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Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
by Ewan Klein Edward Loper Steven BirdThis book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you:Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
Natural Language Processing with Python and spaCy: A Practical Introduction
by Yuli VasilievAn introduction to natural language processing with Python using spaCy, a leading Python natural language processing library.Natural Language Processing with Python and spaCy will show you how to create NLP applications like chatbots, text-condensing scripts, and order-processing tools quickly and easily. You'll learn how to leverage the spaCy library to extract meaning from text intelligently; how to determine the relationships between words in a sentence (syntactic dependency parsing); identify nouns, verbs, and other parts of speech (part-of-speech tagging); and sort proper nouns into categories like people, organizations, and locations (named entity recognizing). You'll even learn how to transform statements into questions to keep a conversation going.You'll also learn how to: • Work with word vectors to mathematically find words with similar meanings (Chapter 5) • Identify patterns within data using spaCy's built-in displaCy visualizer (Chapter 7) • Automatically extract keywords from user input and store them in a relational database (Chapter 9) • Deploy a chatbot app to interact with users over the internet (Chapter 11)"Try This" sections in each chapter encourage you to practice what you've learned by expanding the book's example scripts to handle a wider range of inputs, add error handling, and build professional-quality applications.By the end of the book, you'll be creating your own NLP applications with Python and spaCy.
Natural Language Processing with Python Cookbook
by Krishna Bhavsar<P><P>Learn the tricks and tips that will help you design Text Analytics solutions <P><P>Key Features <P><P>Independent recipes that will teach you how to efficiently perform Natural Language Processing in Python <P><P>Use dictionaries to create your own named entities using this easy-to-follow guide <P><P>Learn how to implement NLTK for various scenarios with the help of example-rich recipes to take you beyond basic Natural Language Processing <P><P>Book Description <P><P>Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages; in particular, it's about programming computers to fruitfully process large natural language corpora. <P><P>This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques. <P><P>By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python. <P><P>What You Will Learn <P><P>Explore corpus management using internal and external corpora <P><P>Learn WordNet usage and a couple of simple application assignments using WordNet <P><P>Operate on raw text <P><P>Learn to perform tokenization, stemming, lemmatization, and spelling corrections, stop words removals, and more <P><P>Understand regular expressions for pattern matching <P><P>Learn to use and write your own POS taggers and grammars <P><P>Learn to evaluate your own trained models <P><P>Explore Deep Learning techniques in NLP <P><P>Generate Text from Nietzsche's writing using LSTM <P><P>Utilize the BABI dataset and LSTM to model episodes
Natural Language Processing with Python Cookbook
by Pratap Dangeti Krishna Bhavsar Naresh KumarLearn the tricks and tips that will help you design Text Analytics solutions About This Book • Independent recipes that will teach you how to efficiently perform Natural Language Processing in Python • Use dictionaries to create your own named entities using this easy-to-follow guide • Learn how to implement NLTK for various scenarios with the help of example-rich recipes to take you beyond basic Natural Language Processing Who This Book Is For This book is intended for data scientists, data analysts, and data science professionals who want to upgrade their existing skills to implement advanced text analytics using NLP. Some basic knowledge of Natural Language Processing is recommended. What You Will Learn • Explore corpus management using internal and external corpora • Learn WordNet usage and a couple of simple application assignments using WordNet • Operate on raw text • Learn to perform tokenization, stemming, lemmatization, and spelling corrections, stop words removals, and more • Understand regular expressions for pattern matching • Learn to use and write your own POS taggers and grammars • Learn to evaluate your own trained models • Explore Deep Learning techniques in NLP • Generate Text from Nietzsche's writing using LSTM • Utilize the BABI dataset and LSTM to model episodes In Detail Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages; in particular, it's about programming computers to fruitfully process large natural language corpora. This book includes unique recipes that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task. You will come across various recipes during the course, covering (among other topics) natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to understand language, plan sentences, and work around various ambiguities. You will learn how to efficiently use NLTK and implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master lexical analysis, syntactic and semantic analysis, pragmatic analysis, and the application of deep learning techniques. By the end of this book, you will have all the knowledge you need to implement Natural Language Processing with Python. Style and Approach This book's rich collection of recipes will come in handy when you are working with Natural Language Processing with Python. Addressing your common and not-so-common pain points, this is a book that you must have on the shelf.
