Artificial intelligence (AI) Books

Book SynopsisThis book provides an argumentation model for means end-reasoning, a distinctive type of reasoning used for problem-solving and decision-making. Means end-reasoning is modelled as goal-directed argumentation from an agent''s goals and known circumstances, and from an action selected as a means, to a decision to carry out the action. Goal-based Reasoning for Argumentation provides an argumentation model of this kind of reasoning showing how it is employed in settings of intelligent deliberation where agents try to collectively arrive at a conclusion on what they should do to move forward in a set of circumstances. The book explains how this argumentation model can help build more realistic computational systems of deliberation and decision-making, and shows how such systems can be applied to solve problems posed by goal-based reasoning in numerous fields, from social psychology and sociology, to law, political science, anthropology, cognitive science, artificial intelligence, multi-agenTable of Contents1. Introduction to practical reasoning; 2. Practical reasoning in health product ads; 3. Formal and computational systems of practical reasoning; 4. Practical reasoning in arguments and explanations; 5. Explanations, motives, and intentions; 6. Practical argumentation in deliberation dialogue; 7. Goal-based argumentation in different types of dialogue; 8. Practical rationality.

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Book SynopsisLanguage resources and computational models are becoming increasingly important for the study of language variation. A main challenge of this interdisciplinary field is that linguistics researchers may not be familiar with these helpful computational tools and many NLP researchers are often not familiar with language variation phenomena. This essential reference introduces researchers to the necessary computational models for processing similar languages, varieties, and dialects. In this book, leading experts tackle the inherent challenges of the field by balancing a thorough discussion of the theoretical background with a meaningful overview of state-of-the-art language technology. The book can be used in a graduate course, or as a supplementary text for courses on language variation, dialectology, and sociolinguistics or on computational linguistics and NLP. Part 1 covers the linguistic fundamentals of the field such as the question of status and language variation. Part 2 discusses Trade Review'Variation is a key aspect of human language, and yet it has been too often overlooked in computational linguistics. The book edited by Marcos Zampieri and Preslav Nakov is an important step towards filling this gap with top-level contributions that offer a new alliance between natural language processing and linguistic theory to understand this complex phenomenon and its impact on applications.' Alessandro Lenci, University of PisaTable of ContentsIntroduction Marcos Zampieri and Preslav Nakov; Part I: Language variation James Walker; Phonetic variation in dialects Rachael Tatman; 3. Similar languages, varieties and dialects Miriam Meyerhoff and Steffen Klaere; 4. Mutual intelligibility Charlotte Gooskens and Vincent J. van Heuven; 5. Dialectology for computational linguists John Nerbonne, Wilbert Heeringa, Jelena Prokić and Martijn Wieling; Part II: 6. Data collection and representation for similar languages, varieties and dialects Tanja Samardžić and Nikola Ljubešić; 7. Adaptation of morphosyntactic taggers Yves Scherrer; 8. Sharing dependency parsers between similar languages Željko Agić; Part III: 9. Dialect and similar language identification Marcos Zampieri; 10. Dialect variation on social media Dong Nguyen; 11. Machine translation between similar languages Preslav Nakov and Jorg Tiedemann; 12. Automatic spoken dialect identification Pedro Torres-Carrasquillo and Bengt Borgström; 13. Arabic dialect processing Nizar Habash; 14. Automatic classification of varieties of Mandarin Chinese Hongzhi Xu, Menghan Jiang, Jingxia Lin, Dingxu Shi and Chu-Ren Huang.

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Book SynopsisAlgorithms are probably the most sophisticated tools that people have had at their disposal since the beginnings of human history. They have transformed science, industry, society. They upset the concepts of work, property, government, private life, even humanity. Going easily from one extreme to the other, we rejoice that they make life easier for us, but fear that they will enslave us. To get beyond this vision of good vs evil, this book takes a new look at our time, the age of algorithms. Creations of the human spirit, algorithms are what we made them. And they will be what we want them to be: it''s up to us to choose the world we want to live in.Trade Review'... written by two computer scientists offering a most accessible view on both what algorithms are (the book starts with a clearest analogy between algorithms and recipes) and how algorithms are severely changing human life.' Simona Chiodo, Metascience'This short and interesting book provides a non-technical introduction to the age of algorithms. The book is worth reading many times even by those unfamiliar with algorithms or computer science.' S.V. Nagaraj, The SIGACT NewsTable of Contents1. Algorithms intrigue, algorithms disturb; 2. What is an algorithm?; 3. Algorithms, computers, and programs; 4. What algorithms do; 5. What algorithms don't do; 6. Computational thinking; 7. The end of employment; 8. The end of work; 9. The end of property; 10. Governing in the age of algorithms; 11. An algorithm in the community; 12. The responsibility of algorithms; 13. Personal data and privacy; 14. Fairness, transparency, and diversity; 15. Computers and ecology; 16. Computer science education; 17. The augmented human; 18. Can an algorithm be intelligent?; 19. Can an algorithm have feelings? 20. Time to choose.

