Multinational organizations have begun to realize that sentiment mining plays an important role for decision making and market strategy. The revolutionary growth of digital marketing not only changes the market game, but also brings forth new opportunities for skilled professionals and expertise. Currently, the technologies are rapidly changing, and artificial intelligence (AI) and machine learning are contributing as game-changing technologies. These are not only trending but are also increasingly popular among data scientists and data analysts. New Opportunities for Sentiment Analysis and Information Processing provides interdisciplinary research in information retrieval and sentiment analysis including studies on extracting sentiments from textual data, sentiment visualization-based dimensionality reduction for multiple features, and deep learning-based multi-domain sentiment extraction. The book also optimizes techniques used for sentiment identification and examines applications of sentiment analysis and emotion detection. Covering such topics as communication networks, natural language processing, and semantic analysis, this book is essential for data scientists, data analysts, IT specialists, scientists, researchers, academicians, and students.

The development of artificial intelligence (AI) involves the creation of computer systems that can do activities that would ordinarily require human intelligence, such as visual perception, speech recognition, decision making, and language translation. Through increasingly complex programming approaches, it has been transforming and advancing the discipline of computer science. Artificial Intelligence Methods and Applications in Computer Engineering illuminates how today's computer engineers and scientists can use AI in real-world applications. It focuses on a few current and emergent AI applications, allowing a more in-depth discussion of each topic. Covering topics such as biomedical research applications, navigation systems, and search engines, this premier reference source is an excellent resource for computer scientists, computer engineers, IT managers, students and educators of higher education, librarians, researchers, and academicians.

Autism spectrum disorder (ASD) is known as a neuro-disorder in which a person may face problems in interaction and communication with people, amongst other challenges. As per medical experts, ASD can be diagnosed at any stage or age but is often noticeable within the first two years of life. If caught early enough, therapies and services can be provided at this early stage instead of waiting until it is too late. ASD occurrences appear to have increased over the last couple of years leading to the need for more research in the field. It is crucial to provide researchers and clinicians with the most up-to-date information on the clinical features, etiopathogenesis, and therapeutic strategies for patients as well as to shed light on the other psychiatric conditions often associated with ASD. In addition, it is equally important to understand how to detect ASD in individuals for accurate diagnosing and early detection. Artificial Intelligence for Accurate Analysis and Detection of Autism Spectrum Disorder discusses the early detection and diagnosis of autism spectrum disorder enabled by artificial intelligence technologies, applications, and therapies. This book will focus on the early diagnosis of ASD through artificial intelligence, such as deep learning and machine learning algorithms, for confirming diagnosis or suggesting the need for further evaluation of individuals. The chapters will also discuss the use of artificial intelligence technologies, such as medical robots, for enhancing the communication skills and the social and emotional skills of children who have been diagnosed with ASD. This book is ideally intended for IT specialists, data scientists, academicians, scholars, researchers, policymakers, medical practitioners, and students interested in how artificial intelligence is impacting the diagnosis and treatment of autism spectrum disorder.

Recent advances in socio-cognitive and affective computing require further study as countless benefits and opportunities have emerged from these innovative technologies that may be useful in a number of contexts throughout daily life. In order to ensure these technologies are appropriately utilized across sectors, the challenges and strategies for adoption as well as potential uses must be thoroughly considered. Principles and Applications of Socio-Cognitive and Affective Computing discusses several aspects of affective interactions and concepts in affective computing, the fundamentals of emotions, and emerging research and exciting techniques for bridging the emotional disparity between humans and machines, all within the context of interactions. The book also considers problem and solution guidelines emerging in cognitive computing, thus summarizing the roadmap of current machine computational intelligence techniques for affective computing. Covering a range of topics such as social interaction, robotics, and virtual reality, this reference work is crucial for scientists, engineers, industry professionals, academicians, researchers, scholars, practitioners, instructors, and students.

In the world of mathematics and computer science, technological advancements are constantly being researched and applied to ongoing issues. Setbacks in social networking, engineering, and automation are themes that affect everyday life, and researchers have been looking for new techniques in which to solve these challenges. Graph theory is a widely studied topic that is now being applied to real-life problems. Advanced Applications of Graph Theory in Modern Society is an essential reference source that discusses recent developments on graph theory, as well as its representation in social networks, artificial neural networks, and many complex networks. The book aims to study results that are useful in the fields of robotics and machine learning and will examine different engineering issues that are closely related to fuzzy graph theory. Featuring research on topics such as artificial neural systems and robotics, this book is ideally designed for mathematicians, research scholars, practitioners, professionals, engineers, and students seeking an innovative overview of graphic theory.

