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MATLAB® With Applications in Mechanics and Tribology
Among the wide range of programming tools available, the technical analysis and calculations are realized by MATLAB®, which is recognized as a convenient and effective tool for modern science and technology. Thus, mastering its latest versions and practical solutions is increasingly essential for the creation of new products in mechanics, electronics, chemistry, life sciences, and modern industry. Modern mechanical and tribology sciences specialists widely use computers and some special programs, but need a universal tool for solving, simulating, and modeling specific problems from their area. There is plenty of information available on MATLAB® for the general engineer, but there is a gap in the field for research that applies MATLAB® to two wide, interdisciplinary, and topical areas: tribology and mechanics. MATLAB® With Applications in Mechanics and Tribology explores how MATLAB® is used as a tool for subsequent computer solutions, applying it to both traditional and modern problems of mechanics and materials sciences. The problem solving in this book includes calculations of the mechanical parts, machine elements, production process, quality assurance, fluid mechanics parameters, thermodynamic and rheological properties of the materials as well as the state equations, descriptive statistics, and more. This book is ideal for scientists, students and professors of engineering courses, self-instructing readers, programmers, computer scientists, practitioners, and researchers looking for concise and clear information on learning and applying MATLAB® software to mechanics, tribology, and material physics.
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Design of Intelligent Applications using Machine Learning and Deep Learning Techniques
Machine learning (ML) and deep learning (DL) algorithms are invaluable resources for Industry 4.0 and allied areas and are considered as the future of computing. A subfield called neural networks, to recognize and understand patterns in data, helps a machine carry out tasks in a manner similar to humans. The intelligent models developed using ML and DL are effectively designed and are fully investigated – bringing in practical applications in many fields such as health care, agriculture and security. These algorithms can only be successfully applied in the context of data computing and analysis. Today, ML and DL have created conditions for potential developments in detection and prediction. Apart from these domains, ML and DL are found useful in analysing the social behaviour of humans. With the advancements in the amount and type of data available for use, it became necessary to build a means to process the data and that is where deep neural networks prove their importance. These networks are capable of handling a large amount of data in such fields as finance and images. This book also exploits key applications in Industry 4.0 including: · Fundamental models, issues and challenges in ML and DL.· Comprehensive analyses and probabilistic approaches for ML and DL.· Various applications in healthcare predictions such as mental health, cancer, thyroid disease, lifestyle disease and cardiac arrhythmia.· Industry 4.0 applications such as facial recognition, feather classification, water stress prediction, deforestation control, tourism and social networking. · Security aspects of Industry 4.0 applications suggest remedial actions against possible attacks and prediction of associated risks. - Information is presented in an accessible way for students, researchers and scientists, business innovators and entrepreneurs, sustainable assessment and management professionals.This book equips readers with a knowledge of data analytics, ML and DL techniques for applications defined under the umbrella of Industry 4.0. This book offers comprehensive coverage, promising ideas and outstanding research contributions, supporting further development of ML and DL approaches by applying intelligence in various applications.
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Hands-On Q-Learning with Python
Leverage the power of reward-based training for your deep learning models with PythonKey FeaturesUnderstand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)Study practical deep reinforcement learning using Q-NetworksExplore state-based unsupervised learning for machine learning modelsBook DescriptionQ-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers.This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you get familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into modelfree Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in real-world applications such as scientific research. Toward the end, you’ll gain a sense of what’s in store for reinforcement learning.By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.What you will learnExplore the fundamentals of reinforcement learning and the state-action-reward processUnderstand Markov decision processesGet well versed with libraries such as Keras, and TensorFlowCreate and deploy model-free learning and deep Q-learning agents with TensorFlow, Keras, and OpenAI GymChoose and optimize a Q-Network’s learning parameters and fine-tune its performanceDiscover real-world applications and use cases of Q-learningWho this book is forIf you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.
