Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. The integration of deep learning improves the performance of Cognitive computing systems in many applications, helping in utilizing heterogeneous data sets and generating meaningful insights.

This book discusses computer vision, a noncontact as well as a nondestructive technique involving the development of theoretical and algorithmic tools for automatic visual understanding and recognition which finds huge applications in agricultural productions. It also entails how rendering of machine learning techniques to computer vision algorithms is boosting this sector with better productivity by developing more precise systems. Computer vision and machine learning (CV-ML) helps in plant disease assessment along with crop condition monitoring to control the degradation of yield, quality, and severe financial loss for farmers. Significant scientific and technological advances have been made in defect assessment, quality grading, disease recognition, pests, insects, fruits, and vegetable types recognition and evaluation of a wide range of agricultural plants, crops, leaves, and fruits. The book discusses intelligent robots developed with the touch of CV-ML which can help farmers to perform various tasks like planting, weeding, harvesting, plant health monitoring, and so on. The topics covered in the book include plant, leaf, and fruit disease detection, crop health monitoring, applications of robots in agriculture, precision farming, assessment of product quality and defects, pest, insect, fruits, and vegetable types recognition.

This book provides extensive research into the use of augmented reality in the three interconnected and overlapping fields of the tourism industry, museum exhibitions, and cultural heritage. It is written by a virtual team of 50 leading researchers and practitioners from 16 countries around the world. The authors explore the opportunities and challenges of augmented reality applications, their current status and future trends, informal learning and heritage preservation, mixed reality environments and immersive installations, cultural heritage education and tourism promotion, visitors with special needs, and emerging post-COVID-19 museums and heritage sites. Augmented Reality in Tourism, Museums and Heritage: A New Technology to Inform and Entertain is essential reading not only for researchers, application developers, educators, museum curators, tourism and cultural heritage promoters, but also for students (both graduates and undergraduates) and anyone who is interested in the efficient and practical use of augmented reality technology.

Inverse problems such as imaging or parameter identification deal with the recovery of unknown quantities from indirect observations, connected via a model describing the underlying context. While traditionally inverse problems are formulated and investigated in a static setting, we observe a significant increase of interest in time-dependence in a growing number of important applications over the last few years. Here, time-dependence affects a) the unknown function to be recovered and / or b) the observed data and / or c) the underlying process. Challenging applications in the field of imaging and parameter identification are techniques such as photoacoustic tomography, elastography, dynamic computerized or emission tomography, dynamic magnetic resonance imaging, super-resolution in image sequences and videos, health monitoring of elastic structures, optical flow problems or magnetic particle imaging to name only a few. Such problems demand for innovation concerning their mathematical description and analysis as well as computational approaches for their solution.

This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.

This book features original research and recent advances in ICT fields related to sustainable development. Based the International Conference on Networks, Intelligent systems, Computing & Environmental Informatics for Sustainable Development, held in Marrakech in April 2020, it features peer-reviewed chapters authored by prominent researchers from around the globe. As such it is an invaluable resource for courses in computer science, electrical engineering and urban sciences for sustainable development. This book covered topics including * Green Networks * Artificial Intelligence for Sustainability* Environment Informatics* Computing Technologies

Contemporary research in science and engineering is seeking to harness the versatility and sustainability of living organisms. By exploiting natural principles, researchers hope to create new kinds of technology that are self-repairing, adaptable, and robust, and to invent a new class of machines that are perceptive, social, emotional, perhaps even conscious. This is the realm of the 'living machine'. Living machines can be divided into two types: biomimetic systems, that harness the principles discovered in nature and embody them in new artifacts, and biohybrid systems in which biological entities are coupled with synthetic ones. Living Machines: A handbook of research in biomimetic and biohybrid systems surveys this flourishing area of research, capturing the current state of play and pointing to the opportunities ahead. Promising areas in biomimetics include self-organization, biologically inspired active materials, self-assembly and self-repair, learning, memory, control architectures and self-regulation, locomotion in air, on land or in water, perception, cognition, control, and communication. Drawing on these advances the potential of biomimetics is revealed in devices that can harvest energy, grow or reproduce, and in animal-like robots that range from synthetic slime molds, to artificial fish, to humanoids. Biohybrid systems is a relatively new field, with exciting and largely unknown potential, but one that is likely to shape the future of humanity. This book surveys progress towards new kinds of biohybrid such as robots that merge electronic neurons with biological tissue, micro-scale machines made from living cells, prosthetic limbs with a sense of touch, and brain-machine interfaces that allow robotic devices to be controlled by human thought. The handbook concludes by exploring some of the impacts that living machine technologies could have on both society and the individual, exploring questions about how we will see and understand ourselves in a world in which the line between the natural and the artificial is increasingly blurred. With contributions from leading researchers from science, engineering, and the humanities, this handbook will be of broad interest to undergraduate and postgraduate students. Researchers in the areas of computational modeling and engineering, including artificial intelligence, machine learning, artificial life, biorobotics, neurorobotics, and human-machine interfaces will find Living Machines an invaluable resource.

Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations. Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms. As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers. This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision. Dr. Wei Qi Yan is an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title, Visual Cryptography for Image Processing and Security.

This two-volume set, CCIS 1453 and CCIS 1454, constitutes refereed proceedings of the 6th International Conference on Data Mining and Big Data, DMBD 2021, held in Guangzhou, China, in October 2021. The 57 full papers and 28 short papers presented in this two-volume set were carefully reviewed and selected from 258 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.

This book is a collection of selected papers presented at the First Congress on Intelligent Systems (CIS 2020), held in New Delhi, India, during September 5-6, 2020. It includes novel and innovative work from experts, practitioners, scientists, and decision-makers from academia and industry. It covers selected papers in the area of computer vision. This book covers new tools and technologies in some of the important areas of medical science like histopathological image analysis, cancer taxonomy, use of deep learning architecture in dental care, and many more. Furthermore, this book reviews and discusses the use of intelligent learning-based algorithms for increasing the productivity in agricultural domain.

This book constitutes the refereed proceedings of the 7th Joint International Conference on Serious Games, JCSG 2021, as virtual event, in January 2022.The 17 full papers presented together with 3 short papers were carefully reviewed and selected from 28 submissions. JSCG 2021 is dedicated to serious games and its interdisciplinary characteristics combining game concepts and technologies required in the different application domains.

This two-volume set CCIS 1498 and CCIS 1499 contains the late breaking posters presented during the 23rd International Conference on Human-Computer Interaction, HCII 2021, which was held virtually in July 2021. The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. Additionally, 174 papers and 146 posters are included in the volumes of the proceedings published after the conference, as "Late Breaking Work" (papers and posters). The posters presented in these two volumes are organized in topical sections as follows: HCI Theory and Practice; UX Design and Research in Intelligent Environments; Interaction with Robots, Chatbots, and Agents; Virtual, Augmented, and Mixed Reality; Games and Gamification; HCI in Mobility, Transport and Aviation; Design for All and Assistive Technologies; Physiology, Affect and Cognition; HCI for Health and Wellbeing; HCI in Learning, Teaching, and Education; Culture and Computing; Social Computing; Design Case Studies; User Experience Studies.

Written by world-renowned authors, this unique compendium presents the most updated progress in pattern recognition and computer vision (PRCV), fully reflecting the strong international research interests in the artificial intelligence arena.Machine learning has been the key to current developments in PRCV. This useful comprehensive volume complements the previous five editions of the book. It places great emphasis on the use of deep learning in many aspects of PRCV applications, not readily available in other reference text.

This book provides a comprehensive review of all aspects relating to visual quality assessment for stereoscopic images, including statistical mathematics, stereo vision and deep learning. It covers the fundamentals of stereoscopic image quality assessment (SIQA), the relevant engineering problems and research significance, and also offers an overview of the significant advances in visual quality assessment for stereoscopic images, discussing and analyzing the current state-of-the-art in SIQA algorithms, the latest challenges and research directions as well as novel models and paradigms. In addition, a large number of vivid figures and formulas help readers gain a deeper understanding of the foundation and new applications of objective stereoscopic image quality assessment technologies. Reviewing the latest advances, challenges and trends in stereoscopic image quality assessment, this book is a valuable resource for researchers, engineers and graduate students working in related fields, including imaging, displaying and image processing, especially those interested in SIQA research.

This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.

This book constitutes the proceedings of the 23rd International Symposium on Fundamentals of Computation Theory, FCT 2021, held in Athens, Greece, in September 2021. The 30 full papers included in this volume were carefully reviewed and selected from 94 submissions. In addition, the book contains 2 invited talks. The papers cover topics of all aspects of theoretical computer science, in particular algorithms, complexity, formal and logical methods.

This book provides an essential overview of the broad range of functional brain imaging techniques, as well as neuroscientific methods suitable for various scientific tasks in fundamental and clinical neuroscience. It also shares information on novel methods in computational neuroscience, mathematical algorithms, image processing, and applications to neuroscience. The mammalian brain is a huge and complex network that consists of billions of neural and glial cells. Decoding how information is represented and processed by this neural network requires the ability to monitor the dynamics of large numbers of neurons at high temporal and spatial resolution over a large part of the brain. Functional brain optical imaging has seen more than thirty years of intensive development. Current light-using methods provide good sensitivity to functional changes through intrinsic contrast and are rapidly exploiting the growing availability of exogenous fluorescence probes. In addition, various types of functional brain optical imaging are now being used to reveal the brain's microanatomy and physiology.

Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented. The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering.

