Dr. Haewon Byeon's Books

FOUNDATION OF DATA SCIENCE

By: Dr. Santosh Kumar Sahu,Dr. Herison Surbakti,Ismail Keshta,Dr. Haewon Byeon

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FOUNDATION OF DATA SCIENCE

By: Dr. Santosh Kumar Sahu,Dr. Herison Surbakti,Ismail Keshta,Dr. Haewon Byeon

The 1960s saw the beginning of computer science as an academic field of study. The programming languages, compilers, and operating systems, as well as the mathematical theory that underpinned these fields, were the primary focuses of this course. Finite automata, regular expressions, context-free languages, and computability were some of the topics that were addressed in theoretical computer science courses. In the 1970s, the study of algorithms became an essential component of theory when it had previously been neglected. The goal was to find practical applications for computers. At this time, a significant shift is taking place, and more attention is being paid to the diverse range of applications. This shift came about for a variety of different causes. The convergence of computer and communication technologies has been a significant contributor to this change. Our current conception of data and how best to work with it in a contemporary environment has to be revised in light of recent advances in the capacity to monitor, collect, and store data in a variety of domains, including the natural sciences, business, and other areas. The rise of the internet and social networks as fundamental components of everyday life carries with it a wealth of theoretical possibilities as well as difficulties. Traditional subfields of computer science continue to hold a significant amount of weight in the field as a whole, but researchers of the future will focus more on how to use computers to comprehend and extract usable information from massive amounts of data arising from applications rather than how to make computers useful for solving particular problems in a well-defined manner. With this in mind, we have prepared this book to cover the theory that we anticipate will be important in the next 40 years, in the same way that a grasp of automata theory, algorithms, and other similar areas provided students an advantage in the previous 40 years. An increased focus on probability, statistical approaches, and numerical methods is one of the key shifts that has taken place. The book's early draughts have been assigned reading at a variety of academic levels, from undergraduate to graduate. The appendix contains the necessary background information for a course taken at the 1 | P a ge undergraduate level. Because of this, the appendix contains problems for your homework.

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240

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Xoffencerpublication

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ISBN 10

8119534212

ISBN 13

9788119534210

PRACTICAL MACHINE LEARNING APPLICATIONS

By: Dr. Sachin R. Jadhav,Pritam Mondal,Ismail Keshta,Dr. Haewon Byeon

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PRACTICAL MACHINE LEARNING APPLICATIONS

By: Dr. Sachin R. Jadhav,Pritam Mondal,Ismail Keshta,Dr. Haewon Byeon

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.

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204

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Xoffencerpublication

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ISBN 10

8119534573

ISBN 13

9788119534579

DATA SCIENCE: FOUNDATION & FUNDAMENTALS

By: Mr. Ramkumar A,Dr. Haewon Byeon,Mohit Tiwari,Dr. Santosh Kumar Sahu

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DATA SCIENCE: FOUNDATION & FUNDAMENTALS

By: Mr. Ramkumar A,Dr. Haewon Byeon,Mohit Tiwari,Dr. Santosh Kumar Sahu

The academic field of computer science did not develop as a separate subject of study until the 1960s after it had been in existence since the 1950s. The mathematical theory that underpinned the fields of computer programming, compilers, and operating systems was one of the primary focuses of this class. Other important topics were the various programming languages and operating systems. Context-free languages, finite automata, regular expressions, and computability were a few of the topics that were discussed in theoretical computer science lectures. The area of study known as algorithmic analysis became an essential component of theory in the 1970s, after having been mostly overlooked for the majority of its existence up to that point in time. The purpose of this initiative was to investigate and identify practical applications for computer technology. At the time, a significant change is taking place, and a greater amount of attention is being paid to the vast number of different applications that may be utilized. This shift is the cumulative effect of several separate variables coming together at the same time. The convergence of computing and communication technology has been a major motivator, and as a result, this change may be primarily attributed to that convergence. Our current knowledge of data and the most effective approach to work with it in the modern world has to be revised in light of recent advancements in the capability to monitor, collect, and store data in a variety of fields, including the natural sciences, business, and other fields. This is necessary because of the recent breakthroughs in these capabilities. This is as a result of recent advancements that have been made in these capacities. The widespread adoption of the internet and other forms of social networking as indispensable components of people's lives brings with it a variety of opportunities for theoretical development as well as difficulties in actual use. Traditional subfields of computer science continue to hold a significant amount of weight in the field as a whole; however, researchers of the future will focus more on how to use computers to comprehend and extract usable information from massive amounts of data arising from applications rather than how to make computers useful for solving particular problems in a well-defined manner. This shift in emphasis is due to the fact that researchers of 1 | P a ge the future will be more concerned with how to use computers to comprehend and extract usable information from massive amounts of data arising from applications. This shift in emphasis is because researchers of the future will be more concerned with how to use the information they find. As a result of this, we felt it necessary to compile this book, which discusses a theory that would, according to our projections, play an important role within the next 40 years. We think that having a grasp of this issue will provide students with an advantage in the next 40 years, in the same way that having an understanding of automata theory, algorithms, and other topics of a similar sort provided students an advantage in the 40 years prior to this one, and in the 40 years after this one. A movement toward placing a larger emphasis on probabilities, statistical approaches, and numerical processes is one of the most significant shifts that has taken place as a result of the developments that have taken place. Early drafts of the book have been assigned reading at a broad variety of academic levels, ranging all the way from the undergraduate level to the graduate level. The information that is expected to have been learned before for a class that is taken at the undergraduate level may be found in the appendix. As a result of this, the appendix will provide you with some activities to do as a component of your project.