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Website Scraping with Python
Closely examine website scraping and data processing: the technique of extracting data from websites in a format suitable for further analysis. You'll review which tools to use, and compare their features and efficiency. Focusing on BeautifulSoup4 and Scrapy, this concise, focused book highlights common problems and suggests solutions that readers can implement on their own.Website Scraping with Python starts by introducing and installing the scraping tools and explaining the features of the full application that readers will build throughout the book. You'll see how to use BeautifulSoup4 and Scrapy individually or together to achieve the desired results. Because many sites use JavaScript, you'll also employ Selenium with a browser emulator to render these sites and make them ready for scraping.By the end of this book, you'll have a complete scraping application to use and rewrite to suit your needs. As a bonus, the author shows you options of how to deploy your spiders into the Cloud to leverage your computer from long-running scraping tasks. What You'll LearnInstall and implement scraping tools individually and togetherRun spiders to crawl websites for data from the cloudWork with emulators and drivers to extract data from scripted sitesWho This Book Is ForReaders with some previous Python and software development experience, and an interest in website scraping.
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Thermal Management for LED Applications
Thermal Management for LED Applications provides state-of-the-art information on recent developments in thermal management as it relates to LEDs and LED-based systems and their applications. Coverage begins with an overview of the basics of thermal management including thermal design for LEDs, thermal characterization and testing of LEDs, and issues related to failure mechanisms and reliability and performance in harsh environments. Advances and recent developments in thermal management round out the book with discussions on advances in TIMs (thermal interface materials) for LED applications, advances in forced convection cooling of LEDs, and advances in heat sinks for LED assemblies.
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Deep Learners and Deep Learner Descriptors for Medical Applications
This book introduces readers to the current trends in using deep learners and deep learner descriptors for medical applications. It reviews the recent literature and presents a variety of medical image and sound applications to illustrate the five major ways deep learners can be utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2) by implementing transfer learning from a pre-trained deep learner and extracting deep features for different CNN layers that can be fed into simpler classifiers, such as the support vector machine; 3) by fine-tuning one or more pre-trained deep learners on an unrelated dataset so that they are able to identify novel medical datasets; 4) by fusing different deep learner architectures; and 5) by combining the above methods to generate a variety of more elaborate ensembles. This book is a value resource for anyone involved in engineering deep learners for medical applications as well as to those interested in learning more about the current techniques in this exciting field. A number of chapters provide source code that can be used to investigate topics further or to kick-start new projects.
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Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques
The two-volume set LNCS 9952 and LNCS 9953 constitutes the refereed proceedings of the 7th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2016, held in Imperial, Corfu, Greece, in October 2016. The papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. Featuring a track introduction to each section, the papers are organized in topical sections named: statistical model checking; evaluation and reproducibility of program analysis and verification; ModSyn-PP: modular synthesis of programs and processes; semantic heterogeneity in the formal development of complex systems; static and runtime verification: competitors or friends?; rigorous engineering of collective adaptive systems; correctness-by-construction and post-hoc verification: friends or foes?; privacy and security issues in information systems; towards a unified view of modeling and programming; formal methods and safety certification: challenges in the railways domain; RVE: runtime verification and enforcement, the (industrial) application perspective; variability modeling for scalable software evolution; detecting and understanding software doping; learning systems: machine-learning in software products and learning-based analysis of software systems; testing the internet of things; doctoral symposium; industrial track; RERS challenge; and STRESS.
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Data Mining With Decision Trees: Theory And Applications (2nd Edition)
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer:
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Theory, Practice, and Applications of Rules on the Web
This book constitutes the refereed proceedings of the 7th International RuleML Symposium, RuleML 2013, held in Seattle, WA, USA, in July 2013 - collocated with the 27th AAAI 2013. The 22 full papers,12 technical papers in main track, 3 technical papers in human language technology track, and 4 tutorials presented together with 3 invited talks were carefully reviewed and selected from numerous submissions. The accepted papers address topics such as rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rules engines/business rules management systems; Semantic Web rule languages and rule standards; rule-based event processing languages (EPLs) and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules.
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Human Mental Workload: Models and Applications
This book constitutes the refereed proceedings of the First International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2017, held in Dublin, Ireland, in June 2017.The 15 revised full papers presented together with two keynotes were carefully reviewed and selected from 35 submissions. The papers are organized in two topical sections on models and applications.
