The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic. The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows: Part I: Theory and algorithms; Part II: Theory and algorithms; human centred computing; AI and cybersecurity; Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications; Part IV: Applications.

This book constitutes the proceedings of the 25th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2021, which took place during May 10-13, 2021. The conference was initially planned to take place in Porto, Portugal, but changed to a virtual event due to the COVID-19 pandemic. The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They were organized in topical sections as follows: medical applications; natural language processing; metaheuristics; image segmentation; databases; deep learning; explainable artificial intelligence; image processing; machine learning; and computer vision.

Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment. What You Will Learn Apply adaptive algorithms to practical applications and examples Understand the relevant data representation features and computational models for time-varying multi-dimensional data Derive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real data Speed up your algorithms and put them to use on real-world stationary and non-stationary data Master the applications of adaptive algorithms on critical edge device computation applications Who This Book Is ForMachine learning engineers, data scientist and architects, software engineers and architects handling edge device computation and data management.

This book constitutes the refereed proceedings of the 22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021, which took place during November 25-27, 2021. The conference was originally planned to take place in Manchester, UK, but was held virtually due to the COVID-19 pandemic.The 61 full papers included in this book were carefully reviewed and selected from 85 submissions. They deal with emerging and challenging topics in intelligent data analytics and associated machine learning paradigms and systems. Special sessions were held on clustering for interpretable machine learning; machine learning towards smarter multimodal systems; and computational intelligence for computer vision and image processing.

This book of Advances in Deep Learning, Artificial Intelligence and Robotics (proceedings of ICDLAIR 2020) is intended to be used as a reference by students and researchers who collect scientific and technical contributions with respect to models, tools, technologies and applications in the field of modern artificial intelligence and robotics. Deep Learning, AI and robotics represent key ingredients for the 4th Industrial Revolution. Their extensive application is dramatically changing products and services, with a large impact on labour, economy and society at all. The research and reports of new technologies and applications in DL, AI and robotics like biometric recognition systems, medical diagnosis, industries, telecommunications, AI petri nets model-based diagnosis, gaming, stock trading, intelligent aerospace systems, robot control and web intelligence aim to bridge the gap between these non-coherent disciplines of knowledge and fosters unified development in next-generation computational models for machine intelligence.

From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans-swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human-AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as well as the human's model of the AI agent's task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), and be ready to provide customized explanations when needed. The authors draw from several years of research in their lab to discuss how an AI agent can use these mental models to either conform to human expectations or change those expectations through explanatory communication. While the focus of the book is on cooperative scenarios, it also covers how the same mental models can be used for obfuscation and deception. The book also describes several real-world application systems for collaborative decision-making that are based on the framework and techniques developed here. Although primarily driven by the authors' own research in these areas, every chapter will provide ample connections to relevant research from the wider literature. The technical topics covered in the book are self-contained and are accessible to readers with a basic background in AI.

This book constitutes refereed proceedings of the 7th International Conference on Technologies and Innovation, CITI 2021, held in Guayaquil, Ecuador, in November 2021.The 14 full papers presented in this volume were carefully reviewed and selected from 36 submissions. They are organized in topical sections named: semantic technologies and machine learning; natural language processing; mobile and collaborative technologies; networks and IoT technologies; ICT for agronomy and environment.

This book constitutes the proceedings of the 10th International Conference on Health Information Science, HIS 2021, which took place in Melbourne, Australia, in October 2021. The 16 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 56 submissions. They are organized in topical sections named: COVID-19, EEG data processing, Medical Data Analysis, Medical Record Mining (I), Medical Data Mining (II), Medical Data Processing.

