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MACHINE LEARNING APPLICATIONS

Mohan Raparthi an accomplished Software Engineer with a remarkable career spanning over 17 years. Holds a Bachelor’s degree in Computer Applications from Osmania University, Hyderabad, India, demonstrating a strong foundation in computer science. Additionally, earned a Master’s degree in Information Systems from Osmania University, further enriching expertise in the field. Completed course from Cal State East Bay. This seasoned engineer is distinguished by certifications from industry giants, including Microsoft, Oracle, and Salesforce, attesting to a broad and versatile skill set. These certifications reflect a commitment to staying updated with cutting-edge technologies.

Astha Sharma received her B.Tech degree in Electronics and Communication Engineering from Uttar Pradesh Technical University, Lucknow, India in 2008 and MTech degree from Jaypee Institute of Information Technology, Noidain 2011.She has received her PhD degree in Electronics Engineering from Indian Institute of Technology (Indian School of Mines), Dhanbad, India. She was selected as a Doctoral Exchange Student during her PhD to work at the Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, in the University of Bologna, Italy under the Erasmus Mundus India4eu II action program. She has previously worked as Assistant Professor in renowned AKTU affiliated institutes in Delhi-NCR region: G.L. Bajaj Institute of Technology & Management, IILM-AHL College of Engineering and Technology, Galgotia College of Engineering and Technology, and Gautam Buddha University, Greater Noida. She is an Innovation Ambassador and has worked in DST sponsored Technology Innovation Hub- IIITB COMET Foundation, Bangalore as Program Manager. She is an IEEE Senior Member and currently working as Associate Professor at Department of Electronics and Communication Engineering in Greater Noida Institute of Technology, Greater Noida. Her research interests include wireless and mobile communication, communication engineering, RF and microwave engineering, energy harvesting, signal processing, machine learning and AI.

Dr. 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, AImedicine, 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 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 343 articles and 19 books.

Sahil Arora is an experienced professional with a solid educational background, having obtained a Bachelor’s degree in Information Technology and a Master’s degree in Information Systems with a specialization in Data Sciences. With over 11 years of experience in Information Technology, Sahil has refined his expertise across various domains, including technical product management, software development, critical edge infrastructure development, and Identity & Access Management (IAM). His breadth of knowledge extends to various areas closely aligned with Data Science, such as machine learning, artificial intelligence, natural language processing (NLP), data mining, and predictive analytics. These proficiencies play a crucial role in his capacity as a Staff Product Manager at Twilio Inc. Known for his dedication to innovation, Sahil is esteemed as a diligent independent researcher, continuously keeping abreast of the latest advancements and trends in the field of Artificial Intelligence.

Description

Machine learning, which is one of the more established subfields within the subject of artificial intelligence, focuses on the study of computer approaches for the discovery of new information and for the management of existing knowledge. Furthermore, machine learning is one of the subfields that has been around the longest. The use of techniques that are associated with machine learning has been adopted in a wide range of application industries. New data, on the other hand, have been available in the most recent few years as a consequence of a multitude of technological developments and research projects (for instance, the completion of the Human Genome Project and the spread of the Web). Consequently, new domains that have the potential to make use of machine learning have come into existence. Learning from biological sequences, learning from email data, and learning in complex environments such as the web are some examples of the modern applications that are currently being explored. Other examples include learning from natural language processing. This study objective is to demonstrate the three application domains that were described previously, as well as some recent attempts that have been made to use machine learning techniques in order to analyze the data that is supplied by these domains. In addition, this article will describe some of the recent attempts that have been made.

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