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Linear Algebra and Its Applications, Global Edition
Learn key concepts of linear algebra to equip yourself in your studies and future career. Linear Algebra and Its Applications 6th edition by Steven R. Lay, Judi J. McDonald and David C. Lay is an excellent introductory guide to the principles and foundations of practical linear algebra. With its learner-friendly approach, the textbook starts with easier material, building confidence by introducing typically challenging concepts early on and gradually developing them. The book revisits those concepts throughout, ensuring you do not become overwhelmed when abstract concepts are introduced, as you progress with your learning. The latest edition provides new and revised content, with a range of features, including: A broad range of introductory vignettes, application examples, and online resourcesNew material and topics to consolidate and enhance your understanding of the subjectNew, modernised applications to prepare your learning of the most innovative topics, such as machine learning, Artificial Intelligence, and digital signal processing With an array of exercises and questions to support your learning, this textbook provides the tools you need to build on your understanding of linear algebra and succeed in your studies. Also available with MyLab® Math MyLab is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab Math personalises the learning experience and improves results for each student. If you would like to purchase both the physical text and MyLab® Math, search for: 9781292351353 Linear Algebra and Its Applications, Global Edition, 6th edition plus MyLab Math with Pearson eText. Package consists of: 9781292351216 Corporate Finance, Global Edition, 5th Edition9781292351285 Corporate Finance, Global Edition, 5th Edition MyLab® Math with Pearson eText MyLab® Math is not included. Students, if MyLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. MyLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content, which is especially relevant to students outside the United States.
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Time Series Analysis with Python Cookbook
Perform time series analysis and forecasting confidently with this Python code bank and reference manualKey FeaturesExplore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithmsLearn different techniques for evaluating, diagnosing, and optimizing your modelsWork with a variety of complex data with trends, multiple seasonal patterns, and irregularitiesBook DescriptionTime series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you'll work with ML and DL models using TensorFlow and PyTorch. Finally, you'll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.What you will learnUnderstand what makes time series data different from other dataApply various imputation and interpolation strategies for missing dataImplement different models for univariate and multivariate time seriesUse different deep learning libraries such as TensorFlow, Keras, and PyTorchPlot interactive time series visualizations using hvPlotExplore state-space models and the unobserved components model (UCM)Detect anomalies using statistical and machine learning methodsForecast complex time series with multiple seasonal patternsWho this book is forThis book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.
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Human Mental Workload: Models and Applications
This book constitutes the refereed proceedings of the 4th International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2020, held in Granda, Spain*, in December 2020.The volume presents one keynote paper as well as 13 revised full papers, which were carefully reviewed and selected from 22 submissions. The papers are organized in two topical sections on models and applications.*The conference was held virtually due to the COVID-19 pandemic.
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Algorithmic Decision Making with Python Resources
This book describes Python3 programming resources for implementing decision aiding algorithms in the context of a bipolar-valued outranking approach. These computing resources, made available under the name Digraph3, are useful in the field of Algorithmic Decision Theory and more specifically in outranking-based Multiple-Criteria Decision Aiding (MCDA).The first part of the book presents a set of tutorials introducing the Digraph3 collection of Python3 modules and its main objects, such as bipolar-valued digraphs and outranking digraphs. In eight methodological chapters, the second part illustrates multiple-criteria evaluation models and decision algorithms. These chapters are largely problem-oriented and demonstrate how to edit a new multiple-criteria performance tableau, how to build a best choice recommendation, how to compute the winner of an election and how to make rankings or ratings using incommensurable criteria. The book’s third part presents three real-world decision case studies, while the fourth part addresses more advanced topics, such as computing ordinal correlations between bipolar-valued outranking digraphs, computing kernels in bipolar-valued digraphs, testing for confidence or stability of outranking statements when facing uncertain or solely ordinal criteria significance weights, and tempering plurality tyranny effects in social choice problems. The fifth and last part is more specifically focused on working with undirected graphs, tree graphs and forests. The closing chapter explores comparability, split, interval and permutation graphs.The book is primarily intended for graduate students in management sciences, computational statistics and operations research. The chapters presenting algorithms for ranking multicriteria performance records will be of computational interest for designers of web recommender systems. Similarly, the relative and absolute quantile-rating algorithms, discussed and illustrated in several chapters, will be of practical interest to public and private performance auditors.
