Haewon Byeon's Books

APPLICATIONS BEYOND CRYPTOCURRENCIES - NON-FINANCIAL USE CASES FOR BLOCKCHAIN TECHNOLOGY

By: Mr. Gunawan Widjaja,Dr. Seema,Ismail Keshta,Dr. Haewon Byeon

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APPLICATIONS BEYOND CRYPTOCURRENCIES - NON-FINANCIAL USE CASES FOR BLOCKCHAIN TECHNOLOGY

By: Mr. Gunawan Widjaja,Dr. Seema,Ismail Keshta,Dr. Haewon Byeon

The blockchain is a distributed ledger that stores information in a way that makes it extremely difficult, if not impossible, for the system to be altered, hacked, or manipulated. A distributed ledger is what a blockchain is. This ledger copies and distributes transactions throughout the network of computers that are part of the blockchain. A data structure known as blockchain has the potential to take the shape of an expanding list of information blocks. Because the knowledge blocks are interconnected, more recent blocks cannot be withdrawn or changed after they have been added. The decentralized digital currency Bitcoin relies on the blockchain technology for its operations. A distributed database that contains records of all transactions or digital activities that have been carried out and shared among parties that are participating is called a blockchain. Each transaction is checked by the system's users who make up the bulk of its members. It includes each and every record of each transaction in its entirety. The most widely used example of a blockchain technology is the cryptocurrency known as Bitcoin. In 2008, a person or group of people going by the name "Satoshi Nakamoto" issued a white paper titled "BitCoin: A peer-to-peer electronic cash system." This was the first time that the Blockchain Technology was brought to public attention. This new technology, known as blockchain, logs transactions in a digital ledger that is shared across the network. Because of this, the ledger cannot be altered in any way. Transactions involving anything of value, like as real estate, automobiles, and so on, may be recorded on the blockchain. Bitcoin is perhaps the most well-known application of Blockchain technology. Bitcoin is a form of cryptocurrency that may be used for the purchase and sale of digital assets over the internet. For two parties to successfully carry out transactions via the internet, Bitcoin relies on cryptographic evidence rather than the confidence of a third party. A unique cryptographic signature is used to secure each transaction.

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193

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Xoffencerpublication

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ISBN 10

8119534727

ISBN 13

9788119534722

AI AND ROBOTICS: A SYMBIOTIC RELATIONSHIP

By: Dr. Alka Dubey,Shikhar Purohit,Mr. Vivek Kumar Bhargava,Dr. Haewon Byeon

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AI AND ROBOTICS: A SYMBIOTIC RELATIONSHIP

By: Dr. Alka Dubey,Shikhar Purohit,Mr. Vivek Kumar Bhargava,Dr. Haewon Byeon

The word "artificial intelligence" is a concept that is used to describe the work processes of robots that would need intelligence if they were really carried out by humans, as stated by. As a result, the term "artificial intelligence" refers to the academic discipline that analyzes the behavior of intelligent problem-solving systems and the creation of intelligent computer systems. There are two distinct classifications that may be used to artificial intelligence:  The computer is merely a tool for examining cognitive processes; the computer duplicates intelligence. This is an example of something that is considered to be weak artificial intelligence. This is an example of artificial intelligence that is not very strong.  The actions that are carried out by the computer are procedures that include intellectual and self-learning activities. This is an illustration of a powerful example of artificial intelligence. Using the necessary software and programming, computers are able to "understand" and are able to modify their own behavior depending on their prior conduct and their experience.  This is also possible since computers are able to learn from their mistakes. In this way, computers are able to develop more intelligence. 4. An illustration of this would be the concept of automatic networking with other computers, which leads to a major influence on scalability.

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242

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Xoffencerpublication

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ISBN 10

8197370869

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9788197370861

CLIMATE CHANGE IMPACT ASSESSMENT ON AGRICULTURE WITH ML

By: Ms. Shreya Malaviya,Ismail Keshta,Dr. Avishek Ghosal,Dr. Haewon Byeon

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CLIMATE CHANGE IMPACT ASSESSMENT ON AGRICULTURE WITH ML

By: Ms. Shreya Malaviya,Ismail Keshta,Dr. Avishek Ghosal,Dr. Haewon Byeon

The fluctuation and change of the climate are issues that humans are concerned about. As a result of the frequent droughts and floods, the livelihoods of billions of people who are dependent on land for the majority of their requirements are in grave danger. The economy of the entire world is being adversely affected on a regular basis as a result of extreme events such as droughts and floods, cold and heat waves, forest fires, landslides, and other similar occurrences. Even though they are not tied to meteorological disasters, natural disasters such as earthquakes, tsunamis, and volcanic eruptions have the potential to alter the chemical composition of the atmosphere due to their impact. This, in turn, will result in calamities that are tied to the weather. The increase in aerosols, which are pollutants in the atmosphere, is caused by the emission of greenhouse gases like carbon dioxide, which is produced when fossil fuels are burned. Other greenhouse gases include chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and others. Other factors that contribute to weather extremes include the depletion of the ozone layer and the emission of UV-B filtered radiation, the eruption of volcanoes, the "human hand" in deforestation in the form of forest fires, and the loss of wet lands. Because of the loss of forest cover, precipitation is able to travel across the ground, causing erosion of the top soil and causing floods and droughts. Forest cover generally acts as a barrier that prevents rainwater from falling and allows it to be absorbed by the soil. The absence of trees, which causes the soil to dry up more rapidly, paradoxically contributes to the severity of drought in years that are dry. As a result of its ability to absorb long-wave radiation and then release it back into the atmosphere, carbon dioxide (CO2) is the most prominent greenhouse gas that contributes to the phenomenon of global warming