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Book SynopsisFinite-state methods are the most efficient mechanisms for analysing textual and symbolic data, providing elegant solutions for an immense number of practical problems in computational linguistics and computer science. This book for graduate students and researchers gives a complete coverage of the field, starting from a conceptual introduction and building to advanced topics and applications. The central finite-state technologies are introduced with mathematical rigour, ranging from simple finite-state automata to transducers and bimachines as ''input-output'' devices. Special attention is given to the rich possibilities of simplifying, transforming and combining finite-state devices. All algorithms presented are accompanied by full correctness proofs and executable source code in a new programming language, C(M), which focuses on transparency of steps and simplicity of code. Thus, by enabling readers to obtain a deep formal understanding of the subject and to put finite-state methodsTrade Review'… this volume is well written and very detailed. It is thus a nice reference for those results for the interested graduate or researcher …' Andreas Maletti, ZB Math ReviewsTable of ContentsPart I. Formal Background: 1. Formal preliminaries; 2. Monoidal finite-state automata; 3. Classical finite-state automata and regular languages; 4. Monoidal multi-tape automata and finite-state transducers; 5. Deterministic transducers; 6. Bimachines; Part II. From Theory to Practice: 7. The C(M) language; 8. C(M) implementation of finite-state devices; 9. The Aho–Corasick algorithm; 10. The minimal deterministic finite-state automaton for a finite language; 11. Constructing finite-state devices for text rewriting; Bibliography; Index.

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Book SynopsisRecent developments in artificial intelligence, especially neural network and deep learning technology, have led to rapidly improving performance in voice assistants such as Siri and Alexa. Over the next few years, capability will continue to improve and become increasingly personalised. Today''s voice assistants will evolve into virtual personal assistants firmly embedded within our everyday lives. Told through the view of a fictitious personal assistant called Cyba, this book provides an accessible but detailed overview of how a conversational voice assistant works, especially how it understands spoken language, manages conversations, answers questions and generates responses. Cyba explains through examples and diagrams the neural network technology underlying speech recognition and synthesis, natural language understanding, knowledge representation, conversation management, language translation and chatbot technology. Cyba also explores the implications of this rapidly evolving techTrade Review'Hey Cyba is based on the author's long history of research and his rich experiences of developing various voice assistant systems. With the current rapid progress and wide deployment of AI-based voice assistant systems all over the world, the publication is very timely, and the book has a very unique and interesting writing style. I strongly recommend it to anyone interested in this area.' Sadaoki Furui, Toyota Technological Institute at Chicago'Hey Cyba, written by one of the giants in the field of man machine interfaces, provides an in depth guide to the workings and future of conversational personal assistants. Written in the first person style of the computer itself this is a highly engaging, informative and authoritative read.' Hermann Hauser, Amadeus Capital Partners'The book to introduce the technology behind our voice assistants to everyone. Voice assistants are among the most complex AI/ML (artificial intelligence/machine learning) systems. Hey, Cyba manages to present this complex AI/ML system in one easy-to-read narrative covering each aspect of the voice assistant in just the right depth. I wonder whether there is anyone but Steve with the deep knowledge and academic and industry experience required to write such a book.' Björn Hoffmeister, Director of Machine Learning at Amazon/Alexa'This enjoyable text deftly illuminates the technology behind a common experience … Highly recommended.' M. Mounts, Choice ConnectTable of Contents1. May I introduce myself?; 2. My inner workings; 3. How my brain works; 4. Knowing what I know; 5. What did you say?; 6. What does that mean?; 7. What should I say next?; 8. Listen to me; 9. How do you say that in…?; 10. Let's chat; 11. Can you trust me?; 12. When all is quiet; 13. Future upgrades and beyond; Glossary; Notes; Index.

