Aim

The European Journal of Emerging Data Science and Machine Learning (EJEDSML) aims to advance pioneering research and innovation in data science, machine learning, and artificial intelligence by providing a dynamic international platform for scholars, researchers, practitioners, and industry experts. The journal is committed to promoting scientific excellence, ethical AI development, and impactful research that accelerates technological transformation, informs real-world decision-making, and strengthens the global knowledge ecosystem in intelligent computing.

EJEDSML seeks to bridge theoretical advancements, computational methodologies, and industrial applications, nurturing a collaborative research environment that supports the evolution of data-driven innovation and smart technology ecosystems.

Scope

EJEDSML publishes original, high-quality contributions that explore emerging ideas, novel algorithms, analytical models, computational frameworks, and applied research across all dimensions of data science and machine learning.

Core Areas of Interest

Data Science & Analytics

• Data mining & predictive analytics

• Statistical modeling & probabilistic methods

• Big data systems & distributed data processing

• Data warehousing & data engineering

• Automated data pipelines & ETL technologies

Machine Learning & Deep Learning

• Supervised, unsupervised & reinforcement learning

• Deep learning architectures & neural network advancements

• Transfer learning, federated learning, & ensemble techniques

• Optimization techniques & model evaluation

Artificial Intelligence & Intelligent Computing

• Knowledge representation & reasoning

• Natural language processing & speech recognition

• Computer vision & image processing

• Robotics & autonomous intelligent systems

Computational Intelligence & Algorithms

• Evolutionary computing & swarm intelligence

• Bayesian learning & probabilistic reasoning

• Graph learning, kernel methods & algorithmic innovation

High-Performance & Cloud Computing

• Distributed computing & parallel processing

• Cloud, edge & fog-based machine learning

• High-performance computing for AI

Human-Centered & Ethical AI

• Explainable AI (XAI) & interpretability

• Fairness, accountability, transparency & privacy in AI

• AI ethics, governance & responsible deployment

Industry Applications

• AI in healthcare, finance, cybersecurity & transportation

• Smart cities, IoT & digital twins

• Decision support systems & process automation

• AI in education, marketing, agriculture & environment

Article Types Accepted

• Original Research Articles

• Review & Survey Papers

• Applied/Industrial Case Studies

• Short Communications & Rapid Insights

• Technical Reports & Algorithmic Contributions

• Perspective & Commentary Papers

Ethical & Editorial Commitment

EJEDSML ensures rigorous research quality through:

• Double-blind peer review

• COPE-aligned publishing ethics

• International editorial & scientific advisory board

• DOI assignment & indexing initiatives

• Transparent, timely & fair publication workflow