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.
Journals
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ISCSITR-INTERNATIONAL JOURNAL OF CLOUD COMPUTING (ISCSITR-IJCC)
ISCSITR - International Journal of Cloud Computing (ISCSITR-IJCC) is an open-access, peer-reviewed journal by the International Society for Computer Science and Information Technology Research. It focuses on cloud computing, covering topics like cloud architecture, services, security, and integration with IoT and AI. The journal publishes high-quality research, case studies, and reviews to advance cloud computing practices and solve industry challenges. With its open-access model, ISCSITR-IJCC ensures wide global dissemination of innovative cloud solutions.
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ISCSITR- INTERNATIONAL JOURNAL OF DATA ANALYTICS (ISCSITR-IJDA)
ISCSITR - International Journal of Data Analytics (ISCSITR-IJDA) is a prestigious open-access, peer-reviewed journal overseen by the International Society for Computer Science and Information Technology Research (ISCSITR). It serves as a specialized forum for the dissemination of significant research and developments in the field of data analytics.
This journal focuses on a broad spectrum of data analytics topics, including advanced analytical techniques, big data analytics, statistical analysis, predictive analytics, descriptive modeling, data warehousing, data cleansing, and real-time data processing. It also explores the application of these techniques across diverse sectors such as marketing, finance, healthcare, education, and public services to provide insights into trends, performance measures, and operational efficiencies.
ISCSITR-IJDA aims to contribute to the field by publishing high-quality research articles, case studies, and review papers that offer new insights, methodologies, and practical solutions for handling complex data and extracting meaningful information. With its open-access policy, the journal ensures that all published content is accessible to a global audience, fostering wider dissemination, engagement, and collaboration within the data analytics community. This journal is an essential resource for academics, industry professionals, and anyone interested in the cutting-edge developments in data analytics.
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ISCSITR- INTERNATIONAL JOURNAL OF HEALTHCARE ANALYTICS (ISCSITR-IJHCA)
ISCSITR - International Journal of Healthcare Analytics (ISCSITR-IJHCA) is an open-access, peer-reviewed journal published by the International Society for Computer Science and Information Technology Research (ISCSITR). It focuses on advancing the field of healthcare analytics by disseminating research on the application of data analysis, data management, and information technology to enhance healthcare delivery and outcomes. The journal covers a wide range of topics, including predictive analytics, medical data privacy, healthcare information systems, and machine learning applications in healthcare. ISCSITR-IJHCA serves as a critical resource for healthcare professionals, researchers, and policymakers, offering insights into how data-driven approaches can improve patient care, operational efficiency, and healthcare policy implementation.
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ISCSITR- INTERNATIONAL JOURNAL OF DATA MINING AND DATA WAREHOUSING (ISCSITR-IJDMDW)
ISCSITR - International Journal of Data Mining and Data Warehousing (ISCSITR-IJDMDW) is a scholarly open-access, peer-reviewed journal supported by the International Society for Computer Science and Information Technology Research (ISCSITR). It focuses on the critical and dynamic fields of data mining and data warehousing, providing a platform for the dissemination of research findings, new methodologies, and technological advancements.
This journal covers a comprehensive range of topics including but not limited to data mining techniques, algorithms, data warehousing design and architecture, big data analytics, knowledge discovery in databases, machine learning applications in data mining, and the integration of data warehousing with business intelligence applications. It also explores the ethical considerations and challenges related to data mining and the handling of large-scale data.
ISCSITR-IJDMDW aims to serve as an essential resource for researchers, data scientists, academicians, and industry professionals by publishing high-quality, original research articles, case studies, and review papers that offer new insights, innovative methods, and practical solutions relevant to data mining and warehousing. The journal's open-access policy ensures that all published works are freely accessible worldwide, enhancing the sharing of knowledge and fostering collaboration among different disciplines, thereby advancing the fields of data mining and data warehousing significantly. This makes it an invaluable resource for anyone engaged in or interested in the latest developments in these areas.
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ISCSITR- INTERNATIONAL JOURNAL OF DATA ENGINEERING (ISCSITR-IJDE)
ISCSITR - International Journal of Data Engineering (ISCSITR-IJDE) is an open-access, peer-reviewed journal published by the International Society for Computer Science and Information Technology Research (ISCSITR). It focuses on the latest research in data engineering, covering topics such as data architecture, database management, big data technologies, and data security. The journal serves as a platform for data engineers, researchers, and academics to share innovations and solutions in managing and manipulating large-scale data systems, promoting the advancement and dissemination of data engineering knowledge globally.