Learn to develop and deploy dashboards as web apps using the Python programming language, and how to integrate algorithms into web apps. Author Tshepo Chris Nokeri begins by introducing you to the basics of constructing and styling static and interactive charts and tables before exploring the basics of HTML, CSS, and Bootstrap, including an approach to building web pages with HTML. From there, he'll show you the key Python web frameworks and techniques for building web apps with them. You'll then see how to style web apps and incorporate themes, including interactive charts and tables to build dashboards, followed by a walkthrough of creating URL routes and securing web apps. You'll then progress to more advanced topics, like building machine learning algorithms and integrating them into a web app. The book concludes with a demonstration of how to deploy web apps in prevalent cloud platforms. Web App Development and Real-Time Web Analytics with Python is ideal for intermediate data scientists, machine learning engineers, and web developers, who have little or no knowledge about building web apps that implement bootstrap technologies. After completing this book, you will have the knowledge necessary to create added value for your organization, as you will understand how to link front-end and back-end development, including machine learning. What You Will Learn Create interactive graphs and render static graphs into interactive ones Understand the essentials of HTML, CSS, and Bootstrap Gain insight into the key Python web frameworks, and how to develop web applications using them Develop machine learning algorithms and integrate them into web apps Secure web apps and deploy them to cloud platforms Who This Book Is For Intermediate data scientists, machine learning engineers, and web developers.

[FIRST EDITION] This accessible textbook presents an introduction to computer vision algorithms for industrially-relevant applications of X-ray testing. Features: introduces the mathematical background for monocular and multiple view geometry; describes the main techniques for image processing used in X-ray testing; presents a range of different representations for X-ray images, explaining how these enable new features to be extracted from the original image; examines a range of known X-ray image classifiers and classification strategies; discusses some basic concepts for the simulation of X-ray images and presents simple geometric and imaging models that can be used in the simulation; reviews a variety of applications for X-ray testing, from industrial inspection and baggage screening to the quality control of natural products; provides supporting material at an associated website, including a database of X-ray images and a Matlab toolbox for use with the book's many examples.

This book constitutes the refereed post-conference proceedings of the 11th EAI International Conference on Research in Computer science and its Applications, CNRIA 2021, held in June 2021. Due to COVID-19 pandemic the conference was held virtually. The 11 full papers presented were selected from 24 submissions and issue different problems in underserved and unserved areas. The papers are arranged in 3 tracks: data science and artificial intelligence; telecom and artificial intelligence; IoT and ICT applications.

This book presents the proceedings of the 2020 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2021), online conference, on 30 October 2021. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.

The two volume set LNCS 13052 and 13053 constitutes the refereed proceedings of the 19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021, held virtually, in September 2021. The 87 papers presented were carefully reviewed and selected from 129 submissions. The papers are organized in the following topical sections across the 2 volumes: 3D vision, biomedical image and pattern analysis; machine learning; feature extractions; object recognition; face and gesture, guess the age contest, biometrics, cryptography and security; and segmentation and image restoration.

This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.

This book constitutes the refereed proceedings of the 10th International Conference on Theory and Practice of Natural Computing, TPNC 2021, held virtually, in December 2021. The 9 full papers presented together with 3 invited talks, in this book were carefully reviewed and selected from 14 submissions. The papers are organized in topical sections named Applications of Natural Computing, Deep Learning and Transfer Learning, Evolutionary and Swarm Algorithms.

This three-volume set CCIS 1467, CCIS 1468, and CCIS 1469 constitutes the thoroughly refereed proceedings of the 7th International Conference on Life System Modeling and Simulation, LSMS 2021, and of the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, held in Hangzhou, China, in October 2021. The 159 revised papers presented were carefully reviewed and selected from over 430 submissions. The papers of this volume are organized in topical sections on: Medical Imaging and Analysis Using Intelligence Computing; Biomedical signal processing, imaging, visualization and surgical robotics; Computational method in taxonomy study and neural dynamics; Intelligent medical apparatus, clinical applications and intelligent design of biochips; Power and Energy Systems; Computational Intelligence in Utilization of Clean and Renewable Energy Resources, and Intelligent Modelling, Control and Supervision for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Control Methods in Energy Infrastructure Development and Distributed Power Generation Systems; Intelligent Modeling, Simulation and Control of Power Electronics and Power Networks; Intelligent Techniques for Sustainable Energy and Green Built Environment, Water Treatment and Waste Management; Intelligent Robot and Simulation; Intelligent Data Processing, Analysis and Control in Complex Systems; Advanced Neural Network Theory and Algorithms; Advanced Computational Methods and Applications; Fuzzy, Neural, and Fuzzy-neuro Hybrids; Intelligent Modelling, Monitoring, and Control of Complex Nonlinear Systems; Intelligent manufacturing, autonomous systems, intelligent robotic systems; Computational Intelligence and Applications.