Natural Language Processing with Python Quick Start Guide: Going from a Python developer to an effective Natural Language Processing Engineer
by Nirant KasliwalBuild and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learningKey FeaturesA no-math, code-driven programmer’s guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook DescriptionNLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP.The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications.We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn.We conclude by deploying these models as REST APIs with Flask.By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.What you will learnUnderstand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpusWork with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clusteringDeep Learning in NLP using PyTorch with a code-driven introduction to PyTorchUsing an NLP project management Framework for estimating timelines and organizing your project into stagesHack and build a simple chatbot application in 30 minutesDeploy an NLP or machine learning application using Flask as RESTFUL APIsWho this book is forProgrammers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.
Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning
by Delip Rao Brian McMahanNatural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.Authors Delip Rao and Brian McMahon provide you with a solid grounding in NLP and deep learning algorithms and demonstrate how to use PyTorch to build applications involving rich representations of text specific to the problems you face. Each chapter includes several code examples and illustrations.Explore computational graphs and the supervised learning paradigmMaster the basics of the PyTorch optimized tensor manipulation libraryGet an overview of traditional NLP concepts and methodsLearn the basic ideas involved in building neural networksUse embeddings to represent words, sentences, documents, and other featuresExplore sequence prediction and generate sequence-to-sequence modelsLearn design patterns for building production NLP systems
Natural Language Processing with Spark NLP: Learning to Understand Text at Scale
by Alex ThomasIf you want to build an enterprise-quality application that uses natural language text but aren’t sure where to begin or what tools to use, this practical guide will help get you started. Alex Thomas, principal data scientist at Wisecube, shows software engineers and data scientists how to build scalable natural language processing (NLP) applications using deep learning and the Apache Spark NLP library.Through concrete examples, practical and theoretical explanations, and hands-on exercises for using NLP on the Spark processing framework, this book teaches you everything from basic linguistics and writing systems to sentiment analysis and search engines. You’ll also explore special concerns for developing text-based applications, such as performance.In four sections, you’ll learn NLP basics and building blocks before diving into application and system building:Basics: Understand the fundamentals of natural language processing, NLP on Apache Stark, and deep learningBuilding blocks: Learn techniques for building NLP applications—including tokenization, sentence segmentation, and named-entity recognition—and discover how and why they workApplications: Explore the design, development, and experimentation process for building your own NLP applicationsBuilding NLP systems: Consider options for productionizing and deploying NLP models, including which human languages to support
Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library
by Thushan GanegedaraWrite modern natural language processing applications using deep learning algorithms and TensorFlowKey Features Focuses on more efficient natural language processing using TensorFlow Covers NLP as a field in its own right to improve understanding for choosing TensorFlow tools and other deep learning approaches Provides choices for how to process and evaluate large unstructured text datasets Learn to apply the TensorFlow toolbox to specific tasks in the most interesting field in artificial intelligenceBook DescriptionNatural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks.Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator.After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.What you will learn Core concepts of NLP and various approaches to natural language processing How to solve NLP tasks by applying TensorFlow functions to create neural networks Strategies to process large amounts of data into word representations that can be used by deep learning applications Techniques for performing sentence classification and language generation using CNNs and RNNs About employing state-of-the art advanced RNNs, like long short-term memory, to solve complex text generation tasks How to write automatic translation programs and implement an actual neural machine translator from scratch The trends and innovations that are paving the future in NLPWho this book is forThis book is for Python developers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks. Fundamental Python skills are assumed, as well as some knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required, although some background in NLP or computational linguistics will be helpful.
