For Authors

About Publishing in IJMLAIDSE

The International Journal of Machine Learning, AI & Data Science Evolution (IJMLAIDSE) is a fully open-access, peer-reviewed journal dedicated to publishing high-quality research in Machine Learning, Artificial Intelligence, and Data Science.

We welcome original research that advances theoretical foundations, introduces innovative algorithms, or demonstrates impactful real-world applications of intelligent technologies.

Our mission is to provide a leading global platform for cutting-edge computational research that drives technological progress and data-driven decision-making. By connecting researchers, industry professionals, and policymakers, IJMLAIDSE supports meaningful knowledge exchange and practical innovation in intelligent systems.

Key Features of IJMLAIDSE

• Open Access: All published articles are freely available worldwide, maximizing visibility, readership, and citation potential.
• Specialized Technical Scope: Focused coverage of Machine Learning, AI, Data Science, and interdisciplinary applications.
• Types of Content: Original research articles, review papers, technical reports, case studies, and special issue contributions.
• Global Reach: Accessible to academics, engineers, data scientists, and industry leaders worldwide.

For more detailed subject coverage, please refer to the Aim and Scope section.

Journal Metrics

IJMLAIDSE values the visibility and real-world influence of the research it publishes. Authors receive access to metrics that demonstrate the reach and engagement of their work.

Citation tracking reflects academic influence, while download and view statistics indicate global readership. Articles are also monitored using alternative metrics capturing online engagement, including mentions on research platforms, technical forums, policy discussions, and social media.

These indicators help authors understand how their research contributes to both scholarly advancement and technological innovation.

Quality Assurance

We maintain high editorial and ethical standards throughout the publication process. Each manuscript undergoes a rigorous peer-review procedure conducted by experts in Machine Learning, AI, and Data Science to ensure originality, technical accuracy, and research significance.

Our editorial policies emphasize transparency, fairness, and research integrity. Submissions are evaluated for methodological rigor, reproducibility, clarity of results, and responsible AI considerations where applicable.

Constructive reviewer feedback supports authors in refining their work to meet international research standards.

Is Your Research Relevant to IJMLAIDSE?

• Introduce novel algorithms, models, or AI frameworks
• Apply ML and data science techniques to solve complex real-world problems
• Address scalability, efficiency, or deployment of AI systems
• Explore ethical AI, fairness, transparency, and responsible data use
• Present case studies in healthcare, finance, smart cities, cybersecurity, education, or industrial AI

We encourage submissions reflecting technological advancement, practical implementation, and societal impact. Authors can explore our Scope section and previously published articles to better understand our focus areas.