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The need for intelligent machines in areas such as medical diagnostics, biometric security systems, and image processing motivates researchers to develop and explore new techniques, algorithms, and applications in this evolving field.Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies provides a common platform for researchers to present theoretical and applied research findings for enhancing and developing intelligent systems. Through its discussions of advances in and applications of pattern recognition technologies and artificial intelligence, this reference highlights core concepts in biometric imagery, feature recognition, and other related fields, along with their applicability.
This book includes reviewed papers by international scholars from the 2020 International Conference on Pattern Recognition and Artificial Intelligence (held online). The papers have been expanded to provide more details specifically for the book. It is geared to promote ongoing interest and understanding about pattern recognition and artificial intelligence. Like the previous book in the series, this book covers a range of topics and illustrates potential areas where pattern recognition and artificial intelligence can be applied. It highlights, for example, how pattern recognition and artificial intelligence can be used to classify, predict, detect and help promote further discoveries related to credit scores, criminal news, national elections, license plates, gender, personality characteristics, health, and more.Chapters include works centred on medical and financial applications as well as topics related to handwriting analysis and text processing, internet security, image analysis, database creation, neural networks and deep learning. While the book is geared to promote interest from the general public, it may also be of interest to graduate students and researchers in the field.
Artificial Intelligence (AI) has become a popular research topic recently. Pattern recognition (PR) is an important part of an AI system. If the AI is considered as the digital brain, then the PR is the visual and auditory cortex that converts the optical signals from the eyes and the acoustic signals from the ears to meaningful symbolic texts that the brain can digest. Over the past 40+ years, the processing speed of a digital computer has increased from kbits/s to tera floating point operations per second (TFLOPS), a 109 times acceleration. PR research has made significant advancements along the advancement of digital hardware, especially the graphical processing unit (GPU) technology that helps the rapid processing of complex images. In this book, the authors have collected the latest work from leading researchers in the PR fields. The topics are broad, which include optical implementation of various filters, digital implementation of state-of-the-art neural network (NN) training methods, and the latest deep leaning (DL) models. We also included applications of PR in various fields.
An accessible undergraduate introduction to the concepts and methods in pattern recognition, machine learning and deep learning.
The nature of handwriting in our society has significantly altered over the ages due to the introduction of new technologies such as computers and the World Wide Web. With increases in the amount of signature verification needs, state of the art internet and paper-based automated recognition methods are necessary. Pattern Recognition Technologies and Applications: Recent Advances provides cutting-edge pattern recognition techniques and applications. Written by world-renowned experts in their field, this easy to understand book is a must have for those seeking explanation in topics such as on- and offline handwriting and speech recognition, signature verification, and gender classification.
"Pattern Recognition, Machine Intelligence and Biometrics" covers the most recent developments in Pattern Recognition and its applications, using artificial intelligence technologies within an increasingly critical field. It covers topics such as: image analysis and fingerprint recognition; facial expressions and emotions; handwriting and signatures; iris recognition; hand-palm gestures; and multimodal based research. The applications span many fields, from engineering, scientific studies and experiments, to biomedical and diagnostic applications, to personal identification and homeland security. In addition, computer modeling and simulations of human behaviors are addressed in this collection of 31 chapters by top-ranked professionals from all over the world in the field of PR/AI/Biometrics. The book is intended for researchers and graduate students in Computer and Information Science, and in Communication and Control Engineering. Dr. Patrick S. P. Wang is a Professor Emeritus at the College of Computer and Information Science, Northeastern University, USA, Zijiang Chair of ECNU, Shanghai, and NSC Visiting Chair Professor of NTUST, Taipei.
The fifth volume in this book series consists of a collection of new papers written by a diverse group of international scholars. Papers and presentations were carefully selected from 160 papers submitted to the International Conference on Pattern Recognition and Artificial Intelligence held in Montreal, Quebec (May 2018) and an associated free public lecture entitled 'Artificial Intelligence and Pattern Recognition: Trendy Technologies in Our Modern Digital World'. Chapters address topics such as the evolution of AI, natural language processing, off and on-line handwriting analysis, tracking and detection systems, neural networks, rating video games, computer-aided diagnosis, and digital learning.Within an increasingly digital world, 'artificial intelligence' is becoming a household term and a topic of great interest to many people worldwide. Pattern recognition, in using key features to classify data, has a strong relationship with artificial intelligence. This book not only complements other monographs in the series, it also provides the latest information. It is geared to promote interest and understanding about pattern recognition and artificial intelligence to the general public. It may also be of interest to graduate students and researchers in the field. Rather than focusing on one specific area, the book introduces readers to various basic concepts and to various potential areas where pattern recognition and artificial intelligence can be applied to make valuable contributions to other fields such as medicine, teaching and learning, forensic science, surveillance, online reviews, computer vision and object tracking.
This volume brings together the results of research into the methodology and applications of pattern recognition, with particular emphasis given to the incorporation of artificial intelligence methodologies into pattern recognition systems.The first part of this volume covers image analysis and processing software, systems and algorithms. Pattern analysis and classifier design are dealt with in part two, while the last part deals with model based and expert systems, including uncertainty calculus methods in pattern analysis and object recognition. A number of specific application areas are considered, including such diverse topics as fingerprinting, astronomy, molecular biology and pathology.
What Is Pattern Recognition The process of automatically recognizing patterns and regularities within data is known as pattern recognition. Statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics, and machine learning are just few of the fields that can benefit from its use. The fields of statistics and engineering are where pattern recognition got its start; some contemporary methods of pattern recognition involve the use of machine learning, which is made possible by the increased availability of huge data and the more abundant computing capacity. Both of these pursuits might be considered to be two facets of the same application sector, and both of these activities have undergone significant development over the course of the last several decades. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Pattern recognition Chapter 2: Supervised learning Chapter 3: Linear classifier Chapter 4: Perceptron Chapter 5: Gaussian process Chapter 6: Expectation-maximization algorithm Chapter 7: Generalized linear model Chapter 8: Statistical learning theory Chapter 9: Kernel method Chapter 10: Probabilistic classification (II) Answering the public top questions about pattern recognition. (III) Real world examples for the usage of pattern recognition in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of pattern recognition. What is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
This book presents latest results in computer recognition systems, pattern recognition, machine learning, web and data mining. It includes coverage of image processing and computer vision; speech and word recognition; and medical applications.