DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

Srinivas Babu Ratnam · Jesús A. Coloma · Dr. Herison Surbakti · Haewon Byeon

Jul 2023 · Xoffencerpublication

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237

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Several empirical research have come to the conclusion that the representation of data plays a vital role in the efficiency with which machine learning algorithms complete their tasks. This indicates that the design of feature extraction, preprocessing, and data transformations requires a disproportionate amount of time and resources when actually executing machine learning algorithms. These steps include preparing the data for analysis, extracting features from the data, and processing the data. This is because each of these components is essential to the algorithm as a whole in order for it to function properly. In spite of the fact that it is of the utmost significance, feature engineering calls for a significant amount of human effort. It also shows a shortcoming of the learning algorithms that are now in use, which is their inability to extract all of the pertinent characteristics from the data that is currently accessible. This is a difficulty with the approaches that are currently utilized in the process of learning. An approach that may be utilized to make up for such a shortfall is called feature engineering, and it involves making use of human intelligence in conjunction with prior information. It would be extremely desired to make learning algorithms less dependent on feature engineering in order to expedite the production of innovative applications and, more crucially, to realize advancements in artificial intelligence (AI). This would be done in order to achieve developments in AI. There are two possible consequences resulting from this. This would make it possible to use machine learning in a larger variety of applications that are simpler to put into action, which would increase the value of machine learning. An artificial intelligence has to have at least a fundamental comprehension of the environment in which humans live, and this may be accomplished if a learner is able to interpret the concealed explanatory factors that are embedded within the visible milieu of low-level sensory input. It is conceivable to combine feature engineering with feature learning in order to obtain state-of-the-art solutions that can be applied to actual circumstances in the real world.

About the author

Srinivas Babu Ratnam Pursuing Master of Science in Data Science, University of Colorado Boulder, USA I have done Bachelors of Engineering in Electrical and Electronics Engineering from Andhra University, Visakhapatnam. I have around 20 years of experience in Software Development Life Cycle .During 20 years of my experience I worked as Developer, Lead, and Architect . I have experience in implementing projects in Cloud, AI/ML/Deep Learning, Blockchain,C RM,Big data at enterprise level. Published 3 research articles in Artificial Intelligence . Editor-in-chief of the textbook “ Introduction to Human Computers Interaction.“ I am a member of professional associations.

Jesús A. Coloma is a Computer Science professor at the School of Systems at Bolivar State University in Ecuador. He holds a degree in Computer Systems Engineering and a Master's degree in Software Engineering from UNIR, and is currently pursuing a Master's degree in Data Science. With over 10 years of experience in both private and public software development, Professor Coloma is a passionate advocate for opensource software and programming. His research focuses on the implementation of web and mobile applications in agriculture and livestock farming. In addition to his research, he teaches courses on software architecture, web programming, information technology, and software security. Outside of his academic work, Jesús enjoys cycling and spending time outdoors

Dr. Herison Surbakti is a highly accomplished educator and researcher with extensive experience in teaching and supervising undergraduate and postgraduate students in three different countries. Over the course of his career, which spans more than a decade, he has made significant contributions to academia in Indonesia, Malaysia, and Thailand. Dr. Surbakti's expertise is widely recognized, evident through his numerous publications in esteemed refereed journals. In addition to his research work, Dr. Surbakti has actively participated in international conferences, where he has presented papers and contributed as a book editor and chapter author. His commitment to advancing knowledge is evident in his primary research focus, which lies in the domains of web-based health information systems and market business trend analytics, with a particular emphasis on data visualization. Dr. Surbakti's research interests revolve around Data Analytics, Business Intelligence, and Knowledge Management. He is dedicated to exploring innovative approaches in these areas and seeks to bridge the gap between academia and industry. His expertise and passion for these fields make him a valuable asset to the academic community and an influential figure in the advancement of knowledge.

Haewon Byeon received the DrSc degree in Biomedical Science from Ajou University School of Medicine. Haewon Byeon currently works at the Department of Medical Big Data, Inje University. His recent interests focus on health promotion, AI-medicine, and biostatistics. He is currently a member of international committee for a Frontiers in Psychiatry, and an editorial board for World Journal of Psychiatry. Also, He were worked on a 4 projects (Principal Investigator) from the Ministry of Education, the Korea Research Foundation, and the Ministry of Health and Welfare. Byeon has published more than 326 articles and 5 books.

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