Advertisement
| an unlabeled hierarchical diagram: The Engineering Design of Systems Dennis M. Buede, William D. Miller, 2016-02-04 New for the third edition, chapters on: Complete Exercise of the SE Process, System Science and Analytics and The Value of Systems Engineering The book takes a model-based approach to key systems engineering design activities and introduces methods and models used in the real world. This book is divided into three major parts: (1) Introduction, Overview and Basic Knowledge, (2) Design and Integration Topics, (3) Supplemental Topics. The first part provides an introduction to the issues associated with the engineering of a system. The second part covers the critical material required to understand the major elements needed in the engineering design of any system: requirements, architectures (functional, physical, and allocated), interfaces, and qualification. The final part reviews methods for data, process, and behavior modeling, decision analysis, system science and analytics, and the value of systems engineering. Chapter 1 has been rewritten to integrate the new chapters and updates were made throughout the original chapters. Provides an overview of modeling, modeling methods associated with SysML, and IDEF0 Includes a new Chapter 12 that provides a comprehensive review of the topics discussed in Chapters 6 through 11 via a simple system – an automated soda machine Features a new Chapter 15 that reviews General System Theory, systems science, natural systems, cybernetics, systems thinking, quantitative characterization of systems, system dynamics, constraint theory, and Fermi problems and guesstimation Includes a new Chapter 16 on the value of systems engineering with five primary value propositions: systems as a goal-seeking system, systems engineering as a communications interface, systems engineering to avert showstoppers, systems engineering to find and fix errors, and systems engineering as risk mitigation The Engineering Design of Systems: Models and Methods, Third Edition is designed to be an introductory reference for professionals as well as a textbook for senior undergraduate and graduate students in systems engineering. |
| an unlabeled hierarchical diagram: Embedded and Ubiquitous Computing - EUC 2005 Workshops Tomoya Enokido, Lu Yan, Bin Xiao, Daeyoung Kim, Yuanshun Dai, Laurence T. Yang, 2005-11-25 |
| an unlabeled hierarchical diagram: Large Scale Hierarchical Classification: State of the Art Azad Naik, Huzefa Rangwala, 2018-10-09 This SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this book, the authors provide a comprehensive overview of various state-of-the-art existing methods and algorithms that were developed to solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in detail such as: 1. High imbalance between classes at different levels of the hierarchy 2. Incorporating relationships during model learning leads to optimization issues 3. Feature selection 4. Scalability due to large number of examples, features and classes 5. Hierarchical inconsistencies 6. Error propagation due to multiple decisions involved in making predictions for top-down methods The brief also demonstrates how multiple hierarchies can be leveraged for improving the HC performance using different Multi-Task Learning (MTL) frameworks. The purpose of this book is two-fold: 1. Help novice researchers/beginners to get up to speed by providing a comprehensive overview of several existing techniques. 2. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC. New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature. |
| an unlabeled hierarchical diagram: Concept Lattices Peter Eklund, 2011-04-02 This volume contains the Proceedings of ICFCA 2004, the 2nd International Conference on Formal Concept Analysis. The ICFCA conference series aims to be the premier forum for the publication of advances in applied lattice and order theory and in particular scienti?c advances related to formal concept analysis. Formal concept analysis emerged in the 1980s from e?orts to restructure lattice theory to promote better communication between lattice theorists and potentialusersoflatticetheory.Sincethen,the?eldhasdevelopedintoagrowing research area in its own right with a thriving theoretical community and an increasing number of applications in data and knowledge processing including data visualization, information retrieval, machine learning, data analysis and knowledge management. In terms of theory, formal concept analysis has been extended into attribute exploration, Boolean judgment, contextual logic and so on to create a powerful general framework for knowledge representation and reasoning. This conference aims to unify theoretical and applied practitioners who use formal concept an- ysis, drawing on the ?elds of mathematics, computer and library sciences and software engineering. The theme of the 2004 conference was ‘Concept Lattices” to acknowledge the colloquial term used for the line diagrams that appear in almost every paper in this volume. ICFCA 2004 included tutorial sessions, demonstrating the practical bene?ts of formal concept analysis, and highlighted developments in the foundational theory and standards. The conference showcased the increasing variety of formal concept analysis software and included eight invited lectures from distinguished speakersinthe?eld.Sevenoftheeightinvitedspeakerssubmittedaccompanying papers and these were reviewed and appear in this volume. |