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215

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8197370826

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9788197370823

REINFORCEMENT LEARNING: FOUNDATIONS, ALGORITHMS AND APPLICATIONS

By: Dr. Darío Salguero García,Deepti Razdan,Ismail Keshta,Dr. Haewon Byeon

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REINFORCEMENT LEARNING: FOUNDATIONS, ALGORITHMS AND APPLICATIONS

By: Dr. Darío Salguero García,Deepti Razdan,Ismail Keshta,Dr. Haewon Byeon

Reinforcement learning, sometimes known as RL, is a catchall word that refers to both a learning problem and a subfield in machine learning. In the context of a problem involving learning, this refers to the process of determining how to guide a computer toward an arbitrary numerical objective. The process of reinforcement learning may be seen in its usual application in the controller is provided with both the present state of the system under their control as well as the reward earned from the most recent transition. After that, the system will calculate an answer and then provide it to you. Because of this, the system goes through a state transition, and the process starts all over again. Figuring out how to have the most possible impact on the system in order to get the greatest possible advantage from it is the task at hand here. The gathering of data and the measurement of performance are two areas in which the learning obstacles are distinct. In this context, we make the assumption that the target system is, by its very nature, unpredictable. In addition, we make the assumption that the measures of state that are now accessible are detailed enough so that the controller does not need to speculate on how to get state information. The Markovian decision processes, often known as MDPs, provide a helpful framework for modeling issues that include these characteristics. MDPs are often "solved" via the use of dynamic programming, which, in practice, does nothing more than recast the initial problem as one involving the selection of an acceptable value function. Dynamic programming, on the other hand, is impractical in all but the most elementary of situations, namely those in which the MDP has a limited number of states and actions. The RL algorithms that we give here may be seen as a method that can be utilized to turn unfeasible dynamic programming into usable algorithms that can be used to real-world applications on a huge scale. The reason why RL algorithms are able to do this task is due to two key assumptions. The fundamental idea is to illustrate the dynamics of the control issue in a more concise way by utilizing samples. This is crucial for two reasons, which are as follows: To begin, it makes it easier to handle learning circumstances that include dynamics that are unknown.

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8119534786

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9788119534784

MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

By: Dilip Siddareddy,Dr. Haewon Byeon,Purvi Makwana,Dr. Vaibhav Bhatnagar

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MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

By: Dilip Siddareddy,Dr. Haewon Byeon,Purvi Makwana,Dr. Vaibhav Bhatnagar

Because of the advancements that have been made in machine learning, the world is being changed in ways that are difficult to conceive. If you stop for a second and take a good look around, you'll see that the area of data science is everywhere you turn. Take, for example, Alexa from Amazon; she is an artificial intelligence that has been developed to be as simple and straightforward to use as is humanly conceivable. There are many other digital assistants similar to Alexa, such as Google Assistant, Cortana, and so on. Alexa is not the only one of its sort. Therefore, the question of why they were formed in the first place is the most crucial one to ask; the question of how they developed is the second most important one to ask. In any event, we are going to make an attempt to study each and every one of these issues, and we are also going to make an effort to devise answers that are both logical and technological in nature. Within the scope of this discussion, the question that has to be inquired about first and foremost is, "What exactly are Machine Learning and Data Science?" A widespread misconception is that data science and machine learning are interchangeable terms for the same thing. Those people do have a point, to some extent, considering that data science is nothing more than taking a huge amount of data and analyzing it using a variety of machine learning approaches, methodologies, and technologies. Therefore, in order to become an expert in data science, you need to have a solid understanding of mathematics and statistics, in addition to a profound comprehension of the area that you intend to specialize in. To be more specific, what does it mean to have "subject expertise"? Subject expertise is nothing more than the knowledge necessary about a given topic in order to be able to abstract and calculate the data that pertains to that field, as the name of this type of expertise indicates. In a nutshell, these three concepts are considered as the foundations of data science, and if you are successful in mastering all of them, then you should rejoice yourself because you have achieved the level of an A-level data scientist.

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203

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8119534603

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9788119534609

KEY PERSONALITY TRAITS FOR SUCCESSFUL LEADERSHIP

By: Lima Nasrin Eni,Dr. A. Geetha,S. Ramesh,Dr. Haewon Byeon

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KEY PERSONALITY TRAITS FOR SUCCESSFUL LEADERSHIP

By: Lima Nasrin Eni,Dr. A. Geetha,S. Ramesh,Dr. Haewon Byeon

There is a segment of the workday that is dedicated to employees discussing their supervisor. It is possible for these to range from positive statements, such as "She will allow me to participate in that executive program and then I will have the opportunity to apply for the job in Hong Kong," to pessimistic statements, such as "You won't believe what he did this time!"—and with a perplexed expression—"He spent a considerable amount of time on the phone once more." These brief views into the working world may seem to be nothing more than the froth that floats on the surface of corporate life; nonetheless, they may really indicate a great deal about the potential for success that groups and organizations possess. According to research conducted by Hay Group, a shift in leadership style may account for as much as seventy percent of the variance in the environment of an organization, which can result in a twenty-five percent improvement in the performance of the business. To put it another way, businesses that have customers who exclaim, "Wonderful!" Their performance is superior to that of those in which people presume, "Oh no! There is a boss present today! It is time for him to return from his vacation! Instead of being soft or indecisive, leaders should express a sophisticated combination of authority, empathy, decision-making, and coaching abilities that bring out the best in both you and your team members. The workforce does not like it when leaders are indecisive or soft. Your thoughts and actions both have an impact on this, which is the most crucial thing to keep in mind. While it may seem to be a simple procedure, it really has the potential to be rather difficult. It is analogous to the discrepancy that exists between learning how to swim and reading about the Olympics. According to Daniel Goleman, author of the book "Emotional Intelligence," emotional or behavioral patterns are more deeply established and require more effort to change than knowledge-based mental patterns. This is because emotional or behavioral patterns occur more often. In addition, it has been convincingly established that the activities of a manager have an effect on the blood pressure of the team. After conducting their investigation, the researchers from Buckinghamshire Chilterns University College came to the conclusion that "your boss could be damaging your health." A controlled study was conducted in which researchers gathered blood pressure measurements from healthcare assistants. These assistants were supervised by two separate individuals, each of whom worked on a different day of the week. Individuals who liked one of their supervisors but hated the other had significantly greater blood pressure when compared to a control group that had a positive attitude towards both of their superiors. January 5, 2002 issue of New Scientist was the source. Every individual is accountable to a superior. Their relevance in their life is something that very few people would dispute. We can all go back to the boss who assisted us in making significant changes in our lives, as well as the boss who was a complete and utter failure. It is important to take into consideration who your real boss is, especially if you have a virtual boss, have had three different employers in the last sixteen months, or have a large number of bosses now.

