Machine learning

Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing metaheuristics techniques with machine learning for solving biomedical problems. This book is organized to present a stepwise progression beginning with the basics of metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of this book presents the fundamental concepts of metaheuristics and machine learning and provides a comprehensive taxonomic view of metaheuristics methods according to a variety of criteria such as data type, scope, and method. The second section of this book explains how to apply metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in metaheuristics for biomedical science. This book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice.

This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023.The 16 full papers included in this volume were carefully reviewed and selected from 30 submissions. The goal of this workshop is to bring together experts from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability.

This book gathers selected, extended and revised contributions to the 17th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and the 5th Conference on Imaging and Visualization (CMBBE 2021), held online on September 7-9, 2021, from Bonn, Germany. It reports on cutting-edge models, algorithms and imaging techniques for studying cells, tissues and organs in normal and pathological conditions. It covers numerical and machine learning methods, finite element modeling and virtual reality techniques, applied to understand biomechanics of movement, fluid and soft tissue biomechanics. It also reports on related advances in rehabilitation, surgery and diagnosis. All in all, this book offers a timely snapshot of the latest research and current challenges at the interface between biomedical engineering, computational biomechanics and biological imaging. Thus, it is expected to provide a source of inspiration for future research and cross-disciplinary collaborations.

This book provides a practical and fairly comprehensive review of Data Science through the lens of dimensionality reduction, as well as hands-on techniques to tackle problems with data collected in the real world. State-of-the-art results and solutions from statistics, computer science and mathematics are explained from the point of view of a practitioner in any domain science, such as biology, cyber security, chemistry, sports science and many others. Quantitative and qualitative assessment methods are described to implement and validate the solutions back in the real world where the problems originated.The ability to generate, gather and store volumes of data in the order of tera- and exo bytes daily has far outpaced our ability to derive useful information with available computational resources for many domains.This book focuses on data science and problem definition, data cleansing, feature selection and extraction, statistical, geometric, information-theoretic, biomolecular and machine learning methods for dimensionality reduction of big datasets and problem solving, as well as a comparative assessment of solutions in a real-world setting.This book targets professionals working within related fields with an undergraduate degree in any science area, particularly quantitative. Readers should be able to follow examples in this book that introduce each method or technique. These motivating examples are followed by precise definitions of the technical concepts required and presentation of the results in general situations. These concepts require a degree of abstraction that can be followed by re-interpreting concepts like in the original example(s). Finally, each section closes with solutions to the original problem(s) afforded by these techniques, perhaps in various ways to compare and contrast dis/advantages to other solutions.

This book explains how to implement a data lake strategy, covering the technical and business challenges architects commonly face. It also illustrates how and why client requirements should drive architectural decisions. Drawing upon a specific case from his own experience, author Nayanjyoti Paul begins with the consideration from which all subsequent decisions should flow: what does your customer need? He also describes the importance of identifying key stakeholders and the key points to focus on when starting a new project. Next, he takes you through the business and technical requirement-gathering process, and how to translate customer expectations into tangible technical goals. From there, yoüll gain insight into the security model that will allow you to establish security and legal guardrails, as well as different aspects of security from the end user¿s perspective. Yoüll learn which organizational roles need to be onboarded into the data lake, their responsibilities, the services they need access to, and how the hierarchy of escalations should work. Subsequent chapters explore how to divide your data lakes into zones, organize data for security and access, manage data sensitivity, and techniques used for data obfuscation. Audit and logging capabilities in the data lake are also covered before a deep dive into designing data lakes to handle multiple kinds and file formats and access patterns. The book concludes by focusing on production operationalization and solutions to implement a production setup. After completing this book, you will understand how to implement a data lake, the best practices to employ while doing so, and will be armed with practical tips to solve business problems. What You Will LearnUnderstand the challenges associated with implementing a data lakeExplore the architectural patterns and processes used to design a new data lakeDesign and implement data lake capabilitiesAssociate business requirements with technical deliverables to drive success Who This Book Is ForData Scientists and Architects, Machine Learning Engineers, and Software Engineers.