| an unlabeled hierarchical diagram: Black History and Black Identity William D. Wright, 2002-02-28 This study contends that historians and intellectuals failed to understand the difference between race and ethnicity, which has in turn impaired their ability to understand who Black people are in America. The author argues that Black Americans are to be distinguished from other categories of black people in the country: black Africans, West Indians, or Hispanics. While Black people are members of the black race, as are other groups of people, they are a distinct ethnic group of that race. This conceptual failure has hampered the ability of historians to define Black experience in America and to study it in the most accurate, authentic, and realistic manner possible. This confusing situation is aggravated further by the fact that many scholars tend to describe Black people in an arbitrary manner, as Africans, African Americans, Afro-Americans, black or Black, which is insufficient for precision. They sometimes downplay the historical evidence regarding African identity, and the identity of Blacks in America. Wright offers a new methodological basis for undertaking Black history: namely, the framework of historical sociology. He argues that this approach will produce a more useful history for Black people and others in America. |
| an unlabeled hierarchical diagram: Graph Machine Learning Claudio Stamile, Aldo Marzullo, Enrico Deusebio, 2021-06-25 Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What you will learn Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book. |
| an unlabeled hierarchical diagram: Artificial Intelligence Dr. V. Deepa, Dr. Jeyanthi, Mrs. P.R.Sukanya Sridevi, Augustin Kirubakaran, 2024-09-27 Artificial Intelligence delves into the transformative world of AI, exploring its foundational theories, practical applications, and ethical implications. Covering core topics like machine learning, neural networks, and natural language processing, the book offers a comprehensive view of AI's potential to reshape industries, enhance decision-making, and drive innovation. With discussions on challenges, advancements, and future trends, this resource serves as an essential guide for students, professionals, and enthusiasts eager to understand and engage with the dynamic field of artificial intelligence. |
| an unlabeled hierarchical diagram: The Common Component Modeling Example Andreas Rausch, Ralf H. Reussner, Raffaela Mirandola, Frantisek Plasil, 2008-08-26 Based on the 2007 Dagstuhl Research Seminar CoCoME, this book defines a common example for modeling approaches of component-based systems. The book makes it possible to compare different approaches and to validate existing models. |
| an unlabeled hierarchical diagram: Graph Representation Learning William L. William L. Hamilton, 2022-06-01 Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning. |
| an unlabeled hierarchical diagram: Evolutionary Theory and the Creation Controversy Olivier Rieppel, 2010-11-01 Evolutionary theory addresses the phenomenon of the origin and diversity of plant and animal species that we observe. In recent times, however, it has become a predominant ideology which has gained currency far beyond its original confines. Attempts to understand the origin and historical development of human culture, civilization and language, of the powers of human cognition, and even the origin of the moral and ethical values guiding and constraining everyday life in human societies are now cast in an evolutionary context. In “Evolutionary Theory and the Creation Controversy” the author examines evolutionary theory from a historical perspective, explaining underlying metaphysical backgrounds and fundamental philosophical questions such as the paradoxical problem of change, existence and creation. He introduces the scientists involved, their research results and theories, and discusses the evolution of evolutionary theory against the background of Creationism and Intelligent Design. |
| an unlabeled hierarchical diagram: A Glossary for English Transformational Grammar Robert Allen Palmatier, 1972 |
| an unlabeled hierarchical diagram: Working Minds Beth Crandall, Gary A. Klein, Robert R. Hoffman, 2006 How to collect data about cognitive processes and events, how to analyze CTA findings, and how to communicate them effectively: a handbook for managers, trainers, systems analysts, market researchers, health professionals, and others. |
| an unlabeled hierarchical diagram: The Smart IoT Blueprint: Engineering a Connected Future Fadi Al-Turjman, |
| an unlabeled hierarchical diagram: Modeling and Simulation Tools for Emerging Telecommunication Networks Nejat Ince, Ercan Topuz, 2006-09-10 This book comprises a selection of papers presented at a symposium organized under the aegis of COST Telecommunications Action 285. The main objective of the book is to enhance existing tools and develop new modeling and simulation tools for research in emerging multi-service telecommunication networks in the areas of model performance improvements, multilayer traffic modeling, and the important issue of evaluation and validation of the new modeling tools. |
| an unlabeled hierarchical diagram: Structural Knowledge David H. Jonassen, Katherine Beissner, Michael Yacci, 2013-05-13 This book introduces the concept of a hypothetical type of knowledge construction -- referred to as structural knowledge -- that goes beyond traditional forms of information recall to provide the bases for knowledge application. Assuming that the validity of the concept is accepted, the volume functions as a handbook for supporting the assessment and use of structural knowledge in learning and instructional settings. It's descriptions are direct and short, and its structure is consistent. Almost all of the chapters describe a technique for representing and assessing structural knowledge acquisition, conveying knowledge structures through direct instruction, or providing learners with strategies that they may use to acquire structural knowledge. These chapters include the following sections in the same sequence: * description of the technique and its theoretical or conceptual rationale * examples and applications * procedures for development and use * effectiveness -- learner interactions and differences, and advantages and disadvantages * references to the literature. The chapters are structured to facilitate access to information as well as to illuminate comparisons and contrasts among the techniques. |
