Machine learning

This book constitutes two challenges that were held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which took place in Singapore during September 18-22, 2022. The peer-reviewed 20 long and 5 short papers included in this volume stem from the following three biomedical image analysis challenges: Mitosis Domain Generalization Challenge (MIDOG 2022), Diabetic Retinopathy Analysis Challenge (CRAC 2022)The challenges share the need for developing and fairly evaluating algorithms that increase accuracy, reproducibility and efficiency of automated image analysis in clinically relevant applications.

Artificial neuronal networks open up radical new pathways for image creation. On the basis of textual prompts, machine-learning algorithms like Midjourney and DALL-E generate imagery that is simultaneously familiar and alien. Graphic designer Floyd Schulze uses this rapidly developing technology to recreate architectural icons. In an accompanying essay, Georg Vrachliotis takes up the topic on the theoretical level. OK Computer is the first book to deal with AI-generated architectural imagery from an artistic perspective.

This book constitutes the proceedings of the 17th International Conference on Research Challenges in Information Sciences, RCIS 2023, which took place in Corfu, Greece, during May 23¿26, 2023. It focused on the special theme "Information Science and the Connected World".The scope of RCIS is summarized by the thematic areas of information systems and their engineering; user-oriented approaches; data and information management; business process management; domain-specific information systems engineering; data science; information infrastructures, and reflective research and practice.The 28 full papers presented in this volume were carefully reviewed and selected from a total of 87 submissions. The book also includes 15 Forum papers and 6 Doctoral Consortium papers. The contributions were organized in topical sections named: Requirements; conceptual modeling and ontologies; machine learning and analytics; conceptual modeling and semantic networks; business process design and computing in the continuum; requirements and evaluation; monitoring and recommending; business process analysis and improvement; user interface and experience; forum papers; doctoral consortium papers. Two-page abstracts of the tutorials can be found in the back matter of the volume.

El aprendizaje profundo forma parte de una familia más amplia de métodos de aprendizaje automático basados en redes neuronales artificiales con aprendizaje de representación. El aprendizaje puede ser supervisado, semisupervisado o no supervisado. Con este libro aprenderás todo lo que necesitas saber sobre el aprendizaje profundo y la historia del aprendizaje automático!

This book presents recent improvements in peridynamic modeling of structures. It provides sufficient theory and numerical implementation helpful to both new and existing researchers in the field.  The main focus of the book is on the non-ordinary state-based (NOSB) peridynamics (PD) and its applications for performing finite deformation. It presents the framework for modeling high stretch polymers, viscoelastic materials, thermoelasticity, plasticity, and creep. It provides a systematic derivation for dimensionally reduced structures such as axisymmetric structures and beams. Also, it presents a novel approach to impose boundary conditions without suffering from displacement kinks near the boundary. Furthermore, it presents refinements to bond-based PD model by including rotation kinematics for modeling isotropic and composite materials. Moreover, it presents a PD - FEM coupling framework in ANSYS based on principle for virtual work. Lastly, it presents an application of neural networks in the peridynamic (PINN) framework. Sample codes are provided for readers to develop hands-on experience on peridynamic modeling.  Describes new developments in peridynamics and their applications in the presence of material and geometric nonlinearity; Describes an approach to seamlessly couple PD with FE; Introduces the use of the neural network in the PD framework to solve engineering problems;Provides theory and numerical examples for researchers and students to self-study and apply in their research (Codes are provided as supplementary material);Provides theoretical development and numerical examples suitable for graduate courses.

This book features selected papers presented at International Conference on Data Analytics and Computing (ICDAC 2022), organized by Department of Mathematics, College of Science and Technology, Wenzhou-Kean University, Wenzhou, China, held during May 28¿29, 2022. This book includes state-of-the-art current trends in data science, data analytics optimization, soft computing and related areas. Its primary readers are postgraduate students, researchers and academic professionals.

