Knowledge Processing and Applied Artificial Intelligence discusses the business potential of knowledge processing and examines the aspects of applied artificial intelligence technology. The book is comprised of nine chapters that are organized into five parts. The text first covers knowledge processing and applied artificial intelligence, and then proceeds to tackling the techniques for acquiring, representing, and reasoning with knowledge. The next part deals with the process of creating and implementing strategically advantageous knowledge-based system applications. The fourth part covers intelligent interfaces, while the last part details alternative approaches to knowledge processing. The book will be of great use to students and professionals of computer or business related disciplines.

Artificial Intelligence and Knowledge Processing: Methods and Applications demonstrates the transformative power of Artificial Intelligence (AI) in our lives. The book is a collection of 14 edited reviews that cover a wide range of topics showcasing the application of AI and machine learning to create knowledge, and facilitate different processes. The book starts by illuminating how AI is employed in robotics, IoT, marketing, and operations. It showcases how AI extracts insights from big data, optimizes museum management, and empowers automated garden path planning using reinforcement learning. The book also explores how AI can be used to predict heart disease using artificial neural networks. Furthermore, the book underscores how AI predicts crop suitability, manages crop systems, and can even help to detect violence in using computer vision. Chapters highlight specific techniques or systems such as recommendation systems and reinforcement learning where appropriate. Key Features: · Showcases a wide range of AI applications · Bridges theory and practice with real-word insights · Uses accessible language to explain complex AI concepts · Includes references for advanced readers This book is intended as a guide for a broad range of readers who want to learn about AI applications and the profound influence it has on our lives.

Economic Modeling Using Artificial Intelligence Methods examines the application of artificial intelligence methods to model economic data. Traditionally, economic modeling has been modeled in the linear domain where the principles of superposition are valid. The application of artificial intelligence for economic modeling allows for a flexible multi-order non-linear modeling. In addition, game theory has largely been applied in economic modeling. However, the inherent limitation of game theory when dealing with many player games encourages the use of multi-agent systems for modeling economic phenomena. The artificial intelligence techniques used to model economic data include: multi-layer perceptron neural networks radial basis functions support vector machines rough sets genetic algorithm particle swarm optimization simulated annealing multi-agent system incremental learning fuzzy networks Signal processing techniques are explored to analyze economic data, and these techniques are the time domain methods, time-frequency domain methods and fractals dimension approaches. Interesting economic problems such as causality versus correlation, simulating the stock market, modeling and controling inflation, option pricing, modeling economic growth as well as portfolio optimization are examined. The relationship between economic dependency and interstate conflict is explored, and knowledge on how economics is useful to foster peace – and vice versa – is investigated. Economic Modeling Using Artificial Intelligence Methods deals with the issue of causality in the non-linear domain and applies the automatic relevance determination, the evidence framework, Bayesian approach and Granger causality to understand causality and correlation. Economic Modeling Using Artificial Intelligence Methods makes an important contribution to the area of econometrics, and is a valuable source of reference for graduate students, researchers and financial practitioners.

Artificial Intelligence and Knowledge Processing play a vital role in various automation industries and their functioning in converting traditional industries to AI-based factories. This book acts as a guide and blends the basics of Artificial Intelligence in various domains, which include Machine Learning, Deep Learning, Artificial Neural Networks, and Expert Systems, and extends their application in all sectors. Artificial Intelligence and Knowledge Processing: Improved Decision-Making and Prediction, discusses the designing of new AI algorithms used to convert general applications to AI-based applications. It highlights different Machine Learning and Deep Learning models for various applications used in healthcare and wellness, agriculture, and automobiles. The book offers an overview of the rapidly growing and developing field of AI applications, along with Knowledge of Engineering, and Business Analytics. Real-time case studies are included across several different fields such as Image Processing, Text Mining, Healthcare, Finance, Digital Marketing, and HR Analytics. The book also introduces a statistical background and probabilistic framework to enhance the understanding of continuous distributions. Topics such as Ensemble Models, Deep Learning Models, Artificial Neural Networks, Expert Systems, and Decision-Based Systems round out the offerings of this book. This multi-contributed book is a valuable source for researchers, academics, technologists, industrialists, practitioners, and all those who wish to explore the applications of AI, Knowledge Processing, Deep Learning, and Machine Learning.

In the light of the challenges that face today's organizations, there is a grow ing recognition that future market success and long term' survival of enter prises will increasingly depend upon the effective usage of information technology. Of late, a new generation of terminology has emerged to describe enterprises. This terminology draws heavily upon the virtual concep- virtual reality, virtual organization, virtual (working) environment, and indeed virtual product. However, developing computerized organisations for the 21st century demands serious thought with regard to the judicious integration of organizational theory, design and practice with research tools and methods from within information processing technology. Within this book, we approach this aim from the perspective of a radically decentralized (possibly virtual) enterprise. We assume that organizations are becoming increasingly process-orientated, rather than adhering to the former more traditional organizational structures based upon task oriented models. This approach has proved illuminating in that, due to the inherent autonomy of organizational subunits any approach to coordinating decentralized activ ities (including workflows and business processes) necessitates a cooperative style of problem solving. This book introduces the reader to a stimulating new field of interdiscipli nary research in cooperative problem solving. In Chapter 1 Kim presents a view of three central discip14tes, namely those of Organizational Theory, Computer Supported Cooperative Work (CSCW) and Distributed Artificial Intelligence (DAI). The applications given here demonstrate how future enterprises will benefit from recent advances in the technological arena of cooperative knowledge processing.

"Nowadays, Artificial Intelligence is transforming most failed businesses into highly profitable businesses. The major aspect is historical data. With data and Artificial Intelligence and Knowledge Processing, we can find the current financial status of the organization, predict the future of the organization and if the loss has happened in the past consecutive years, we can know the reason behind the loss. With the help of knowledge processing, we can build decision support systems to make better decisions in business. AI-powered IoT creates intelligent technologies that imitate intelligent human behavior and aid in decision-making with little or no human interaction. AI can read, comprehend, and correlate data at breakneck speed, adding a new level of intelligence to blockchain-based business networks. The major research in Semantics, Artificial Intelligence, and Domain-specific applications are becoming more and more common in our daily lives, and the bulk of knowledge processing is becoming intellectual in a very wide sense. Even Artificial Intelligence with IoT and Blockchain found in a broad range of scenarios, from simple everyday tasks to more complex ones, such as transportation systems, business, finance, and even the medical field. Digital news, relationship networking, and advancements generated from expert decision handling are only a few examples of common usage. This research book seeks to give a broad understanding of the concepts, tools, and techniques that underpin data science, artificial intelligence with IoT and Blockchain and knowledge processing in business and industry"--

"This book is devoted mainly to applied expert systems. It does cover four additional applied AI Topics: natural language processing, computer vision, speech understanding and intelligent robotics"--Preface