Editor-in-chief
- Dr. Parikshit N. Mahalle
The Vishwakarma Journal of Artificial Intelligence (VJAI) is a peer-reviewed, open-access journal dedicated to the latest advancements in Artificial Intelligence (AI) and its applications. VJAI serves as a global platform for researchers, academicians, industry professionals, and students to publish high-quality research that contributes to the development and implementation of AI technologies.
The journal aims to bridge the gap between theoretical AI research and real-world applications by fostering collaboration among researchers from academia and industry. VJAI covers a broad spectrum of AI-related topics, including machine learning, deep learning, natural language processing, computer vision, robotics, AI ethics, and AI-driven innovations.
With the rapid evolution of AI technologies, VJAI seeks to provide timely insights into emerging trends, challenges, and solutions in artificial intelligence. By following a rigorous peer-review process, the journal ensures that only high-quality and original research is published, making it a valuable resource for AI enthusiasts, researchers, and practitioners worldwide.
VJAI follows an open-access policy, allowing free and unrestricted access to published research. This ensures that knowledge dissemination is not limited by financial barriers, enabling AI researchers from different regions to collaborate and innovate.
The journal is published regularly and welcomes various types of contributions, including original research papers, review articles, case studies, technical notes, and short communications. It adheres to strict ethical publishing standards, ensuring integrity and reliability in scientific research.
Scope and Areas of Research:
The Vishwakarma Journal of Artificial Intelligence (VJAI) focuses on a wide range of topics related to AI. The journal encourages interdisciplinary research that combines AI with other fields such as healthcare, finance, education, robotics, and more. Key areas of research include:
- Machine Learning & Deep Learning
- Supervised, unsupervised, and reinforcement learning
- Neural networks and deep learning architectures
- Transfer learning and self-supervised learning
- Explainable AI (XAI)
- Natural Language Processing (NLP)
- Text analysis and sentiment detection
- Speech recognition and generation
- Machine translation
- Large language models (LLMs)
- Computer Vision & Image Processing
- Object detection and recognition
- Facial recognition and biometrics
- Medical image analysis
- Generative AI for image synthesis
- AI in Robotics & Automation
- Human-robot interaction
- Autonomous vehicles and drones
- AI-driven industrial automation
- Swarm intelligence
- AI in Healthcare & Medicine
- AI-powered diagnostics and imaging
- Drug discovery and genomics
- Personalized medicine and AI-driven healthcare systems
- Remote patient monitoring
- Ethics, Fairness, and AI Governance
- Bias and fairness in AI algorithms
- AI regulations and policy frameworks
- Privacy and security concerns in AI
- Ethical considerations in autonomous AI systems
- AI Applications in Various Industries
- AI in finance (fraud detection, algorithmic trading)
- AI in education (adaptive learning, AI tutors)
- AI in agriculture (smart farming, precision agriculture)
- AI in cybersecurity (threat detection, risk assessment)