Unlock the Power of Machine Learning with PythonAre you ready to take your data analysis skills to the next level? Look no further than this comprehensive guide to Machine Learning with Python.In this tutorial, you will learn everything you need to know to become an expert in Machine Learning with Python, from the basics of data analysis to advanced techniques like deep learning and neural networks. With step-by-step instructions and real-world examples, this tutorial is perfect for both beginners and experienced programmers alike.Discover how to build and train predictive models, analyze large datasets, and automate complex tasks with ease. With the power of Python at your fingertips, you'll be able to tackle any data challenge and unlock new insights into your business or research.Whether you're looking to break into the field of data science or simply want to enhance your skills, this tutorial has everything you need to succeed. So why wait? Dive into the exciting world of Machine Learning with Python today and unleash your full potential!With this tutorial, you'll gain expertise in the following areas:Deep Learning and Neural NetworksPredictive Modeling and AlgorithmsBig Data and Data AnalysisSupervised and Unsupervised LearningReinforcement Learning and Decision TreesAnd much more!So why wait? Order your copy of this must-have tutorial today and take the first step towards becoming a data analysis expert!

This book stems from the growing use of learning-based techniques, such as reinforcement learning and adaptive control, in the control of autonomous and safety-critical systems. Safety is critical to many applications, such as autonomous driving, air traffic control, and robotics. As these learning-enabled technologies become more prevalent in the control of autonomous systems, it becomes increasingly important to ensure that such systems are safe. To address these challenges, the authors provide a self-contained treatment of learning-based control techniques with rigorous guarantees of stability and safety. This book contains recent results on provably correct control techniques from specifications that go beyond safety and stability, such as temporal logic formulas. The authors bring together control theory, optimization, machine learning, and formal methods and present worked-out examples and extensive simulation examples to complement the mathematical style of presentation. Prerequisites are minimal, and the underlying ideas are accessible to readers with only a brief background in control-theoretic ideas, such as Lyapunov stability theory.

With the increase in urban population, it became necessary to keep track of the object of interest. In favor of SDGs for sustainable smart city, with the advancement in technology visual tracking extends to track multi-target present in the scene rather estimating location for single target only. In contrast to single object tracking, multi-target introduces one extra step of detection. Tracking multi-target includes detecting and categorizing the target into multiple classes in the first frame and provides each individual target an ID to keep its track in the subsequent frames of a video stream. One category of multi-target algorithms exploits global information to track the target of the detected target. On the other hand, some algorithms consider present and past information of the target to provide efficient tracking solutions. Apart from these, deep leaning-based algorithms provide reliable and accurate solutions. But, these algorithms are computationally slow when applied in real-time. This book presents and summarizes the various visual tracking algorithms and challenges in the domain. The various feature that can be extracted from the target and target saliency prediction is also covered. It explores a comprehensive analysis of the evolution from traditional methods to deep learning methods, from single object tracking to multi-target tracking. In addition, the application of visual tracking and the future of visual tracking can also be introduced to provide the future aspects in the domain to the reader. This book also discusses the advancement in the area with critical performance analysis of each proposed algorithm. This book will be formulated with intent to uncover the challenges and possibilities of efficient and effective tracking of single or multi-object, addressing the various environmental and hardware challenges. The intended audience includes academicians, engineers, postgraduate students, developers, professionals, military personals, scientists, data analysts, practitioners, and people who are interested in exploring more about tracking.· Another projected audience are the researchers and academicians who identify and develop methodologies, frameworks, tools, and applications through reference citations, literature reviews, quantitative/qualitative results, and discussions.

The book offers many ideas based on the artificial intelligence techniques, fueled by digital technologies that can be implemented in human lives to make them smarter and more intelligent to survive in this rapidly changing world. This book contains diverse applications based on intelligence systems, such as in the development of innovative digital technologies based on cloud-based infrastructure, agriculture supply-chain management, technology that enables the visually impaired to live more comfortably and confidently in their daily lives. It contains new techniques of encryption and decryption, development of various decision-making systems, artificial intelligence techniques in medical areas, etc. This book addresses newly developed technologies and key ideas for new theory which can be used in the development of more advanced and intelligent world for better future.