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256

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Xoffencerpublication

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8119534476

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9788119534470

SUPERVISED LEARNING ALGORITHMS - CLASSIFICATION AND REGRESSION ALGORITHMS

By: Dr. Sami Ahmed Haider,Dr. Gargishankar Verma,Arijeet Chandra,Dr. Haewon Byeon

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SUPERVISED LEARNING ALGORITHMS - CLASSIFICATION AND REGRESSION ALGORITHMS

By: Dr. Sami Ahmed Haider,Dr. Gargishankar Verma,Arijeet Chandra,Dr. Haewon Byeon

The subset of machine learning algorithms known as supervised learning is an essential component that makes a substantial contribution to the resolution of a wide variety of problems that are associated with the study of artificial intelligence (AI). A dataset that has been labeled is given to the algorithm during the supervised learning phase. This dataset contains not only the input data but also the target labels that correlate to those data. Both sets of information are included. The objective of this activity is to construct a model or a mapping that is able to reliably predict the labels for data that has not yet been observed. There are a large number of algorithms that are commonly used for supervised learning, and each of these techniques has a number of benefits as well as some drawbacks. The technique known as linear regression, which is applied in situations involving continuous numerical data, is one method that is frequently used. Creating a linear link between the input features and the variable that you want to change is the method that is used to accomplish this goal. Logistic regression is often utilized when the objective is to categorize individual data points into a number of separate groups or classes. It constructs a model that calculates the probability that a certain data point belongs to a particular category. Decision trees are a type of general-purpose algorithm that can be put to use for a variety of different classification and regression-related projects. They do this by constructing a tree-like structure, where each leaf node represents a projected class or value and each inside node represents a decision that was taken based on a feature. In other words, each node in the structure represents a decision that was made. The performance of prediction tasks can be improved using ensemble methods such as Random Forests and Gradient Boosting. These methods work by combining many decision trees into a single model. They are especially useful when it comes to managing difficult datasets. Support Vector Machines, often known as SVMs, are useful tools for binary classification because they pinpoint the hyperplane that achieves the optimal margin between classes. Because of this, they are able to deliver satisfactory results whenever there is a noticeable divide between the classes.

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209

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811953476X

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9788119534760

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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8119534573

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9788119534579

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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8119534212

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9788119534210

ARTIFICIAL INTELLIGENCE: A MODERN APPROACH

By: Dr. Anil Kumar,Sivasubramanian Balasubramanian,Dr. Haewon Byeon,Prof. Ganesh Vasudeo Manerkar

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ARTIFICIAL INTELLIGENCE: A MODERN APPROACH

By: Dr. Anil Kumar,Sivasubramanian Balasubramanian,Dr. Haewon Byeon,Prof. Ganesh Vasudeo Manerkar

Here we try to define artificial intelligence (AI) and explain why we think it deserves more attention than other worthy research topics; obviously, this is a prerequisite to doing any kind of study in this area. We humans take great pride in our intelligence; in fact, we call ourselves Homo sapiens, which means "man the wise." Human cognition has long baffled scientists, who have sought to explain how a little particle of stuff like us can see, understand, predict, and control an enormous and complex cosmos. Beyond that, the field of artificial intelligence (AI) aims to do more than just understand; it aims to build intelligent objects. One of the newest innovations in engineering and science is AI. The name wasn't even thought of until 1956, although development started in earnest almost immediately after WWII ended. Science professionals from several disciplines often mention artificial intelligence (AI) as the "field I would most like to be in" next to molecular biology. If you're a physics student, you could think that all the great thinkers like Galileo, Newton, Einstein, and others have thought of everything. Conversely, AI is still on the market for a handful of brilliant minds to join their team full-time. At now, AI encompasses a wide variety of subfields, from the broad (perception and learning) to the narrow (proving mathematical theorems, writing poetry, operating a car on a congested street, and disease detection, among many others). These are but a few of the many activities that might be categorised as AI-related. Artificial intelligence (AI) is a field that really covers all intellectual pursuits; it is relevant to everyone

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251

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8197370818

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9788197370816

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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8119534271

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9788119534272

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

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8119534573

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9788119534579

MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

By: Dr. Rachit Adhvaryu,Dr. Kruti Sutaria,Ismail Keshta,Dr. Haewon Byeon

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MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