Natural Language Processing with TensorFlow: The definitive NLP book to implement the most sought-after machine learning models and tasks, 2nd Edition
by Thushan Ganegedara Andrei LopatenkoFrom introductory NLP tasks to Transformer models, this new edition teaches you to utilize powerful TensorFlow APIs to implement end-to-end NLP solutions driven by performant ML (Machine Learning) modelsKey FeaturesLearn to solve common NLP problems effectively with TensorFlow 2.xImplement end-to-end data pipelines guided by the underlying ML model architectureUse advanced LSTM techniques for complex data transformations, custom models and metricsBook DescriptionLearning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures. The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP. TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models. By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you'll be able to confidently use TensorFlow throughout your machine learning workflow.What you will learnLearn core concepts of NLP and techniques with TensorFlowUse state-of-the-art Transformers and how they are used to solve NLP tasksPerform sentence classification and text generation using CNNs and RNNsUtilize advanced models for machine translation and image caption generationBuild end-to-end data pipelines in TensorFlowLearn interesting facts and practices related to the task at handCreate word representations of large amounts of data for deep learningWho this book is forThis book is for Python developers and programmers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks.Fundamental Python skills are assumed, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required.
Natural Language Processing with Transformers
by Lewis Tunstall Leandro Von Werra Thomas WolfSince their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve.Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answeringLearn how transformers can be used for cross-lingual transfer learningApply transformers in real-world scenarios where labeled data is scarceMake transformer models efficient for deployment using techniques such as distillation, pruning, and quantizationTrain transformers from scratch and learn how to scale to multiple GPUs and distributed environments
Natural Language Processing with Transformers, Revised Edition
by Lewis Tunstall Leandro Von Werra Thomas WolfSince their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve.Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answeringLearn how transformers can be used for cross-lingual transfer learningApply transformers in real-world scenarios where labeled data is scarceMake transformer models efficient for deployment using techniques such as distillation, pruning, and quantizationTrain transformers from scratch and learn how to scale to multiple GPUs and distributed environments
The Natural Language Processing Workshop: Confidently design and build your own NLP projects with this easy-to-understand practical guide
by Dwight Gunning Rohan Chopra Sohom Ghosh Nipun Sadvilkar Aniruddha M. Godbole Muzaffar Bashir ShahMake NLP easy by building chatbots and models, and executing various NLP tasks to gain data-driven insights from raw text dataKey FeaturesGet familiar with key natural language processing (NLP) concepts and terminologyExplore the functionalities and features of popular NLP toolsLearn how to use Python programming and third-party libraries to perform NLP tasksBook DescriptionDo you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you've never done it before?With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises.The book starts with an introduction to NLP. You'll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you'll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you'll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews.By the end of this book, you'll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.What you will learnObtain, verify, clean and transform text data into a correct format for useUse methods such as tokenization and stemming for text extractionDevelop a classifier to classify comments in Wikipedia articlesCollect data from open websites with the help of web scrapingTrain a model to detect topics in a set of documents using topic modelingDiscover techniques to represent text as word and document vectorsWho this book is forThis book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly.
Natural Language Understanding and Intelligent Applications
by Chin-Yew Lin Nianwen Xue Dongyan Zhao Xuanjing Huang Yansong FengThis book constitutes the joint refereed proceedings of the 5th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2016, and the 24th International Conference on Computer Processing of Oriental Languages, ICCPOL 2016, held in Kunming, China, in December 2016. The 48 revised full papers presented together with 41 short papers were carefully reviewed and selected from 216 submissions. The papers cover fundamental research in language computing, multi-lingual access, web mining/text mining, machine learning for NLP, knowledge graph, NLP for social network, as well as applications in language computing.