This book presents the concept of Control Barrier Function (CBF), which captures the evolution of safety requirements during the execution of a system and can be used to enforce safety. Safety is formalized using an emerging state-of-the-art approach based on CBFs, and many illustrative examples from autonomous driving, traffic control, and robot control are provided. Safety is central to autonomous systems since they are intended to operate with minimal or no human supervision, and a single failure could result in catastrophic results. The authors discuss how safety can be guaranteed via both theoretical and application perspectives. This presented method is computationally efficient and can be easily implemented in real-time systems that require high-frequency reactive control. In addition, the CBF approach can easily deal with nonlinear models and complex constraints used in a wide spectrum of applications, including autonomous driving, robotics, and traffic control. Withthe proliferation of autonomous systems, such as self-driving cars, mobile robots, and unmanned air vehicles, safety plays a crucial role in ensuring their widespread adoption. This book considers the integration of safety guarantees into the operation of such systems including typical safety requirements that involve collision avoidance, technological system limitations, and bounds on real-time executions. Adaptive approaches for safety are also proposed for time-varying execution bounds and noisy dynamics.

Microbial ecosystems are complex, with hundreds of members interacting with each other and the environment. The intricate and hidden behaviors underlying these interactions make research questions challenging - but can be better understood through machine learning. However, most machine learning that is used in microbiome work is a black box form of investigation, where accurate predictions can be made, but the inner logic behind what is driving prediction is hidden behind nontransparent layers of complexity

This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.

Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc.Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging.This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.

Automation tools like machine learning are a necessity in our big data world. Thanks to the Internet and advancements in all facets of computer and storage technology, almost everyone has a voice in the Internet connected world. However, there are still very real physical limits in our physical world. This dichotomy-the seemingly limitless nature of technology enabled data colliding with the physical limits of the real world-has made automation tools a necessity, and predictive models powered by machine learning algorithms are one such tool.

This book constitutes the proceedings of the 9th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2023, which took place in Santa Margherita di Pula, Italy, in May 2023. The 57 papers presented in this volume were carefully reviewed and selected from 72 submissions. They were organized in topical sections as follows: Inverse Problems in Imaging; Machine and Deep Learning in Imaging; Optimization for Imaging: Theory and Methods; Scale Space, PDEs, Flow, Motion and Registration.

Ihr Unternehmen hat sich für KI entschieden. Glückwunsch, was nun? Dieses praktische Buch bietet einen ganzheitlichen Plan für die Implementierung von KI aus der Perspektive der IT und des IT-Betriebs im Unternehmen. Sie erfahren etwas über die Fähigkeiten, das Potenzial, die Grenzen und die Herausforderungen von KI. In diesem Buch erfahren Sie, welche Rolle KI im Kontext etablierter Bereiche wie Design Thinking und DevOps, Governance und Change Management, Blockchain und Quantum Computing spielt, und diskutieren die Konvergenz von KI in diesen Schlüsselbereichen des Unternehmens.Deploying AI in the Enterprise bietet Anleitungen und Methoden zur effektiven Bereitstellung und Operationalisierung nachhaltiger KI-Lösungen. Sie lernen die Herausforderungen bei der Implementierung kennen, wie z. B. Probleme bei der KI-Operationalisierung und Hindernisse bei der Umsetzung von Erkenntnissen in umsetzbare Prognosen. Sie werden auch lernen, wie Sie die Schlüsselkomponenten der KI-Informationsarchitektur erkennen und welche Rolle sie für eine erfolgreiche und nachhaltige KI-Implementierung spielt. Und Sie werden verstehen, wie Sie KI effektiv einsetzen können, um die Nutzung von Kerninformationen in Master Data Management (MDM)-Lösungen zu verbessern.Was Sie lernen werdenVerstehen der wichtigsten KI-Konzepte, einschließlich maschinelles Lernen und Deep LearningBefolgen von Best Practices und Methoden zur erfolgreichen Bereitstellung und Operationalisierung von KI-LösungenErkennen der kritischen Komponenten der KI-Informationsarchitektur und der Bedeutung eines PlansIntegration von KI in bestehende Initiativen innerhalb einer OrganisationErkennen der aktuellen Grenzen von KI und wie sich dies auf Ihr Unternehmen auswirken könnteBewusstsein für wichtige und aktuelle KI-Forschung schaffenIhre Denkweise anpassen, um KI von einem ganzheitlichen Standpunkt aus zu betrachtenMachen Sie sich mit den Möglichkeiten von KI in verschiedenen Branchen vertraut.Für wen ist dieses Buch gedacht?IT-Profis, Datenwissenschaftler und Architekten, die sich mit den Herausforderungen bei der Implementierung und dem Betrieb von KI auseinandersetzen müssen und einen umfassenden Überblick darüber benötigen, wie sich KI auf andere geschäftskritische Bereiche auswirkt. Es ist keine Einführung, sondern richtet sich an Leser, die nach Beispielen für die Nutzung von Daten suchen, um daraus verwertbare Erkenntnisse und Vorhersagen abzuleiten, und die die aktuellen Risiken und Grenzen von KI verstehen und berücksichtigen müssen und wissen wollen, was dies in einem branchenrelevanten Kontext bedeutet.

