This book constitutes the refereed proceedings of the 42nd German Conference on Pattern Recognition, DAGM GCPR 2020, which took place during September 28 until October 1, 2020. The conference was planned to take place in Tubingen, Germany, but had to change to an online format due to the COVID-19 pandemic.The 34 papers presented in this volume were carefully reviewed and selected from a total of 89 submissions. They were organized in topical sections named: Normalizing Flow, Semantics, Physics, Camera Calibration and Computer Vision, Pattern Recognition, Machine Learning.

This book constitutes selected and revised papers of the 5th International Conference on Space Information Networks, SINC 2020, held in Shenzhen, China, in December 2020.The 11 full and 2 short papers presented in this volume were carefully reviewed and selected from 104 submissions. The papers present the latest research in the fields of space information networks.

The recent developments in wireless communications, networking, and embedded systems have driven various innovative Internet of Things (IoT) applications, e.g., smart cities, mobile healthcare, autonomous driving and drones. A common feature of these applications is the stringent requirements for low-latency communications. Considering the typical small payload size of IoT applications, it is of critical importance to reduce the size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead communications also help to improve the energy efficiency of IoT devices. Recently, structured signal processing techniques have been introduced and developed to reduce the overheads for key design problems in IoT networks, such as channel estimation, device identification, and message decoding. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This book provides an overview of four general structured signal processing models: a sparse linear model, a blind demixing model, a sparse blind demixing model, and a shuffled linear model, and discusses their applications in enabling low-overhead communications in IoT networks. Further, it presents practical algorithms based on both convex and nonconvex optimization approaches, as well as theoretical analyses that use various mathematical tools.

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model. The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O. After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data. What You Will Learn Design, develop, train, and validate machine learning and deep learning models Find optimal hyper parameters for superior model performance Improve model performance using techniques such as dimension reduction and regularization Extract meaningful insights for decision making using data visualization Who This Book Is For Beginning and intermediate level data scientists and machine learning engineers

This book constitutes the proceedings of the 7th International Conference on Decision Support Systems Technologies, ICDSST 2021, held during May 26-28, 2021. The conference was planned to take place in Loughborough, UK, and changed to an online format due to the COVID-19 pandemic.The EWG-DSS series of International Conference on Decision Support System Technology (ICDSST) is planned to consolidate the tradition of annual events organized by the EWG-DSS in offering a platform for European and international DSS communities, comprising the academic and industrial sectors, to present state-of-the-art DSS research and developments, to discuss current challenges that surround decision-making processes, to exchange ideas about realistic and innovative solutions, and to co-develop potential business opportunities. The main aim of this year's conference is to investigate the role DSS and related technologies can play in mitigating the impact of pandemics and post-crisis recovery. The 10 papers presented in this volume were carefully reviewed and selected from 44 submissions. They were organized in two topical sections named: multiple criteria approaches and advances in decision support systems' technologies and methods.

This book constitutes the proceedings of the First International Conference on Spatial Data and Intelligence, SpatialDI 2020, which was held on May 8-9, 2020. The conference was planned to take place in Shenzhen, China, and changed to an online format due to the COVID-19 pandemic. The 21 full papers presented in this volume were carefully reviewed and selected from 50 submissions. They were organized in topical sections named: traffic management; data science; and visualization science.

This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN 2019, held in Paris, France, in December 2019. The 26 revised full papers included in the volume were carefully reviewed and selected from 75 submissions. They present and discuss new trends in deep and reinforcement learning, pattern recognition and classification for networks, machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection, optimization and new innovative machine learning methods, performance analysis of machine learning algorithms, experimental evaluations of machine learning, data mining in heterogeneous networks, distributed and decentralized machine learning algorithms, intelligent cloud-support communications, ressource allocation, energy-aware communications, software de ned networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.

This book constitutes revised selected papers from two VLDB workshops: The International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2020, and the 6th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2020, which were held virtually on August 31 and September 4, 2020.For Poly 2020, 4 full and 3 short papers were accepted from 10 submissions; and for DMAH 2020, 7 full and 2 short papers were accepted from a total of 15 submissions. The papers were organized in topical sections as follows: Privacy, Security and/or Policy Issues for Heterogenous Data; COVID-19 Data Analytics and Visualization; Deep Learning based Biomedical Data Analytics; NLP based Learning from Unstructured Data; Biomedical Data Modelling and Prediction.

Changsheng Hua proposes two approaches, an input/output recovery approach and a performance index-based approach for robustness and performance optimization of feedback control systems. For their data-driven implementation in deterministic and stochastic systems, the author develops Q-learning and natural actor-critic (NAC) methods, respectively. Their effectiveness has been demonstrated by an experimental study on a brushless direct current motor test rig. The author: Changsheng Hua received the Ph.D. degree at the Institute of Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, Germany, in 2020. His research interests include model-based and data-driven fault diagnosis and fault-tolerant techniques.

This book includes high impact papers presented at the International Conference on Communication, Computing and Electronics Systems 2019, held at the PPG Institute of Technology, Coimbatore, India, on 15-16 November, 2019. Discussing recent trends in cloud computing, mobile computing, and advancements of electronics systems, the book covers topics such as automation, VLSI, embedded systems, integrated device technology, satellite communication, optical communication, RF communication, microwave engineering, artificial intelligence, deep learning, pattern recognition, Internet of Things, precision models, bioinformatics, and healthcare informatics.

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.

This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. * Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks * Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks * Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

This book highlights recent advances in computational intelligence for signal processing, computing, imaging, artificial intelligence, and their applications. It offers support for researchers involved in designing decision support systems to promote the societal acceptance of ambient intelligence, and presents the latest research on diverse topics in intelligence technologies with the goal of advancing knowledge and applications in this rapidly evolving field. As such, it offers a valuable resource for researchers, developers and educators whose work involves recent advances and emerging technologies in computational intelligence.