| an unlabeled hierarchical diagram: Advances in Conceptual Modeling - Foundations and Applications Jean-Luc Hainaut, Elke Al. Rundensteiner, Markus Kirchberg, Michaela Bertolotto, Mathias Brochhausen, Phoebe Chen, Samira Sisaid Cherfi, Martin Doerr, Hyoil Han, Sven Hartmann, Jeffrey Parsons, Geert Poels, Colette Rolland, Eric Yu, Esteban Zimlanyi, 2007-11-13 This book constitutes the refereed joint proceedings of six workshops held in conjunction with the 26th International Conference on Conceptual Modeling. Topics include conceptual modeling for life sciences applications, foundations and practices of UML, ontologies and information systems for the semantic Web , quality of information systems, requirements, intentions and goals in conceptual modeling, and semantic and conceptual issues in geographic information systems. |
| an unlabeled hierarchical diagram: The Role of Organic Matter in Structuring Microbial Communities L. Kaplan, M. Hullar, L. Sappelsa, D. Stahl, P. Hatcher, S. Frazier, 2005-03-01 Natural organic matter is important to the quality of drinking water. It constitutes precursors for disinfectant by-product formation and supports regrowth of bacteria. The drinking water industry is involved in work designed to improve biological treatment of water, control bacterial regrowth in distribution systems, and measure biodegradable NOM concentrations. These efforts would benefit from a knowledge of NOM composition and structure and the composition of microbial communities that colonize biological filters and distribution systems. In this project the researchers addressed four major goals: (1) to determine the structure and composition of natural organic matter (NOM), (2) to describe the structure of heterotrophic bacterial communities supported by raw and treated source water, (3) to measure the responses of heterotrophic bacterial communities to seasonally driven variations in NOM and temperature, and (4) to determine whether bioreactor systems can serve as small-scale models for the development and refinement of drinking water treatment processes. The five source waters selected for this project included a broad range of physiographic provinces, vegetation zones, and NOM concentrations. The research team analyzed NOM and microbial communities from an analytical hierarchy involving assessment of concentration, composition, and structure. Concentrations of NOM and BOM were estimated from dissolved organic carbon (DOC) and biodegradable DOC concentrations. NOM composition was assessed from analyses of carbohydrates with ion chromatography with pulsed amperometric detection, humic substances with XAD-8 resin, and functional groups with NMR. Molecular structure was determined from tetramethylammonium hydroxide thermochemolysis (TMAH) GC/MS. Microbial community composition was assessed from comparative ribosomal ribonucleic acid (RNA) sequencing, specifically, terminal restriction fragment length polymorphisms (t-RFLP), to provide an overview of microbial population structure and detect population shifts at the level of species. NOM Composition NOM and BOM concentrations showed extensive temporal variation in all of the source waters, but a general pattern of concentration ranges was discernable, indicating that each watershed has a particular concentration signal. Compositional studies revealed that humic substances and complex carbohydrates are components of both NOM and BOM. Structural and compositional studies identified unique NOM signatures for the different source waters, with some classes of molecules observed only in specific source waters. The BOM pool included humic substances and lignin, sources generally presumed to be relatively resistant to biodegradation. Additional novel insights included the quantitative contribution of aromatic molecules to the BOM pool and the potential for bacterial demethylation of lignin. Bacterial Communities The communities of microorganisms that developed in bioreactors that were fed water from different watersheds were unique. NOM influenced the genetic composition of resulting microbial communities, and seasonal shifts were observed for watersheds possessing strong seasonal temperature signals. Thus, temperature and organic matter quantity and quality probably influenced parameters important to the biological treatment of drinking water. A comparison of bioreactor metabolism with rapid sand filters showed some overlap, suggesting the bioreactors may indicate the ultimate potential of rapid sand filters for BOM processing. The researchers recommend the following: Bioreactors designed to monitor a BOM source should ideally be inoculated, colonized, and maintained by that source; at a minimum, acclimation to the source over several months is needed. Seasonal changes in the microbial community colonizing a biologically active filter may diminish filter performance and require an acclimation period to restore performance. Molecular-based methods for both microbial and chemical analyses of drinking water and treatment processes should be targeted for continued development and implementation within the drinking water industry. Originally published by AwwaRF for its subscribers in 2004. |
| an unlabeled hierarchical diagram: Industrial Machine Learning Andreas François Vermeulen, 2019-11-30 Understand the industrialization of machine learning (ML) and take the first steps toward identifying and generating the transformational disruptors of artificial intelligence (AI). You will learn to apply ML to data lakes in various industries, supplying data professionals with the advanced skills required to handle the future of data engineering and data science. Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakes. Industrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory, supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptors. What You Will Learn Generate and identify transformational disruptors of artificial intelligence (AI) Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environment Hone the skills required to handle the future of data engineering and data science Who This Book Is For Intermediate to expert level professionals in the fields of data science, data engineering, machine learning, and data management |