This book includes original, peer-reviewed articles from the 2nd International Conference on Cognitive & Intelligent Computing (ICCIC-2022), held at Vasavi College of Engineering Hyderabad, India. It covers the latest trends and developments in areas of cognitive computing, intelligent computing, machine learning, smart cities, IoT, artificial intelligence, cyber-physical systems, cybernetics, data science, neural network, and cognition. This book addresses the comprehensive nature of computational intelligence, cognitive computing, AI, ML, and DL to emphasize its character in modeling, identification, optimization, prediction, forecasting, and control of future intelligent systems. Submissions are original, unpublished, and present in-depth fundamental research contributions either from a methodological/application perspective in understanding artificial intelligence and machine learning approaches and their capabilities in solving diverse range of problems in industries and its real-world applications.

This book introduces how to enhance the context capture ability of the model, improve the position information perception ability of the pretrained models, and identify and denoise the unlabeled entities. The Chinese medical named entity recognition is an important branch of the intelligent medicine, which is beneficial to mine the information hidden in medical texts and provide the medical entity information for clinical medical decision-making and medical classification. Researchers, engineers and post-graduate students in the fields of medicine management and software engineering.

This book presents recent developments in multivariate and robust statistical methods. Featuring contributions by leading experts in the field it covers various topics, including multivariate and high-dimensional methods, time series, graphical models, robust estimation, supervised learning and normal extremes. It will appeal to statistics and data science researchers, PhD students and practitioners who are interested in modern multivariate and robust statistics. The book is dedicated to David E. Tyler on the occasion of his pending retirement and also includes a review contribution on the popular Tyler¿s shape matrix.

Using machine and deep learning techniques the authors introduce pre-processing methods applied to satellite images to identify land cover features, detect object, classify crops, recognize targets, and monitor and support earth resources. Readers will need a basic understanding of computing, remote sensing and image interpretation.

The book (proceedings of the 4th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR) 2022) introduces key topics from artificial intelligence algorithms and programming organisations and explains how they contribute to health care, manufacturing, law, finance, retail, real estate, accountancy, digital marketing, and various other fields. Although artificial intelligence (AI) has generated a lot of hype over the past ten years, these consequences on how we live, work, and play are still in their infancy and will likely have a significant impact in the future. The supremacy of AI in areas like speech and picture recognition, navigational apps, personal assistants for smartphones, ride-sharing apps, and many other areas is already well established. The book is primarily meant for academics, researchers, and engineers who want to employ AI applications to address real-world issues. The authors hope that businesses and technology creators will also find it appealing to utilise in industry.

Knowledge Science beschäftigt sich mit Konzepten, Methoden und Prozessen zur systematischen Erzeugung, Extraktion, Speicherung und Bereitstellung von Wissen zur Lösung von Problemen und lässt sich somit dem Wissensmanagement zuordnen. Kognitive Assistenten sorgen dafür, das richtige Wissen zur richtigen Zeit in der richtigen Art und Weise seinen Anwendern und Anwenderinnen bereitzustellen. Damit dies gelingen kann, kommen inzwischen zahlreiche Methoden der Künstlichen Intelligenz (KI) zur Unterstützung unterschiedlicher Aufgaben des Wissensmanagements zum Einsatz.