This book discusses the recent research trends and upcoming applications based on artificial intelligence. It includes best selected research papers presented at the International Conference on Research and Applications in Artificial Intelligence (RAAI 2020), organized by Department of Information Technology, RCC Institute of Information technology, Kolkata, West Bengal, India during 19 – 20, December, 2020. Many versatile fields of artificial intelligence are categorically addressed through different chapters of this book. The book is a valuable resource and reference for researchers, instructors, students, scientists, engineers, managers and industry practitioners in these important areas.
This book constitutes the refereed proceedings of the 13th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2018, held in Beijing, China in April, 2018. The 64 papers presented were carefully reviewed and selected from 138 submissions. They provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR; as well as image technology applications.
*Searchable CD ROM containing the entire book (including images) *Over 450 color images, plus never before published images provided by the George Eastman House collection, as well as images from Ansel Adams, Howard Schatz, and Jerry Uelsmann to name just a few The role and value of the picture cannot be matched for accuracy or impact. This comprehensive treatise, featuring the history and historical processes of photography, contemporary applications, and the new and evolving digital technologies, will provide the most accurate technical synopsis of the current, as well as early worlds of photography ever compiled. This Encyclopedia, produced by a team of world renown practicing experts, shares in highly detailed descriptions, the core concepts and facts relative to anything photographic. This Fourth edition of the Focal Encyclopedia serves as the definitive reference for students and practitioners of photography worldwide, expanding on the award winning 3rd edition. In addition to Michael Peres (Editor in Chief), the editors are: Franziska Frey (Digital Photography), J. Tomas Lopez (Contemporary Issues), David Malin (Photography in Science), Mark Osterman (Process Historian), Grant Romer (History and the Evolution of Photography), Nancy M. Stuart (Major Themes and Photographers of the 20th Century), and Scott Williams (Photographic Materials and Process Essentials)
Advances in Molecular Pathology is an annual review publication that covers the current practices and latest developments in the field of Molecular Pathology. Each issue is divided into sections for comprehensive coverage of all subspecialty areas within molecular pathology, including, Genetics, Hematopathology, Infectious Disease, Pharmacogenomics, Informatics, Solid Tumors, and special topics on COVID-19. The Editor-in-Chief of the publication is Dr. Gregory Tsongalis, a leading expert in the field. Topics covered this year include but are not limited to: Phenotype Association and Variant Pathogenicity Prediction Tools in Genomic Analysis; The application of noninvasive prenatal screening to detect copy number variations; Next generation cytogenomics using optical mapping; Review of molecular in APL; NGS for MRD in acute leukemia; Review of emerging technologies as they pertain to infectious disease testing; Germline genetic variants that predict drug response; Nutrigenomics; PGx of hypertension; Genomic data for blood typing, specifically both through NGS and arrays; Preanalytic Variables and Tissue Stewardship for Reliable Next-Generation Sequencing (NGS) Clinical Analysis; and Cell-free nucleic acids in cancer: Current approaches, challenges, and future directions.
This authoritative text/reference presents a comprehensive review of algorithms and techniques for face recognition (FR), with an emphasis on systems that can be reliably used in operational environments. Insights are provided by an international team of pre-eminent experts into the processing of multispectral and hyperspectral face images captured under uncontrolled environments. These discussions cover a variety of imaging sensors ranging from state-of-the-art visible and infrared imaging sensors, to RGB-D and mobile phone image sensors. A range of different biometric modalities are also examined, including face, periocular and iris. This timely volume is a mine of useful information for researchers, practitioners and students involved in image processing, computer vision, biometrics and security.
This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
"This book is an in-depth collection aimed at developers and scholars of research articles from the expanding field of digital libraries"--Provided by publisher.
In recent years, libraries have embraced new technologies that organize and store a variety of digital information, such as multimedia databases, digital medical images, and content-based images. Modern Library Technologies for Data Storage, Retrieval, and Use highlights new features of digital library technology in order to educate the database community. By contributing research from case studies on the emerging technology use in libraries, this book is essential for academics and scientists interested in the efforts to understand the applications of data acquisition, retrieval and storage.