This book offers a detailed and comprehensive analysis of multi-aspect data learning, focusing especially on representation learning approaches for unsupervised machine learning. It covers state-of-the-art representation learning techniques for clustering and their applications in various domains. This is the first book to systematically review multi-aspect data learning, incorporating a range of concepts and applications. Additionally, it is the first to comprehensively investigate manifold learning for dimensionality reduction in multi-view data learning. The book presents the latest advances in matrix factorization, subspace clustering, spectral clustering and deep learning methods, with a particular emphasis on the challenges and characteristics of multi-aspect data. Each chapter includes a thorough discussion of state-of-the-art of multi-aspect data learning methods and important research gaps. The book provides readers with the necessary foundational knowledge to apply these methods to new domains and applications, as well as inspire new research in this emerging field.

This book demonstrates that the reliable and secure communication performance of maritime communications can be significantly improved by using intelligent reflecting surface (IRS) aided communication, privacy-aware Internet of Things (IoT) communications, intelligent resource management and location privacy protection. In the IRS aided maritime communication system, the reflecting elements of IRS can be intelligently controlled to change the phase of signal, and finally enhance the received signal strength of maritime ships (or sensors) or jam maritime eavesdroppers illustrated in this book.The power and spectrum resource in maritime communications can be jointly optimized to guarantee the quality of service (i.e., security and reliability requirements), and reinforcement leaning is adopted to smartly choose the resource allocation strategy. Moreover, learning based privacy-aware offloading and location privacy protection are proposed to intelligently guarantee the privacy-preserving requirements of maritime ships or (sensors). Therefore, these communication schemes based on reinforcement learning algorithms can help maritime communication systems to improve the information security, especially in dynamic and complex maritime environments.This timely book also provides broad coverage of the maritime wireless communication issues, such as reliability, security, resource management, and privacy protection. Reinforcement learning based methods are applied to solve these issues. This book includes four rigorously refereed chapters from prominent international researchers working in this subject area. The material serves as a useful reference for researchers, graduate students. Practitioners seeking solutions to maritime wireless communication and security related issues will benefit from this book as well.

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book¿s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

This open access book presents the proceedings of the 10th Machine Intelligence and Digital Interaction Conference. Artificial intelligence (AI) is rapidly affecting more aspects of our lives as a result of significant advancements in its research and the widespread usage of interactive technologies. This has led to the birth of several new social phenomena. Many nations have been working to comprehend these phenomena and discover solutions for moving artificial intelligence development in the proper direction to benefit individuals and communities at large. These efforts necessitate multidisciplinary approaches, encompassing not only the scientific fields involved in the creation of artificial intelligence and human¿computer interaction but also strong collaboration between academics and practitioners. Because of this, the primary objective of the MIDI conference, which was conducted online on December 13¿15, 2022, is to combine two up until recently distinct disciplines of research¿artificial intelligence and human¿technology interaction.

This book offers an excellent source of knowledge for readers who are interested in keeping up with the developments in the field of cyber security and social media analysis. It covers the possibility of using augmented reality to monitor cyber security feeds in a multitasking environment. It describes a real-time scheduled security scanner. E-commerce concept labeling is tackled by introducing a lightweight global taxonomy induction system. Blogsphere analytics and online video narratives and networks are explored. The effect of global and local network structure, credibility based prevention of fake news dissemination, and detection of trending topics and influence from social media are investigated.This book helps the reader in developing their own perspective about how to deal with cyber security and how to benefit from the development in technology to tackle cyber security issues. The reader of this book will realize how to use various machine learning techniques for tackling various applications involving social medial analysis.

This book focuses on methods and models in classification and data analysis and presents real-world applications at the interface with data science. Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, engineering, marketing, and cyber risk.The contents comprise selected and peer-reviewed contributions presented at the 13th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2021, held (online) in Florence, Italy, on September 9¿11, 2021. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results at the interface between classification and data science.

This book constitutes the refereed proceedings of the 7th International Conference on Advances in Computing and Data Sciences, ICACDS 2023, held in Kolkata, India, during April 27¿28, 2023. The 47 full papers included in this book were carefully reviewed and selected from 22 submissions. The papers focus on advances of next generation computing technologies in the areas of advanced computing and data sciences.