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244

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Xoffencerpublication

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ISBN 10

8119534271

ISBN 13

9788119534272

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

By: Mr. Srinivas Rao Adabala,Dr. Ravi Kumar,Purvi Makwana,Dr. Haewon Byeon

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DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

By: Mr. Srinivas Rao Adabala,Dr. Ravi Kumar,Purvi Makwana,Dr. Haewon Byeon

Deep learning has developed as a useful approach for data mining tasks such as unsupervised feature learning and representation. This is thanks to its ability to learn from examples with no prior guidance. Unsupervised learning is the process of discovering patterns and structures in unlabeled data without the use of any explicit labels or annotations. This type of learning does not require the data to be annotated or labelled. This is especially helpful in situations in which labelled data are few or nonexistent. Unsupervised feature learning and representation have seen widespread application of deep learning methods such as auto encoders and generative adversarial networks (GANs). These algorithms learn to describe the data in a hierarchical fashion, where higher-level characteristics are stacked upon lower-level ones, capturing increasingly complicated and abstract patterns as they progress. Neural networks are known as Auto encoders, and they are designed to reconstruct their input data from a compressed representation known as the latent space. The hidden layers of the network are able to learn to encode valuable characteristics that capture the underlying structure of the data when an auto encoder is trained on input that does not have labels attached to it. It is possible to use the reconstruction error as a measurement of how well the auto encoder has learned to represent the data. GANs are made up of two different types of networks: a generator network and a discriminator network. While the discriminator network is taught to differentiate between real and synthetic data, the generator network is taught to generate synthetic data samples that are an accurate representation of the real data. By going through an adversarial training process, both the generator and the discriminator are able to improve their skills. The generator is able to produce more realistic samples, and the discriminator is better able to tell the difference between real and fake samples. One meaningful representation of the data could be understood as being contained within the latent space of the generator. After the deep learning model has learned a reliable representation of the data, it can be put to use for a variety of data mining activities.

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207

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Xoffencerpublication

Published Date

ISBN 10

8119534174

ISBN 13

9788119534173

ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ADVANTAGES AND DISADVANTAGES

By: Amit Vyas,Varun Dixit,Davinderjit Kaur,Dr. Haewon Byeon

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ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ADVANTAGES AND DISADVANTAGES

By: Amit Vyas,Varun Dixit,Davinderjit Kaur,Dr. Haewon Byeon

Homo sapiens have always had the goal of finding a means to upgrade and improve their living situations in a manner that is in line with how they see life. This is parallel to the history of our ancestors, which covers a thousand miles, beginning with the caveman who struggled only to find food and finishing with the voyage that embarked upon the greatest blessing as the evolution of the human mind to critically analyze the environment and surrounding. The journey began with the caveman and ended with the evolution of the human mind to critically analyze the environment and surrounding. The concept of "Artificial Intelligence" (AI) has allowed mankind to leap into an environment that is entirely diversified and imaginative. This is an environment in which we can separate the typical industrial revolution from the dilemma of the information technology revolution. A computerized information technology-based system that is capable of doing many activities simultaneously in order to integrate a large number of algorithmic pieces of data and consult with either built-in or internet data bases in order to provide you with answers to your questions. When we talk about artificial intelligence, we are referring to the following: 3. At this point in time, artificial intelligence has permeated every imaginable facet of our existence, resulting in an abundance of time-saving inventions and straightforward answers to issues that were previously intractable. As a direct consequence of this, our lives have been forced into comfort zones that are apparently more pleasurable. The newly found corporate empire that is healthcare is quickly getting connected with information technology on an ever-increasing scale across all of its domains. This is not just true for the healthcare industry but certainly for other fields as well. When it comes to addressing "AI in healthcare," it appears that a situation that is very similar to the one that arises when discussing any new notion or concept. This is something that is always important for a productive dialogue and progress. However, as of right now, there is a little stronger lean toward the latter group than there is towards the former group. 1 | P a ge Equipment that interacts with hospital information and management systems will simply take in all of the data based on the clinical presentation and will provide you with beepers, flashers, and changing color codes to alert you to issues about patients in real time. Not only has the movement of data between and within departments become far quicker than it was in the past, but the machinery that interacts with these systems also gives you access to the data in real time. The next stage was to supply the treating physicians with an algorithmic method that would aid in the interpretation of the data. At the same time, the material was going to be provided to consultants and the necessary data repositories so that it could be better understood. In a word, the purpose of this cutting-edge human-machine interface was to facilitate the management of the most optimal medical judgments that could possibly be made. Not only does the system prevent the laborious entering of data, but it also has the potential to provide feedback to interventions along with a time line. This is all made possible by the system's use of barcodes. In the case that a mistake was made, this helps to reduce the number of comments that need to be made, which can be verbally conflicting and take up a lot of time.

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226

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Xoffencerpublication

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ISBN 10

8119534239

ISBN 13

9788119534234

Book's by Dr. Haewon Byeon

FOUNDATION OF DATA SCIENCE

FOUNDATION OF DATA SCIENCE

PRACTICAL MACHINE LEARNING APPLICATIONS

PRACTICAL MACHINE LEARNING APPLICATIONS

DATA SCIENCE: FOUNDATION & FUNDAMENTALS

DATA SCIENCE: FOUNDATION & FUNDAMENTALS

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ADVANTAGES AND DISADVANTAGES

ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ADVANTAGES AND DISADVANTAGES

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