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Innovative LibGuides Applications
Innovative LibGuides Applications: Real World Examples shows both new and experienced users methods to utilize the platform in ways that they may not have previously considered. This includes topics such as website and learning management system integration, digital collections, data-drive decision-making, information literacy instruction, library administration, and a system-wide case study. Each chapter features ideas that you can implement immediately, or over time, as is appropriate to your own needs. Further, they are adaptable—tweak as you see fit! Divided into sections, this book examines different uses and approaches to LibGuides:· Website and learning management system (LMS) integration: examine how LibGuides can be used as, or closely integrated with, the library website and/or your institutions’ LMS. Various examples are provided that you may be able to adapt at your own organization. Not every solution worked as anticipated—our authors address this, as well.· Digital collections: Two alternate approaches are presented. One looks at exhibitions and their application as outreach tools. The other looks at creating a digital museum within LibGuides. These are “can’t miss” chapters for those that wish to more deeply explore digital collections and LibGuides viability.· Data-drive decision-making: Learn how some institutions are mining and utilizing data collected within LibApps/LibGuides. In both cases, the data examined helped institutions to improve library services while articulating a clear purpose (and use) for each data point. · Information literacy: Explore two different methods for revamping your instruction program through utilization of the platform. · Library administration: Discover how internal and external processes may be augmented through the use of LibApps/LibGuides.· System-wide case study: See how a community college system in North Carolina has each implemented (or not) LibApps/LibGuides on their respective campuses. This section shows how different institutions may decide to use the platform in different ways—all while attempting to improve their own services.Upon completion of this authoritative LITA Guide, readers will come away with the knowledge and tools to maximize their LibGuides experience
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Rewriting Logic and Its Applications
This book constitutes the thoroughly refereed post-workshop proceedings of the 11th International Workshop on Rewriting Logic and its Applications, WRLA 2016, held as a satellite event of ETAPS 2016, in Eindhoven, The Netherlands, in April 2016.The 8 revised full papers presented together with 2 invited papers and 3 abstracts were carefully reviewed and selected from 14 submissions. The papers cover several topics such as: foundations; rewriting as a logical and semantic framework; rewriting languages; verification techniques; and applications.
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Intelligent Computing, Networked Control, and Their Engineering Applications
The three-volume set CCIS 761, CCIS 762, and CCIS 763 constitutes the thoroughly refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2017, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, held in Nanjing, China, in September 2017. The 208 revised full papers presented were carefully reviewed and selected from over 625 submissions.The papers of this volume are organized in topical sections on: Biomedical Signal Processing; Computational Methods in Organism Modeling; Medical Apparatus and Clinical Applications; Bionics Control Methods, Algorithms and Apparatus; Modeling and Simulation of Life Systems; Data Driven Analysis; Image and Video Processing; Advanced Fuzzy and Neural Network Theory and Algorithms; Advanced Evolutionary Methods and Applications; Advanced Machine Learning Methods and Applications; Intelligent Modeling, Monitoring, and Control of Complex Nonlinear Systems; Advanced Methods for Networked Systems; Control and Analysis of Transportation Systems; Advanced Sliding Mode Control and Applications; Advanced Analysis of New Materials and Devices; Computational Intelligence in Utilization of Clean and Renewable Energy Resources; Intelligent Methods for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Computing and Control in Power Systems; Modeling, Simulation and Control in Smart Grid and Microgrid; Optimization Methods; Computational Methods for Sustainable Environment.