| an unlabeled hierarchical diagram: Python Machine Learning Sebastian Raschka, 2015-09-23 Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models. |
| an unlabeled hierarchical diagram: Biblical Hebrew Grammar Visualized Francis I. Andersen, A. Dean Forbes, 2012-03-25 In Biblical Hebrew Grammar Visualized, Andersen and Forbes approach the grammar of Biblical Hebrew from the perspective of corpus linguistics. Their pictorial representations of the clauses making up the biblical texts show the grammatical functions (subject, object, and so on) and semantic roles (surrogate, time interval, and so on) of clausal constituents, as well as the grammatical relations that bind the constituents into coherent structures. The book carefully introduces the Andersen-Forbes approach to text preparation and characterization. It describes and tallies the kinds of phrases and clauses encountered across all of Biblical Hebrew. It classifies and gives examples of the major constituents that form clauses, focusing especially on the grammatical functions and semantic roles. The book presents the structures of the constituents and uses their patterns of incidence both to examine constituent order (“word order”) and to characterize the relations among verb corpora. It expounds in detail the characteristics of quasiverbals, verbless clauses, discontinuous and double-duty clausal constituents, and supra-clausal structures. The book is intended for students of Biblical Hebrew at all levels. Beginning students will readily grasp the basic grammatical structures making up the clauses, because these are few and fairly simple. Intermediate and advanced students will profit from the detailed descriptions and comparative analyses of all of the structures making up the biblical texts. Scholars will find fresh ways of addressing open problems, while gaining glimpses of new research approaches and topics along the way. |
| an unlabeled hierarchical diagram: Transformation in Healthcare with Emerging Technologies Pushpa Singh, Divya Mishra, Kirti Seth, 2022-04-27 The book, Transformation in Healthcare with Emerging Technologies, presents healthcare industrial revolution based on service aggregation and virtualisation that can transform the healthcare sector with the aid of technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Bigdata and Blockchain. These technologies offer fast communication between doctors and patients, protected transactions, safe data storage and analysis, immutable data records, transparent data flow service, transaction validation process, and secure data exchanges between organizations. Features: • Discusses the Integration of AI, IoT, big data and blockchain in healthcare industry • Highlights the security and privacy aspect of AI, IoT, big data and blockchain in healthcare industry • Talks about challenges and issues of AI, IoT, big data and blockchain in healthcare industry • Includes several case studies It is primarily aimed at graduates and researchers in computer science and IT who are doing collaborative research with the medical industry. Industry professionals will also find it useful. |
| an unlabeled hierarchical diagram: Mathematical Reviews , 2005 |
| an unlabeled hierarchical diagram: Critical Thinking Across the Curriculum Diane F. Halpern, 2014-02-04 Consider that many of the people who are alive today will be working at jobs that do not currently exist and that the explosion of information means that today's knowledge will quickly become outdated. As a result, two goals for education clearly emerge -- learning how to learn and how to think critically about information that changes at a rapid rate. We face a multitude of new challenges to our natural environment, difficult dilemmas concerning the use of weapons of mass destruction, political agendas for the distribution of scarce commodities and wealth, psychological problems of loneliness and depression, escalating violence, and an expanding elderly population. International in scope and in magnitude, these new problems strain resources and threaten the continuance of life on earth. To creatively and effectively attack these imminent problems, a well educated, thinking populace is essential. An abridged edition of Halpern's best-selling text, Critical Thinking Across the Curriculum is designed to help students enhance their thinking skills in every class. The skills discussed are needed in every academic area and setting -- both in and out of class. They are: determining cause; assessing likelihood and uncertainty; comprehending complex text; solving novel problems; making good decisions; evaluating claims and evidence; and thinking creatively. In this adaptation of her best-selling text, Diane Halpern applies the theories and research of cognitive psychology to the development of critical thinking and learning skills needed in the increasingly complex world in which we work and live. The book is distinguished by its clear writing style, humorous tone, many practical examples and anecdotes, and rigorous academic grounding. Everyday examples and exercises promote the transfer of critical thinking skills and dispositions to real-world settings and problems. The goal is to help readers recognize when and how to apply the thinking skills needed to analyze arguments, reason clearly, identify and solve problems, and make sound decisions. Also of importance, a general thinking skills framework ties the chapters together, but each is written so that it can stand alone. This organization allows for maximum flexibility in the selection of topics and the order in which they are covered. This book is intended for use in any course emphasizing critical thinking as an approach to excellence in thinking and learning. |
