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FUZZY COMPUTING IN DATA SCIENCE This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges. The book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. Audience Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.
This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.
Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field of big data, cloud computing, the Internet of Things, and quantum information. At present, the processing of this kind of data is not very mature. The authors carried out more than 20 years of research, and show in this book their most important results. The seven chapters of the book are devoted to key topics such as dynamic fuzzy machine learning models, dynamic fuzzy self-learning subspace algorithms, fuzzy decision tree learning, dynamic concepts based on dynamic fuzzy sets, semi-supervised multi-task learning based on dynamic fuzzy data, dynamic fuzzy hierarchy learning, examination of multi-agent learning model based on dynamic fuzzy logic. This book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artificial intelligence, machine learning, automation, data analysis, mathematics, management, cognitive science, and finance. It can be also used as the basis for teaching the principles of dynamic fuzzy learning.
This volume contains the proceedings of the Eighth Austrian Artificial Intelligence Conference, held in Linz, Austria, in June 1993. The focus of the conference was on "Fuzzy Logic in Artificial Intelligence". The volume contains abstracts of two invited talks and full versions of 17 carefully selected papers. The invited talks were: "The role of fuzzylogic and soft computing in the conception and design of intelligent systems" by Lotfi A. Zadeh, and "A contextual approach for AI systems development" by Irina V. Ezhkova. The contributed papers are grouped into sections on theoretical issues, machine learning, expert systems, robotics and control, applications to medicine, and applications to car driving. Additionally, the volume contains descriptions of the four workshops that took place during the conference.
"This book presents the most innovative systematic and practical facets of fuzzy computing technologies to students, scholars, and academicians, as well as practitioners, engineers, and professionals"--
"This book presents up-to-date techniques for addressing data management problems with logic and memory use"--Provided by publisher.
The modern origin of fuzzy sets, fuzzy algebra, fuzzy decision making, and “computing with words” is conventionally traced to Lotfi Zadeh’s publication in 1965 of his path-breaking refutation of binary set theory. In a sixteen-page article, modestly titled “Fuzzy Sets” and published in the journal Information and Control, Zadeh launched a multi-disciplinary revolution. The start was relatively slow, but momentum gathered quickly. From 1970 to 1979 there were about 500 journal publications with the word fuzzy in the title; from 2000 to 2009 there were more than 35,000. At present, citations to Zadeh’s publications are running at a rate of about 1,500-2,000 per year, and this rate continues to rise. Almost all applications of Zadeh’s ideas have been in highly technical scientific fields, not in the social sciences. Zadeh was surprised by this development. In a personal note he states: “When I wrote my l965 paper, I expected that fuzzy set theory would be applied primarily in the realm of human sciences. Contrary to my expectation, fuzzy set theory and fuzzy logic are applied in the main in physical and engineering sciences.” In fact, the first comprehensive examination of fuzzy sets by a social scientist did not appear until 1987, a full twenty-two years after the publication of Zadeh’s seminal article, when Michael Smithson, an Australian psychologist, published Fuzzy Set Analysis for Behavioral and Social Sciences.
This book constitutes the refereed proceedings of the International Conference on Soft Computing in Data Science, SCDS 2017, held in Yogyakarta, Indonesia, November 27-28, 2017. The 26 revised full papers presented were carefully reviewed and selected from 68 submissions. The papers are organized in topical sections on deep learning and real-time classification; image feature classification and extraction; classification, clustering, visualization; applications of machine learning; data visualization; fuzzy logic; prediction models and e-learning; text and sentiment analytics.
Fuzzy Logic for Data Science the application of fuzzy logic principles to solve complex data science challenges, focusing on handling uncertainty and imprecision in data. This introduces readers to foundational concepts of fuzzy sets, fuzzy reasoning, and fuzzy inference systems, bridging the gap between traditional data science techniques and fuzzy logic methodologies. It demonstrates how fuzzy logic can enhance predictive modeling, decision-making, and data interpretation across various real-world applications, making it ideal for data scientists, analysts, and students looking to broaden their analytical skill set with adaptable, flexible approaches.
Fuzzy systems and data mining are indispensible aspects of the computer systems and algorithms on which the world has come to depend. This book presents papers from FSDM 2021, the 7th International Conference on Fuzzy Systems and Data Mining. The conference, originally due to take place in Seoul, South Korea, was held online on 26-29 October 2021, due to ongoing restrictions connected with the COVID-19 pandemic. The annual FSDM conference provides a platform for knowledge exchange between international experts, researchers, academics and delegates from industry. This year, the committee received 266 submissions, and this book contains 52 papers, including keynotes and invited presentations, oral and poster contributions. The papers cover four main areas: 1) fuzzy theory, algorithms and systems – including topics like stability; 2) fuzzy applications – which are widely used and cover various types of processing as well as hardware and architecture for big data and time series; 3) the interdisciplinary field of fuzzy logic and data mining; and 4) data mining itself. The topic most frequently addressed this year is fuzzy systems. The book offers an overview of research and developments in fuzzy logic and data mining, and will be of interest to all those working in the field of data science.