Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications.Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks. "

This volume links the concept of granular computing using deep learning and the Internet of Things to object tracking for video analysis. It describes how uncertainties, involved in the task of video processing, could be handled in rough set theoretic granular computing frameworks. Issues such as object tracking from videos in constrained situations, occlusion/overlapping handling, measuring of the reliability of tracking methods, object recognition and linguistic interpretation in video scenes, and event prediction from videos, are the addressed in this volume. The book also looks at ways to reduce data dependency in the context of unsupervised (without manual interaction/ labeled data/ prior information) training.This book may be used both as a textbook and reference book for graduate students and researchers in computer science, electrical engineering, system science, data science, and information technology, and is recommended for both students and practitioners working in computer vision, machine learning, video analytics, image analytics, artificial intelligence, system design, rough set theory, granular computing, and soft computing.

Soft computing comprises various paradigms dedicated to approximately solving real-world problems, e.g., in decision making, classification or learning; among these paradigms are fuzzy sets, rough sets, neural networks, and genetic algorithms. It is well understood now in the soft computing community that hybrid approaches combining various paradigms provide very promising attempts to solving complex problems. Exploiting the potential and strength of both neural networks and rough sets, this book is devoted to rough-neurocomputing which is also related to the novel aspect of computing based on information granulation, in particular to computing with words. It provides foundational and methodological issues as well as applications in various fields.

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Symposium on Intelligent Informatics (ISI 2013) held in Mysore, India during August 23-24, 2013. The 47 revised papers presented were carefully reviewed and selected from 126 initial submissions. The papers are organized in topical sections on pattern recognition, signal and image processing; data mining, clustering and intelligent information systems; multi agent systems; and computer networks and distributed systems. The book is directed to the researchers and scientists engaged in various fields of intelligent informatics.

Soft computing comprises various paradigms dedicated to approximately solving real-world problems, e.g. in decision making, classification or learning; among these paradigms are fuzzy sets, rough sets, neural networks, genetic algorithms, and others.

It is well understood now in the soft computing community that hybrid approaches combining various paradigms are very promising approaches for solving complex problems. Exploiting the potential and strength of both neural networks and rough sets, this book is devoted to rough-neuro computing which is also related to the novel aspect of computing based on information granulation, in particular to computing with words. It provides foundational and methodological issues as well as applications in various fields.

This volume contains the proceedings of the third international conference on Pattern Recognition and Machine Intelligence (PReMI 2009) which was held at the Indian Institute of Technology, New Delhi, India, during December 16-20, 2009. This was the third conference in the series. The first two conferences were held in December at the Indian Statistical Institute, Kolkata in 2005 and 2007. PReMI has become a premier conference in India presenting state-of-art research findings in the areas of machine intelligence and pattern recognition. The conference is also successful in encouraging academic and industrial interaction, and in prom- ing collaborative research and developmental activities in pattern recognition, - chine intelligence and other allied fields, involving scientists, engineers, professionals, researchers and students from India and abroad. The conference is scheduled to be held every alternate year making it an ideal platform for sharing views and expe- ences in these fields in a regular manner. The focus of PReMI 2009 was soft-computing, machine learning, pattern recognition and their applications to diverse fields. As part of PReMI 2009 we had two special workshops. One workshop focused on text mining. The other workshop show-cased industrial and developmental projects in the relevant areas. Premi 2009 attracted 221 submissions from different countries across the world.

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks. "

The 8th International Workshop on Fuzzy Logic and Applications (WILF 2009) held in Palermo (Italy), June 9-12, 2009, covered topics related to theoretical and experimental areas of fuzzy sets and systems with emphasis on di?erent applications. This eventrepresentsthe continuation ofan established traditionof biannual interdisciplinary meetings. The previous editions of WILF were held, with an increasing number of participants, in Naples (1995), Bari (1997), Genoa (1999), Milan (2001), Naples (2003), Crema (2005) and Camogli (2007). Each event focused on distinct main thematic areas of fuzzy logic and related applications. WILF 2009 aimed to highlight connections and synergiesof fuzzy sets theory with nonconventional computing (e.g., neural networks, evolutionary compu- tion, support vector machines, molecular computing, quantum computing) and cognitivescience,inordertoreachabetterunderstandingofbothnaturaland- ti?cial complex systems as well as computing systems, inspired by nature, which are able to solve complex problems. From this perspective one of the main goals of the WILF workshops is to bring together researchers and developers from both academia and high-tech companies. WILF 2009receivedmorethan 60 paper submissionsfrom all overthe world, including Algeria, Belgium, Benin, Brazil, Canada, China, France, Greece, India, Iran, Italy, Japan, Poland, Romania, Slovakia, Spain and the USA. A rigorous peer-review selection process was applied to ultimately select nearly 40 high-quality manuscripts to be published in this volume. Moreover,thevolumealsoincludespresentationsfromthreekeynotespeakers EtienneKerre(GhentUniversity,Belgium),SankarK.Pal(ISI,India)andEnric Trillas (ECSC, Spain).

The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019. The 131 revised full papers presented were carefully reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning; Soft and Evolutionary Computing; Image Processing; Medical Image Processing; Bioinformatics and Biomedical Signal Processing; Information Retrieval; Remote Sensing; Signal and Video Processing; and Smart and Intelligent Sensors.

The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019. The 131 revised full papers presented were carefully reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning; Soft and Evolutionary Computing; Image Processing; Medical Image Processing; Bioinformatics and Biomedical Signal Processing; Information Retrieval; Remote Sensing; Signal and Video Processing; and Smart and Intelligent Sensors.