الصفحة 30
الصفحة 30
Diagnosis and Treatment of Human Mycoses
Diagnosis and Treatment of Human Mycoses brings together globally-recognized mycoses experts to guide readers in the use of current knowledge in the field of medical mycology to manage those who suffer from fungal infections (mycoses). Often, diagnostic strategies and tests, including basic and directed culturing techniques, histopathology with standard and special stains, serological methods, and radiological studies all need to be considered and commonly combined to make the diagnosis of fungal infection. This volume first introduces and reviews these tools separately and then as they pertain to specific infections or groups of diseases. The volume consists of four parts. Parts I-III provide an overview of diagnostic and therapeutic tools, and part IV presents the human mycoses.Diagnosis and Treatment of Human Mycoses is meant to be a concise text that will provide the busy infectious disease, hematology–oncology, pulmonology, or critical care specialist a practical tool to diagnose and manage fungal infections. In addition, the depth of the material in the text will provide these and other medical specialists and trainees an excellent reference and learning resource.
Diagnosing dental and orofacial Pain : A clinical manual
Offers valuable advice on ways to observe and communicate effectively with patients in pain, how to analyze a patients' pain descriptions, and how to provide a proper diagnosis of orofacial pain problems that can arise from a myriad of sources-anywhere from teeth, joint and muscle pain, and paranasal sinuses to cluster headaches, neuralgias, neuropathic pain and viral infections. Helps the student and practitioner understand the diagnostic process by addressing the exact questions that need to be asked and then analyzing verbal and non-verbal responses to theseEdited by experts with decades of clinical and teaching experience, and with contributions from international specialistsCompanion website provides additional learning materials including videos, case studies and further practical tips for examination and diagnosisIncludes numerous color photographs and illustrations throughout to enhance text clarityoffers valuable advice on ways to observe and communicate effectively with patients in pain, how to analyze a patients' pain descriptions, and how to provide a proper diagnosis of orofacial pain problems that can arise from a myriad of sources-anywhere from teeth, joint and muscle pain, and paranasal sinuses to cluster headaches, neuralgias, neuropathic pain and viral infections.
Developing Students Statistical Reasoning : Connecting Research and Teaching Practice
This book is written for mathematics and statistics educators and researchers. It summarizes the research and highlights the important concepts for teachers to emphasize, and shows the interrelationships among concepts. It makes specific suggestions regarding how to build classroom activities, integrate technological tools, and assess students’ learning.
Developing BIM talent : A guide to the BIM body of knowledge with Metrics, KSAs, and learning outcomes
A systematic Building Information Modeling (BIM) framework features cutting-edge use cases and competencies for students and professionals pursuing BIM careers. Offers: A solid foundation and guidelines for educators and practitioners for starting or enhancing a BIM curriculum or training program Templates, expert interviews, and case studies that provide in-depth knowledge and lessons learned that can facilitate process changes and strategic action plans Strategies for standardizing emerging BIM job tasks, descriptions, and methods for benchmarking performance
Deterministic and statistical methods in Machine Learning ; 1st International Workshop, Sheffield, UK, September 7-10, 2004. Revised Lectures
This book consitutes the refereed proceedings of the First International Workshop on Machine Learning held in Sheffield, UK, in September 2004. The 19 revised full papers presented were carefully reviewed and selected for inclusion in the book. They address all current issues in the rapidly maturing field of machine learning that aims to provide practical methods for data discovery, categorisation and modelling. The particular focus of the workshop was advanced research methods in machine learning and statistical signal processing.
Designing machine learning systems : An iterative process for production-ready applications
Machine learning systems are both complex and unique. Each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. The book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems
Design Science and Its Importance in the German Mathematics Educational Discussion
This ICME-13 Topical Survey reviews the state-of-the-art by first exploring the roots and scope of design science. Second, it presents two examples of current design science projects that focus on substantial learning environments including a student and a teacher perspective. Subsequently, the book elaborates on how empirical research can be conceptualised within design science. Lastly, it explores developments in design science from a national and international perspective, while also discussing current trends in design research. Within the German-language tradition, considering ‘mathematics education as a design science’ primarily draws on the works of Wittmann.
Design computing and cognition 06 ; 1st ed. ; Proceedings of the 2nd International conference on design computing and cognition
This is the second volume of the new conference series Design Computing and Cognition (DCC) that takes over from and subsumes the successful series Artificial Intelligence in Design (AID) published by Kluwer since 1992. The AID volumes have become standard reference texts for the field. It is expected that the DCC volumes will perform the same role.