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Building Modern Business Applications
Discover a new way of thinking about business applications in light of the massive industry shift toward cloud computing and reactive programming technologies. This book synthesizes technologies and techniques such as event sourcing, command query responsibility segregation (CQRS), property-based testing, and GraphQL into a cohesive guide for modern business applications that benefit every developer.The book begins with a look at the fundamentals of modern business applications. These fundamentals include business rules and the managing of data over time. The benefits of reactive techniques are explained, including how they are fundamentally aligned with what application developers strive to achieve in their work.Author Peter Royal equips you with sound guidance to follow as you evolve your existing systems, as well as examples of how to build those systems using modern techniques in Spring, Java, and PostgreSQL.What You Will LearnArchitect business applications for cloud-based environmentsDesign sustainable business applicationsIntegrate GraphQL best practices into business applicationsUse property-based testing to exhaustively test possible system statesThink about business applications in terms of message flowsRelate the benefits of reactive systems to business goalsModel time appropriately for business requirementsWho This Book Is ForPracticing software developers who are building business applications, developers who are being asked to deploy into cloud environments that are more volatile than statically provisioned data centers, developers who want to increase the reliability of their systems and are struggling to find the right paradigms and architectures to achieve their goals, developers who see and use capabilities in software in other areas of their lives and want to bring those capabilities into their own work, and developers with experience designing other types of software who want to learn how to design business applications
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Oracle and Open Source - Tools and Applications
Oracle & Open Source is the first book to tie together the commercial world of Oracle and the free-wheeling world of open source software. As this book reveals, these two worlds are not as far apart as they may seem. Today, there are many excellent and freely available software tools that Oracle developers and database administrators can use, at no cost, to improve their own coding productivity and their system's performance. Moreover, many of the finest Oracle developers are now making their source code freely available so their peers can build upon this code base. Oracle Corporation is even porting its RDBMS to Linux and starting to incorporate a growing number of open source tools in the company's own software.Oracle & Open Source describes close to 100 open source tools you can use for Oracle development and database administration, from large and widely known open source systems (like Linux, Perl, Apache, TCL/Tk and Python) to more Oracle-specific tools (like Orasoft, Orac, OracleTool, and OraSnap). You'll learn how to obtain the software and how to adapt it to best advantage. The book abounds with code examples, download and installation instructions, and helpful usage hints.Not only does it tell you how to find and use existing open source code; Oracle & Open Source gives you the details and the motivation to build your own open source contributions and release them to the Oracle community. You'll learn all about tools like the Oracle Call Interface (OCI) and Perl-DBI (Database Interface), which provide the glue allowing new open source tools to link into commercial Oracle software. With Oracle & Open Source as a guide, you'll discover an enormous number of highly effective open source tools, while getting involved with the thriving community of open source development.
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Object-Oriented Analysis and Design with Applications
Object-Oriented Design with Applications has long been the essential reference to object-oriented technology, which, in turn, has evolved to join the mainstream of industrial-strength software development. In this third edition--the first revision in 13 years--readers can learn to apply object-oriented methods using new paradigms such as Java, the Unified Modeling Language (UML) 2.0, and .NET. The authors draw upon their rich and varied experience to offer improved methods for object development and numerous examples that tackle the complex problems faced by software engineers, including systems architecture, data acquisition, cryptoanalysis, control systems, and Web development. They illustrate essential concepts, explain the method, and show successful applications in a variety of fields. You'll also find pragmatic advice on a host of issues, including classification, implementation strategies, and cost-effective project management. New to this new edition are An introduction to the new UML 2.0, from the notation's most fundamental and advanced elements with an emphasis on key changesNew domains and contextsA greatly enhanced focus on modeling--as eagerly requested by readers--with five chapters that each delve into one phase of the overall development lifecycle.Fresh approaches to reasoning about complex systemsAn examination of the conceptual foundation of the widely misunderstood fundamental elements of the object model, such as abstraction, encapsulation, modularity, and hierarchyHow to allocate the resources of a team of developers and mange the risks associated with developing complex software systemsAn appendix on object-oriented programming languages This is the seminal text for anyone who wishes to use object-oriented technology to manage the complexity inherent in many kinds of systems. Sidebars Preface Acknowledgments About the Authors Section I: Concepts Chapter 1: Complexity Chapter 2: The Object Model Chapter 3: Classes and Objects Chapter 4: Classification Section II: Method Chapter 5: Notation Chapter 6: Process Chapter 7: Pragmatics Chapter 8: System Architecture: Satellite-Based Navigation Chapter 9: Control System: Traffic Management Chapter 10: Artificial Intelligence: Cryptanalysis Chapter 11: Data Acquisition: Weather Monitoring Station Chapter 12: Web Application: Vacation Tracking System Appendix A: Object-Oriented Programming Languages Appendix B: Further Reading Notes Glossary Classified Bibliography Index
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Database Systems for Advanced Applications
This two volume set LNCS 6587 and LNCS 6588 constitutes the refereed proceedings of the 16th International Conference on Database Systems for Advanced Applications, DASFAA 2011, held in Saarbrücken, Germany, in April 2010. The 53 revised full papers and 12 revised short papers presented together with 2 invited keynote papers, 22 demonstration papers, 4 industrial papers, 8 demo papers, and the abstract of 1 panel discussion, were carefully reviewed and selected from a total of 225 submissions. The topics covered are social network, social network and privacy, data mining, probability and uncertainty, stream processing, graph, XML, XML and graph, similarity, searching and digital preservation, spatial queries, query processing, as well as indexing and high performance.
