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Special Topics in Information Technology (SpringerBriefs in Applied Sciences and Technology)

by Barbara Pernici

This open access book presents nine outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Controls, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the nine best theses defended in 2018-19 and selected for the IT PhD Award. Each of the nine authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists.

Special Topics in Multimedia, IoT and Web Technologies

by Valter Roesler Eduardo Barrére Roberto Willrich

This book presents a set of recent advances that involve the areas of multimedia, IoT, and web technologies. These advances incorporate aspects of clouds, artificial intelligence, data analysis, user experience, and games. In this context, the work will bring the reader the opportunity to understand new possibilities of use and research in these areas. We think that this book is suitable for students (postgraduates and undergraduates) and lecturers on these specific topics. Professionals can also benefit from the book since some chapters work with practical aspects relevant to the industry.

Speech-to-Speech Translation (SpringerBriefs in Computer Science)

by Yutaka Kidawara Eiichiro Sumita Hisashi Kawai

This book provides the readers with retrospective and prospective views with detailed explanations of component technologies, speech recognition, language translation and speech synthesis.Speech-to-speech translation system (S2S) enables to break language barriers, i.e., communicate each other between any pair of person on the glove, which is one of extreme dreams of humankind.People, society, and economy connected by S2S will demonstrate explosive growth without exception.In 1986, Japan initiated basic research of S2S, then the idea spread world-wide and were explored deeply by researchers during three decades.Now, we see S2S application on smartphone/tablet around the world.Computational resources such as processors, memories, wireless communication accelerate this computation-intensive systems and accumulation of digital data of speech and language encourage recent approaches based on machine learning.Through field experiments after long research in laboratories, S2S systems are being well-developed and now ready to utilized in daily life.Unique chapter of this book is end-2-end evaluation by comparing system’s performance and human competence. The effectiveness of the system would be understood by the score of this evaluation.The book will end with one of the next focus of S2S will be technology of simultaneous interpretation for lecture, broadcast news and so on.

The Spirit in the Stone: An Unofficial Graphic Novel for Minecrafters (Unofficial Battle Station Prime #4)

by Cara J. Stevens

An evil spirit has invaded the Battle Station. Will the cadets be ready for the fight? Find out in the fourth installment of Battle Station Prime! With the Prime Knight by their side, the young cadets of the Battle Station are convinced that no enemy is a match for their forces. But when one of their own group gets possessed by an evil spirit, it will take everything they have to get their friend back and banish the spirit from the realm for good. Meanwhile, attacks are coming in from all sides. Waves of skeletons and zombies continue to assault the Battle Station and all of the outposts in the land. An unexpected alliance with a witch leads to an even more unexpected trip to the Far Lands, while a mission to discover the identity of their enemy will take the others through a desert temple. All of this is a lot of responsibility for six young warriors, and most of the Battle Station leaders are not sure they can handle it. But fortunately, Ned, the Prime Knight, believes these young heroes have what it takes to survive and succeed to save the Battle Station and the entire realm. Pell, Logan, Maddy, Brooklyn, Cloud, and Zoe will do everything they can to make sure his bet on them pays off.

Spring Boot Persistence Best Practices: Optimize Java Persistence Performance in Spring Boot Applications

by Anghel Leonard

This book is a collection of developer code recipes and best practices for persisting data using Spring, particularly Spring Boot. The book is structured around practical recipes, where each recipe discusses a performance case or performance-related case, and almost every recipe has one or more applications. Mainly, when we try to accomplish something (e.g., read some data from the database), there are several approaches to do it, and, in order to choose the best way, you have to know the implied trades-off from a performance perspective. You’ll see that in the end, all these penalties slow down the application. Besides presenting the arguments that favor a certain choice, the application is written in Spring Boot style which is quite different than plain Hibernate. Persistence is an important set of techniques and technologies for accessing and using data, and this book demonstrates that data is mobile regardless of specific applications and contexts. In Java development, persistence is a key factor in enterprise, ecommerce, cloud and other transaction-oriented applications. After reading and using this book, you'll have the fundamentals to apply these persistence solutions into your own mission-critical enterprise Java applications that you build using Spring. What You Will Learn Shape *-to-many associations for best performancesEffectively exploit Spring Projections (DTO) Learn best practices for batching inserts, updates and deletes Effectively fetch parent and association in a single SELECTLearn how to inspect Persistent Context contentDissect pagination techniques (offset and keyset)Handle queries, locking, schemas, Hibernate types, and more Who This Book Is For Any Spring and Spring Boot developer that wants to squeeze the persistence layer performances.