By: Dr. Rachit Adhvaryu,Dr. Kruti Sutaria,Ismail Keshta,Dr. Haewon Byeon

Machine learning is an area of artificial intelligence that focuses on teaching computers how to learn without being explicitly instructed to do so. This ability allows computers to acquire knowledge and competence via experience rather than being taught to do so. In recent years, as a consequence of the many different applications it has in a broad variety of fields, it has become an increasingly important topic of debate as a result of the multiple practical uses it has. Throughout the course of this blog, we will discuss how machine learning is being utilized to address difficulties in the real world, as well as study the principles of machine learning and go into more advanced topics. Whether you are a newbie interested in learning about machine learning or an experienced data scientist wishing to keep up to speed on the latest breakthroughs in the field, we hope that you will find something here that is of interest to you. If you are a novice interested in learning about machine learning, go here. Machine learning is an application of artificial intelligence that makes use of statistical methods to teach computers how to learn on their own and make judgements without being expressly programmed to do so. This is accomplished via the use of statistical methods. It is predicated on the notion that computers are able to learn from data, spot patterns, and make decisions with relatively little input from human beings

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205

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8197211914

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9788197211911

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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8119534174

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9788119534173

BLOCKCHAIN FUNDAMENTALS CORE AND CONCEPTS AND DECENTRALIZED LEDGER TECHNOLOGY

By: Mr. Gunawan Widjaja,Niraj Upadhayaya ,Ismail Keshta,Dr. Haewon Byeon

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BLOCKCHAIN FUNDAMENTALS CORE AND CONCEPTS AND DECENTRALIZED LEDGER TECHNOLOGY

By: Mr. Gunawan Widjaja,Niraj Upadhayaya ,Ismail Keshta,Dr. Haewon Byeon

Let's take a look at the beginnings of the technology that is now known as blockchain before delving into the specifics of how the blockchain works and the various other components of it. In 1991, a team of academic academics was the first to present the intellectual framework that underpins blockchain technology. The concept was first conceived for the purpose of time-stamping digital documents in such a way that it would be impossible to retroactively change their dates afterward. Despite this, the concept was mostly ignored until Satoshi Nakamoto brought it up once more in the white paper he published. It is possible that this is the first time in the history of the world that the creator of a game-changing technology has chosen to remain fully nameless. An unknown individual or group is said to be behind the creation of the first blockchain, which was Bitcoin. This person or group goes by the name Satoshi Nakamoto. 2009 marked the year when Bitcoin became the world's first cryptocurrency to use a blockchain. In the years that followed, bitcoin gained traction, and the technology that it was based on went on to gain an even greater following. Therefore, the uncertainty and lack of clarity among people began at the very beginning of the phenomenon itself; a product and the terminology associated with it became viral before the technology that underpinned it. And when the blockchain exhibited its true potential, people were attempting to associate it with the terminology of bitcoin, which resulted in a complete misunderstanding and confusion on everyone's part. On the other hand, you should begin with blockchain and work your way up to trying to grasp bitcoin. Before delving further into the particulars of the technology, there is another issue that has to be answered first. In order to label a piece of technology as revolutionary, it must, of course, offer significant advantages over previously existing technologies. The following are some advantages that blockchain technology has over pre-existing solutions in various industries: What is Blockchain? When we look at the data structure, data distribution, data validation (which refers to the authentication of a piece of data in blockchain), and other associated terminology of blockchain, we can get a good understanding of the characteristics. IBM defines blockchain as a shared and distributed ledger that makes it easier to record transactions and keep track of assets inside a network. Blockchain was developed by the company IBM. The asset might be a tangible one such as a piece of real estate, a house, or a vehicle, or it could be an intangible one such as digital money, the rights to intellectual property, or something similar. In its most basic form, it takes care of storing data and tracking where it goes throughout a decentralised network. Let's check at its specifics. On a P2P network, it functions as either a decentralised database or a public register that maintains information on assets and the transactions involving those assets. The use of encryption will be employed to ensure the safety of each transaction, and at some point in the future, the history of all transactions will be compiled into blocks of data and stored away. After that, the blocks are protected against alteration and connected to one another through the use of cryptography. The entirety of the procedure will result in the production of an unalterable and unfalsifiable record of the transactions that took place throughout the network. In addition to this, blocks of records are duplicated to all of the computers that are participating in the network, making it possible for everyone to have access to it. The fact that blockchain can store any form of asset together with facts about its ownership, a history of that ownership, and the placement of assets within the network is one of the technology's most significant advantages. Whether it be the virtual currency bitcoin or any other type of digital asset such as a certificate, personal information, a contract, title of ownership of intellectual property, or even the physical things themselves, digital assets may be used to store and transfer value. The most significant aspect of Blockchain is its ability to enable the creation of a shared reality between entities that cannot be trusted. That is to say that none of these participating nodes in the network are required to know or trust one another because each node possesses the capability to independently monitor and validate the chain. It's a cruel twist of fate that participants' inherent mistrust of one another is what ultimately ensures the blockchain's integrity and veracity.

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8119534301

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9788119534302

APPLICATIONS BEYOND CRYPTOCURRENCIES - NON-FINANCIAL USE CASES FOR BLOCKCHAIN TECHNOLOGY

By: Mr. Gunawan Widjaja,Dr. Seema,Ismail Keshta,Dr. Haewon Byeon

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APPLICATIONS BEYOND CRYPTOCURRENCIES - NON-FINANCIAL USE CASES FOR BLOCKCHAIN TECHNOLOGY

By: Mr. Gunawan Widjaja,Dr. Seema,Ismail Keshta,Dr. Haewon Byeon

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8119534727

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9788119534722

KEY PERSONALITY TRAITS FOR SUCCESSFUL LEADERSHIP

By: Lima Nasrin Eni,Dr. A. Geetha,S. Ramesh,Dr. Haewon Byeon

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KEY PERSONALITY TRAITS FOR SUCCESSFUL LEADERSHIP

By: Lima Nasrin Eni,Dr. A. Geetha,S. Ramesh,Dr. Haewon Byeon

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256

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8119534476

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9788119534470

MACHINE LEARNING EXPLAINED: A PRACTICAL GUIDE TO DATA-DRIVEN DECISION MAKING

By: Abdelhamid ZAIDI,Renato Racelis Maaliw III,Mrs. K. P. Maheswari,Dr. Haewon Byeon