| an unlabeled hierarchical diagram: Physiology of the Gastrointestinal Tract, Two Volume Set Hamid M. Said, 2012-07-04 Physiology of the Gastrointestinal Tract, Fifth Edition — winner of a 2013 Highly Commended BMA Medical Book Award for Internal Medicine — covers the study of the mechanical, physical, and biochemical functions of the GI Tract while linking the clinical disease or disorder, bridging the gap between clinical and laboratory medicine. The gastrointestinal system is responsible for the breakdown and absorption of various foods and liquids needed to sustain life. Other diseases and disorders treated by clinicians in this area include: food allergies, constipation, chronic liver disease and cirrhosis, gallstones, gastritis, GERD, hemorrhoids, IBS, lactose intolerance, pancreatic, appendicitis, celiac disease, Crohn's disease, peptic ulcer, stomach ulcer, viral hepatitis, colorectal cancer and liver transplants. The new edition is a highly referenced and useful resource for gastroenterologists, physiologists, internists, professional researchers, and instructors teaching courses for clinical and research students. - 2013 Highly Commended BMA Medical Book Award for Internal Medicine - Discusses the multiple processes governing gastrointestinal function - Each section edited by preeminent scientist in the field - Updated, four-color illustrations |
| an unlabeled hierarchical diagram: The Handbook of Task Analysis for Human-Computer Interaction Dan Diaper, Neville Stanton, 2003-09-01 A comprehensive review of the current state of research and use of task analysis for Human-Computer Interaction (HCI), this multi-authored and diligently edited handbook offers the best reference source available on this diverse subject whose foundations date to the turn of the last century. Each chapter begins with an abstract and is cross-referen |
| an unlabeled hierarchical diagram: The Deep Learning with Keras Workshop Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat, 2020-07-29 Discover how to leverage Keras, the powerful and easy-to-use open source Python library for developing and evaluating deep learning models Key FeaturesGet to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scoresExplore advanced concepts such as sequential memory and sequential modelingReinforce your skills with real-world development, screencasts, and knowledge checksBook Description New experiences can be intimidating, but not this one! This beginner's guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks. What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework. The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you'll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you'll explore recurrent neural networks and learn how to train them to predict values in sequential data. By the end of this book, you'll have developed the skills you need to confidently train your own neural network models. What you will learnGain insights into the fundamentals of neural networksUnderstand the limitations of machine learning and how it differs from deep learningBuild image classifiers with convolutional neural networksEvaluate, tweak, and improve your models with techniques such as cross-validationCreate prediction models to detect data patterns and make predictionsImprove model accuracy with L1, L2, and dropout regularizationWho this book is for If you know the basics of data science and machine learning and want to get started with advanced machine learning technologies like artificial neural networks and deep learning, then this is the book for you. To grasp the concepts explained in this deep learning book more effectively, prior experience in Python programming and some familiarity with statistics and logistic regression are a must. |
| an unlabeled hierarchical diagram: Clustering And Classification Phips Arabie, Larry Hubert, Geert De Soete, 1996-01-29 At a moderately advanced level, this book seeks to cover the areas of clustering and related methods of data analysis where major advances are being made. Topics include: hierarchical clustering, variable selection and weighting, additive trees and other network models, relevance of neural network models to clustering, the role of computational complexity in cluster analysis, latent class approaches to cluster analysis, theory and method with applications of a hierarchical classes model in psychology and psychopathology, combinatorial data analysis, clusterwise aggregation of relations, review of the Japanese-language results on clustering, review of the Russian-language results on clustering and multidimensional scaling, practical advances, and significance tests. |
| an unlabeled hierarchical diagram: Auto-Segmentation for Radiation Oncology Jinzhong Yang, Gregory C. Sharp, Mark J. Gooding, 2021-04-18 This book provides a comprehensive introduction to current state-of-the-art auto-segmentation approaches used in radiation oncology for auto-delineation of organs-of-risk for thoracic radiation treatment planning. Containing the latest, cutting edge technologies and treatments, it explores deep-learning methods, multi-atlas-based methods, and model-based methods that are currently being developed for clinical radiation oncology applications. Each chapter focuses on a specific aspect of algorithm choices and discusses the impact of the different algorithm modules to the algorithm performance as well as the implementation issues for clinical use (including data curation challenges and auto-contour evaluations). This book is an ideal guide for radiation oncology centers looking to learn more about potential auto-segmentation tools for their clinic in addition to medical physicists commissioning auto-segmentation for clinical use. Features: Up-to-date with the latest technologies in the field Edited by leading authorities in the area, with chapter contributions from subject area specialists All approaches presented in this book are validated using a standard benchmark dataset established by the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of American Association of Physicists in Medicine |