SQL Injection Strategies: Practical techniques to secure old vulnerabilities against modern attacks

by Ettore Galluccio Edoardo Caselli Gabriele Lombari

Learn to exploit vulnerable database applications using SQL injection tools and techniques, while understanding how to effectively prevent attacks Key Features Understand SQL injection and its effects on websites and other systems Get hands-on with SQL injection using both manual and automated tools Explore practical tips for various attack and defense strategies relating to SQL injection Book Description SQL injection (SQLi) is probably the most infamous attack that can be unleashed against applications on the internet. SQL Injection Strategies is an end-to-end guide for beginners looking to learn how to perform SQL injection and test the security of web applications, websites, or databases, using both manual and automated techniques. The book serves as both a theoretical and practical guide to take you through the important aspects of SQL injection, both from an attack and a defense perspective. You'll start with a thorough introduction to SQL injection and its impact on websites and systems. Later, the book features steps to configure a virtual environment, so you can try SQL injection techniques safely on your own computer. These tests can be performed not only on web applications but also on web services and mobile applications that can be used for managing IoT environments. Tools such as sqlmap and others are then covered, helping you understand how to use them effectively to perform SQL injection attacks. By the end of this book, you will be well-versed with SQL injection, from both the attack and defense perspective. What you will learn Focus on how to defend against SQL injection attacks Understand web application security Get up and running with a variety of SQL injection concepts Become well-versed with different SQL injection scenarios Discover SQL injection manual attack techniques Delve into SQL injection automated techniques Who this book is for This book is ideal for penetration testers, ethical hackers, or anyone who wants to learn about SQL injection and the various attack and defense strategies against this web security vulnerability. No prior knowledge of SQL injection is needed to get started with this book.

SQL Server 2019 Administrator's Guide: A definitive guide for DBAs to implement, monitor, and maintain enterprise database solutions, 2nd Edition

by Marek Chmel Vladimír Mužný

Use Microsoft SQL Server 2019 to implement, administer, and secure a robust database solution that is disaster-proof and highly available Key Features Explore new features of SQL Server 2019 to set up, administer, and maintain your database solution successfully Develop a dynamic SQL Server environment and streamline big data pipelines Discover best practices for fixing performance issues, database access management, replication, and security Book Description SQL Server is one of the most popular relational database management systems developed by Microsoft. This second edition of the SQL Server Administrator's Guide will not only teach you how to administer an enterprise database, but also help you become proficient at managing and keeping the database available, secure, and stable. You'll start by learning how to set up your SQL Server and configure new and existing environments for optimal use. The book then takes you through designing aspects and delves into performance tuning by showing you how to use indexes effectively. You'll understand certain choices that need to be made about backups, implement security policy, and discover how to keep your environment healthy. Tools available for monitoring and managing a SQL Server database, including automating health reviews, performance checks, and much more, will also be discussed in detail. As you advance, the book covers essential topics such as migration, upgrading, and consolidation, along with the techniques that will help you when things go wrong. Once you've got to grips with integration with Azure and streamlining big data pipelines, you'll learn best practices from industry experts for maintaining a highly reliable database solution. Whether you are an administrator or are looking to get started with database administration, this SQL Server book will help you develop the skills you need to successfully create, design, and deploy database solutions. What you will learn Discover SQL Server 2019's new features and how to implement them Fix performance issues by optimizing queries and making use of indexes Design and use an optimal database management strategy Combine SQL Server 2019 with Azure and manage your solution using various automation techniques Implement efficient backup and recovery techniques in line with security policies Get to grips with migrating, upgrading, and consolidating with SQL Server Set up an AlwaysOn-enabled stable and fast SQL Server 2019 environment Understand how to work with Big Data on SQL Server environments Who this book is for This book is for database administrators, database developers, and anyone who wants to administer large and multiple databases single-handedly using Microsoft's SQL Server 2019. Basic awareness of database concepts and experience with previous SQL Server versions is required.