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MACHINE LEARNING EXPLAINED: A PRACTICAL GUIDE TO DATA-DRIVEN DECISION MAKING

By: Abdelhamid ZAIDI,Renato Racelis Maaliw III,Mrs. K. P. Maheswari,Dr. Haewon Byeon

During the course of the process of making a choice, we rely on a variety of presumptions, premises, and the circumstances; all of this is directed by the goal that is related with the decision itself. However, the premises and the knowledge of the corporation are dependent on our data since they are an essential component of our organization as a system. The context and the assumptions are both external factors that are beyond the control of any decision maker. Both the background and the assumptions represent outside forces that are not within the control of any decision maker. A prominent example of a conceptual error is the misunderstanding that exists between data and information, which in reality correspond to entirely distinct ideas. This misunderstanding is a common occurrence. In point of fact, information and data cannot in any way be substituted for one another in any context. To put this another way, there is no guarantee that the data will be consistent, comparable, or traceable, despite the fact that we are able to collect data from a broad variety of distinct data sources. This is because there are so many diverse data sources. Because of this, in order for us to make a decision, we need to have a good comprehension of both the component that is presently being examined and the data that is linked with it at the present time. Only then will we be able to make an informed choice. The identification of the system itself is the first step that must be taken before any other aspects of the system, such as its boundaries, context, subsystems, feedback, inputs, and outputs, can be determined. Because of this, it is significant because, according to the point of view connected with general system theory, it is necessary to identify the system that is being discussed. In order to get a more in-depth understanding of the system, we must first begin by defining it. After that, we may proceed to quantifying each associated quality in order to achieve this goal. This would make it possible for us to have a better understanding of the system. Because of this, in order for us to collect information on the topic of the research, we will initially need to measure it in order to quantify the characteristics that are associated with it. For this, we will need to perform certain measurements on the subject. After that, we will establish the indicators that will be applied for the purpose of determining the value of each measure, and we will do so by utilizing the results of the stage that came before it. Within the context of this method, the Measurement and Evaluation (M&E) process can gain an advantage from making use of a conceptual framework that is built on top of an underlying ontology. The M&E framework makes it possible to describe the basic ideas, which prepares the way for a measurement process to be carried out in a manner that is consistent and repeatable. This is made possible by the fact that the framework makes it possible to specify the essential concepts. The ability of a measuring process to be automated is of the utmost significance, even if it is required for a measuring process to give findings that are consistent, comparable, and traceable. The ability of a measuring process to be automated is of the utmost relevance. Because the activities that take place in today's economy take place in real time, we need to pay considerable attention to the use of online monitoring in order to notice and avoid a variety of different scenarios while they are happening. Because of this, we will be able to reduce risk while maximizing our efficiency. In this regard, the functionality of the measurement and evaluation frameworks is an extremely valuable asset, as they make it possible to organize and automate the process of measuring in a manner that is consistent. This makes the frameworks an exceptionally helpful asset. As a result of this, the frameworks are a very useful asset. As soon as it is feasible to guarantee that the measurements are comparable, consistent, and traceable, the method of decision-making will naturally be based on their history, which will consist of the measurements collected throughout the years. This will be the case as soon as it is possible to guarantee that the measurements are comparable, consistent, and traceable. This will take place as soon as it is practical to assure that the measurements are comparable, consistent, and traceable. In this regard, the organizational memory is of special importance due to the fact that it makes it possible to store prior organizational experience and knowledge in order to get ready for future proposals (that is, as the foundation for a range of different assumptions and premises, among other things). In this regard, the organizational memory is of particular use. Because of this, the organizational memory is a component that is of very high importance. Measurements and the experiences that are associated with them provide continuous nourishment for the organizational memory, and the organizational memory provides the foundation for the feedback that is utilized in the process of decision making.

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811953445X

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9788119534456

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

By: Srinivas Babu Ratnam,Jesús A. Coloma,Dr. Herison Surbakti ,Haewon Byeon

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DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

By: Srinivas Babu Ratnam,Jesús A. Coloma,Dr. Herison Surbakti ,Haewon Byeon

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.

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237

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8196401884

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9788196401887

MACHINE LEARNING FOR HEALTHCARE: PREDICTIVE ANALYTICS AND PERSONALIZED MEDICINE

By: Dr. G. Vishnuvarthanan,Mr. Gunawan Widjaja,Dr. Shahazad Niwazi Qurashi,Haewon Byeon

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MACHINE LEARNING FOR HEALTHCARE: PREDICTIVE ANALYTICS AND PERSONALIZED MEDICINE

By: Dr. G. Vishnuvarthanan,Mr. Gunawan Widjaja,Dr. Shahazad Niwazi Qurashi,Haewon Byeon

In the discipline of healthcare informatics, the study of how data relevant to healthcare may be obtained, transmitted, processed, stored, and retrieved is known as the study of how data can be gathered, transferred, processed, stored, and retrieved. In this area of study, early sickness prevention, early illness detection, early illness diagnosis, and early illness therapy are all crucial components. In the subject of healthcare informatics, the only types of data that are considered trustworthy are those that belong to illnesses, patient histories, and the computer operations that are required in order to analyze this data. Conventional medical practices all across the United States have made significant investments in cutting-edge technology and computational infrastructure over the course of the previous two decades in order to improve their potential to support academic institutions, medical experts, and patients. There has been a large investment of resources made in order to increase the quality of medical care that can be provided by utilizing a variety of different options, and this improvement has been made possible as a result of the expenditure. The impetus for all of these numerous programs was the overriding objective of giving patients with access to healthcare that is not only reasonably priced and of excellent quality, but also completely and wholly free of any and all fear. This goal was the driving force behind all of these many programs. As a direct result of these efforts, the benefits and usefulness of applying computational tools to help with referrals and prescriptions, to set up and manage electronic health records (EHR), and to make technical advancements in digital medical imaging have become more obvious. This is particularly the case with regard to electronic health records (EHR), which are becoming increasingly prevalent. This is a direct consequence of the fact that the benefits of utilizing computational tools have been more readily apparent in recent times. With the aid of these technologies, electronic health records (EHR) are something that can be set up and managed.