| an unlabeled hierarchical diagram: Chemometric Monitoring Madhusree Kundu, Palash Kumar Kundu, Seshu K. Damarla, 2017-10-10 Data collection, compression, storage, and interpretation have become mature technologies over the years. Extraction of meaningful information from the process historical database seems to be a natural and logical choice. In view of this, the proposed book aims to apply the data driven knowledge base in ensuring safe process operation through timely detection of process abnormal and normal operating conditions, assuring product quality and analyzing biomedical signal leading to diagnostic tools. The book poses an open invitation for an interface which is required henceforth, in practical implementation of the propositions and possibilities referred in the book. It poses a challenge to the researchers in academia towards the development of more sophisticated algorithms. The proposed book also incites applications in diversified areas. Key Features: Presents discussion of several modern and popular chemometric techniques Introduces specific illustrative industrial applications using the chemometric techniques Demonstrates several applications to beverage quality monitoring Provides all the algorithms developed for the automated device design, data files, sources for biomedical signals and their pre-processing steps, and all the process models requited to simulate process normal/faulty data Includes casestudy-based approach to the topics with MATLAB and SIMULINK source codes |
| an unlabeled hierarchical diagram: IAENG Transactions on Engineering Technologies Haeng Kon Kim, Sio-Iong Ao, Mahyar A. Amouzegar, Burghard B. Rieger, 2013-09-12 IAENG Transactions on Engineering Technologies contains forty-nine revised and extended research articles, written by prominent researchers participating in the conference. Topics covered include circuits, engineering mathematics, control theory, communications systems, systems engineering, manufacture engineering, computational biology, chemical engineering, and industrial applications. This book offers the state of art of tremendous advances in engineering technologies and physical science and applications, and also serves as an excellent source of reference for researchers and graduate students working with/on engineering technologies and physical science and applications. |
| an unlabeled hierarchical diagram: Health Informatics Meets EHealth G. Schreier, D. Hayn, 2018-05-18 Biomedical engineering and health informatics are closely related to each other, and it is often difficult to tell where one ends and the other begins, but ICT systems in healthcare and biomedical systems and devices are already becoming increasingly interconnected, and share the common entity of data. This is something which is set to become even more prevalent in future, and will complete the chain and flow of information from the sensor, via processing, to the actuator, which may be anyone or anything from a human healthcare professional to a robot. Methods for automating the processing of information, such as signal processing, machine learning, predictive analytics and decision support, are increasingly important for providing actionable information and supporting personalized and preventive healthcare protocols in both biomedical and digital healthcare systems and applications. This book of proceedings presents 50 papers from the 12th eHealth conference, eHealth2018, held in Vienna, Austria, in May 2018. The theme of this year’s conference is Biomedical Meets eHealth – From Sensors to Decisions, and the papers included here cover a wide range of topics from the field of eHealth. The book will be of interest to all those working to design and implement healthcare today. |
| an unlabeled hierarchical diagram: Handbook of Graph Grammars and Computing by Graph Transformation Grzegorz Rozenberg, 1997-01-01 Graph grammars originated in the late 60s, motivated by considerations about pattern recognition and compiler construction. Since then the list of areas which have interacted with the development of graph grammars has grown quite impressively. Besides the aforementioned areas it includes software specification and development, VLSI layout schemes, database design, modeling of concurrent systems, massively parallel computer architectures, logic programming, computer animation, developmental biology, music composition, visual languages, and many others. The area of graph grammars and graph transformations generalizes formal language theory based on strings and the theory of term rewriting based on trees. As a matter of fact within the area of graph grammars, graph transformation is considered a fundamental programming paradigm where computation includes specification, programming, and implementation. |
| an unlabeled hierarchical diagram: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2009 Guang-Zhong Yang, David J. Hawkes, Daniel Rueckert, Alison Noble, Chris Taylor, 2009-10-01 The two-volume set LNCS 5761 and LNCS 5762 constitute the refereed proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009, held in London, UK, in September 2009. Based on rigorous peer reviews, the program committee carefully selected 259 revised papers from 804 submissions for presentation in two volumes. The first volume includes 125 papers divided in topical sections on cardiovascular image guided intervention and robotics; surgical navigation and tissue interaction; intra-operative imaging and endoscopic navigation; motion modelling and image formation; image registration; modelling and segmentation; image segmentation and classification; segmentation and atlas based techniques; neuroimage analysis; surgical navigation and robotics; image registration; and neuroimage analysis: structure and function. |
| an unlabeled hierarchical diagram: Machine Learning V.K. Jain, Machine Learning employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the sud-domains of machine learning, classification, cluster analysis, data mining, database, and visualization. Machine learning is perhaps the hottest thing in Silicon Valley right now, especially deep learning. We have Google's class on Tensor Flow, which teaches you everything you need to know to work in Silicon Valley's top companies. The reason why it is so hot is because it can take over many repetitive, mindless tasks. It'll make doctor better doctors, and lawyers better lawyers and it makes cars drive themselves. For example, when you're booking a taxi, you're shown how much the trip would cost. Or when you're on the trip, you're shown the path the taxi would take to reach your destination. While booking a ride on Uber, you're always told the amount of time the trip would take and how much it would cost. All of that, is Machine Learning! The overall goal of this book Machine Learning is to provide a broad understanding of various faces of Machine Learning environment in an integrated manner. It covers the syllabi of all technical universities in India and aboard. The first edition of this book is also been awarded by AICTE and placed in AICTE's latest Model Curriculum in Engineering & Technology as well as Emerging Technology. |
| an unlabeled hierarchical diagram: The Prefrontal Cortex Joaquin Fuster, 2015-05-22 The Prefrontal Cortex, Fifth Edition, provides users with a thoroughly updated version of this comprehensive work that has historically served as the classic reference on this part of the brain. The book offers a unifying, interdisciplinary perspective that is lacking in other volumes written about the frontal lobes, and is, once again, written by the award-winning author who discovered memory cells, the physiological substrate of working memory. The fifth edition constitutes a comprehensive update, including all the major advances made on the physiology and cognitive neuroscience of the region since publication in 2008. All chapters have been fully revised, and the overview of prefrontal functions now interprets experimental data within the theoretical framework of the new paradigm of cortical structure and dynamics (the Cognit Paradigm), addressing the accompanying social, economic, and cultural implications. - Provides a distinctly interdisciplinary view of the prefrontal cortex, covering all major methodologies, from comparative anatomy to modern imaging - Unique analysis and synthesis of a large body of basic and clinical data on the subject (more than 2000 references) - Written by an award-winning author who discovered memory cells, the physiological substrate of working memory - Synthesizes evidence that the prefrontal cortex constitutes a complex pre-adaptive system - Incorporates emerging study of the role of the frontal lobes in social, economic, and cultural adaptation |
| an unlabeled hierarchical diagram: Cyber Intelligence and Information Retrieval João Manuel R. S. Tavares, Paramartha Dutta, Soumi Dutta, Debabrata Samanta, 2021-09-28 This book gathers a collection of high-quality peer-reviewed research papers presented at International Conference on Cyber Intelligence and Information Retrieval (CIIR 2021), held at Institute of Engineering & Management, Kolkata, India during 20–21 May 2021. The book covers research papers in the field of privacy and security in the cloud, data loss prevention and recovery, high-performance networks, network security and cryptography, image and signal processing, artificial immune systems, information and network security, data science techniques and applications, data warehousing and data mining, data mining in dynamic environment, higher-order neural computing, rough set and fuzzy set theory, and nature-inspired computing techniques. |
| an unlabeled hierarchical diagram: An Internet in Your Head Daniel Graham, 2021-05-04 Whether we realize it or not, we think of our brains as computers. In neuroscience, the metaphor of the brain as a computer has defined the field for much of the modern era. But as neuroscientists increasingly reevaluate their assumptions about how brains work, we need a new metaphor to help us ask better questions. The computational neuroscientist Daniel Graham offers an innovative paradigm for understanding the brain. He argues that the brain is not like a single computer—it is a communication system, like the internet. Both are networks whose power comes from their flexibility and reliability. The brain and the internet both must route signals throughout their systems, requiring protocols to direct messages from just about any point to any other. But we do not yet understand how the brain manages the dynamic flow of information across its entire network. The internet metaphor can help neuroscience unravel the brain’s routing mechanisms by focusing attention on shared design principles and communication strategies that emerge from parallel challenges. Highlighting similarities between brain connectivity and the architecture of the internet can open new avenues of research and help unlock the brain’s deepest secrets. An Internet in Your Head presents a clear-eyed and engaging tour of brain science as it stands today and where the new paradigm might take it next. It offers anyone with an interest in brains a transformative new way to conceptualize what goes on inside our heads. |
| an unlabeled hierarchical diagram: Discovering Smalltalk Wilf LaLonde, 1994 From a well-known developer of object-oriented systems in Smalltalk, this book provides an introduction to programming for the novice alongside complete coverage of the Smalltalk language. The coverage provides a complete introduction to the syntax of Smalltalk, including the Smalltalk libraries and the Smalltalk environment using Digitalk/V as the example environment. |
| an unlabeled hierarchical diagram: The Soar Cognitive Architecture John E. Laird, 2012-04-13 The definitive presentation of Soar, one AI's most enduring architectures, offering comprehensive descriptions of fundamental aspects and new components. In development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements. |
| an unlabeled hierarchical diagram: Computer Vision – ECCV 2020 Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm, 2020-11-26 The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation. |
What is the difference between labeled and unlabeled data?
Oct 4, 2013 · Labeled data typically takes a set of unlabeled data and augments each piece of that unlabeled data with some sort of meaningful "tag," "label," or "class" that is somehow informative …
algorithm - Labelled vs Unlabelled Binary tree? - Stack Overflow
Apr 16, 2015 · A binary tree can have labels assigned to each node or not. For a given unlabeled binary tree with n nodes we have n! ways to assign labels. (Consider an in-order traversal of the …
linux - unconfined_t vs unlabeled_t in SELinux - Stack Overflow
Oct 18, 2019 · The unlabeled isid is used to automatically associate the type (in this case unlabeled_t) with entities that have an invalid context, and the file isid is used to automatically …
Unlabeled vs unstructured data - Stack Overflow
Feb 1, 2020 · Unlabeled data means that you don’t have labels and you should use unsupervised methods to deal with ...
How to pre-train a deep neural network (or RNN) with unlabeled …
Nov 15, 2018 · There are lots of ways to deep-learn from unlabeled data. Layerwise pre-training was developed back in the 2000s by Geoff Hinton's group, though that's generally fallen out of …
nlp - Weka ignoring unlabeled data - Stack Overflow
May 8, 2013 · I am working on an NLP classification project using Naive Bayes classifier in Weka. I intend to use semi-supervised machine learning, hence working with unlabeled data. When I test …
r - Warning message: ggrepel: 1 unlabeled data points (too many ...
Nov 11, 2021 · However, sometimes the data points are too crowded together and the algorithm finds no solution to place all labels. This is what your message means by "1 unlabeled data …
continue - Unlabeled Statement in java - Stack Overflow
Nov 3, 2015 · The unlabeled form skips to the end of the innermost loop's body and evaluates the boolean expression that ...
nlp - How to fine tune BERT on unlabeled data? - Stack Overflow
May 22, 2020 · I want to do additional pretraining. Looking at the link to "Sentence Transformers," it looks like what I want is in the section "Continue Training on Other Data." Can I use unlabeled …
machine learning - How to find instances in an unlabeled dataset, …
My problem is that I have a large unlabeled dataset, but over time I want it to become labeled and build a confident classifier. This can be done by active learning, but active learning needs an …
What is the difference between labeled and unlabeled data?
Oct 4, 2013 · Labeled data typically takes a set of unlabeled data and augments each piece of that unlabeled data with some sort of meaningful "tag," "label," or "class" that is somehow …
algorithm - Labelled vs Unlabelled Binary tree? - Stack Overflow
Apr 16, 2015 · A binary tree can have labels assigned to each node or not. For a given unlabeled binary tree with n nodes we have n! ways to assign labels. (Consider an in-order traversal of …
linux - unconfined_t vs unlabeled_t in SELinux - Stack Overflow
Oct 18, 2019 · The unlabeled isid is used to automatically associate the type (in this case unlabeled_t) with entities that have an invalid context, and the file isid is used to automatically …
Unlabeled vs unstructured data - Stack Overflow
Feb 1, 2020 · Unlabeled data means that you don’t have labels and you should use unsupervised methods to deal with ...
How to pre-train a deep neural network (or RNN) with unlabeled …
Nov 15, 2018 · There are lots of ways to deep-learn from unlabeled data. Layerwise pre-training was developed back in the 2000s by Geoff Hinton's group, though that's generally fallen out of …
nlp - Weka ignoring unlabeled data - Stack Overflow
May 8, 2013 · I am working on an NLP classification project using Naive Bayes classifier in Weka. I intend to use semi-supervised machine learning, hence working with unlabeled data. When I …
r - Warning message: ggrepel: 1 unlabeled data points (too many ...
Nov 11, 2021 · However, sometimes the data points are too crowded together and the algorithm finds no solution to place all labels. This is what your message means by "1 unlabeled data …
continue - Unlabeled Statement in java - Stack Overflow
Nov 3, 2015 · The unlabeled form skips to the end of the innermost loop's body and evaluates the boolean expression that ...
nlp - How to fine tune BERT on unlabeled data? - Stack Overflow
May 22, 2020 · I want to do additional pretraining. Looking at the link to "Sentence Transformers," it looks like what I want is in the section "Continue Training on Other Data." Can I use …
machine learning - How to find instances in an unlabeled dataset, …
My problem is that I have a large unlabeled dataset, but over time I want it to become labeled and build a confident classifier. This can be done by active learning, but active learning needs an …