SQL Server Big Data Clusters: Data Virtualization, Data Lake, and AI Platform

by Benjamin Weissman Enrico van de Laar

Use this guide to one of SQL Server 2019’s most impactful features—Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will LearnInstall, manage, and troubleshoot Big Data Clusters in cloud or on-premise environmentsAnalyze large volumes of data directly from SQL Server and/or Apache SparkManage data stored in HDFS from SQL Server as if it were relational dataImplement advanced analytics solutions through machine learning and AIExpose different data sources as a single logical source using data virtualizationWho This Book Is ForData engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments

SQL Server Database Programming with Visual Basic.NET: Concepts, Designs and Implementations

by Ying Bai

A guide to the practical issues and applications in database programming with updated Visual Basic.NET SQL Server Database Programming with Visual Basic.NET offers a guide to the fundamental knowledge and practical techniques for the design and creation of professional database programs that can be used for real-world commercial and industrial applications. The author—a noted expert on the topic—uses the most current version of Visual Basic.NET, Visual Basic.NET 2017 with Visual Studio.NET 2017. In addition, he introduces the updated SQL Server database and Microsoft SQL Server 2017 Express. All sample program projects can be run in the most updated version, Visual Basic.NET 2019 with Visual Studio.NET 2019. Written in an accessible, down-to-earth style, the author explains how to build a sample database using the SQL Server management system and Microsoft SQL Server Management Studio 2018. The latest version of ASP.NET, ASP.NET 4.7, is also discussed to provide the most up-to-date Web database programming technologies. This important book: • Offers illustrative practical examples and detailed descriptions to aid in comprehension of the material presented • Includes both fundamental and advanced database programming techniques • Integrates images into associated database tables using a DevExpress UI tools - WindowsUI Written for graduate and senior undergraduate students studying database implementations and programming courses, SQL Server Database Programming with Visual Basic.NET shows how to develop professional and practical database programs in Visual Basic.NET 2017/Visual Basic.NET 2019.

The SQL Server DBA’s Guide to Docker Containers: Agile Deployment without Infrastructure Lock-in

by Edwin M Sarmiento

Get introduced to the world of Docker containers from a SQL Server DBA’s perspective. This book explains container technology and how it can improve the deployment of your SQL Server databases without infrastructure lock-in. You will be equipped with the right technical skills to guide stakeholders in your business as they adopt and adapt to new technologies to improve time-to-market and competitiveness. You will learn how to build a lab environment at home on which to build skills that transfer directly into your day job. This book teaches you how to install and configure Docker on both Windows Server and Linux operating systems. You will learn the most common Docker commands that you need to know as a DBA to deploy and manage SQL Server on containers. Support for SQL Server on Linux is new, and this book has your back with guidance on creating Docker images specifically for deployment to a Linux platform. Included is coverage of key Linux commands needed to manage SQL Server on that operating system. By the end of the book you will have learned how to create your own custom SQL Server container images with configuration settings that are specific to your organization, that are capable of being deployed to both Windows Server and Linux. What You Will LearnCreate Docker containers for agile deployment of SQL ServerRun multiple SQL Server instances on a single Linux machineDeploy custom images specific to your organization’s needsKnow the benefits and architecture of container technologyInstall and configure Docker on Windows Server and Linux Manage and persist SQL Server data in Docker containersWho This Book Is ForIntermediate to senior SQL Server DBAs who are familiar with SQL Server on Windows and want to build their existing skills to deploy and manage SQL Server on Linux and through Docker containers. Readers should have a grasp of relational database concepts and be comfortable with the Transact-SQL language.