This book highlights contemporary state of research in multidisciplinary areas in computer science, computer engineering, chemical engineering, mechanical engineering, physics, biomedical sciences, life sciences, medicine, and health care. The accepted submissions to the 7th IRC Conference on Science, Engineering and Technology (IRC-SET 2021) that were presented on August 7, 2021, are published in this conference proceedings. The papers presented here were shortlisted after extensive rounds of rigorous reviews by a panel of esteemed individuals who are pioneers and experts in their respective domains.

Alzheimer's Disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Detecting this condition in its early stages is critical for timely intervention and better management of the disease. "Early Detection of Alzheimer's Disease using Machine Learning Algorithms" presents a groundbreaking approach to identify early signs of Alzheimer's through advanced machine learning techniques.By harnessing the power of machine learning algorithms, this innovative system analyzes vast amounts of data, including cognitive assessments, brain imaging, and genetic markers. The algorithms can recognize subtle patterns and anomalies indicative of Alzheimer's disease, even before noticeable symptoms manifest.This cutting-edge technology holds great promise in transforming healthcare by enabling early identification of Alzheimer's, potentially leading to more effective treatments and therapies. Moreover, it can provide valuable insights for researchers and medical professionals to better understand the disease's progression and improve patient care.The utilization of machine learning algorithms not only enhances the accuracy and efficiency of Alzheimer's detection but also expedites the diagnostic process, reducing the burden on both patients and healthcare providers. As this field continues to evolve, the application of machine learning in Alzheimer's detection brings hope for a future with improved quality of life for those affected by this challenging condition.Early Alzheimer's Detection with Machine Learning represents a significant leap in the fight against Alzheimer's, offering a beacon of hope for early intervention, better care, and improved outcomes for individuals and their families facing this debilitating disease.

The Adjuster is a work of fiction and delves into a world where machine learning, robotics and artificial intelligence in general are used and exploited to serve purposes that are sometimes not so ethical or moral. Corporate executives, insurance company personnel, attorneys and mysterious characters all intersect in this dark and bleak story Of deception, manipulation and prejudice, A sobering View Of Old school job professionalism, honesty and integrity running headlong into adversaries that are well prepared to advance their agenda. The adjuster, Dave Easton, looks to a retirement from his life-long profession in his near future, unaware of the challenge he is about to face with a newly assigned claim. Easton is world-weary and alone, but still has his embedded job skills to help guide him on this fast-moving investigation. This work Of fiction will engage the reader and provoke the "what if" thoughts.

This focuses on enhancing the classification of medical data using learning algorithms. With the increasing availability and complexity of medical data, accurate and efficient classification techniques are crucial for effective healthcare decision-making. The research aims to explore various learning algorithms and their potential to improve the classification accuracy of medical data.By leveraging machine learning algorithms, this research seeks to optimize the process of categorizing medical data into specific classes or categories. The study will investigate the performance and effectiveness of different algorithms, such as decision trees, support vector machines, neural networks, and ensemble methods. These algorithms will be evaluated based on their ability to handle diverse medical data types, including patient records, diagnostic reports, medical images, and laboratory results.The outcomes of this research have the potential to contribute significantly to the field of medical data analysis. The enhanced classification techniques can help healthcare professionals accurately interpret and utilize medical data, leading to improved diagnoses, treatment planning, and patient care. Additionally, the findings may pave the way for developing automated systems that can assist medical professionals in data-driven decision-making, reducing human errors and enhancing overall healthcare efficiency.Overall, its aims to advance the field of medical data classification by leveraging learning algorithms to achieve more accurate and reliable results. The research findings have the potential to positively impact healthcare practices, facilitating better healthcare outcomes and improving patient well-being.

This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity.This book also provides insight into the difficult "e;how"e; and "e;why"e; questions that arise in AI within the security domain. For example, this book includes chapters covering "e;explainable AI"e;, "e;adversarial learning"e;, "e;resilient AI"e;, and a wide variety of related topics. It's not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more.Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.