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241

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ISBN 10

8196401868

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9788196401863

BLOCKCHAIN AND THE INTERNET OF THINGS (IOT): A CONVERGENCE OF TECHNOLOGIES

By: Dr. Gargishankar Verma,Aadam Quraishi,Ismail Keshta,Dr. Haewon Byeon

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BLOCKCHAIN AND THE INTERNET OF THINGS (IOT): A CONVERGENCE OF TECHNOLOGIES

By: Dr. Gargishankar Verma,Aadam Quraishi,Ismail Keshta,Dr. Haewon Byeon

The Internet of Things (IoT) is a technology that enables a network of physical items (things) to sense physical events, transmit data, and interact with their environment in order to make decisions or monitor certain processes and occurrences without the need for human contact. This may be accomplished through the use of the internet. The desire to make it simpler to collect data in real time and to offer automatic and remotecontrol mechanisms as a substitute for the conventional monitoring and control systems used in many sectors today was one of the most significant reasons for the development of IoT systems. This goal has been one of the most important reasons for the development of IoT systems. Manufacturing, environmental monitoring, digital agriculture, smart cities and homes, business management, and asset tracking are some of the sectors that fall under this category. It is expected that the number of devices that are connected to one another will have topped 20 billion by the year 2020. Because of these growing demands and the huge penetration of IoT across a wide variety of rising industries, quick innovation in the existing IoT protocols, technologies, and architectures is necessary, as well as significant work to define IoT standards that will enable these developments. The Internet of Things (IoT) generates large volumes of data, which demands the availability of network connectivity as well as power, processing, and storage resources in order to transform this data into information or services that have any value. When implementing IoT networks, it is vital to emphasize cybersecurity and data privacy in addition to guaranteeing consistent connections and the scalability of the network. Other important considerations include ensuring that the network can be expanded. At the moment, centralized architectural models are utilized in an extensive manner to authenticate, authorize, and link the numerous nodes that make up an Internet of Things network. Moreover, these models are used to represent the Internet of Things. Because there will be a rising number of devices, which might reach hundreds of billions, centralized systems will break down and fail when the centralized server is not accessible. As a potential answer to this issue, a decentralized architecture for the Internet of Things was proposed. This design relocates some of the processing tasks that occur within the network to the periphery of the network.

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207

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8119534840

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9788119534845

REINFORCEMENT LEARNING FUNDAMENTALS - LEARNING THROUGH REWARDS AND PUNISHMENTS

By: Dr. Chithra K,Anirudhan Adukkathayar C,Ismail Keshta,Dr. Haewon Byeon

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REINFORCEMENT LEARNING FUNDAMENTALS - LEARNING THROUGH REWARDS AND PUNISHMENTS

By: Dr. Chithra K,Anirudhan Adukkathayar C,Ismail Keshta,Dr. Haewon Byeon

Reinforcement learning is a subfield within the broader domain of machine learning. The crux of the matter is in selecting the optimal course of action to maximize prospective profitability within a given set of conditions. It is utilized by various software and computers to determine the optimal course of action or action route to effectively respond to a given event. In the process of supervised learning, the training data includes the ground truth, and the model is trained using the correct response. In contrast, in the context of reinforcement learning, the absence of a definitive correct answer is seen. Instead, the reinforcement agent exercises its discretion in selecting the appropriate behaviors required to successfully complete the assigned task. This observation highlights a significant distinction between the two modalities of learning. In supervised learning, the training dataset contains the solution key, enabling the model to be trained using the correct answers directly. In the context of unsupervised learning, the model is trained using erroneous or inaccurate responses. Without access to a training dataset, it is implausible for the system to acquire knowledge by any alternative means. The mathematical impossibility of the situation is evident. Reinforcement learning (RL) is a subfield within the domain of artificial intelligence (AI) that focuses on the examination and analysis of decision-making processes. The objective of this study is to ascertain the optimal approach for individuals to navigate a certain context, with the aim of maximizing the potential outcomes resulting from their endeavors. The data employed in reinforcement learning (RL) is obtained through many machine learning algorithms, each of which acquires knowledge through its distinct iteration of the trial-and-error process. Data is not considered a constituent of the input employed in either supervised or unsupervised machine learning methodologies. Both of these machine learning algorithms are not classified as "supervised." Reinforcement learning is a computational approach that involves the utilization of algorithms to acquire knowledge from previous actions' consequences and afterwards choose the most advantageous path of action. Following each stage, the algorithm is provided with input that aids in evaluating the appropriateness, neutrality, or inaccuracy.

