This book presents appealing contributions on computational intelligence and mathematics, connecting both areas and offering solutions to a number of interesting, real-world problems. Such problems often require novel solutions, as complexity exceeds the tractable size. At the same time, the need for good-quality realistic solutions results in models and algorithms with a good balance of resource intensiveness and model quality (accuracy). Many areas of knowledge call for hybrid solutions that combine traditional mathematical techniques and computational intelligence based on subsymbolic knowledge representation. Important research topics are focused on developing the interaction between computational intelligence and mathematics, in order to address various challenges of the current technological age. Written by influential, leading researchers, this book discusses the latest trends in hybridising mathematics and computational intelligence.
This book is a collection of selected papers presented at the 17th FAI International Conference on Engineering, Mathematical and Computational Intelligence (ICEMCI 2019), held at Jabalpur Engineering College, India, from 21–23 December 2019. This book discusses mathematical, computational intelligence and engineering approaches for tourism, agriculture and health care. It is a unique combination of a wide spectrum of topics, such as tourism destination ranking, medical diagnosis-based intelligent systems, drivers for hotel objectives, irrigation systems and more, which are discussed by using fuzzy, statistical and neural network tools. This book will be valuable to faculty members, postgraduate students, research scholars as well as readers from the industrial sector.
The volume contains original research papers as the Proceedings of the International Conference on Advances in Mathematics and Computing, held at Veer Surendra Sai University of Technology, Odisha, India, on 7-8 February, 2020. It focuses on new trends in applied analysis, computational mathematics and related areas. It also includes certain new models, image analysis technique, fluid flow problems, etc. as applications of mathematical analysis and computational mathematics. The volume should bring forward new and emerging topics of mathematics and computing having potential applications and uses in other areas of sciences. It can serve as a valuable resource for graduate students, researchers and educators interested in mathematical tools and techniques for solving various problems arising in science and engineering.
As the amount of accumulated data across a variety of fields becomes harder to maintain, it is essential for a new generation of computational theories and tools to assist humans in extracting knowledge from this rapidly growing digital data. Global Trends in Intelligent Computing Research and Development brings together recent advances and in depth knowledge in the fields of knowledge representation and computational intelligence. Highlighting the theoretical advances and their applications to real life problems, this book is an essential tool for researchers, lecturers, professors, students, and developers who have seek insight into knowledge representation and real life applications.
Applications of Computational Intelligence in Multi-Disciplinary Research provides the readers with a comprehensive handbook for applying the powerful principles, concepts, and algorithms of computational intelligence to a wide spectrum of research cases. The book covers the main approaches used in computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods, all of which can be collectively viewed as soft computing. Other key approaches included are swarm intelligence and artificial immune systems. These approaches provide researchers with powerful tools for analysis and problem-solving when data is incomplete and when the problem under consideration is too complex for standard mathematics and the crisp logic approach of Boolean computing. - Provides an overview of the key methods of computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods - Includes case studies and real-world examples of computational intelligence applied in a variety of research topics, including bioinformatics, biomedical engineering, big data analytics, information security, signal processing, machine learning, nanotechnology, and optimization techniques - Presents a thorough technical explanation on how computational intelligence is applied that is suitable for a wide range of multidisciplinary and interdisciplinary research
This volume contains contributions from participants in the 2007 International Multiconference of Engineers and Computer Scientists. It covers a variety of subjects in the frontiers of intelligent systems and computer engineering and their industrial applications. The book offers up-to-date information on advances in intelligent systems and computer engineering and also serves as an excellent reference work for researchers and graduate students working in the field.
This book provides an introduction to nineteen popular multiple intelligences. Part One discusses general intelligence, psychological testing, naturalistic intelligence, social intelligence, emotional intelligence, interpersonal intelligence, and cultural intelligence. Part Two tackles machine intelligence, the development of artificial intelligence, computational intelligence, and digital intelligence, or the ability for humans to adapt to a digital environment. Finally, Part Three discusses the role of intelligence in business development, using technology to augment intelligence, abstract thinking, swarm and animal intelligence, military intelligence, and musical intelligence. A Primer on Multiple Intelligences is a must-read for graduate students or scholars considering researching cognition, perception, motivation, and artificial intelligence. It will also be of use to those in social psychology, computer science, and pedagogy. It is as a valuable resource for anyone interested in learning more about the multifaceted study of intelligence.
Disruptive Trends in Computer Aided Diagnosis collates novel techniques and methodologies in the domain of content based image classification and deep learning/machine learning techniques to design efficient computer aided diagnosis architecture. It is aimed to highlight new challenges and probable solutions in the domain of computer aided diagnosis to leverage balancing of sustainable ecology. The volume focuses on designing efficient algorithms for proposing CAD systems to mitigate the challenges of critical illnesses at an early stage. State-of-the-art novel methods are explored for envisaging automated diagnosis systems thereby overriding the limitations due to lack of training data, sample annotation, region of interest identification, proper segmentation and so on. The assorted techniques addresses the challenges encountered in existing systems thereby facilitating accurate patient healthcare and diagnosis. Features: An integrated interdisciplinary approach to address complex computer aided diagnosis problems and limitations. Elucidates a rich summary of the state-of-the-art tools and techniques related to automated detection and diagnosis of life threatening diseases including pandemics. Machine learning and deep learning methodologies on evolving accurate and precise early detection and medical diagnosis systems. Information presented in an accessible way for students, researchers and medical practitioners. The volume would come to the benefit of both post-graduate students and aspiring researchers in the field of medical informatics, computer science and electronics and communication engineering. In addition, the volume is also intended to serve as a guiding factor for the medical practitioners and radiologists in accurate diagnosis of diseases.
From the winner of the Turing Award and the Abel Prize, an introduction to computational complexity theory, its connections and interactions with mathematics, and its central role in the natural and social sciences, technology, and philosophy Mathematics and Computation provides a broad, conceptual overview of computational complexity theory—the mathematical study of efficient computation. With important practical applications to computer science and industry, computational complexity theory has evolved into a highly interdisciplinary field, with strong links to most mathematical areas and to a growing number of scientific endeavors. Avi Wigderson takes a sweeping survey of complexity theory, emphasizing the field’s insights and challenges. He explains the ideas and motivations leading to key models, notions, and results. In particular, he looks at algorithms and complexity, computations and proofs, randomness and interaction, quantum and arithmetic computation, and cryptography and learning, all as parts of a cohesive whole with numerous cross-influences. Wigderson illustrates the immense breadth of the field, its beauty and richness, and its diverse and growing interactions with other areas of mathematics. He ends with a comprehensive look at the theory of computation, its methodology and aspirations, and the unique and fundamental ways in which it has shaped and will further shape science, technology, and society. For further reading, an extensive bibliography is provided for all topics covered. Mathematics and Computation is useful for undergraduate and graduate students in mathematics, computer science, and related fields, as well as researchers and teachers in these fields. Many parts require little background, and serve as an invitation to newcomers seeking an introduction to the theory of computation. Comprehensive coverage of computational complexity theory, and beyond High-level, intuitive exposition, which brings conceptual clarity to this central and dynamic scientific discipline Historical accounts of the evolution and motivations of central concepts and models A broad view of the theory of computation's influence on science, technology, and society Extensive bibliography
This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.
