الصفحة 52
الصفحة 52
Bioimage Data Analysis Workflows
This book provides students and researchers in the life sciences with essential practical information on how to quantitatively analyze data images. It refrains from focusing on theory, and instead uses practical examples and step-by step protocols to familiarize readers with the most commonly used image processing and analysis platforms such as ImageJ, MatLab and Python.
Biochemistry and cell biology of ageing ; Part III : Biomedical science
Covering interesting and significant biomedical ageing topics not included in the earlier volumes. Comprehensive and cutting-edge, this book is a valuable resource for experienced researchers and early career scientist alike, who are interested in learning more about the fascinating and challenging question of why and how our cells age.
Big data and artificial intelligence in digital finance : Increasing personalization and trust in digital finance using big data and AI
This book presents how cutting-edge digital technologies like Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTechs, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also introduces some of the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data.
Big data analytics and machine intelligence in biomedical and health informatics : Concepts, methodologies, tools and applications
Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. Covers the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT).
Big Data : Conceptual Analysis and Applications
The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used.
Big Data – BigData 2020; 9th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Honolulu, HI, USA, September 18-20, 2020, Proceedings
Constitutes the proceedings of the 9th International Conference on Big Data, BigData 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic. The 16 full and 3 short papers presented were carefully reviewed and selected from 52 submissions. The topics covered are Big Data Architecture, Big Data Modeling, Big Data As A Service, Big Data for Vertical Industries (Government, Healthcare, etc.), Big Data Analytics, Big Data Toolkits, Big Data Open Platforms, Economic Analysis, Big Data for Enterprise Transformation, Big Data in Business Performance Management, Big Data for Business Model Innovations and Analytics, Big Data in Enterprise Management Models and Practices, Big Data in Government Management Models and Practices, and Big Data in Smart Planet Solutions.
Beyond the Worst-Case Analysis of Algorithms
There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.
Beyond the apparent Banality of the mathematics classroom
New research in mathematics education deals with the complexity of the mathematics’ classroom. The classroom teaching situation constitutes a pertinent unit of analysis for research into the ternary didactic relationship which binds teachers, students and mathematical knowledge. The classroom is considered as a complex didactic system, which offers the researcher an opportunity to gauge the boundaries of the freedom that is left with regard to choices about the knowledge to be taught and the ways of organizing the students’ learning, while giveing rise to the study of interrelations between three main elements of the teaching process the: mathematical content to be taught and learned, management of the various time dimensions, and activity of the teacher who prepares and manages the class, to the benefit of the students' knowledge and the teachers' own experience.
Beyond knowledge : The legacy of competence : Meaningful computer-based learning environments
The edited and peer reviewed volume presents selected papers of the conference "Beyond knowlegde: the legacy of competence" It reflects the current state-of-the-art work of scholars worldwide within the area of learning and instruction with computers. Mainly, areas of computer-based learning environments supporting competence-focused knowledge acquisition but also foundational scientific work are addressed. More specific, contents cover cognitive processes in hypermedia and multimedia learning, social issues in computer-supported collaborative learning, motivation and emotion in Blended Learning and e-Learning.
Beyond Cartesian Dualism : Encountering affect in the teaching and learning of science.
There is surprisingly little known about affect in science education. Despite periodic forays into monitoring students’ attitudes-toward-science, the effect of affect is too often overlooked. Beyond Cartesian Dualism gathers together contemporary theorizing in this axiomatic area. In fourteen chapters, senior scholars of international standing use their knowledge of the literature and empirical data to model the relationship between cognition and affect in science education. Their revealing discussions are grounded in a broad range of educational contexts including school classrooms, universities, science centres, travelling exhibits and refugee camps, and explore an array of far reaching questions. What is known about science teachers’ and students’ emotions? How do emotions mediate and moderate instruction? How might science education promote psychological
Beliefs about SLA : New Research Approaches
This edited collection of articles illustrates more recent work on beliefs about SLA, drawing on the thinking of (educational) philosophers and (discursive) psychologists, including Dewey, Bakhtin, Vygotsky, and Potter.
Beginning Java Objects : From concepts to code
Learning to design objects effectively with Java is the goal of Beginning Java Objects: From Concepts to Code, Second Edition. Plenty of titles dig into the Java language in massive detail, but this one takes the unique approach of stepping back and looking at fundamental object concepts first. Mastery of Java—from understanding the basic language features to building complete industrial-strength Java applications—emerges only after a thorough tour of thinking in objects. The first edition of Beginning Java Objects has been a bestseller; this second edition includes material on the key features of J2SE 5, conceptual introductions to JDBC and J2EE, and an in-depth treatment of the critical design principles of model-data layer separation and model-view separation.
Beginning Java Data Structures and Algorithms : Sharpen your problem solving skills by learning core computer science concepts in a pain-free manner
Teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You’ll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications.
Beginning Java 17 Fundamentals : Object-Oriented Programming in Java 17
Learn the fundamentals of the Java 17 LTS or Java Standard Edition version 17 Long Term Support release, including basic programming concepts and the object-oriented fundamentals necessary at all levels of Java development. You will: Write your first Java programs with emphasis on learning object-oriented programming / How to work with switch expressions, value types (records), local variable type inference, pattern matching switch and more from Java 17 / Handle exceptions, assertions, strings and dates, and object formatting / Learn about how to define and use modules / Dive in depth into classes, interfaces, and inheritance in Java / Use regular expressions / Take advantage of the JShell REPL tool
Beginning deep learning with TensorFlow : Work with Keras, MNIST data sets, and advanced neural networks
Stats with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! You will: Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications
Beginning C# 2008 : From novice to professional
This book is for anyone who wants to write good C# code—even if you have never programmed before. Writing good code can be a challenge—there are so many options, especially in a .NET language like C#. If you want to really get the best from a programming language, you need to know which features work best in which situations, and understand their strengths and weaknesses. It is this understanding that makes the difference between coding and coding well. Beginning C# 2008: From Novice to Professional, Second Edition has been written to teach you how to use the C# programming language to solve problems. From the earliest chapters and the first introductory concepts, you'll be looking at real–world programming challenges and learning how C# can be used to overcome them.
Becoming a teacher educator : Theory and practice for teacher educators
It is the first book that addresses a range of important topics related to the work of teacher educators, the induction of teacher educators and their further professional development.Becoming a Teacher Educator has a practical focus and it provides theoretical insights, experiences of experts and practical recommendations. The book is rooted in the Association of Teacher Education in Europe (ATEE) and many of the chapters are written by authors who are active members of the ATEE. Distinguished researchers and practitioners from different parts of Europe, and beyond, joined their efforts to write a book that is truly international and combines research, practice and reflection.
Bayesian networks and Influence diagrams : A guide to construction and analysis
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty.
Bayesian Networks and Decision Graphs
Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams.It contians two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems.
Basiswissen pharmakologie = Basic knowledge of pharmacology
Offers a clear and concise overview of all exam-relevant pharmacology content. It guides you through the entire foundational knowledge, from the basics to the most important clinical pictures, in an easily understandable way and aligned with the German National Competency-Based Learning Objectives for Medicine (GK) and National Competency-Based Learning Objectives for Medicine (NKLM). Benefit from the lecturer's many years of experience, who has carefully selected and presented the essential information for you.