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8119534964

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9788119534968

BLOCKCHAIN FOR HEALTHCARE: SECURING PATIENT DATA AND TRANSFORMING MEDICAL RECORDS

By: Srinivas Babu Ratnam,Shuvam Purkait,Dr. Indu,Haewon Byeon

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BLOCKCHAIN FOR HEALTHCARE: SECURING PATIENT DATA AND TRANSFORMING MEDICAL RECORDS

By: Srinivas Babu Ratnam,Shuvam Purkait,Dr. Indu,Haewon Byeon

The healthcare business is a huge and intricate sector that includes a diverse array of organizations and people committed to the diagnosis, treatment, management, and prevention of illnesses and diseases. It is one of the most important industries in the world. Hospitals, clinics, medical device manufacturers, pharmaceutical firms, insurance companies, research institutes, and a great many more kinds of businesses and organizations are included in this category. Challenges in the healthcare industry Rising healthcare costs: The ever-increasing expense of medical treatment is one of the key obstacles that must be overcome. There are a number of factors that contribute to the growing financial burden that is placed on people, governments, and healthcare systems. Some of these factors include enhanced medical technology, costly pharmaceuticals, an ageing population, and chronic illnesses. Access to healthcare: Access to high-quality medical treatment may be difficult to get in many parts of the world, especially for underserved groups, persons with low incomes, and people who live in rural or distant places. There are a number of reasons that might contribute to limited access, including geographical limitations, a deficiency in healthcare infrastructure, an inadequate number of healthcare professionals, and socioeconomic inequities. Fragmented healthcare systems: The fragmentation of many healthcare systems throughout the globe results in patients receiving treatment from a myriad of different providers and organisations, which often suffers from a lack of efficient coordination and communication. The fragmentation of healthcare systems may result in inefficiencies, disconnected treatment, medical mistakes, and higher expenses.

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231

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8196401825

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9788196401825

REINFORCEMENT LEARNING: FOUNDATIONS, ALGORITHMS AND APPLICATIONS

By: Dr. Darío Salguero García,Deepti Razdan,Ismail Keshta,Dr. Haewon Byeon

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REINFORCEMENT LEARNING: FOUNDATIONS, ALGORITHMS AND APPLICATIONS

By: Dr. Darío Salguero García,Deepti Razdan,Ismail Keshta,Dr. Haewon Byeon

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8119534786

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9788119534784

5G TECHNOLOGY AND IT'S APPLICATION

By: S. R. Jena,Dr. Aniruddha Deka,Dr. Jyoti Prasad Kalita,Dr. Haewon Byeon

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5G TECHNOLOGY AND IT'S APPLICATION

By: S. R. Jena,Dr. Aniruddha Deka,Dr. Jyoti Prasad Kalita,Dr. Haewon Byeon

Since the invention of communication, it has been the focus of a significant amount of research and development effort ever since it was first invented. Only a limited few individuals still make use of landlines, but the vast majority of people in today's world communicate via the use of sophisticated, varied, and multi-functional mobile phones. Because we are able to maintain our connections with the rest of the world via the use of portable electronic devices, our lives are simplified in a number of different ways. For example, we no longer have to worry about missing out on important information. Throughout the course of its existence, the industry of telecommunications has been a participant in a number of remarkable technological advancements. These developments have spanned from 1G to 2G to 3G to 4G to 5G, with each generation bringing about a significant boost in performance. The most recent generation, 5G, is the result of these breakthroughs. With 2G and the following generation of mobile communications, users were able to use their electronic handheld devices with volume levels of calls and data transmission; however, the degree of control with 5G is much more promising and may open up a great deal more possibilities. It is now feasible for VOIP-enabled devices to communicate massive amounts of data as a result of breakthroughs made in 5G technology. This capability was not previously available. Because the development of the very finest and most up-to-date technology is a major contributor to and one of the most critical components that drives this competition, mobile phone makers are compelled to engage in competition with other firms that are inventive. With the arrival of 5G, these possibilities have virtually achieved their limitless potential and are getting closer all the time. The advent of 5G technology has just marked the beginning of a change in the way data is transferred. This transformation is being ushered in by 5G. In addition to this, it will make changes to the way in which mobile services are provided in every region of the globe. The concept of worldwide connectivity achieved via the use of mobile phones is no longer science fiction. It is increasing more and more likely that it will turn out to be true. This position is only going to expand as the 5G era brings in further online risks; 1 | P a ge data security has become one of the most critical components of effectively managing a company, and it has emerged as one of the most significant aspects of successfully operating a business. There will be a significant contribution made towards an increase in the quality of communication between persons as a result of the fact that areas in Japan will soon be able to contact and use local phones in India. Soon, people all around the world will be able to take advantage of enhanced global connection that is also conveniently located, all because of the use of technology and the many devices that it permits. At this stage, all that is needed of you is technology from the 21st century that is comparable to a Personal Digital Assistant (PDA). Telkomsel and Indosat are the only two providers of telecommunications services in Indonesia that are capable of operating 5G networks. May of 2021 is when Telkomsel is expected to begin delivering 5G services, while June of 2021 is when Indosat is expected to make 5G services fully commercially available. Both Telkomsel and Indosat are now in the process of rolling out 5G services; however, they are doing so by employing two unique frequency bands, namely the N40 band (2,300 MHZ) and the N3 band (1,800 MHz). Other service providers, including as XL Axiata, have submitted an application to Kemkominfo for a Certificate of Operation (SKLO), which will enable them to begin providing 5G services in August of 2021. On the other hand, XL Axiata has not yet commenced providing services based on its 5G network. Only a select few 5G service alternatives are currently accessible to residents of Indonesia at this time. According to the information that was readily accessible when 2018 came to a close, there were just nine cities that were claimed to have access to 5G networks. Jabodetabek, Bandung, Batam, Balikpapan, Makassar, Surakarta, Surabaya, Denpasar, and Medan are some of the cities in Indonesia that have made 5G networks accessible to their citizens. It was anticipated that there were 277.7 million people living in Indonesia in the month of January 2022. In 2018, just half of all individuals in Indonesia made use of the internet, making it the country with the lowest internet penetration rate. This demonstrates how there has been a significant growth over the course of the previous several years in the percentage of people in the nation who have internet access.

