ICTACT Journal on Data Science and Machine Learning (IJDSML) is a peer-reviewed International Journal published quarterly. IJDSML welcomes Scientists, Researchers, Engineers to submit their original research papers which is neither published nor currently under review by other journals or conferences. Papers should emphasize original results relating to the theory and/or applications of Data Science and Machine Learning. Review articles, focusing on multidisciplinary views of data science and machine learning, are also welcome. The Journal will highlight the continued growth and new challenges in Data Science and Machine Learning, for both basic research and application development.
(a) Papers for which all results could be easily reproducible. For such papers, the authors will be asked to upload "instructions for reproduction'', possibly with the source codes or stable URLs (from where the codes could be downloaded). (b) Papers with negative results. For such papers, the experimental setting and negative results must be presented in detail. Also, why the negative results are important for the research community must be explained clearly. The rationale behind this kind of paper is that this would help researchers choose the correct approaches to solve problems and avoid the (already worked out) failed approaches. (c) Detailed report, case study and literature review articles about innovative software / hardware, new technology, high impact applications and future development with sufficient background and subject coverage.