Aim and Scope
International Journal of Machine Learning and Predictive Analytics (IJMLPA) a double blind peer-reviewed journal that publishes cutting-edge research focused on machine learning models, predictive systems, statistical learning, neural networks, pattern recognition, and applied AI. The journal welcomes advanced methodologies, conceptual frameworks, mathematical models, and real-world applications that demonstrate the capability of machine learning in forecasting, decision-making, automation, and optimization. IJMLPA aims to contribute to global research progress in intelligent predictive technologies.
IJMLPA focuses on both foundational and applied aspects of machine learning and predictive analytics. The journal welcomes original research articles, review papers, case studies, and technical contributions that address challenges in modeling, forecasting, and data-driven inference. The scope includes supervised and unsupervised learning, deep learning architectures, statistical learning, and predictive modeling techniques. It also emphasizes real-world applications of predictive analytics across healthcare, finance, business intelligence, engineering, and social systems. Contributions that address model performance, interpretability, scalability, and ethical considerations in predictive systems are highly encouraged.
ISSN: 3142-788X | Editor-in-Chief: Prof. Isabelle Dupont | Frequency: Quarterly
