Aim and Scope

Aim:
The International Journal of Intelligent Data and Machine Learning (IJIDML) aims to promote and disseminate innovative research that advances the theory and practice of intelligent data analysis and machine learning. The journal provides a high-impact platform for academics, researchers, and professionals to explore cutting-edge methods, tools, and applications in data-driven technologies and intelligent systems.

Scope:
IJIDML publishes original research articles, review papers, and case studies in a broad range of topics, including but not limited to:

• Machine Learning Algorithms and Optimization Techniques

• Deep Learning and Neural Networks

• Intelligent Data Mining and Knowledge Discovery

• Big Data Analytics and Scalable Data Processing

• Natural Language Processing and Text Mining

• Reinforcement Learning and Decision-Making Systems

• AI-Driven Predictive Modeling and Forecasting

• Data Privacy, Ethics, and Fairness in Machine Learning

• Computational Intelligence and Hybrid Systems

• Intelligent Recommender Systems

• AI Applications in Healthcare, Finance, Education, and Smart Cities

• Edge AI and Real-Time Data Analysis

• Feature Engineering and Dimensionality Reduction

• Graph Neural Networks and Structured Data Learning

• AutoML and Model Explainability

IJIDML welcomes interdisciplinary research that combines machine learning with fields such as computer vision, IoT, cybersecurity, bioinformatics, and social network analysis. The journal emphasizes original contributions with clear relevance, practical utility, and potential for advancing intelligent systems and data-driven decision-making.