A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.

"This book investigages granular computing (GrC), which emerged as one of the fastest growing information processing paradigms in computational intelligence and human-centric systems"--Provided by publisher.

Ontology, originally a fundamental part of philosophical enquiry, is concerned with the analysis and categorization of what exists. The advent of complex information systems which rely on robust and coherent formal representations of their subject matter has led to a renewed focus on ontological enquiry, and the systematic study of such representations are at the center of the modern discipline of formal ontology. This is now a research focus in domains as diverse as conceptual modeling, database design, software engineering, artificial intelligence, computational linguistics, the life sciences, bioinformatics, geographic information science, knowledge engineering, information retrieval and the semantic web. This book presents the proceedings of the 9th edition of the Formal Ontology in Information Systems conference (FOIS 2016) held in Annecy, France, in July 2016. It contains the 25 full papers delivered at the conference (an acceptance rate of 30.9% for the main track), as well as the abstracts of the 3 keynotes by Gilberto Câmara, Stephen Mumford and Friederike Moltmann. The remainder of the book is divided into the sections: Foundations; Space, Time and Change; Cognition, Language and Semantics; Empiricism and Measurement; Ontology for Engineering; Biomedical Ontologies; and Ontology of Social Reality. The domains addressed by the papers include geography, biomedicine, economics, social reality and engineering, and the book will be of interest to all those working in these fields, as well as to anybody with an interest in formal ontology.

The merging of Artificial Intelligence (AI) and Internet-of-Things is known as Artificial Intelligence-of-Things (AIoT). IoT consists of interlinked computing devices and machines which can acquire, transfer, and execute field/industrial operations without human involvement, while AI processes the acquired data and helps extract the required information. The technologies work in synergy: AI enriches IoT through machine learning and deep learning-based data analysis and learning capabilities, whereas IoT enriches AI through data acquisition, connectivity, and data exchange. Precision agriculture is becoming critically important for sustainable food production to meet the growing food demand. In recent decades, AI and IoT techniques have played an increasing role within industrial operations (e.g. autonomous manufacturing, automated supply chain management, predictive maintenance, smart energy grids, smart home appliances, and wearables), however, agricultural field operations are still heavily dependent on human labor. This is because these operations are ill-defined, unstructured, and susceptible to variation in natural conditions (e.g. illumination, landscape, atmosphere) plus the biological nature of crops (fruits, stems, leaves, and/or shoots continuously change their shape and/or color as they grow).

This book delves into the dynamic synergy between AI and IoT, offering a comprehensive exploration of their transformative potential. With a keen eye on the present and future landscapes, this book navigates through real-world applications, showcasing how AI enriches IoT ecosystems, amplifying their capabilities across diverse sectors. From smart homes and cities to industrial automation and healthcare, each chapter unfolds compelling case studies illustrating how AI augments IoT devices to optimize processes, enhance decision-making, and drive innovation. As the technological horizon expands, the book anticipates emerging trends, paving the way for readers to grasp the profound impact AI will continue to wield on the IoT landscape. Whether you're a seasoned professional or an enthusiast curious about the intersection of AI and IoT, this book offers invaluable insights into the boundless opportunities that await in today's interconnected world and the possibilities that lie ahead.

"This book gives insight into technological advances for dental practice, research and education, for general dental clinician, the researcher and the computer scientist"--Provided by publisher.

More than 30 leading experts from around the world provide comprehensive coverage of various branches of face image analysis, making this text a valuable asset for students, researchers, and practitioners engaged in the study, research, and development of face image analysis techniques.

Computer Vision and Pattern Recognition (CVPR) together play an important role in the processes involved in environmental informatics due to their pervasive, non-destructive, effective, and efficient natures. As a result, CVPR has made significant contributions to the field of environmental informatics by enabling multi-modal data fusion and feature extraction, supporting fast and reliable object detection and classification, and mining the intrinsic relationship between different aspects of environmental data. Computer Vision and Pattern Recognition in Environmental Informatics describes a number of methods and tools for image interpretation and analysis, which enables observation, modelling, and understanding of environmental targets. In addition to case studies on monitoring and modeling plant, soil, insect, and aquatic animals, this publication includes discussions on innovative new ideas related to environmental monitoring, automatic fish segmentation and recognition, real-time motion tracking systems, sparse coding and decision fusion, and cell phone image-based classification and provides useful references for professionals, researchers, engineers, and students with various backgrounds within a multitude of communities.

This reference text presents the knowledge base of computer vision and soft computing techniques with their applications for sustainable developments. Features: ∙ Covers a variety of deep learning architectures useful for computer vision tasks. ∙ Demonstrates the use of different soft computing techniques and their applications for different computer vision tasks. ∙ Highlights the unified strengths of hybrid techniques based on deep learning and soft computing taken together that give the interpretable, adaptive, and optimized solution to a given problem. ∙ Addresses the different issues and further research opportunities in computer vision and soft computing. ∙ Describes all the concepts with practical examples and case studies with appropriate performance measures that validate the applicability of the respective technique to a certain domain. ∙ Considers recent real word problems and the prospective solutions to these problems. This book will be useful to researchers, students, faculty, and industry personnel who are eager to explore the power of deep learning and soft computing for different computer vision tasks.