Communication and artificial intelligence (AI) are closely related. It is communication - particularly interpersonal conversational interaction - that provides AI with its defining test case and experimental evidence. Likewise, recent developments in AI introduce new challenges and opportunities for communication studies. Technologies such as machine translation of human languages, spoken dialogue systems like Siri, algorithms capable of producing publishable journalistic content, and social robots are all designed to communicate with users in a human-like way. This timely and original textbook provides educators and students with a much-needed resource, connecting the dots between the science of AI and the discipline of communication studies. Clearly outlining the topic's scope, content and future, the text introduces key issues and debates, highlighting the importance and relevance of AI to communication studies. In lively and accessible prose, David Gunkel provides a new generation with the information, knowledge, and skills necessary to working and living in a world where social interaction is no longer restricted to humans. The first work of its kind, An Introduction to Communication and Artificial Intelligence is the go-to textbook for students and scholars getting to grips with this crucial interdisciplinary topic.

Discusses the way technological advancements have revolutionized solar technology with the help of and case studies. Covers advanced artificial intelligence techniques useful for improving the study related to solar systems. Focuses on finding the solution to the actual operating difficulties. Discusses core concepts and principles of artificial intelligence in the control and management of solar energy systems.

We are crossing a new frontier in the evolution of computing and entering the era of cognitive systems. The victory of IBM's Watson on the television quiz show Jeopardy! revealed how scientists and engineers at IBM and elsewhere are pushing the boundaries of science and technology to create machines that sense, learn, reason, and interact with people in new ways to provide insight and advice. In Smart Machines, John E. Kelly III, director of IBM Research, and Steve Hamm, a writer at IBM and a former business and technology journalist, introduce the fascinating world of "cognitive systems" to general audiences and provide a window into the future of computing. Cognitive systems promise to penetrate complexity and assist people and organizations in better decision making. They can help doctors evaluate and treat patients, augment the ways we see, anticipate major weather events, and contribute to smarter urban planning. Kelly and Hamm's comprehensive perspective describes this technology inside and out and explains how it will help us conquer the harnessing and understanding of "big data," one of the major computing challenges facing businesses and governments in the coming decades. Absorbing and impassioned, their book will inspire governments, academics, and the global tech industry to work together to power this exciting wave in innovation.

Over the past two decades, swarm intelligence has emerged as a powerful approach to solving optimization as well as other complex problems. Swarm intelligence models are inspired by social behaviors of simple agents interacting among themselves as well as with the environment, e.g., cat swarm, crow search, elephant herding, foraging of bees and ants. The collective behaviors that emerge out of the interactions at the colony level are useful in achieving complex goals. The main aim of this book is to present a sample of recent innovations and advances in techniques and swarm intelligence. Among the topics covered in this book include: Introduction of swarm Intelligence Foundation of swarm Intelligence The particle Swarm and collective intelligence optimization, and complex social swarms Application of various swarm intelligence models to operational planning of energy plants, modeling and control of robots, organic computing, techniques of cloud services, bioinspired optimization, routing protocols for next-generation networks inspired by collective behaviors of insect societies and cybernetic organisms.

Offering a wide range of programming examples implemented in MATLAB(r), Computational Intelligence Paradigms: Theory and Applications Using MATLAB(r) presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research.

The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization.

Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.

This book explores various renewal processes in the context of probability theory, uncertainty theory and chance theory. It also covers the applications of these renewal processes in maintenance models and insurance risk models. The methods used to derive the limit of the renewal rate, the reward rate, and the availability rate are of particular interest, as they can easily be extended to the derivation of other models. Its comprehensive and systematic treatment of renewal processes, renewal reward processes and the alternating renewal process is one of the book's major features, making it particularly valuable for readers who are interested in learning about renewal theory. Given its scope, the book will benefit researchers, engineers, and graduate students in the fields of mathematics, information science, operations research, industrial engineering, etc.

While mathematically sophisticated methods can be used to better understand and improve processes, the nonlinear nature of food processing models can make their dynamic optimization a daunting task. With contributions from a virtual who s who in the food processing industry, Optimization in Food Engineering evaluates the potential uses and limitations of optimization techniques for food processing, including classical methods, artificial intelligence-genetic algorithms, multi-objective optimization procedures, and computational fluid dynamics.