Apply supervised and unsupervised learning to solve practical and real-world big data problems. This book teaches you how to engineer features, optimize hyperparameters, train and test models, develop pipelines, and automate the machine learning (ML) process. The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras. The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked. This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics. What You Will Learn Understand widespread supervised and unsupervised learning, including key dimension reduction techniques Know the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learning Integrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworks Design, build, test, and validate skilled machine models and deep learning models Optimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alteration Who This Book Is For Data scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics

This book constitutes the refereed proceedings of the 14th International Conference on Similarity Search and Applications, SISAP 2021, held in Dortmund, Germany, in September/October 2021. The conference was held virtually due to the COVID-19 pandemic.The 23 full papers presented together with 5 short and 3 doctoral symposium papers were carefully reviewed and selected from 50 submissions. The papers are organized in the topical sections named: Similarity Search and Retrieval; Intrinsic Dimensionality; Clustering and Classification; Applications of Similarity Search; Similarity Search in Graph-Structured Data; Doctoral Symposium.

This two-volume book constitutes the post-conference proceedings of the 5th International Conference on Advances in Computing and Data Sciences, ICACDS 2021, held in Nashik, India, in April 2021.* The 103 full papers were carefully reviewed and selected from 781 submissions. Part II is devoted to data sciences, organizing principles, medical technologies, computational linguistics etc. *The conference was held virtually due to the COVID-19 pandemic.

This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example-a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions.

This book constitutes the refereed proceedings of the 6th International Conference on Information, Communication and Computing Technology, ICICCT 2021, held in New Delhi, India, in May 2021.The 16 full papers and 4 short paper presented in this volume were carefully reviewed and selected from 83 qualified submissions. The papers are organized in topical sections on communication and network systems; computational intelligence techniques.

Learn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking. You'll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, you'll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. You'll learn not only to understand and apply methods successfully but to think critically about it. Modern Deep Learning Design and Methods is ideal for readers looking to utilize modern, flexible, and creative deep-learning design and methods. Get ready to design and implement innovative deep-learning solutions to today's difficult problems. What You'll Learn Improve the performance of deep learning models by using pre-trained models, extracting rich features, and automating optimization. Compress deep learning models while maintaining performance. Reframe a wide variety of difficult problems and design effective deep learning solutions to solve them. Use the Keras framework, with some help from libraries like HyperOpt, TensorFlow, and PyTorch, to implement a wide variety of deep learning approaches. Who This Book Is For Data scientists with some familiarity with deep learning to deep learning engineers seeking structured inspiration and direction on their next project. Developers interested in harnessing modern deep learning methods to solve a variety of difficult problems.

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: * the setting of learning problems based on the model of minimizing the risk functional from empirical data * a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency * non-asymptotic bounds for the risk achieved using the empirical risk minimization principle * principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds * the Support Vector methods that control the generalization ability when estimating function using small sample size. The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include: * the theory of direct method of learning based on solving multidimensional integral equations for density, conditional probability, and conditional density estimation * a new inductive principle of learning. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. Vladimir N. Vapnik is Technology Leader AT&T Labs-Research and Professor of London University. He is one of the founders of statistical learning theory, and the author of seven books published in English, Russian, German, and Chinese.

This book constitutes the refereed proceedings of the Third International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2021, held in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.For UNSURE 2021, 13 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. PIPPI 2021 accepted 14 papers from the 18 submissions received. The workshop aims to bring together methods and experience from researchers and authors working on these younger cohorts and provides a forum for the open discussion of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.

This book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.