This book constitutes revised selected papers of the 9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020, held in Moscow, Russia, in october 2020. Due to the COVID-19 pandemic the conference was held online. The 14 full papers, 9 short papers and 4 poster papers were carefully reviewed and selected from 108 qualified submissions. The papers are organized in topical sections on natural language processing; computer vision; social network analysis; data analysis and machine learning; theoretical machine learning and optimization; process mining; posters.

In nonparametric and high-dimensional statistical models, the classical Gauss-Fisher-Le Cam theory of the optimality of maximum likelihood estimators and Bayesian posterior inference does not apply, and new foundations and ideas have been developed in the past several decades. This book gives a coherent account of the statistical theory in infinite-dimensional parameter spaces. The mathematical foundations include self-contained 'mini-courses' on the theory of Gaussian and empirical processes, approximation and wavelet theory, and the basic theory of function spaces. The theory of statistical inference in such models - hypothesis testing, estimation and confidence sets - is presented within the minimax paradigm of decision theory. This includes the basic theory of convolution kernel and projection estimation, but also Bayesian nonparametrics and nonparametric maximum likelihood estimation. In a final chapter the theory of adaptive inference in nonparametric models is developed, including Lepski's method, wavelet thresholding, and adaptive inference for self-similar functions. Winner of the 2017 PROSE Award for Mathematics.

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The "parent problem" of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.

This two-volume set constitutes the refereed proceedings of the Third International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R) 2020, held in Aurangabad, India, in January 2020.The 78 revised full papers presented were carefully reviewed and selected from 329 submissions. The papers are organized in topical sections in the two volumes. Part I: Computer vision and applications; Data science and machine learning; Document understanding and Recognition. Part II: Healthcare informatics and medical imaging; Image analysis and recognition; Signal processing and pattern recognition; Image and signal processing in Agriculture.

Build a chatbot using the Microsoft Conversational AI platform. This book will teach you, step by step, how to save time and money by including chatbots in your enterprise's strategy. You will learn how to be proficient at every phase of development, from collaboration on a chatbot in an end-to-end scenario, to the first mock-up phase, and on through to the deployment and evaluation phases. Microsoft built a cloud service ecosystem for running artificial intelligence workloads in public cloud scenarios and a robust AI platform that offers a broad range of services targeting conversational artificial intelligence solutions such as chatbots. Building a chatbot requires not just developer coding skills but special considerations, including input from business stakeholders such as domain matter experts and power users. You will learn by example how to use a great set of tools and services to bridge the gap between business and engineering. You will learn how to successfully morph business requirements into actionable IT and engineering requirements. You will learn about Bot Framework Composer, which allows power users to initiate the building of a chatbot that can then be handed over to the development team to add capabilities through code. Coverage is given to the process of sharing implementation tasks and workloads between power users, who are using a low-code or no-code approach, and developers, who are building out the enhanced features for the chatbot. What You Will Learn Understand Microsoft's comprehensive AI ecosystem and its services and solutions Recognize which solutions and services should be applied in each business scenario Discover no-code/low-code approaches for building chatbots Develop chatbots using the conversational AI stack Align business and development for improved chatbot outcomes and reduced time-to-market Who This Book Is For Developers and power users who want to build chatbots. An understanding of the core principles of writing code (.NET or JavaScript) for modern web applications is expected.

heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.

This book covers selected high-quality research papers presented at the International Conference on Big Data, Machine Learning, and Applications (BigDML 2019). It focuses on both theory and applications in the broad areas of big data and machine learning. It brings together the academia, researchers, developers and practitioners from scientific organizations and industry to share and disseminate recent research findings.

This proceedings book includes the results from the International Conference on Deep Learning, Artificial Intelligence and Robotics, held in Malaviya National Institute of Technology, Jawahar Lal Nehru Marg, Malaviya Nagar, Jaipur, Rajasthan, 302017. The scope of this conference includes all subareas of AI, with broad coverage of traditional topics like robotics, statistical learning and deep learning techniques. However, the organizing committee expressly encouraged work on the applications of DL and AI in the important fields of computer/electronics/electrical/mechanical/chemical/textile engineering, health care and agriculture, business and social media and other relevant domains. The conference welcomed papers on the following (but not limited to) research topics: * Deep Learning: Applications of deep learning in various engineering streams, neural information processing systems, training schemes, GPU computation and paradigms, human-computer interaction, genetic algorithm, reinforcement learning, natural language processing, social computing, user customization, embedded computation, automotive design and bioinformatics * Artificial Intelligence: Automatic control, natural language processing, data mining and machine learning tools, fuzzy logic, heuristic optimization techniques (membrane-based separation, wastewater treatment, process control, etc.) and soft computing * Robotics: Automation and advanced control-based applications in engineering, neural networks on low powered devices, human-robot interaction and communication, cognitive, developmental and evolutionary robotics, fault diagnosis, virtual reality, space and underwater robotics, simulation and modelling, bio-inspired robotics, cable robots, cognitive robotics, collaborative robotics, collective and social robots and humanoid robots It was a collaborative platform for academic experts, researchers and corporate professionals for interacting their research in various domain of engineering like robotics, data acquisition, human-computer interaction, genetic algorithm, sentiment analysis as well as usage of AI and advanced computation in various industrial challenges based applications such as user customization, augmented reality, voice assistants, reactor design, product formulation/synthesis, embedded system design, membrane-based separation for protecting environment along with wastewater treatment, rheological properties estimation for Newtonian and non-Newtonian fluids used in micro-processing industries and fault detection.