PRACTICAL MACHINE LEARNING APPLICATIONS

Dr. Sachin R. Jadhav · Pritam Mondal · Ismail Keshta · Dr. Haewon Byeon

sep 2023 · Xoffencerpublication

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Over dit e-boek

It is not feasible to arrive at an accurate estimate of the total quantity of knowledge that has been accumulated as a direct consequence of man's activity. Every single day, millions of new tuples are added to the databases, and each of those tuples represents an observation, an experience that can be learned from it, and a situation that may occur again in the future in a way that is comparable to the one it happened in when it was first observed. As human beings, we have the innate capacity to gain knowledge from our experiences, and this is something that occurs constantly throughout our lives. Nevertheless, what does place when the number of occurrences to which we are exposed is more than our capacity to comprehend each of them? What would happen if a fact were to be repeated millions of times, but it would never happen precisely the same way again? What would the results be? What kind of outcomes may we anticipate? It is a subfield of artificial intelligence that focuses on learning from experience, or, to be more specific, the process of automatically extracting implicit knowledge from information that is stored in the form of data. This subfield was named after the concept of learning from experience. Machine learning, which is sometimes shortened as ML and referred to in certain contexts as ML, is sometimes referred to simply as ML. In this study, we will investigate two problems that have been solved in the actual world of business by using machine learning. These problems were faced by companies throughout the globe. Companies were tasked with overcoming both of these obstacles. The first of these responsibilities is to provide an accurate forecast of the final product quality that will be supplied by an oil and gas refinery, which is discussed in Section 2. The second component is a model that, as will be covered in Section 3, may be used in order to acquire an estimate of the amount of wear and tear that will be experienced by a collection of micro gas turbines. This will be accomplished by calculating the amount of wear and tear that can be expected from the collection of micro gas turbines. In the phrase that follows, we will talk about the theoretical components that are essential for the creation of our solutions. An explanation of the ML approaches that we have used may be found in Section 1.1 for any reader who is interested in reading it and would want to read it.

Over de auteur

He has completed PhD in Computer Engineering, from Visvesvaraya Technological University (VTU)Public university in Belgaum, Karnataka. He has completed graduation in Computer science & Engineering, in first class with distinction. He has completed his Post Graduation in Computer Engineering from University of Mumbai. He has published many papers in area of image processing, Artificial Intelligence and Machine Learning. He is awarded with Best Educator award by AWS Academy, Best Faculty mentor by Zensar, IBM, Infosys etc. He has certified with AWS certification. He has given training on various technologies on AWS Cloud Services and associate certifications, python, java, C, C++ etc.

Pritam Mondal began his academic journey at Techno India University, where he earned a Bachelor’s degree in Computer Science and Engineering. He later furthered his education by obtaining a Master’s degree in Artificial Intelligence and Machine Learning from the prestigious Birla Institute of Technology and Science (BITS) Pilani. With over five years of experience in the corporate industry, Pritam has cemented his place as a leading voice in the Software Engineering with AI and ML sector, collaborating with top-tier companies to create groundbreaking solutions and propel advancements in the field. Pritam’s expertise is widely sought by businesses, conferences, and training institutions across the globe. Beyond his professional pursuits, Pritam is dedicated to mentoring the next generation of AI enthusiasts and is an active advocate for “AI For All”. “Practical Machine Learning Applications” marks Pritam’s latest endeavor to give back to the AI community, distilling years of unparalleled experience into essential insights for readers.

Ismail Keshta Received his B.Sc. and the M.Sc. degrees in computer engineering and his Ph.D. in computer science and engineering from the King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, in 2009, 2011, and 2016, respectively. He was a lecturer in the Computer Engineering Department of KFUPM from 2012 to 2016. Prior to that, in 2011, he was a lecturer in Princess Nourah bint Abdulrahman University and Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia. He is currently an assistant professor in the computer science and information systems department of AlM aarefa University, Riyadh, Saudi Arabia. His research interests include software process improvement, modeling, and intelligent systems.

Dr. Haewon Byeon Received the Dr Sc 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 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 343 articles and 19 books.

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