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Book SynopsisGame developers will use this book to gain a basic knowledge of programming artificial intelligence using Unity and C#. You will not be bored learning the theory underpinning AI. Instead, you will learn by experience and practice, and complete an engaging project in each chapter. AI is the one of the most popular subjects in gaming today, ranging from controlling the behavior of non-player characters to procedural generated levels. This book starts with an introduction to AI and its use in games. Basic moving behaviors and pathfinding are covered, and then you move through more complex concepts of pathfinding and decision making.What You Will Learn Understand the fundamentals of AI Create gameplay-based AI to address navigation and decision-making problems Put into practice graph theory and behavior models Address pathfinding problems Use the A* algorTable of ContentsChapter 1: Introduction Chapter Goal: An introduction to the book where goals and main topics are introduced to the reader. Sub -Topics 1. What is AI? 2. AI in games 3. Intelligent agents 4. Knowledge representation Chapter 2: Movements Chapter Goal: Introducing the reader to steering and basic AI moving behaviors, in particular wandering and following the player. Sub - Topics 1. Moving in a 2D world 2. Moving in a 3D world 3. Steering 4. Moving behaviors (wandering vs following) 5. A case study: car games 6. Project: mini car traffic simulator Chapter 3: Pathfinding Chapter Goal: Introducing the reader to pathfinding algorithms and problem-solving approaches. Sub - Topics: 1. Graphs 2. Pathfinding algorithms: Dijkstra 3. Pathfinding algorithms: A* 4. World representation 5. Constraint Satisfaction Problems (CSP) 6. Improving on pathfinding 7. A case study: Warcraft 8. Project: Labyrinth Chapter 4: Decision Making Chapter Goal: How does AI takes decisions? In this chapter, the reader will understand how to implement the ability to reason and plan actions using data structures to represent knowledge and search algorithms to find the best sequence of actions. Sub - Topics: 1. Decision trees 2. Finite-state machines (FSM) 3. Behavior trees 4. Fuzzy logic 5. Goal-oriented behavior 7. Rule-based systems 9. A case study: Halo 10. Project: Wumpus’ Cave Explorer Chapter 5: Tactics and Strategy Chapter Goal: Putting together all the knowledge acquired in the previous chapters to build intelligent agents that can perform well against the player. Sub - Topics: 1. Putting things together: intelligent agents in action 2. Strategy planning 3. Tactical pathfinding 4. Coordination and tactics in PVE: ambushing the player 5. A case study: 007 Goldeneye 6. Project: Chess with guns

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Book SynopsisTable of ContentsChapter 1: What is Machine Learning (ML)? Basics of Java Script (JS) Programming in the browser using Java Script Graphics and Interactive processing in the browser using Java Script libraries Getting started with P5.JS and ML5.JS ReferencesChapter 2: Human Pose Estimation in the Browser Browser based data processing Posenet vs Openpose models Human pose estimation using ML5.Posenet Inputs, Outputs and Data structures of Posenet model ReferencesChapter 3: Human Pose Classification Classification techniques using ML Neural Network in the browser Human Pose classification based on the outputs of Posenet model Consideration of poses using Confidence scores of Posenet model Storage of data using JSON formats related to the outputs of Posenet model ReferencesChapter 4: Gait Analysis Normal vs Abnormal Gait patterns Determination of Gait patterns using threshold values of the models User Interface design and development for monitoring of Gait patterns Real-Time data visualization of the Gait patterns on the browser ReferencesChapter 5: Future Possible Applications of Key Concepts

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Book SynopsisGet started with Azure Cognitive Services and its APIs that expose machine learning as a service. This book introduces the suite of Azure Cognitive Services and helps you take advantage of the proven machine learning algorithms that have been developed by experts and made available through Cognitive Services, easily integrating those algorithms into your own applications without having to develop the algorithms from scratch. The book also shows you how to use the algorithms provided by Cognitive Services to accelerate data analysis and development within your organization.   The authors begin by introducing the tools and describing the steps needed to invoke libraries to analyze structured and unstructured text, speech, and pictures, and you will learn to create interactive chatbots using the Cognitive Services libraries. Each chapter contains the information you need to implement artificial intelligence (AI) via Azure Cognitive Services in your peTable of Contents1. Introducing Cognitive Services2. Prerequisites and Tools3. Vision4. Language in Cognitive Services5. Speech Services6. Power Platform & Cognitive Services7. Chatbots8. Ethics in AI

