Aims and scope
Journal of Data Science and Machine Learning is an international refereed journal publishes quarterly online open access by KMF. The Journal publishes research works on a wide range of topics that involving understanding and making effective use of field data. We prefer applied research and emphasis is on the relevance of the underlying problem rather than pure mathematical depth. Our goal is to enable scientists to do their research on applied science and through effective use of data. The Journal will provide a platform for all data workers to present their views and exchange ideas. It has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applications of data science and analytics, with a primary focus on:
statistical and mathematical foundations for data science and analytics;
understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;
creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;
data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data, behaviors and systems;
active, realtime, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation;
big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interoperability, exchange, and recommendation;
in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;
review, surveys, trends, prospects and opportunities of data science research, innovation and applications;
data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial domains; and
ethics, quality, privacy, safety and security, trust, and risk of data science and analytics.
KMF Publication Ethics Statement
Journal of Data Science and Machine Learning is a member of the Committee on Publication Ethics. KMF takes the responsibility to enforce a rigorous peer-review together with strict ethical policies and standards to ensure to add high quality scientific works to the field of scholarly publication. Unfortunately, cases of plagiarism, data falsification, inappropriate authorship credit, and the like, do arise. KMF takes such publishing ethics issues very seriously and our editors are trained to proceed in such cases with a zero tolerance policy. To verify the originality of content submitted to our journals, we use latest software to check submissions against previous publications.
Authors and publishers are encouraged to send review copies of their recent related books to the following address. Received books will be listed as Books Received within the journal’s News & Announcements section.
Copyright / Open Access
Articles published in Journal of Data Science and Machine Learning will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution Law. The copyright is retained by the author(s). KMF will insert the following note at the end of the published text:
© 2020 by the authors; licensee KMF. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution Law.
Reprints may be ordered. Please contact for more information on how to order reprints.
Announcement and Advertisement
Announcements regarding academic activities such as conferences are published for free in the News & Announcements section of the journal. Advertisement can be either published or placed on the pertinent website.