About the Journal
Aim & Scope
Aim
The primary aim of the International Journal of Generative AI and Machine Learning (IJGAIML) is to provide a high-quality international platform for the dissemination of original research and scholarly knowledge in Generative AI, Machine Learning, Deep Learning, and intelligent systems.
The journal seeks to promote research that addresses both the theoretical foundations and practical challenges of developing intelligent computational systems. It encourages innovative approaches that improve the performance, scalability, interpretability, security, reliability, and responsible deployment of AI technologies.
Scope
The journal welcomes original research articles, review papers, case studies, technical studies, and application-oriented research covering, but not limited to, the following areas:
- Generative Artificial Intelligence
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Diffusion Models
- Large Language Models (LLMs)
- Foundation Models
- Multimodal Generative AI
- Generative AI for Text, Image, Audio and Video
- Retrieval-Augmented Generation (RAG)
- AI Agents and Agentic AI
- Natural Language Processing and Understanding
- Machine Translation and Conversational AI
- Speech Recognition and Speech Generation
- Computer Vision and Image Generation
- Deep Learning and Neural Networks
- Reinforcement Learning
- Supervised, Unsupervised and Semi-Supervised Learning
- Self-Supervised and Transfer Learning
- Federated and Distributed Machine Learning
- Explainable and Interpretable AI
- Responsible and Trustworthy AI
- AI Ethics, Governance and Regulation
- AI Safety, Security and Privacy
- Adversarial Machine Learning
- Automated Machine Learning (AutoML)
- Few-Shot, Zero-Shot and In-Context Learning
- Knowledge Graphs and Neural-Symbolic AI
- Recommender and Intelligent Decision-Support Systems
- Predictive Analytics and Intelligent Data Mining
- Edge AI and TinyML
- AI for IoT and Smart Systems
- Robotics and Autonomous Intelligent Systems
- Generative AI in Healthcare and Biomedical Applications
- Generative AI in Education and E-Learning
- AI in Engineering and Scientific Computing
- AI in Finance, Business and Management
- AI in Cybersecurity
- AI in Software Engineering and Programming
- AI for Digital Transformation
- Human-AI Interaction and Collaboration
- AI Optimization and Computational Intelligence
- Benchmarking, Evaluation and Performance Analysis of AI Models
- Emerging Trends and Future Directions in Generative AI and Machine Learning
The journal particularly welcomes interdisciplinary and application-driven research demonstrating how Generative AI and Machine Learning can address complex real-world problems. Studies presenting novel algorithms, architectures, datasets, evaluation methodologies, AI applications, and empirical findings are encouraged.
International Journal of Generative AI and Machine Learning (IJGAIML) is committed to supporting the exchange of innovative ideas and research findings that contribute to the responsible advancement of next-generation artificial intelligence and machine learning technologies.