SQL Server on Azure Virtual Machines: A hands-on guide to provisioning Microsoft SQL Server on Azure VMs

by Allan Hirt John Martin Louis Davidson Joey D'Antoni Anthony Nocentino Tim Radney Randolph West

Learn how to combine SQL Server's analytics with Azure's flexibility and hybrid connectivity to achieve industry-leading performance and manageability for your cloud database. Key Features Understand platform availability for SQL Server in Azure Explore the benefits and deployment choices offered by SQL IaaS Get to grips with deploying SQL Server on the Linux development ecosystem Book Description Deploying SQL Server on Azure virtual machines allows you to work on full versions of SQL Server in the cloud without having to maintain on-premises hardware. The book begins by introducing you to the SQL portfolio in Azure and takes you through SQL Server IaaS scenarios, before explaining the factors that you need to consider while choosing an OS for SQL Server in Azure VMs. As you progress through the book, you'll explore different VM options and deployment choices for IaaS and understand platform availability, migration tools, and best practices in Azure. In later chapters, you'll learn how to configure storage to achieve optimized performance. Finally, you'll get to grips with the concept of Azure Hybrid Benefit and find out how you can use it to maximize the value of your existing on-premises SQL Server. By the end of this book, you'll be proficient in administering SQL Server on Microsoft Azure and leveraging the tools required for its deployment. What you will learn Choose an operating system for SQL Server in Azure VMs Use the Azure Management Portal to facilitate the deployment process Verify connectivity and network latency in cloud Configure storage for optimal performance and connectivity Explore various disaster recovery options for SQL Server in Azure Optimize SQL Server on Linux Discover how to back up databases to a URL Who this book is for SQL Server on Azure VMs is for you if you are a developer, data enthusiast, or anyone who wants to migrate SQL Server databases to Azure virtual machines. Basic familiarity with SQL Server and managed identities for Azure resources will be a plus.

SRE with Java Microservices: Patterns For Reliable Microservices In The Enterprise

by Jonathan Schneider

In a microservices architecture, the whole is indeed greater than the sum of its parts. But in practice, individual microservices can inadvertently impact others and alter the end user experience. Effective microservices architectures require standardization on an organizational level with the help of a platform engineering team.This practical book provides a series of progressive steps that platform engineers can apply technically and organizationally to achieve highly resilient Java applications. Author Jonathan Schneider covers many effective SRE practices from companies leading the way in microservices adoption. You’ll examine several patterns discovered through much trial and error in recent years, complete with Java code examples.Chapters are organized according to specific patterns, including:Application metrics: Monitoring for availability with MicrometerDebugging with observability: Logging and distributed tracing; failure injection testingCharting and alerting: Building effective charts; KPIs for Java microservicesSafe multicloud delivery: Spinnaker, deployment strategies, and automated canary analysisSource code observability: Dependency management, API utilization, and end-to-end asset inventoryTraffic management: Concurrency of systems; platform, gateway, and client-side load balancing

The Stakeholder Perspective: Relationship Management to Increase Value and Success Rates of Complex Projects

by Massimo Pirozzi

The Stakeholder Perspective places people at the center of both projects and project management. It gives to the project management community a helpful, innovative, stakeholder-centered approach to increase projects’ delivered value and success rate. It presents a logical model also called the "Stakeholder Perspective," which acts as the reference point in a structured path to effectiveness. Starting from the analysis of a project’s stakeholders, the model integrates both rational and relational innovative approaches. Its continuous focus on stakeholder requirements and expectations helps to set a proper path, and to maintain it, in order to target success and to achieve goals in a variety of projects with different size and complexity. The book presents a set of innovative and immediately applicable techniques for effective stakeholder identification and classification, as well as analysis of stakeholder requirements and expectations, key stakeholders management, stakeholder network management, and, more generally, stakeholder relationship management. The proposed stakeholder classification model consists of just four communities, each one based on the commonality of main interests and behavior. This model features an accurate and stable identification process to increase effective communication and drastic reduce relationship complexity. A systemic approach is proposed to analyze both stakeholder requirements and expectations. The approach aids in detecting otherwise unclear stakeholder requirements and/or hidden stakeholder expectations. An interactive communication model is presented along with its individual and organizational frames of reference. Also presented are relevant cues to maximize effective and purposeful communication with key stakeholders as well as with the stakeholder network. The importance of satisfying not only the project requirements but also the stakeholder expectations is demonstrated to be the critical success factor in all projects. An innovative approach based on the perceived value and key performance indicators shows how to manage different levels of project complexity. The book also defines a complete structured path to relationship effectiveness called "Relationship Management Project," which can be tailored to enhance stakeholder and communication management processes in each one of the project management process groups (i.e. initiating, planning, executing, monitoring and controlling, and closing). The book concludes with a look ahead at Project Management X.0 and the stakeholder-centered evolution of both project and portfolio management.

