Aim & Scope

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