This book introduces a novel perspective on machine learning, offering distinct advantages over neural network-based techniques. This approach boasts a reduced hardware requirement, lower power consumption, and enhanced interpretability. The applications of this approach encompass high-speed classifications, including packet classification, network intrusion detection, and exotic particle detection in high-energy physics. Moreover, it finds utility in medical diagnosis scenarios characterized by small training sets and imbalanced data. The resulting rule generated by this method can be implemented either in software or hardware. In the case of hardware implementation, circuit design can employ look-up tables (memory), rather than threshold gates.The methodology described in this book involves extracting a set of rules from a training set, composed of categorical variable vectors and their corresponding classes. Unnecessary variables are eliminated, and the rules are simplified before being transformed into a sum-of-products (SOP) form. The resulting SOP exhibits the ability to generalize and predict outputs for new inputs. The effectiveness of this approach is demonstrated through numerous examples and experimental results using the University of California-Irvine (UCI) dataset.This book is primarily intended for graduate students and researchers in the fields of logic synthesis, machine learning, and data mining. It assumes a foundational understanding of logic synthesis, while familiarity with linear algebra and statistics would be beneficial for readers.

This book focuses on the acceleration of emerging irregular sparse workloads, posed by novel artificial intelligent (AI) models and sparse linear algebra. Specifically, the book outlines several co-optimized hardware-software solutions for a highly promising class of emerging sparse AI models called Probabilistic Circuit (PC) and a similar sparse matrix workload for triangular linear systems (SpTRSV). The authors describe optimizations for the entire stack, targeting applications, compilation, hardware architecture and silicon implementation, resulting in orders of magnitude higher performance and energy-efficiency compared to the existing state-of-the-art solutions. Thus, this book provides important building blocks for the upcoming generation of edge AI platforms.

This book constitutes the refereed proceedings of the 8th International Conference on Business Intelligence, CBI 2023, which held in Istanbul, Turkey, during July 19¿21, 2023.The 15 full papers included in this book were carefully reviewed and selected from 50 submissions. They were organized in topical sections as follows: artificial intelligence and business intelligence; and optimization and decision support.

This book provides both the developers and the users with an awareness of the challenges and opportunities of advancements in healthcare paradigm with the application and availability of advanced hardware, software, tools, technique or algorithm development stemming the Internet of Things. The book helps readers to bridge the gap in their three understanding of three major domains and their interconnections: Hardware tested and software APP development for data collection, intelligent protocols for analysis and knowledge extraction. Medical expertise to interpret extracted knowledge towards disease prediction or diagnosis and support. Security experts to ensure data correctness for precise advice. The book provides state-of-the-art overviews by active researchers, technically elaborating healthcare architectures/frameworks, protocols, algorithms, methodologies followed by experimental results and evaluation. Future direction and scope will be precisely documented for interested readers.

This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics.The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI).

This edited book discusses the challenges in handling medical data to provide effective healthcare services in real-time and outlines the approaches and the impact of, AI, IOT, cloud, big data, and blockchain technologies for building intelligent Healthcare 4.0 systems.

Medical Imaging Informatics is an edited book that discusses how medical images can be processed using machine learning techniques and big data analysis methods. These tools help physicians to gain a full overview of a patient's data, which in turn assists with diagnosis, prognosis or intervention.

This edited book explores new and emerging technologies in the field of medical image processing using deep learning models, neural networks and machine learning architectures. Multimodal medical imaging and optimisation techniques are discussed in relation to the advances, challenges and benefits of computer-aided diagnoses.

This book provides a comprehensive introduction to processing-in-memory (PIM) technology, from its architectures to circuits implementations on multiple memory types and describes how it can be a viable computer architecture in the era of AI and big data. The authors summarize the challenges of AI hardware systems, processing-in-memory (PIM) constraints and approaches to derive system-level requirements for a practical and feasible PIM solution. The presentation focuses on feasible PIM solutions that can be implemented and used in real systems, including architectures, circuits, and implementation cases for each major memory type (SRAM, DRAM, and ReRAM).