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Database Systems for Advanced Applications
This two volume set LNCS 9642 and LNCS 9643 constitutesthe refereed proceedings of the 21st International Conference on DatabaseSystems for Advanced Applications, DASFAA 2016, held in Dallas, TX, USA, inApril 2016.The 61 full papers presented were carefully reviewed andselected from a total of 183 submissions. The papers cover the followingtopics: crowdsourcing, data quality, entity identification, data mining andmachine learning, recommendation, semantics computing and knowledge base,textual data, social networks, complex queries, similarity computing, graphdatabases, and miscellaneous, advanced applications.
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Data Mining for Biomedical Applications
This book constitutes the refereed proceedings of the International Workshop on Data Mining for Biomedical Applications, BioDM 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The 14 revised full papers presented together with one keynote talk were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections
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Hands-On Serverless Applications with Go
Learn to build, secure, deploy, and manage your serverless application in Golang with AWS Lambda Key FeaturesImplement AWS lambda to build scalable and cost-efficient applications in GoDesign and set the data flow between cloud services and custom business logicLearn to design Lambda functions using real-world examples and implementation scenariosBook DescriptionServerless architecture is popular in the tech community due to AWS Lambda. Go is simple to learn, straightforward to work with, and easy to read for other developers; and now it's been heralded as a supported language for AWS Lambda. This book is your optimal guide to designing a Go serverless application and deploying it to Lambda.This book starts with a quick introduction to the world of serverless architecture and its benefits, and then delves into AWS Lambda using practical examples. You'll then learn how to design and build a production-ready application in Go using AWS serverless services with zero upfront infrastructure investment. The book will help you learn how to scale up serverless applications and handle distributed serverless systems in production. You will also learn how to log and test your application.Along the way, you'll also discover how to set up a CI/CD pipeline to automate the deployment process of your Lambda functions. Moreover, you'll learn how to troubleshoot and monitor your apps in near real-time with services such as AWS CloudWatch and X-ray. This book will also teach you how to secure the access with AWS Cognito.By the end of this book, you will have mastered designing, building, and deploying a Go serverless application.What you will learnUnderstand how AWS Lambda works and use it to create an applicationUnderstand how to scaleup serverless applicationsDesign a cost-effective serverless application in AWSBuild a highly scalable and fault-tolerant CI/CD pipelineUnderstand how to troubleshoot and monitor serverless apps in AWSDiscover the working of APIs and single page applicationsBuild a production-ready serverless application in GoWho this book is forThis book is for Go developers who would like to learn about serverless architecture. Go programming knowledge is assumed. DevOps and Solution Architects who are interested in building serverless applications in Go can also choose this book.
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Learning Software Organizations: Methodology and Applications
This book constitutes the thoroughly refereed and revised post-conference documentation of the 11th International Conference on Software Engineering and Knowledge Engineering, SEKE'99, held in Kaiserslautern, Germany in June 1999. The book provides a unique overview of current activities, approaches, and trends in learning software organizations. The first part gives an overview on the topic, covering foundations in the software engineering domain, enabling techniques for organizational learning, and learning support techniques. The second and the third part of the book on methodology and applications present thoroughly revised full papers of the most interesting papers on learning software organizations presented during SEKE'99 and its satellite workshop LSO'99.
