Special Issue on Machine Learning and its Applications

Machine Learning and its Applications studies algorithms and statistical models that enable computers to learn from data and improve performance without explicit programming. It covers supervised, unsupervised, reinforcement learning, as well as deep learning techniques. The field applies predictive modeling, pattern recognition, and data-driven decision-making across domains such as healthcare, finance, natural language processing, computer vision, robotics and intelligent systems.

In this special issue, we intend to invite front-line researchers and authors to submit original research and review articles on Machine Learning and its Applications. Potential topics include, but are not limited to:

• Supervised learning
• Unsupervised learning
• Deep learning
• Reinforcement learning & decision-making
• Genetic algorithms and evolutionary algorithms
• Generative models
• Natural language processing
• Computer vision
• Transfer learning
• Explainable and interpretable machine learning
• Federated and distributed learning
• Probabilistic machine learning & Bayesian methods
• Time series and sequential data analysis
• Data mining and machine learning
• Artificial neural networks
• Recommender systems
• Autonomous systems and robotics
• ML for scientific discovery
• Application in business and health science

Authors should read over the journal’s For Authors carefully before submission. Prospective authors should submit an electronic copy of their complete manuscript through the journal’s Paper Submission System.

According to the following timetable:

Guest Editors: Nandkumar Niture (Harrisburg University of Science and Technology, USA)

Dr. Malek Almobarek (AlMaarefa University, Saudi Arabia)

For publishing inquiries, please feel free to contact the Editorial Assistant at submission.entrance1@scirp.org

JCC Editorial Office