Visualizations of virtual objects in the real environment is often done by a simplified representation with simple surfaces and without reference to the surrounding environment. The seamless fusion of the virtual and real environment is, however, an essential factor in many areas, which is of particular importance when calculating lighting in mixed realities on mobile devices. Current approaches focus on approximations, which allow the calculation of diffuse lighting, whereby the rendering of glossy reflection properties is often neglected. The aim of this book is to enable the visualization of mirror-like reflective surfaces in mixed reality. In order to achieve this goal, various approaches are explored enabling high-quality visualization of virtual objects in realtime with a focus on the use of common hardware such as cameras, sensors in mobile devices, and partially depth sensors. Complete ambient lighting can be estimated, which enables detailed reflections. The results provide a novel way to embed complex and simple geometric shapes with glossy surfaces in the real world which offers a higher level of detail in the reflections without using additional hardware.

This is the first book that focuses entirely on the fundamental questions in visualization. Unlike other existing books in the field, it contains discussions that go far beyond individual visual representations and individual visualization algorithms. It offers a collection of investigative discourses that probe these questions from different perspectives, including concepts that help frame these questions and their potential answers, mathematical methods that underpin the scientific reasoning of these questions, empirical methods that facilitate the validation and falsification of potential answers, and case studies that stimulate hypotheses about potential answers while providing practical evidence for such hypotheses. Readers are not instructed to follow a specific theory, but their attention is brought to a broad range of schools of thoughts and different ways of investigating fundamental questions. As such, the book represents the by now most significant collective effort for gathering a large collection of discourses on the foundation of data visualization. Data visualization is a relatively young scientific discipline. Over the last three decades, a large collection of computer-supported visualization techniques have been developed, and the merits and benefits of using these techniques have been evidenced by numerous applications in practice. These technical advancements have given rise to the scientific curiosity about some fundamental questions such as why and how visualization works, when it is useful or effective and when it is not, what are the primary factors affecting its usefulness and effectiveness, and so on. This book signifies timely and exciting opportunities to answer such fundamental questions by building on the wealth of knowledge and experience accumulated in developing and deploying visualization technology in practice.

This book constitutes the refereed proceedings of the 12th International Conference on the Theory and Application of Diagrams, Diagrams 2021, held virtually in September 2021. The 16 full papers and 25 short papers presented together with 16 posters were carefully reviewed and selected from 94 submissions. The papers are organized in the following topical sections: design of concrete diagrams; theory of diagrams; diagrams and mathematics; diagrams and logic; new representation systems; analysis of diagrams; diagrams and computation; cognitive analysis; diagrams as structural tools; formal diagrams; and understanding thought processes. 10 chapters are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

PREVIOUS EDITIONThis textbook introduces the "Fundamentals of Multimedia", addressing real issues commonly faced in the workplace. The essential concepts are explained in a practical way to enable students to apply their existing skills to address problems in multimedia. Fully revised and updated, this new edition now includes coverage of such topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks, and their attendant technologies. Features: presents an overview of the key concepts in multimedia, including color science; reviews lossless and lossy compression methods for image, video and audio data; examines the demands placed by multimedia communications on wired and wireless networks; discusses the impact of social media and cloud computing on information sharing and on multimedia content search and retrieval; includes study exercises at the end of each chapter; provides supplementary resources for both students and instructors at an associated website.

This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.

This cutting-edge volume focuses on how artificial intelligence can be used to give computers the ability to imitate human sight. With contributions from researchers in diverse countries, including Thailand, Spain, Japan, Turkey, Australia, and India, the book explains the essential modules that are necessary for comprehending artificial intelligence experiences to provide machines with the power of vision. The volume also presents innovative research developments, applications, and current trends in the field. The chapters cover such topics as visual quality improvement, Parkinson's disease diagnosis, hypertensive retinopathy detection through retinal fundus, big image data processing, N-grams for image classification, medical brain images, chatbot applications, credit score improvisation, vision-based vehicle lane detection, damaged vehicle parts recognition, partial image encryption of medical images, and image synthesis. The chapter authors show different approaches to computer vision, image processing, and frameworks for machine learning to build automated and stable applications. Deep learning is included for making immersive application-based systems, pattern recognition, and biometric systems. The book also considers efficiency and comparison at various levels of using algorithms for real-time applications, processes, and analysis.

This book discusses email spam detection and its challenges such as text classification and categorization. The book proposes an efficient spam detection technique that is a combination of Character Segmentation and Recognition and Classification (CSRC). The author describes how this can detect whether an email (text and image based) is a spam mail or not. The book presents four solutions: first, to extract the text character from the image by segmentation process which includes a combination of Discrete Wavelet Transform (DWT) and skew detection. Second, text characters are via text recognition and visual feature extraction approach which relies on contour analysis with improved Local Binary Pattern (LBP). Third, extracted text features are classified using improvised K-Nearest Neighbor search (KNN) and Support Vector Machine (SVM). Fourth, the performance of the proposed method is validated by the measure of metric named as sensitivity, specificity, precision, recall, F-measure, accuracy, error rate and correct rate. Presents solutions to email spam detection and discusses its challenges such as text classification and categorization; Analyzes the proposed techniques' performance using precision, F-measure, recall and accuracy; Evaluates the limitations of the proposed research thereby recommending future research.