Standards and Innovations in Information Technology and Communications

by Dina Šimunić Ivica Pavić

This book gives a thorough explanation of standardization, its processes, its life cycle, and its related organization on a national, regional and global level. The book provides readers with an insight in the interaction cycle between standardization organizations, government, industry, and consumers. The readers can gain a clear insight to standardization and innovation process, standards, and innovations life-cycle and the related organizations with all presented material in the field of information and communications technologies. The book introduces the reader to understand perpetual play of standards and innovation cycle, as the basis for the modern world.

Statistical Analysis of Network Data with R (Use R! #65)

by Eric D. Kolaczyk Gábor Csárdi

The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualization and characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.

Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges: 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers (Lecture Notes in Computer Science #12009)

by Shuo Li Oscar Camara Tommaso Mansi Mihaela Pop Maxime Sermesant Alistair Young Xiahai Zhuang Avan Suinesiaputra

This book constitutes the thoroughly refereed post-workshop proceedings of the 10th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 42 revised full workshop papers were carefully reviewed and selected from 76 submissions. The topics of the workshop included: cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods.

Statistical Learning Using Neural Networks: A Guide for Statisticians and Data Scientists with Python

by Basilio de Braganca Pereira Calyampudi Radhakrishna Rao Fabio Borges de Oliveira

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.

Statistical Machine Learning: A Unified Framework (Chapman & Hall/CRC Texts in Statistical Science)

by Richard Golden

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

Statistical Methods and Applications in Forestry and Environmental Sciences (Forum for Interdisciplinary Mathematics)

by Girish Chandra Raman Nautiyal Hukum Chandra

This book presents recent developments in statistical methodologies with particular relevance to applications in forestry and environmental sciences. It discusses important methodologies like ranked set sampling, adaptive cluster sampling, small area estimation, calibration approach-based estimators, design of experiments, multivariate techniques, Internet of Things, and ridge regression methods. It also covers the history of the implementation of statistical techniques in Indian forestry and the National Forest Inventory of India.The book is a valuable resource for applied statisticians, students, researchers, and practitioners in the forestry and environment sector. It includes real-world examples and case studies to help readers apply the techniques discussed. It also motivates academicians and researchers to use new technologies in the areas of forestry and environmental sciences with the help of software like R, MATLAB, Statistica, and Mathematica.

Statistical Modeling in Biomedical Research: Contemporary Topics and Voices in the Field (Emerging Topics in Statistics and Biostatistics)

by Ding-Geng Din Chen Yichuan Zhao

This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate further collaborations. This book will appeal to industry data scientists and statisticians, researchers, and graduate students in biostatistics and biomedical science. It covers topics in:Next generation sequence data analysisDeep learning, precision medicine, and their applicationsLarge scale data analysis and its applicationsBiomedical research and modelingSurvival analysis with complex data structure and its applications.