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Creating Cross-Platform C# Applications with Uno Platform
Discover how to leverage the Uno Platform to write single-codebase, cross-platform mobile, desktop, and web applications using C# and XAMLKey FeaturesEnhance your Windows apps by running them on all operating systems and browsersUse tools and APIs you already know to remain productive as you target new platformsCreate realistic apps for various lines of business (LOBs) and consumer scenariosBook DescriptionDevelopers are increasingly being asked to build native applications that run on multiple operating systems and in the browser. In the past, this would have meant learning new technologies and making multiple copies of an application. But the Uno Platform allows you to use tools, languages, and APIs you already know from building Windows apps to develop apps that can also run on other platforms. This book will help you to create customer-facing as well as line-of-business apps that can be used on the device, browser, or operating system of your choice.This practical guide enables developers to put their C# and XAML knowledge to work by writing cross-platform apps using the Uno Platform. Packed with tips and practical examples, this book will help you to build applications for common scenarios. You'll begin by learning about the Uno Platform through step-by-step explanations of essential concepts, before moving on to creating cross-platform apps for different lines of business. Throughout this book, you'll work with examples that will teach you how to combine your existing knowledge to manage common development environments and implement frequently needed functionality.By the end of this Uno development book, you will have learned how to write your own cross-platform apps with the Uno Platform and use additional tools and libraries to speed up your app development process.What you will learnUnderstand how and why Uno could be the right fit for your needsSet up your development environment for cross-platform app development with the Uno Platform and create your first Uno Platform appFind out how to create apps for different business scenariosDiscover how to combine technologies and controls to accelerate developmentGo beyond the basics and create 'world-ready' applicationsGain the confidence and experience to use Uno in your own projectsWho this book is forThis book is for developers who are familiar with app development for Windows and want to use their existing skills to build cross-platform apps. Basic knowledge of C# and XAML is required to get started with this book. Anyone with basic experience in app development using WPF, UWP, or WinUI will be able to learn how to create cross-platform applications with the Uno Platform.
R 1,172.00
Practical Data Science with Python 3
Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.What You'll LearnPlay the role of a data scientist when completing increasingly challenging exercises using Python 3Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big DataApply theory of probability, statistical inference, and algebra to understand the data science practicesWho This Book Is ForAnyone who would like to embark into the realm of data science using Python 3.
R 1,450.76
Artificial Intelligence with Python Cookbook
Work through practical recipes to learn how to solve complex machine learning and deep learning problems using PythonKey FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook DescriptionArtificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research.Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems.By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.What you will learnImplement data preprocessing steps and optimize model hyperparametersDelve into representational learning with adversarial autoencodersUse active learning, recommenders, knowledge embedding, and SAT solversGet to grips with probabilistic modeling with TensorFlow probabilityRun object detection, text-to-speech conversion, and text and music generationApply swarm algorithms, multi-agent systems, and graph networksGo from proof of concept to production by deploying models as microservicesUnderstand how to use modern AI in practiceWho this book is forThis AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.
R 1,498.00
Applications of the Input-Output Framework
This book provides a fresh perspective on the ever-growing relevance of input-output analysis in problem solving. It is based on the “19th National Conference of the Input-Output Research Association of India (IORA)”, held in 2017 in Mumbai, India. The conference promoted the exchange of ideas on input-output analysis and related methods among economists, government officials, policymakers, academicians and industrialists. The book captures the unique ideas of prominent scholars, extends the basic “input-output framework,” analytical tool, outlines the possible impacts of some major policy decisions adopted by the Government of India, and puts forward concrete policy suggestions. In addition, it highlights the versatility of the Leontief model, which is currently being extended to cover a diverse spectrum of policy issues, ranging from agricultural productivity to science and technology and from carbon hotspots to energy and environmental consequences. A perfect blend of theory and application, the book provides a realistic outlook on sensitive economies and interdependencies between sectors.
R 4,030.00
Leveraging Applications of Formal Methods, Verification and Validation. Verification Principles
This four-volume set LNCS 13701-13704 constitutes contributions of the associated events held at the 11th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2022, which took place in Rhodes, Greece, in October/November 2022. The contributions in the four-volume set are organized according to the following topical sections: specify this - bridging gaps between program specification paradigms; x-by-construction meets runtime verification; verification and validation of concurrent and distributed heterogeneous systems; programming - what is next: the role of documentation; automated software re-engineering; DIME day; rigorous engineering of collective adaptive systems; formal methods meet machine learning; digital twin engineering; digital thread in smart manufacturing; formal methods for distributed computing in future railway systems; industrial day.
R 3,941.86
Database and Expert Systems Applications
This two volume set of LNCS 11029 and LNCS 11030 constitutes the refereed proceedings of the 29th International Conference on Database and Expert Systems Applications, DEXA 2018, held in Regensburg, Germany, in September 2018.The 35 revised full papers presented together with 40 short papers were carefully reviewed and selected from 160 submissions. The papers of the first volume discuss a range of topics including: Big data analytics; data integrity and privacy; decision support systems; data semantics; cloud data processing; time series data; social networks; temporal and spatial databases; and graph data and road networks. The papers of the second volume discuss a range of the following topics: Information retrieval; uncertain information; data warehouses and recommender systems; data streams; information networks and algorithms; database system architecture and performance; novel database solutions; graph querying and databases; learning; emerging applications; data mining; privacy; and text processing.
