Machine Learning and Knowledge Extraction - MDPI | 2024 Impact Factor: 6.0 | Cite Score: 9.9 | Q1

About this Journal:
Machine Learning and Knowledge Extraction (MAKE) is an international, peer-reviewed open-access journal published by MDPI, focusing on cutting-edge research in machine learning, data mining, knowledge discovery, and intelligent systems.Machine Learning and Knowledge Extraction (ISSN 2504-4990) provides an advanced forum for studies related to all areas of machine learning and knowledge extraction. It publishes reviews, regular research papers, communications, perspectives, and viewpoints, as well as Special Issues on particular subjects. The aim of Machine Learning and Knowledge Extraction is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. Therefore, the journal has no restrictions regarding the length of papers. Full experimental details should be provided so that the results can be reproduced.
It provides a platform for researchers and practitioners working on the theory, algorithms, systems, and applications of machine learning and knowledge extraction.
The journal emphasizes both foundational advances and innovative applications in real-world domains such as healthcare, finance, cybersecurity, multimedia, and industrial systems.
MAKE also encourages contributions on novel data-centric approaches, responsible AI, explainability, and knowledge representation.

Scope of the Journal:
Machine learning algorithms, deep learning architectures, and statistical learning methods.
Knowledge extraction, data mining, pattern recognition, clustering, and classification techniques.
Explainable AI (XAI), interpretable models, transparency, and responsible machine learning.
Knowledge graphs, semantic technologies, and representation learning.
Big data analytics, data processing pipelines, and scalable ML systems.
Applications of ML in healthcare, IoT, cybersecurity, business intelligence, robotics, and other domains.
Hybrid AI models combining machine learning with symbolic reasoning.
Evaluation metrics, benchmarking, and reproducibility in machine learning research.