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222

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8119534220

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9788119534227

DATA STRUCTURES FOR MODERN APPLICATIONS

By: S. R. Jena,Dr. Jeevan,Ismail Keshta,Dr. Haewon Byeon

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DATA STRUCTURES FOR MODERN APPLICATIONS

By: S. R. Jena,Dr. Jeevan,Ismail Keshta,Dr. Haewon Byeon

The development of computers has progressed to the point where they are now an essential component of the machinery required for the operation of human civilization in the modern world and the maintenance of that civilization. Problems that do not require numerical calculations can be solved more efficiently with the assistance of computers in a variety of fields, including text processing, information retrieval, data management, image processing, video processing, and artificial intelligence, to name just a few of the available options. Computers can also assist in the solution of problems that do require numerical calculations. Computers not only aid us in efficiently resolving a huge number of numerical issues that emerge in engineering and scientific calculations, but they also assist us in doing so for a wide range of other problems that do not involve numerical computing. This is because computers are capable of doing parallel processing, which allows them to perform several computations in parallel. In contrast, mathematical models of numerical computation take the form of a variety of equations and solutions. Mathematical models of non-numerical computing take the form of the content of applications that make use of data structures. As a direct result of this, data structure is the basis upon which the study of computer science is built. Consequently, this makes it the cornerstone of the discipline of computer science. The core curriculum is the most important course in the computer course system. It covers a variety of topics, including operating systems, databases, compilation principles, computer networks, and other types of professional essential courses. This curriculum also covers topics such as computer graphics, image processing, artificial intelligence, as well as other areas that are analogous to these subjects. This is because the demarcation of disciplines is what determines which classes are considered to be among the most essential ones. As a result, this distinction is responsible for the fact that this is the case.

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8119534549

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9788119534548

INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT

By: Dr. Aadam Quraishi MD,Shajeni Justin,Ismail Keshta,Dr. Haewon Byeon

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INTERPRETABLE AI: TECHNIQUES FOR MAKING MACHINE LEARNING MODELS TRANSPARENT

By: Dr. Aadam Quraishi MD,Shajeni Justin,Ismail Keshta,Dr. Haewon Byeon

The capacity to understand and have trust in the results generated by models is one of the distinguishing characteristics of high-quality scientific research. Because of the significant impact that models and the outcomes of modeling will have on both our work and our personal lives, it is imperative that we have a solid understanding of models and have faith in the results of modeling. This is something that should be kept in mind by analysts, engineers, physicians, researchers, and scientists in general. Many years ago, picking a model that was transparent to human practitioners or customers often meant selecting basic data sources and simpler model forms such as linear models, single decision trees, or business rule systems. This was the case since selecting a model that was transparent required less processing power. This was the situation as a result of the fact that picking a model that was transparent to human practitioners or customers in general entailed picking a model. Even though these more easy approaches were typically the best option, and even though they continue to be the best option today, they are subject to failure in real-world circumstances in which the phenomena being replicated are nonlinear, uncommon or weak, or very distinctive to particular individuals. Despite the fact that they continue to be the best option, they are sensitive to failure in these kinds of scenarios. The conventional trade-off that existed between the precision of prediction models and the simplicity with which they could be interpreted has been abolished; nevertheless, it is likely that this trade-off was never truly required in the first place. There are technologies that are now accessible that can be used to develop modeling systems that are accurate and sophisticated, based on heterogeneous data and techniques for machine learning, and that can also aid human comprehension of and

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268

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Xoffencerpublication

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ISBN 10

8119534670

ISBN 13

9788119534678

MACHINE LEARNING APPLICATIONS

By: Mohan Raparthi,Astha Sharma,Dr. Haewon Byeon,Sahil Arora

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MACHINE LEARNING APPLICATIONS

By: Mohan Raparthi,Astha Sharma,Dr. Haewon Byeon,Sahil Arora

There is a great deal of individualization involved in the process of learning for every one of us. The subject "Is Man a Machine?" was presented by Will Durant in his wellknown book, The Pleasures of Philosophy. In the study titled "Is Man a Machine?", Durant composed lines that are regarded to be masterpieces. These statements include: "Here is a youth; When you take into consideration the fact that it is striving to lift itself to a vertical dignity for the very first time, it is doing it with both fear and courage; why should it be so eager to stand and walk? In addition, why should it shake with an insatiable curiosity, with a hazardous and unquenchable ambition, with touching and tasting, with watching and listening, with manipulating and experimenting, with observing and wondering, with growing—until it weighs the globe and charts and measures the stars at the same time? The ability to learn, on the other hand, is not something that is exclusive to human beings to possess. This extraordinary phenomenon may be seen in even the most fundamental of species, such as amoeba and paramecium, which are examples of simpler organisms. There is also the possibility that plants exhibit intelligent activity. When it comes to the natural world, the only things that do not take part in the process of learning are those that are not alive. From this perspective, it would seem that learning and living are inextricably linked to one another. It is not possible to acquire a great deal of knowledge about the domain of nonliving items that are produced by nature. Machines are nonliving organisms that humans have developed and referred to as machines. Is it possible for us to put learning into these devices? It is considered a pipe dream that one day we will be able to create a computer that is capable of learning in the same way that humans do. In the event that this objective is accomplished, it will result in the development of deterministic machines that possess freedom (or, alternatively, the illusion of freedom to be more precise). We will be able to boldly say that our humanoids are a depiction of people in the form of machines, and that they are the most comparable to humans in appearance. This will be possible throughout that period of time.

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232

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Xoffencerpublication

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ISBN 10

8119534581

ISBN 13

9788119534586

Book's by Haewon Byeon

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