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications (Algorithms for Intelligent Systems)

by K. G. Srinivasa G. M. Siddesh S. R. Manisekhar

This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

Statistical Robust Beamforming for Broadcast Channels and Applications in Satellite Communication (Foundations in Signal Processing, Communications and Networking #22)

by Andreas Gründinger

This book investigates adaptive physical-layer beamforming and resource allocation that ensure reliable data transmission in the multi-antenna broadcast channel. The book provides an overview of robust optimization techniques and modelling approximations to deal with stochastic performance metrics. One key contribution of the book is a closed-form description of the achievable rates with unlimited transmit power for a rank-one channel error model. Additionally, the book provides a concise duality framework to transform mean square error (MSE) based beamformer designs, e.g., quality of service and balancing optimizations, into equivalent uplink filter designs. For the algorithmic solution, the book analyses the following paradigm: transmission to receivers with large MSE targets (low demands) is switched off if the transmit power is low. The book also studies chance constrained optimizations for limiting the outage probability. In this context, the book provides two novel conservative outage probability approximations, that result in convex beamformer optimizations. To compensate for the remaining inaccuracy, the book introduces a post-processing power allocation. Finally, the book applies the introduced beamformer designs for SatCom, where interference from neighboring spotbeams and channel fading are the main limitations.

The Statistics and Calculus with Python Workshop: A comprehensive introduction to mathematics in Python for artificial intelligence applications

by Peter Farrell Alvaro Fuentes Quan Nguyen Ajinkya Sudhir Kolhe Alexander Joseph Sarver Marios Tsatsos

With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easyKey FeaturesDiscover how most programmers use the main Python libraries when performing statistics with PythonUse descriptive statistics and visualizations to answer business and scientific questionsSolve complicated calculus problems, such as arc length and solids of revolution using derivatives and integralsBook DescriptionAre you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python.The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions.By the end of this book, you'll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.What you will learnGet to grips with the fundamental mathematical functions in PythonPerform calculations on tabular datasets using pandasUnderstand the differences between polynomials, rational functions, exponential functions, and trigonometric functionsUse algebra techniques for solving systems of equationsSolve real-world problems with probabilitySolve optimization problems with derivatives and integralsWho this book is forIf you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Python.

Statistics for Data Science and Policy Analysis

by Azizur Rahman

This book brings together the best contributions of the Applied Statistics and Policy Analysis Conference 2019. Written by leading international experts in the field of statistics, data science and policy evaluation. This book explores the theme of effective policy methods through the use of big data, accurate estimates and modern computing tools and statistical modelling.

STEM Education for the 21st Century (Springerbriefs In Education Ser.)

by Bryan Edward Penprase

This book chronicles the revolution in STEM teaching and learning that has arisen from a convergence of educational research, emerging technologies, and innovative ways of structuring both the physical space and classroom activities in STEM higher education. Beginning with a historical overview of US higher education and an overview of diversity in STEM in the US, the book sets a context in which our present-day innovation in science and technology urgently needs to provide more diversity and inclusion within STEM fields. Research-validated pedagogies using active learning and new types of research-based curriculum is transforming how physics, biology and other fields are taught in leading universities, and the book gives profiles of leading innovators in science education and examples of exciting new research-based courses taking root in US institutions. The book includes interviews with leading scientists and educators, case studies of new courses and new institutions, and descriptions of site visits where new trends in 21st STEM education are being developed. The book also takes the reader into innovative learning environments in engineering where students are empowered by emerging technologies to develop new creative capacity in their STEM education, through new centers for design thinking and liberal arts-based engineering. Equally innovative are new conceptual frameworks for course design and learning, and the book explores the concepts of Scientific Teaching, Backward Course Design, Threshold Concepts and Learning Taxonomies in a systematic way with examples from diverse scientific fields. Finally, the book takes the reader inside the leading centers for online education, including Udacity, Coursera and EdX, interviews the leaders and founders of MOOC technology, and gives a sense of how online education is evolving and what this means for STEM education. This book provides a broad and deep exploration into the historical context of science education and into some of the cutting-edge innovations that are reshaping how leading universities teach science and engineering. The emergence of exponentially advancing technologies such as synthetic biology, artificial intelligence and materials sciences has been described as the Fourth Industrial Revolution, and the book explores how these technologies will shape our future will bring a transformation of STEM curriculum that can help students solve many the most urgent problems facing our world and society.

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