The book begins by delineating the fundamentals and methods for analytical and numerical procedures. It then covers optimization techniques and how they specifically apply to food processing. The final section digs deep into fundamental food processes and provides detailed explanation and examples from the most experienced and published authors in the field. This includes a range of processes from optimization strategies for improving the performance of batch reactors to the optimization of conventional thermal processing, microwave heating, freeze drying, spray drying, and refrigeration systems, to structural optimization techniques for developing beverage containers, optimization approaches for impingement processing, and optimal operational planning methodologies. Each chapter presents the required parameters for the given process with the optimization procedure to apply.

An increasing part of the food processor s job is to optimize systems to squeeze more dollars out of overhead to offset rising utility and transportation costs. Logically combining optimization techniques from many sources into a single volume focused on food production processes, this book provides real solutions to increases in energy, healthcare, and product liability costs that impact the bottom line in food production.

This book proposal deals with the emerging advances in Artificial Intelligence and the Fourth Industrial Revolution taking place around the world right now and likely to change the face of healthcare in both the short- and long-term future. Seeks out theoretical, methodological, well-established and validated empirical work dealing with these different topics, in depth experimental or application aspect focused on relevant topics instead of giving an overview on the entire spectrum. The editors are expert in the field with proven track record.

A new industrial revolution has begun. Like mechanization or electricity before it, artificial intelligence will touch every aspect of our lives-and cause profound disruptions in the balance of global power, especially among the AI superpowers: China, the United States, and Europe. Autonomous weapons expert Paul Scharre takes readers inside the fierce competition to develop and implement this game-changing technology and dominate the future. Four Battlegrounds argues that four key elements define this struggle: data, computing power, talent, and institutions. Data is a vital resource like coal or oil, but it must be collected and refined. Advanced computer chips are the essence of computing power-control over chip supply chains grants leverage over rivals. Talent is about people: which country attracts the best researchers and most advanced technology companies? The fourth "battlefield" is maybe the most critical: the ultimate global leader in AI will have institutions that effectively incorporate AI into their economy, society, and especially their military. Scharre's account surges with futuristic technology. He explores the ways AI systems are already discovering new strategies via millions of war-game simulations, developing combat tactics better than any human, tracking billions of people using biometrics, and subtly controlling information with secret algorithms. He visits China's "National Team" of leading AI companies to show the chilling synergy between China's government, private sector, and surveillance state. He interviews Pentagon leadership and tours U.S. Defense Department offices in Silicon Valley, revealing deep tensions between the military and tech giants who control data, chips, and talent. Yet he concludes that those tensions, inherent to our democratic system, create resilience and resistance to autocracy in the face of overwhelmingly powerful technology. Engaging and direct, Four Battlegrounds offers a vivid picture of how AI is transforming warfare, global security, and the future of human freedom-and what it will take for democracies to remain at the forefront of the world order.

A Financial Times and Economist Book of the Year 'Wonderfully stimulating... will teach you to see around corners' - TIM HARFORD 'A paean to cognitive agility and the elasticity of the imagination' - ECOMOMIST 'Captivating... will transform the way you think' MARISSA KING, PROFESSOR AT YALE SCHOOL OF MANAGEMENT The power of mental models to make better decisions We're often told that humans make bad decisions and that more data is better. But this is backwards: people are good at decisions precisely because we use mental models and can envision new realities outside of data. Great outcomes don't depend so much on the final moment of choosing but on generating better alternatives to choose between. That's framing. It's a cognitive muscle we can strengthen to improve our lives, work and future -- to meet this historical moment. Framers shows how.

Deep Learning Approaches for Security Threats in IoT Environments An expert discussion of the application of deep learning methods in the IoT security environment In Deep Learning Approaches for Security Threats in IoT Environments, a team of distinguished cybersecurity educators deliver an insightful and robust exploration of how to approach and measure the security of Internet-of-Things (IoT) systems and networks. In this book, readers will examine critical concepts in artificial intelligence (AI) and IoT, and apply effective strategies to help secure and protect IoT networks. The authors discuss supervised, semi-supervised, and unsupervised deep learning techniques, as well as reinforcement and federated learning methods for privacy preservation. This book applies deep learning approaches to IoT networks and solves the security problems that professionals frequently encounter when working in the field of IoT, as well as providing ways in which smart devices can solve cybersecurity issues. Readers will also get access to a companion website with PowerPoint presentations, links to supporting videos, and additional resources. They'll also find: A thorough introduction to artificial intelligence and the Internet of Things, including key concepts like deep learning, security, and privacy Comprehensive discussions of the architectures, protocols, and standards that form the foundation of deep learning for securing modern IoT systems and networks In-depth examinations of the architectural design of cloud, fog, and edge computing networks Fulsome presentations of the security requirements, threats, and countermeasures relevant to IoT networks Perfect for professionals working in the AI, cybersecurity, and IoT industries, Deep Learning Approaches for Security Threats in IoT Environments will also earn a place in the libraries of undergraduate and graduate students studying deep learning, cybersecurity, privacy preservation, and the security of IoT networks.

By 2020, experts forecast that up to 28 billion devices will be connected to the Internet, with only one third of them being computers, smartphones, smartwatches, and tablets. The remaining two thirds will be other "devices" - sensors, terminals, household appliances, thermostats, televisions, automobiles, production machinery, urban infrastructure and many other "things", which traditionally have not been Internet enabled. This "Internet of Things" (IoT) represents a remarkable transformation of the way in which our world will soon interact. Much like the World Wide Web connected computers to networks, and the next evolution mobile devices connected people to the Internet and other people, IoT looks poised to interconnect devices, people, environments, virtual objects and machines in ways that only science fiction writers could have imagined. In a nutshell the Internet of Things (IoT) is the convergence of connecting people, things, data and processes; it is transforming our life, business and everything in between. Secure and Smart IoT explores many aspects of the Internet of Things and explains many of the complicated principles of IoT and the new advancements in IoT including using Fog Computing, AI and Blockchain technology. The topics discussed in the book include: - Internet of Things (IoT) - Industrial Internet of Things (IIoT) - Fog Computing - Artificial Intelligence - Blockchain Technology - Network Security - Zero-Trust Model - Data Analytics - Digital Transformation - DDoS - Smart Devices - Cybersecurity

In diesem Buch lernst du Schritt fA1/4r Schritt, wie du mit dem kostenlosen MIT App Inventor einen Chatbot entwickelst. Du erfAhrst zuerst, was Chatbots eigentlich sind und dass diese als Alexa, Siri, Bixby, Cortana und Google schon um uns sind. Du gehst der Frage nach, auf welche Weise Chatbots intelligent sind. Die erste Chatbot-App, die du nach Anleitung entwickelst, beantwortet eine einfache Frage von dir. Am Ende erzAhlt der Chatbot Witze. Weitere Beispiele zeigen dir, was sonst noch alles mAglich ist: Der Chatbot wird immer schlauer und geht immer besser auf seinen Chatpartner ein. Bestens geeignet fA1/4r Kinder und Jugendliche ab 10 Jahren.

This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.

Focusing on grid computing and asynchronism, Parallel Iterative Algorithms explores the theoretical and practical aspects of parallel numerical algorithms. Each chapter contains a theoretical discussion of the topic, an algorithmic section that fully details implementation examples and specific algorithms, and an evaluation of the advantages and drawbacks of the algorithms. Several exercises also appear at the end of most chapters. The first two chapters introduce the general features of sequential iterative algorithms and their applications to numerical problems. The book then describes different kinds of parallel systems and parallel iterative algorithms. It goes on to address both linear and nonlinear parallel synchronous and asynchronous iterative algorithms for numerical computation, with an emphasis on the multisplitting approach. The final chapter discusses the features required for efficient implementation of asynchronous iterative algorithms. Providing the theoretical and practical knowledge needed to design and implement efficient parallel iterative algorithms, this book illustrates how to apply these algorithms to solve linear and nonlinear numerical problems in parallel environments, including local, distant, homogeneous, and heterogeneous clusters.

Comprehensive textbook resource on distributed systems--integrates foundational topics with advanced topics of contemporary importance within the field Distributed Computing: Theory and Applications is organized around three aspects of distributed systems: networks, middleware tools, and applications. It presents architectural uniform methods to implement a distributed shared memory system for realization data consistency models suited to distributed applications' requirements. To aid in understanding how these concepts apply to real-world situations, the work illustrates topics with practical applications and includes references for further reading. Downloadable lecture slides are also included to help professors and instructors convey key concepts to their students. Additional topics discussed in Distributed Computing: Theory and Applications include: The abstractions and the implementations of several middleware for distributed computing, which constitute the bulk of core foundational topics Network issues and high-level connection tools Distributed knowledge management and distributed multi-agent systems Gossip Protocol, peer-to-peer systems and block-chain technology, and techniques for unstructured data management P2P, coordinated architectures, and advanced middleware for building large modern distributed applications Researchers, industry professionals, and students in the fields of science, technology, and medicine will be able to use Distributed Computing: Theory and Applications as a comprehensive textbook resource for understanding distributed systems, the specifics behind the modern elements which relate to them, and their practical applications.

This book includes selected papers presented at the International Conference on Marketing and Technologies (ICMarkTech 2022), held at Universidade de Santiago de Compostela, Spain, during December 1-3, 2022. It covers up-to-date cutting-edge research on artificial intelligence applied in marketing, virtual and augmented reality in marketing, business intelligence databases and marketing, data mining and big data, marketing data science, web marketing, e-commerce and v-commerce, social media and networking, geomarketing and IoT, marketing automation and inbound marketing, machine learning applied to marketing, customer data management and CRM, and neuromarketing technologies.

Intelligent and adaptive techniques are rapidly being used in all stages of medical treatment, from the initial diagnosis to planning delivery and follow-up therapy.To realize the full potential of these techniques, developers and end users must understand both the underlying technology and the specifics of the medical application considered. Focusing on this growing area of interest, Intelligent and Adaptive Systems in Medicine clearly and concisely explains a range of adaptive and intelligent systems, highlighting their benefits and limitations with realistic medical examples.

Bringing together theory and practice, this volume describes the application of adaptive and intelligent control as well as intelligent systems in the diagnosis, planning, treatment, and follow up of diseases such as cancer. Each chapter presents a family of an intelligent and adaptive system, explains the techniques and algorithms behind these systems, and explores how to solve medical and biomedical problems using intelligent and adaptive systems. The book focuses on the methods of fuzzy logic, artificial neural networks, neuro-fuzzy modeling, adaptive and predictive control, systems and statistical modeling, and image processing.

By assessing the use of intelligent and adaptive techniques for medical diagnosis and therapy, this guide promotes further research in this area of "techno-medicine." It provides researchers and clinicians with the tools and processes that are leading to the invaluable use of intelligent systems in early diagnoses and effective treatment.

Discusses the industry transformation aligned with computerization of manufacturing and the concept of required skills needed to build the new workforce Covers the role of Artificial Intelligence in the management of resource flow and decision-making in the industrial transformation of operations and supply chain management Presents sustainability efficiency for industrial transformation with IoT, machine learning, data analysis and blockchain technologies Focuses on industrial development, design, implementation, and transformation using technologies such as Big Data Analytics, Blockchain among others Showcases the incorporation of complex processes and functions as one central component of digital systems Explores current trends that are accelerating the industrial transformation and influencing the transition of the fourth industrial revolution related to industrial infrastructure, biodiversity, and production enhancement

This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.

COGNITIVE COMPUTING MODELS IN COMMUNICATION SYSTEMS A concise book on the latest research focusing on problems and challenges in the areas of data transmission technology, computer algorithms, AI-based devices, computer technology, and their solutions. The book provides a comprehensive overview of state-of-the-art research work on cognitive models in communication systems and computing techniques. It also bridges the gap between various communication systems and solutions by providing the current models and computing techniques, their applications, the strengths and limitations of the existing methods, and the future directions in this area. The contributors showcase their latest research work focusing on the issues, challenges, and solutions in the field of data transmission techniques, computational algorithms, artificial intelligence (AI)-based devices, and computing techniques. Readers will find in this succinctly written and unique book: Topics covering the applications of advanced cognitive devices, models, architecture, and techniques. A range of case studies and applications that will provide readers with the tools to apply cutting-edge models and algorithms. In-depth information about new cognitive computing models and conceptual frameworks and their implementation. Audience The book is designed for researchers and electronics engineers, computer science engineers, industrial engineers, and mechanical engineers (both in academia and industry) working in the fields of machine learning, cognitive computing, mobile communication, and wireless network system.

utilizes the Consolidated Serial Homicide Offender Database, one of the largest and most robust open access databases of multiple murders available illustrated with in-depth case studies of SHOs, such as Felix Vail, Michael Sumpter, the Seminole Heights Killer, and the Austin Bomber provides commentary from those who have used these patterning methods in practice, in addition to laying out how to put the current suite of data tools to use within organizations

REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of "decision, information, decision, information," are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a "diary problem" that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.

A holistic and real-world approach to operationalizing artificial intelligence in your company In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jagare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you'll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI) The importance of efficient and reproduceable data pipelines, including how to manage your company's data An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world--and not just the lab--Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment.