Bio-inspired Algorithms for Pattern Recognition in Audio and Image Processing PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Bio-inspired Algorithms for Pattern Recognition in Audio and Image Processing PDF full book. Access full book title Bio-inspired Algorithms for Pattern Recognition in Audio and Image Processing by . Download full books in PDF and EPUB format.
Author:
Publisher:
ISBN: 9789036789318
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author:
Publisher:
ISBN: 9789036789318
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Simon James Fong
Publisher: Springer Nature
ISBN: 981156695X
Category : Technology & Engineering
Languages : en
Pages : 228
Get Book Here
Book Description
This book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases. The subject area of this book is within the realm of computer science, notably algorithms (meta-heuristic and, more particularly, bio-inspired algorithms). Although application domains of these new algorithms may be mentioned, the scope of this book is not on the application of algorithms to specific or general domains but to provide an update on recent research trends for bio-inspired algorithms within a specific application domain or emerging area. These areas include data streaming, fog computing, and phases of big data management. One of the reasons for writing this book is that the bio-inspired approach does not receive much attention but shows considerable promise and diversity in terms of approach of many issues in big data and streaming. Some novel approaches of this book are the use of these algorithms to all phases of data management (not just a particular phase such as data mining or business intelligence as many books focus on); effective demonstration of the effectiveness of a selected algorithm within a chapter against comparative algorithms using the experimental method. Another novel approach is a brief overview and evaluation of traditional algorithms, both sequential and parallel, for use in data mining, in order to provide an overview of existing algorithms in use. This overview complements a further chapter on bio-inspired algorithms for data mining to enable readers to make a more suitable choice of algorithm for data mining within a particular context. In all chapters, references for further reading are provided, and in selected chapters, the author also include ideas for future research.
Author: Katrin Amunts
Publisher: Springer Nature
ISBN: 3030824276
Category : Computers
Languages : en
Pages : 159
Get Book Here
Book Description
This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures.
Author: Pradipta Maji
Publisher: John Wiley & Sons
ISBN: 1118119711
Category : Technology & Engineering
Languages : en
Pages : 312
Get Book Here
Book Description
Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection. Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as: Soft computing in pattern recognition and data mining A mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the set Selection of non-redundant and relevant features of real-valued data sets Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis Segmentation of brain MR images for visualization of human tissues Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text covering the latest findings as well as directions for future research is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.
Author: Patrick S. P. Wang
Publisher: Springer Science & Business Media
ISBN: 3642224075
Category : Computers
Languages : en
Pages : 883
Get Book Here
Book Description
"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.
Author: S. Smys
Publisher: Springer Nature
ISBN: 3030418626
Category : Computers
Languages : en
Pages : 1664
Get Book Here
Book Description
This volume gathers selected, peer-reviewed original contributions presented at the International Conference on Computational Vision and Bio-inspired Computing (ICCVBIC) conference which was held in Coimbatore, India, on November 29-30, 2018. The works included here offer a rich and diverse sampling of recent developments in the fields of Computational Vision, Fuzzy, Image Processing and Bio-inspired Computing. The topics covered include computer vision; cryptography and digital privacy; machine learning and artificial neural networks; genetic algorithms and computational intelligence; the Internet of Things; and biometric systems, to name but a few. The applications discussed range from security, healthcare and epidemic control to urban computing, agriculture and robotics. In this book, researchers, graduate students and professionals will find innovative solutions to real-world problems in industry and society as a whole, together with inspirations for further research.
Author: Al-Haj, Ali Mohammad
Publisher: IGI Global
ISBN: 1615209042
Category : Education
Languages : en
Pages : 566
Get Book Here
Book Description
"This book introduces readers to state-of-art research in multimedia watermarking in the different disciplines of watermarking, addressing the different aspects of advanced watermarking research; modeling and theoretical analysis, advanced embedding and extraction techniques, software and hardware implementations, and performance evaluations of watermarking systems"--Provided by publisher.
Author: Akash Kumar Bhoi
Publisher: Springer Nature
ISBN: 9811554951
Category : Technology & Engineering
Languages : en
Pages : 427
Get Book Here
Book Description
This book covers the latest technological advances in neuro-computational intelligence in biological processes where the primary focus is on biologically inspired neuro-computational techniques. The theoretical and practical aspects of biomedical neural computing, brain-inspired computing, bio-computational models, artificial intelligence (AI) and machine learning (ML) approaches in biomedical data analytics are covered along with their qualitative and quantitative features. The contents cover numerous computational applications, methodologies and emerging challenges in the field of bio-soft computing and bio-signal processing. The authors have taken meticulous care in describing the fundamental concepts, identifying the research gap and highlighting the problems with the strategical computational approaches to address the ongoing challenges in bio-inspired models and algorithms. Given the range of topics covered, this book can be a valuable resource for students, researchers as well as practitioners interested in the rapidly evolving field of neurocomputing and biomedical data analytics.
Author: Dr. Kang Li
Publisher: Springer Science & Business Media
ISBN: 3540747680
Category : Computers
Languages : en
Pages : 823
Get Book Here
Book Description
This book is part of a two-volume work that constitutes the refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2007, held in Shanghai, China, September 2007. Coverage includes advanced neural network theory, advanced evolutionary computing theory, ant colonies and particle swarm optimization, intelligent modeling, monitoring, and control of complex nonlinear systems, as well as biomedical signal processing, imaging and visualization.
Author: Sourabh Ravindran
Publisher:
ISBN: 9781109871449
Category :
Languages : en
Pages : 115
Get Book Here
Book Description
Human-like performance by machines in tasks of speech and audio processing has remained an elusive goal. In an attempt to bridge the gap in performance between humans and machines there has been an increased effort to study and model physiological processes. However, the widespread use of biologically inspired features proposed in the past have been hampered mainly by either the lack of robustness across a range of signal-to-noise ratios or the formidable computational costs. It is possible that the biologically inspired features proposed in the past have been unsuccessful because the classifiers that employed them were not well suited to the characteristics of these features. In physiological systems, sensor processing occurs in several stages. It is likely the case that signal features and biological processing techniques evolved together and are complementary or well matched. It is precisely for this reason that modeling the feature extraction processes should go hand in hand with modeling of the processes that use these features. This research presents a front-end feature extraction method for audio signals inspired by the human peripheral auditory system. It is shown that the noise robustness issues of current state-of-the-art features, specifically, mel-frequency cepstral coefficients (MFCCs) can be addressed by paying closer attention to peripheral auditory processing. Features based on modeling processing in the primary auditory cortex have a distinctly different flavor and classifiers such as Gaussian mixture models (GMMs) cannot fully exploit the potential of these features. New developments in the field of machine learning are leveraged to build classifiers to exploit the performance gains afforded by the features based on advanced models of the human auditory system. Further, a classification structure similar to what might be expected in physiological processing is used to demonstrate the clear advantage of incorporating biologically inspired features into mainstream audio processing. The feature extraction and classification system can be efficiently implemented using the low-power cooperative analog-digital signal processing platform. The usefulness of the features are demonstrated for tasks of audio classification, speech versus non-speech discrimination, and speech recognition. The low-power nature of the classification system makes it ideal for use in applications such as hearing aids, hand-held devices, and surveillance through acoustic scene monitoring. It is clear that biologically inspired features have huge potential with respect to advancing the state-of-the-art in audio signal processing. There is a clear need to address the issue of how best to use these features. This thesis strives to demonstrate the possible advantages to be gained by using biologically inspired features and also suggests ways to incorporate these features into current classification methods, thereby opening the door to exciting research possibilities.
