Innovative AI solutions for healthcare, including medical imaging, disease diagnosis, and patient care improvement.</p> </li> <li> <p><strong>AI and Education</strong>: Utilization of AI in educational technology, personalized learning, and intelligent tutoring systems.</p> </li> </ol> <p><strong>Publication Formats:</strong></p> <p>The journal publishes a wide range of article types, including:</p> <ul> <li>Original Research Papers</li> <li>Review Articles</li> <li>Short Communications</li> <li>Case Studies</li> <li>Survey Papers</li> <li>Technical Notes</li> </ul> <p><strong>Editorial Board:</strong></p> <p><em>International Transactions in Artificial Intelligence</em> boasts a distinguished editorial board comprising experts and researchers from diverse subfields of AI. The editorial board ensures the highest standards of quality and rigor in the review process.</p> <p><strong>Audience:</strong></p> <p>This journal is a valuable resource for researchers, academics, industry professionals, policymakers, and students interested in the latest developments and breakthroughs in artificial intelligence. It serves as a platform for exchanging ideas, fostering collaboration, and shaping the future of AI.</p> <p><em>International Transactions in Artificial Intelligence</em> is committed to fostering excellence and innovation in AI research and welcomes contributions that advance the understanding and application of AI across various domains. Researchers and practitioners are invited to submit their work to be considered for publication in this esteemed journal.</p> <p><strong>Impact Factor:</strong> 7.565</p> en-US Fri, 24 Jan 2025 06:22:03 +0000 OJS 3.3.0.13 http://blogs.law.harvard.edu/tech/rss 60 https://isjr.co.in/index.php/ITAI/article/view/347 <p>The use of Artificial Intelligence (AI) in vocational education raises several ethical challenges, particularly in ensuring fairness, transparency, and accountability in AI-driven systems. This paper investigates the ethical implications of deploying AI technologies in vocational training, with a focus on issues such as algorithmic bias, data privacy, and decision-making transparency. Through a review of existing literature and case studies, the paper identifies the potential risks associated with AI in vocational education and proposes guidelines for ethical AI deployment. The study also emphasizes the importance of human oversight and regulatory frameworks to ensure that AI applications in vocational education promote equitable and responsible learning environments.</p> Prof. Chan Koi Copyright (c) 2025 https://isjr.co.in/index.php/ITAI/article/view/347 Fri, 10 Jan 2025 00:00:00 +0000 https://isjr.co.in/index.php/ITAI/article/view/371 <p>The integration of legacy systems with modern microservices architectures has become a critical challenge for organizations aiming to achieve digital transformation while preserving existing investments. This paper presents a comprehensive approach that combines RESTful web services with AI-based data mapping techniques to enable seamless interoperability between outdated monolithic systems and scalable microservices environments. Legacy systems, often constrained by rigid data schemas, proprietary protocols, and limited scalability, hinder organizational agility and innovation. To address these challenges, the proposed framework utilizes RESTful APIs as a standardized communication layer, enabling loosely coupled interactions and facilitating real-time data exchange across heterogeneous platforms. Furthermore, the incorporation of artificial intelligence, particularly machine learning-based data mapping models, automates the transformation and alignment of legacy data structures with modern microservice schemas. This significantly reduces manual intervention, minimizes integration errors, and enhances data consistency. The methodology includes intelligent schema matching, semantic data interpretation, and adaptive learning mechanisms that improve mapping accuracy over time.</p> Mallikarjun Bellundagi Copyright (c) 2026 https://isjr.co.in/index.php/ITAI/article/view/371 Thu, 24 Jul 2025 00:00:00 +0000