Natural Science Imaging and Photography (Applications in Scientific Photography)
by Michael R. PeresThis book provides an in-depth exploration of scientific photography. Highlighting the best practices needed to make, distribute, and preserve scientific visual information using digital photographic methods and technologies, it offers solutions to some of the biggest challenges facing photographers. Written by a team of international, award-winning image makers with over 300 years of cumulative experience, this comprehensive resource explains the foundations used, the tools required, and the steps to needed for creating the optimal photograph in a range of environments and circumstances. Topics covered include: • ethical practices • aerial photography • close-up and macro photography • computational photography • field photography • geological photography • imaging with invisible spectrums • photographing small animals in captivity • time-based imaging • image processing in science Showcasing modern methods, this book equips readers with the skills needed to capture and process the best image possible. Designed for basic and intermediate photographers, Natural Science Imaging and Photography exists as an essential contemporary handbook.
Natural User Interfaces in Medical Image Analysis
by Marek R. Ogiela Tomasz HachajThis unique text/reference highlights a selection of practical applications of advanced image analysis methods for medical images. The book covers the complete methodology for processing, analysing and interpreting diagnostic results of sample CT images. The text also presents significant problems related to new approaches and paradigms in image understanding and semantic image analysis. To further engage the reader, example source code is provided for the implemented algorithms in the described solutions. Features: describes the most important methods and algorithms used for image analysis; examines the fundamentals of cognitive computer image analysis for computer-aided diagnosis and semantic image description; presents original approaches for the semantic analysis of CT perfusion and CT angiography images of the brain and carotid artery; discusses techniques for creating 3D visualisations of large datasets; reviews natural user interfaces in medical imaging systems, including GDL technology.
Nature and Landscape Photography
by Martin BorgWhat happens when you bring two of your passions together? Magic, of course. Photography offers a perfect outlet for creativity and emotions. Nature provides peace, serenity, and a wellspring of energy. To combine both--to photograph nature--is a unique and fulfilling experience. In this book, renowned Swedish nature photographer Martin Borg shares his experience and insight along with 71 of his beautiful images that illustrate each point. He offers helpful advice for beginning to intermediate photographers, ranging from technical tips, to aesthetics, to philosophical thoughts on the essence of being a nature photographer.
Nature-Inspired Algorithms and Applications
by S. Balamurugan Anupriya Jain Sachin Sharma Dinesh Goyal Sonia Duggal Seema SharmaThe purpose of designing this book is to portray certain practical applications of nature-inspired computation in machine learning for the better understanding of the world around us. The focus is to portray and present recent developments in the areas where nature- inspired algorithms are specifically designed and applied to solve complex real-world problems in data analytics and pattern recognition, by means of domain-specific solutions. Various nature-inspired algorithms and their multidisciplinary applications (in mechanical engineering, electrical engineering, machine learning, image processing, data mining and wireless network domains are detailed, which will make this book a handy reference guide.
A Nature-Inspired Approach to Cryptology (Studies in Computational Intelligence #1122)
by Shishir Kumar Shandilya Agni Datta Atulya K. NagarThis book introduces nature-inspired algorithms and their applications to modern cryptography. It helps the readers to get into the field of nature-based approaches to solve complex cryptographic issues. This book provides a comprehensive view of nature-inspired research which could be applied in cryptography to strengthen security. It will also explore the novel research directives such as Clever algorithms and immune-based cyber resilience. New experimented nature-inspired approaches are having enough potential to make a huge impact in the field of cryptanalysis. This book gives a lucid introduction to this exciting new field and will promote further research in this domain. The book discusses the current landscape of cryptography and nature-inspired research and will be helpful to prospective students and professionals to explore further.
Nature-Inspired Computation in Data Mining and Machine Learning (Studies in Computational Intelligence #855)
by Xin-She Yang Xing-Shi HeThis book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.
