Machine Learning Foundations
Research covering supervised, unsupervised, semi-supervised learning, optimization, and statistical modeling.
Research covering supervised, unsupervised, semi-supervised learning, optimization, and statistical modeling.
Work involving CNNs, RNNs, LSTMs, GANs, transformers, and next-generation neural architectures.
Breakthrough applications in vision, NLP, audio processing, robotics, biomedical AI, and automation.
Research on explainability, fairness, federated learning, privacy-preserving AI, and model security.
Each manuscript is evaluated by qualified reviewers to ensure originality, clarity, and scientific contribution.
All articles are freely accessible to researchers worldwide, increasing the reach and visibility of your work.
The journal hosts special issues focusing on current breakthroughs in ML and DL technologies.
Authors receive the first decision within 4–6 weeks, ensuring timely and reliable communication.
The journal aims for indexing in Google Scholar, CrossRef, Scopus (future), and other major academic platforms.
All submissions undergo plagiarism checks and must comply with international publishing ethics guidelines.