R 2,213.85
Stochastic Simulation and Applications in Finance with MATLAB Programs
Stochastic Simulation and Applications in Finance with MATLAB Programs explains the fundamentals of Monte Carlo simulation techniques, their use in the numerical resolution of stochastic differential equations and their current applications in finance. Building on an integrated approach, it provides a pedagogical treatment of the need-to-know materials in risk management and financial engineering. The book takes readers through the basic concepts, covering the most recent research and problems in the area, including: the quadratic re-sampling technique, the Least Squared Method, the dynamic programming and Stratified State Aggregation technique to price American options, the extreme value simulation technique to price exotic options and the retrieval of volatility method to estimate Greeks. The authors also present modern term structure of interest rate models and pricing swaptions with the BGM market model, and give a full explanation of corporate securities valuation and credit risk based on the structural approach of Merton. Case studies on financial guarantees illustrate how to implement the simulation techniques in pricing and hedging. NOTE TO READER: The CD has been converted to URL. Go to the following website www.wiley.com/go/huyhnstochastic which provides MATLAB programs for the practical examples and case studies, which will give the reader confidence in using and adapting specific ways to solve problems involving stochastic processes in finance.
R 4,396.00
Formal Methods: Foundations and Applications
This book constitutes the refereed proceedings of the 25th Brazilian Symposium on Formal Methods, SBMF 2022, which was held virtually in December 2022. The 8 regular papers presented in this book were carefully reviewed and selected from 15 submissions. The symposium focuses on the development, dissemination, and use of formal methods for the constructionof high-quality computational systems, aiming to promote opportunities for researchers and practitioners with an interest in formal methods to discuss the recent advances in this area.
R 2,342.51
Large-Scale Data Analytics with Python and Spark
Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.
R 1,735.99
Computational Probability Applications
This focuses on the developing field of building probability models with the power of symbolic algebra systems. The book combines the uses of symbolic algebra with probabilistic/stochastic application and highlights the applications in a variety of contexts. The research explored in each chapter is unified by the use of A Probability Programming Language (APPL) to achieve the modeling objectives. APPL, as a research tool, enables a probabilist or statistician the ability to explore new ideas, methods, and models. Furthermore, as an open-source language, it sets the foundation for future algorithms to augment the original code. Computational Probability Applications is comprised of fifteen chapters, each presenting a specific application of computational probability using the APPL modeling and computer language. The chapter topics include using inverse gamma as a survival distribution, linear approximations of probability density functions, and also moment-ratio diagrams for univariate distributions. These works highlight interesting examples, often done by undergraduate students and graduate students that can serve as templates for future work. In addition, this book should appeal to researchers and practitioners in a range of fields including probability, statistics, engineering, finance, neuroscience, and economics.
R 4,834.54
Practical Solutions for Diverse Real-World NLP Applications
This book unveils the most advanced techniques and innovative applications in the natural language processing (NLP) field. It uncovers the secrets to enhancing language understanding, and presents practical solutions to different NLP tasks, as text augmentation, paraphrase generation, and restoring spaces and punctuation in multiple languages. It unlocks the potential of hierarchical multi-task learning for cross-lingual phoneme recognition, and allows readers to explore more real-world applications such as error correction, aggregating industrial security findings as well as predicting music emotion values from social media conversations. "Practical Solutions for Diverse Real-World NLP Applications" is the suitable guidebook for researchers, students, and practitioners as it paves the way for them by delivering invaluable insights and knowledge.
R 6,478.19
Rewriting Logic and Its Applications
This book constitutes the thoroughly refereed post-workshop proceedings of the 10th International Workshop on Rewriting Logic and its Applications, WRLA 2014, held as a satellite event of ETAPS 2014, in Grenoble, France, in March 2014.The 13 revised full papers presented together with 3 invited papers were carefully reviewed and selected from 21 submissions. The papers address a great diversity of topics in the fields of foundations and models of RL; languages based on RL; RL as a logical framework; RL as a semantic framework; use of RL to provide rigorous support for model-based software engineering; formalisms related to RL; verification techniques for RL specifications; comparisons of RL with existing formalisms having analogous aims; application of RL to specification and analysis of distributed systems and physical systems.
R 2,175.08
