Machine learning is a multidisciplinary subject that includes field of computer science, artificial intelligence, statistics and operation research etc. The objective of machine learning is to understand the structure of data and fit that data into models that can be understood and utilized by people. Machine Learning facilitates computers in building models from sample data in order to automate decision-making processes based on data inputs. Currently Machine Learning techniques are very much useful in research work to improve efficiency, speed and accuracy of outcomes in the presence of large-scale data. The continuous development of machine learning tools and techniques is responsible for understanding the non-linearity existing in the data, extracting several features, finding hidden patterns in data, classifying data into labels and so on. This has led to the tremendous advancement and spread in applications ranging from personalized product recommendations to speech recognition in cell phones; from predictive analytics to security applications that leverage machine learning for implementing filters and safe guards against new threats; from data mining programs that discover general rules in large data sets, to information filtering systems that automatically learn users' interests. It also innovatively powers various application sectors such as manufacturing, retail, healthcare, finance, travel, hospitality, science and engineering etc.
ICAML2019, ICAML2020 and ICAML2021 have been published and indexed by EI!
All papers, both invited and contributed, will be reviewed by at least 2 experts from the committees. After a careful reviewing process before the final decision and detailed presentation at the conference.
All accepted papers of ICAML2023 will be published by Conference Publishing Services (CPS). Conference publication of CPS is submitted to EI Compendex, Scopus, INSPEC and Current Contents on Diskette for indexing.
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