ISCSITR - International Journal of Machine Learning (ISCSITR-IJML)

ISCSITR - International Journal of Machine Learning (ISCSITR-IJML) is a prominent open-access, peer-reviewed journal published by the International Society for Computer Science and Information Technology Research (ISCSITR). It focuses on the fast-growing field of machine learning, covering areas like supervised and unsupervised learning, deep learning, reinforcement learning, and predictive analytics. The journal encourages papers offering theoretical, experimental, and methodological advances, with applications in sectors like healthcare, finance, and autonomous vehicles. By providing global open access, ISCSITR-IJML facilitates the widespread dissemination of cutting-edge machine learning research. The ISCSITR - International Journal of Machine Learning (ISCSITR-IJML) is a highly regarded, open-access, peer-reviewed journal managed by the International Society for Computer Science and Information Technology Research (ISCSITR). This journal is dedicated to the dynamic and rapidly advancing field of machine learning, serving as a global platform for researchers, academics, and professionals to publish their findings, methodologies, and innovative applications. The ISCSITR-IJML covers a wide spectrum of machine learning topics, from fundamental research to cutting-edge developments. Areas of interest include but are not limited to: Supervised and unsupervised learning Deep learning and neural networks Reinforcement learning Algorithmic innovations and optimization techniques Predictive analytics and statistical learning Machine learning applications in various industries, including healthcare, finance, robotics, autonomous systems, and more The journal seeks to publish papers that contribute significant theoretical, methodological, and experimental advances in the field. Submissions that introduce novel perspectives, address emerging challenges, or offer innovative approaches to existing problems are highly encouraged. As an open-access publication, ISCSITR-IJML ensures that all its content is freely available to the global community of machine learning researchers, educators, and practitioners. This accessibility enhances the visibility and impact of the research, promoting collaboration and knowledge-sharing across academic, industrial, and governmental sectors. With a rigorous peer-review process and a commitment to high-quality publications, ISCSITR-IJML is an invaluable resource for anyone seeking to stay updated with the latest trends and breakthroughs in machine learning. The journal plays a crucial role in advancing the state of the art in machine learning, contributing to the broader understanding and application of intelligent systems in today’s data-driven world. Researchers and authors are invited to submit their manuscripts to the International Journal of Machine Learning (ISCSITR-IJML) for consideration. The journal welcomes high-quality research papers that align with its focus on machine learning and related topics, including supervised and unsupervised learning, deep learning, reinforcement learning, predictive analytics, and algorithmic developments.

ISCSITR - International Journal of Machine Learning (ISCSITR-IJML) is :-

• International, Peer-Reviewed, Open Access, Refereed, machine learning, Supervised Learning, Unsupervised Learning, Deep Learning, Reinforcement Learning, Predictive Analytics, Algorithmic Developments, Natural Language Processing (NLP), Computer Vision, Applications of Machine Learning, Hybrid and Advanced Methods , Online , Half-Yearly Journal

• UGC Approved, ISSN Approved: P-ISSN E-ISSN: 3429-5187, Established: 2020,
• Does Not Provide Crossref DOI

• Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE