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Artificial Intelligence in Theory and Practice IV by Tharam Dillon Pdf
This book constitutes the refereed proceedings of the 4th IFIP TC 12 International Conference on Artificial Intelligence, IFIP AI 2015, Held as Part of WCC 2015, in Daejeon, South Korea, in October 2015. The 13 full papers presented were carefully reviewed and selected from 36 submissions. The papers are organized in topical sections on artificial intelligence techniques in biomedicine, artificial intelligence for knowledge management, computational intelligence and algorithms, and intelligent decision support systems.
Artificial Intelligence by Thomas L. Dean,James Allen,John Aloimonos Pdf
This book provides a detailed understanding of the broad issues in artificial intelligence and a survey of current AI technology. The author delivers broad coverage of innovative representational techniques, including neural networks, image processing and probabilistic reasoning, alongside the traditional methods of symbolic reasoning. The work is intended for students in artificial intelligence, researchers and LISP programmers.
Artificial Intelligence in Medical Imaging by Lia Morra,Silvia Delsanto,Loredana Correale Pdf
Choice Recommended Title, January 2021 This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-par performance with clinical experts. In the last section, the book offers a view on the complexity of the validation of artificial intelligence applications for commercial use, describing the recently introduced concept of software as a medical device, as well as good practices and relevant considerations for training and testing machine learning systems for medical use. Open problematics on the validation for public use of systems which by nature continuously evolve through new data is also explored. The book will be of interest to graduate students in medical physics, biomedical engineering and computer science, in addition to researchers and medical professionals operating in the medical imaging domain, who wish to better understand these technologies and the future of the field. Features: An accessible yet detailed overview of the field Explores a hot and growing topic Provides an interdisciplinary perspective
Advances in Artificial Intelligence: From Theory to Practice by Salem Benferhat,Karim Tabia,Moonis Ali Pdf
The two-volume set LNCS 10350 and 10351 constitutes the thoroughly refereed proceedings of the 30th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, held in Arras, France, in June 2017. The 70 revised full papers presented together with 45 short papers and 3 invited talks were carefully reviewed and selected from 180 submissions. They are organized in topical sections: constraints, planning, and optimization; data mining and machine learning; sensors, signal processing, and data fusion; recommender systems; decision support systems; knowledge representation and reasoning; navigation, control, and autonome agents; sentiment analysis and social media; games, computer vision; and animation; uncertainty management; graphical models: from theory to applications; anomaly detection; agronomy and artificial intelligence; applications of argumentation; intelligent systems in healthcare and mhealth for health outcomes; and innovative applications of textual analysis based on AI.
Edge Intelligence by Javid Taheri,Schahram Dustdar,Albert Zomaya,Shuiguang Deng Pdf
This graduate-level textbook is ideally suited for lecturing the most relevant topics of Edge Computing and its ties to Artificial Intelligence (AI) and Machine Learning (ML) approaches. It starts from basics and gradually advances, step-by-step, to ways AI/ML concepts can help or benefit from Edge Computing platforms. The book is structured into seven chapters; each comes with its own dedicated set of teaching materials (practical skills, demonstration videos, questions, lab assignments, etc.). Chapter 1 opens the book and comprehensively introduces the concept of distributed computing continuum systems that led to the creation of Edge Computing. Chapter 2 motivates the use of container technologies and how they are used to implement programmable edge computing platforms. Chapter 3 introduces ways to employ AI/ML approaches to optimize service lifecycles at the edge. Chapter 4 goes deeper in the use of AI/ML and introduces ways to optimize spreading computational tasks along edge computing platforms. Chapter 5 introduces AI/ML pipelines to efficiently process generated data on the edge. Chapter 6 introduces ways to implement AI/ML systems on the edge and ways to deal with their training and inferencing procedures considering the limited resources available at the edge-nodes. Chapter 7 motivates the creation of a new orchestrator independent object model to descriptive objects (nodes, applications, etc.) and requirements (SLAs) for underlying edge platforms. To provide hands-on experience to students and step-by-step improve their technical capabilities, seven sets of Tutorials-and-Labs (TaLs) are also designed. Codes and Instructions for each TaL is provided on the book website, and accompanied by videos to facilitate their learning process.
Machine Learning in Finance by Matthew F. Dixon,Igor Halperin,Paul Bilokon Pdf
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Philosophy and Theory of Artificial Intelligence 2017 by Vincent C. Müller Pdf
This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing.
Communicating Artificial Intelligence (AI) by Seungahn Nah,Jasmine E. McNealy,Jang Hyun Kim,Jungseock Joo Pdf
Despite increasing scholarly attention to artificial intelligence (AI), studies at the intersection of AI and communication remain ripe for exploration, including investigations of the social, political, cultural, and ethical aspects of machine intelligence, interactions among agents, and social artifacts. This book tackles these unexplored research areas with special emphasis on conditions, components, and consequences of cognitive, attitudinal, affective, and behavioural dimensions toward communication and AI. In doing so, this book epitomizes communication, journalism and media scholarship on AI and its social, political, cultural, and ethical perspectives. Topics vary widely from interactions between humans and robots through news representation of AI and AI-based news credibility to privacy and value toward AI in the public sphere. Contributors from such countries as Brazil, Netherland, South Korea, Spain, and United States discuss important issues and challenges in AI and communication studies. The collection of chapters in the book considers implications for not only theoretical and methodological approaches, but policymakers and practitioners alike. The chapters in this book were originally published as a special issue of Communication Studies.
Understanding Machine Learning by Shai Shalev-Shwartz,Shai Ben-David Pdf
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence by Hamido Fujita,Philippe Fournier-Viger,Moonis Ali,Yinglin Wang Pdf
This book constitutes the thoroughly refereed proceedings of the 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, held in Kitakyushu, Japan, in July 2022. The 67 full papers and 11 short papers presented were carefully reviewed and selected from 127 submissions. The IEA/AIE 2022 conference focuses on focuses on applications of applied intelligent systems to solve real-life problems in all areas including business and finance, science, engineering, industry, cyberspace, bioinformatics, automation, robotics, medicine and biomedicine, and human-machine interactions.
Genetic Programming Theory and Practice IV by Rick Riolo,Terence Soule,Bill Worzel Pdf
Genetic Programming Theory and Practice IV was developed from the fourth workshop at the University of Michigan’s Center for the Study of Complex Systems. The workshop was convened in May 2006 to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application.
Advances in Data Science and Artificial Intelligence by Rajiv Misra,Nishtha Kesswani,Muttukrishnan Rajarajan,Bharadwaj Veeravalli,Imene Brigui,Ashok Patel,T. N. Singh Pdf
With the intriguing development of technologies in several industries along with the advent of accrescent and ubiquitous computational resources, it creates an ample number of opportunities to develop innovative intelligence technologies in order to solve the wide range of uncertainties, imprecision, and vagueness issues in various real-life problems. Hybridizing modern computational intelligence with traditional computing methods has attracted researchers and academicians to focus on developing innovative AI techniques using data science. International Conference on Data Science and Artificial Intelligence (ICDSAI) 2022, organized on April 23-24, 2022 by the Indian Institute of Technology, Patna at NITIE Mumbai (India) in collaboration with the International Association of Academicians (IAASSE) USA collected scientific and technical contributions with respect to models, tools, technologies, and applications in the field of modern Artificial Intelligence and Data Science, covering the entire range of concepts from theory to practice, including case studies, works-in-progress, and conceptual explorations.
Cooperative Information Agents IV - The Future of Information Agents in Cyberspace by Matthias Klusch,Larry Kerschberg Pdf
These arethe proceedingsof the Fourth InternationalWorkshopon Cooperative Information Agents, held in Boston Massachusetts, USA, July 7-9, 2000. Cooperative information agent research and development focused originally onaccessingmultiple,heterogeneous,anddistributedinformationsources. Ga- ingaccesstothesesystems,throughInternetsearchengines,applicationprogram interfaces, wrappers, and web-based screens has been an important focus of - operative intelligent agents. Research has also focused on the integration of this information into a coherent model that combined data and knowledge from the multiple sources. Finally, this information is disseminated to a wide audience, giving rise to issues such as data quality, information pedigree, source reliability, information security, personal privacy, and information value. Research in - operative information agents has expanded to include agent negotiation, agent communities, agent mobility, as well as agent collaboration for information d- covery in constrained environments. TheinterdisciplinaryCIAworkshopseriesencompassesa widevarietyoft- ics dealing with cooperative information agents. All workshop proceedings have been published by Springer as Lecture Notes in Arti?cial Intelligence, Volumes 1202 (1997), 1435 (1998), and 1652 (1999), respectively. This year, the theme of the CIA workshop was ”’The Future of Information Agents in Cyberspace”, a very ?tting topic as the use of agents for information gathering, negotiation, correlation, fusion, and dissemination becomes ever more prevalent. We noted a marked trend in CIA 2000 towards addressing issues related to communities of agents that: (1) negotiate for information resources, (2) build robust ontologies to enhance search capabilities, (3) communicate for planning and problem so- ing, (4) learn and evolve based on their experiences, and (5) assume increasing degrees of autonomy in the control of complex systems.