Design basics
Filled with hundreds of stunning examples of successful two-dimensional design, this how-to book explains design theory and gives students the tools needed to create successful designs. DESIGN BASICS presents art fundamentals concepts in full two- to four-page spreads, making the text practical and easy for students to refer to while they work.
Design and Analysis of Learning Classifier Systems : A Probabilistic Approach
This book provides a comprehensive introduction to the design and analysis of Learning Classifier Systems (LCS) from the perspective of machine learning. LCS are a family of methods for handling unsupervised learning, supervised learning and sequential decision tasks by decomposing larger problem spaces into easy-to-handle subproblems.
Design Added Value : How Design Increases Value for Architects and Engineers
Enables architects, engineers, contractors and owner-clients of buildings to benefit from extraordinary design and construction features. It explains the rationale and motivation for D-AV methodology, outlines and illustrates this methodology with examples, provides complete and detailed examples of how the key analysis techniques work through historical case studies, and describes specific methods used in application of the D-AV methodology, such as Bayesian statistics, cost benefit analysis, pairwise comparison techniques, cognitive walkthroughs, and optimization.
Deploying .NET Applications : Learning MSBuild and ClickOnce
Whether building a console application, a web service, or a smart client, you eventually need to distribute your finished work. Deploying .NET Applications is a complete guide to delivering applications built with .NET. Packed with hands-on guidance, practical examples, and war stories from the authors many experiences with deployment scenarios, this book provides everything you need to know. The book begins by introducing the deployment problem, then examines why deployment is an engineering problem for organizations. Subsequent chapters provide detail about deploying each type of application, then discuss automated deployments.
Dental materials : Properties and manipulation ; 11th ed.
Comprehensive and cutting-edge content describes the latest materials commonly used in dental practice, including those in esthetics, ceramics, dental implants, and impressions. Approximately 500 illustrations and photographs make it easier to understand properties and differences in both materials and specific types of products. Review questions provide an excellent study tool with 20 to 30 self-test questions in each chapter. Quick Review boxes summarize the material in each chapter. Note boxes highlight key points and important terminology throughout the text. Key terms are bolded at their initial mention in the text and defined in the glossary. A logical and consistent format sets up a solid foundation before progressing into discussions of specific materials, moving from the more common and simple applications such as composites to more specialized areas such as polymers and dental implants. Learning objectives in each chapter focus readers’ attention on essential information. Supplemental readings in each chapter cite texts and journal articles for further research and study.
Deepfake detection
The rise of large language models (LLMs) and the increasing sophistication of deepfake images have made detecting synthetic content a pressing challenge. Several approaches have been proposed to tackle this problem, including statistical analysis, and machine learning algorithms. In this project, A novel zero-shot approach is proposed that utilizes the power of LLMs to detect fake text. The pre-trained LLM is fine-tuned to enhance its ability to differentiate real and fake text. The approach uses the LLM to detect text by analyzing the log probabilities of the text. For detecting fake images, computer vision algorithms and neural networks are used to analyze facial features. The facial region is cropped and preprocessed and the neural network identifies patterns indicative of synthetic content.
Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
Presents a comprehensive comparison of the performance of stochastic optimization algorithms / Includes an introduction to benchmarking and statistical analysis / Provides a web-based tool for making statistical comparisons of optimization algorithms / Overviews of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches.
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety
Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
Deep Learning-Based Face Analytics
Provides an overview of different deep learning-based methods for face recognition and related problems. Specifically, the authors present methods based on autoencoders, restricted Boltzmann machines, and deep convolutional neural networks for face detection, localization, tracking, recognition, etc. The authors also discuss merits and drawbacks of available approaches and identifies promising avenues of research in this rapidly evolving field.
Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics : Techniques and Applications
Examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept, the need for AI (artificial intelligence) techniques has become more significant than ever.
Deep Learning with PyTorch Lightning : Build and train high-performance artificial intelligence and self-supervised models using Python
You’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning.
Deep Learning to See : Towards New Foundations of Computer Vision
Topics and features: Presents a curiosity-driven approach, posing questions to stimulate readers to design novel computational models of vision Offers a rethinking of computer vision, arguing for an approach based on vision in nature, versus regarding visual signals as collections of images Provides an interdisciplinary commentary, aiming to unify computer vision, machine learning, human vision, and computational neuroscience Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions.