Nature-Inspired Computation in Engineering
by Xin-She YangThis timely review book summarizes the state-of-the-art developments in nature-inspired optimization algorithms and their applications in engineering. Algorithms and topics include the overview and history of nature-inspired algorithms, discrete firefly algorithm, discrete cuckoo search, plant propagation algorithm, parameter-free bat algorithm, gravitational search, biogeography-based algorithm, differential evolution, particle swarm optimization and others. Applications include vehicle routing, swarming robots, discrete and combinatorial optimization, clustering of wireless sensor networks, cell formation, economic load dispatch, metamodeling, surrogated-assisted cooperative co-evolution, data fitting and reverse engineering as well as other case studies in engineering. This book will be an ideal reference for researchers, lecturers, graduates and engineers who are interested in nature-inspired computation, artificial intelligence and computational intelligence. It can also serve as a reference for relevant courses in computer science, artificial intelligence and machine learning, natural computation, engineering optimization and data mining.
Nature-Inspired Computation in Navigation and Routing Problems: Algorithms, Methods and Applications (Springer Tracts in Nature-Inspired Computing)
by Xin-She Yang Yu-Xin ZhaoThis book discusses all the major nature-inspired algorithms with a focus on their application in the context of solving navigation and routing problems. It also reviews the approximation methods and recent nature-inspired approaches for practical navigation, and compares these methods with traditional algorithms to validate the approach for the case studies discussed. Further, it examines the design of alternative solutions using nature-inspired techniques, and explores the challenges of navigation and routing problems and nature-inspired metaheuristic approaches.
Nature-Inspired Computing: Physics and Chemistry-Based Algorithms
by Hojjat Adeli Nazmul H. SiddiqueNature-Inspired Computing: Physics and Chemistry-Based Algorithms provides a comprehensive introduction to the methodologies and algorithms in nature-inspired computing, with an emphasis on applications to real-life engineering problems. The research interest for Nature-inspired Computing has grown considerably exploring different phenomena observed in nature and basic principles of physics, chemistry, and biology. The discipline has reached a mature stage and the field has been well-established. This endeavour is another attempt at investigation into various computational schemes inspired from nature, which are presented in this book with the development of a suitable framework and industrial applications. Designed for senior undergraduates, postgraduates, research students, and professionals, the book is written at a comprehensible level for students who have some basic knowledge of calculus and differential equations, and some exposure to optimization theory. Due to the focus on search and optimization, the book is also appropriate for electrical, control, civil, industrial and manufacturing engineering, business, and economics students, as well as those in computer and information sciences. With the mathematical and programming references and applications in each chapter, the book is self-contained, and can also serve as a reference for researchers and scientists in the fields of system science, natural computing, and optimization.
Nature-Inspired Computing for Control Systems
by Hiram Eredín Ponce EspinosaThe book presents recent advances in nature-inspired computing, giving a special emphasis to control systems applications. It reviews different techniques used for simulating physical, chemical, biological or social phenomena at the purpose of designing robust, predictive and adaptive control strategies. The book is a collection of several contributions, covering either more general approaches in control systems, or methodologies for control tuning and adaptive controllers, as well as exciting applications of nature-inspired techniques in robotics. On one side, the book is expected to motivate readers with a background in conventional control systems to try out these powerful techniques inspired by nature. On the other side, the book provides advanced readers with a deeper understanding of the field and a broad spectrum of different methods and techniques. All in all, the book is an outstanding, practice-oriented reference guide to nature-inspired computing addressing graduate students, researchers and practitioners in the field of control engineering.
Nature Inspired Computing for Data Science (Studies in Computational Intelligence #871)
by Himansu Das Minakhi Rout Jitendra Kumar RoutThis book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.
Nature-Inspired Computing for Smart Application Design (Springer Tracts in Nature-Inspired Computing)
by Santosh Kumar Das Thanh-Phong Dao Thinagaran PerumalThis book focuses primarily on the nature-inspired approach for designing smart applications. It includes several implementation paradigms such as design and path planning of wireless network, security mechanism and implementation for dynamic as well as static nodes, learning method of cloud computing, data exploration and management, data analysis and optimization, decision taking in conflicting environment, etc. The book fundamentally highlights the recent research advancements in the field of engineering and science.