As environmental issues remain at the forefront of energy research, renewable energy is now an all-important field of study. And as smart technology continues to grow and be refined, its applications broaden and increase in their potential to revolutionize sustainability studies. This potential can only be fully realized with a thorough understanding of the most recent breakthroughs in the field. Research Advancements in Smart Technology, Optimization, and Renewable Energy is a collection of innovative research that explores the recent steps forward for smart applications in sustainability. Featuring coverage on a wide range of topics including energy assessment, neural fuzzy control, and biogeography, this book is ideally designed for advocates, policymakers, engineers, software developers, academicians, researchers, and students.

There is a tremendous need for computer scientists, data scientists, and software developers to learn how to develop Socratic problem-solving applications. While the amount of data and information processing has been accelerating, our ability to learn and problem-solve with that data has fallen behind. Meanwhile, problems have become too complex to solve in the workplace without a concerted effort to follow a problem-solving process. This problem-solving process must be able to deal with big and disparate data. Furthermore, it must solve problems that do not have a "rule" to apply in solving them. Moreover, it must deal with ambiguity and help humans use informed judgment to build on previous steps and create new understanding. Computer-based Socratic problem-solving systems answer this need for a problem-solving process using big and disparate data. Furthermore, computer scientists, data scientists, and software developers need the knowledge to develop these systems. Socrates Digital (TM) for Learning and Problem Solving presents the rationale for developing a Socratic problem-solving application. It describes how a computer-based Socratic problem-solving system called Socrates DigitalTM can keep problem-solvers on track, document the outcome of a problem-solving session, and share those results with problem-solvers and larger audiences. In addition, Socrates DigitalTM assists problem-solvers to combine evidence about their quality of reasoning for individual problem-solving steps and their overall confidence in the solution. Socrates DigitalTM also captures, manages, and distributes this knowledge across organizations to improve problem-solving. This book also presents how to build a Socrates DigitalTM system by detailing the four phases of design and development: Understand, Explore, Materialize, and Realize. The details include flow charts and pseudo-code for readers to implement Socrates DigitalTM in a general-purpose programming language. The completion of the design and development process results in a Socrates DigitalTM system that leverages artificial intelligence services from providers that include Apple, Microsoft, Google, IBM, and Amazon. In addition, an appendix provides a demonstration of a no-code implementation of Socrates DigitalTM in Microsoft Power Virtual Agent.

Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks (HONNs) play an important role in this new direction. HONNs will challenge traditional artificial neural network products and change the research methodology that people are currently using in control and recognition areas for the control signal generating, pattern recognition, nonlinear recognition, classification, and prediction. Since HONNs are open box models, they can be easily accepted and used by individuals working in information science, information technology, management, economics, and business fields. Emerging Capabilities and Applications of Artificial Higher Order Neural Networks contains innovative research on how to use HONNs in control and recognition areas and explains why HONNs can approximate any nonlinear data to any degree of accuracy, their ease of use, and how they can have better nonlinear data recognition accuracy than SAS nonlinear procedures. Featuring coverage on a broad range of topics such as nonlinear regression, pattern recognition, and data prediction, this book is ideally designed for data analysists, IT specialists, engineers, researchers, academics, students, and professionals working in the fields of economics, business, modeling, simulation, control, recognition, computer science, and engineering research.

Machine Learning is evolving computation and its application like never before. It is now widely recognized that machine learning is playing a similar role as electricity played in modernizing the world. From simple high school science projects to large-scale radio astronomy, machine learning has revolutionized it all. However, a few of the applications stand out as transforming the world and opening up a new era. The book intends to showcase applications of machine learning that are leading us to the next generation of computing and living standards. The book portrays the application of machine learning to cutting-edge technologies that are playing a prominent role in improving the quality of life and the progress of civilization. The focus of the book is not just machine learning, but its application to specific domains that are resulting in substantial progress of civilization. It is ideal for scientists and researchers, academic and corporate libraries, students, lecturers and teachers, and practitioners and professionals.

Emerging technologies continue to affect a variety of industries, making processes more effective and efficient. However, they also impact society by promoting opportunities to encourage social change and socioeconomic advancement. Blockchain is one that is already influencing third world countries and disrupting the globe. Blockchain Technology for Global Social Change is an essential research publication that provides insight into advancements being made in blockchain and some potential applications of the technology that can improve the lives of individuals in emerging markets. This publication covers a range of topics such as digital government, health systems, and urbanization and is ideal for policymakers, academicians, researchers, sociologists, government officials, economists, and financial experts seeking current and relevant research on evolving blockchain technologies.

The book's core argument is that an artificial intelligence that could equal or exceed human intelligence-sometimes called artificial general intelligence (AGI)-is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system-the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank's computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI's potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society.