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Book SynopsisIntermediate-Advanced user levelTable of Contents​Chapter 1: The Power of Cognitive Services Chapter Goal: This first chapter sets up the values, reasons, and impacts you can achieve through Microsoft Azure Cognitive Services. It provides an overview of the features and capabilities. The chapter also introduces you to our case study and structures that we’ll use throughout the rest of the book. No of pages: 14 Sub - Topics 1. Overview of Azure Cognitive Services 2. Understanding the Use Cases 3. Exploring the Cognitive Services APIs: Vision, Speech, Language, Search, and Decision 4. Overview of Machine Leaning 5. The COVID-19 SmartApp Scenario Chapter 2: The Azure Portal for Cognitive Services Chapter Goal: The aim of this chapter to get started with Microsoft Cognitive services by exploring the Azure Portal. This chapter will explore the Cognitive Azure Portal and some of the common features. Finally, the chapter will take you inside the Azure Marketplace for Bot Service, Cognitive Services, and Machine Learning. No of pages: 18 Sub - Topics 1. Getting started with Azure Portal and Microsoft Cognitive Services 2. Azure Marketplace – an overview of AI + Machine Learning 3. Getting started with Azure Bot Service 4. Understanding software development kits (SDKs) – to get started with a favorite programing language [Ref. https://docs.microsoft.com/en-us/azure/cognitive-services/] 5. Setting up your Visual Studio template Chapter 3: Vision – Identify and Analyze Images and Videos Chapter Goal: This chapter will provide insight on Computer Vision with a full of hands-on example, where we build an application to analyze an Image. There are two features currently in preview that this chapter will also cover: Form Recognizer and Ink Recognizer. No of pages: 24 Sub - Topics 1. Understanding the Vision API with Computer Vision 2. Analyzing images 3. Identifying a face 4. Understanding the working behavior of vision APIs for Video Analysis 5. Recognizing forms, tables, and ink 6. Summary of the Vision API Chapter 4: Language – Gain an Understanding of Unstructured Text and Models Chapter Goal: This chapter will provide insight on NLP (Natural language processing) by evaluating user sentiments. The chapter will also touch preview features – including Immersive Reader. No of pages: 20 Sub - Topics 1. Creating and understanding language models 2. Training language models 3. Translating text to create your own translator application 4. Using QnA Maker to host conversational discussions about your data 5. Using Immersive Reader to understand text via audio and visual cues 6. Summary of the Language API Chapter 5: Speech – Talk to Your Application Chapter Goal: This chapter will provide insight on speech services by evaluating translating text to speech and vice versa. Enabling a speaker and translating into multiple languages. The chapter will also touch a preview feature – Speaker Recognition. The Bing speech feature will not be covered as it is retiring soon. No of pages: 18 Sub - Topics 1. Understanding speech and speech services 2. Converting speech into text and vice versa 3. Translating speech real-time into your application 4. Identifying the speaker from speech using Speaker Recognition 5. Customizing speech 6. Summary of the Speech API Chapter 6: Decision – Make Smarter Decisions In Your Applications Chapter Goal: This chapter will provide insight on decision services by adding content a moderation facility in the application. The chapter will also touch on a preview feature – Anomaly Detector. No of pages: 17 Sub - Topics 1. Understanding the decision service and decision APIs 2. Creating an auto Content Moderator application 3. Creating personalized experiences with the Personalizer 4. Identifying future problems with the Anomaly Detector 5. Summary of the Decision API Chapter 7: Search – Add Search Capabilities to Your Application Chapter Goal: This chapter will provide insight on Bing Search APIs by adding various search functionalities to the application. No of pages: 18 Sub - Topics 1. Understanding search and the Bing Search APIs 2. Creating a smart application by adding Bing Search 3. Suggesting a user with auto suggestions 4. Summary of the Search API Chapter 8: Deploy and Host Services Using Containers Chapter Goal: This chapter will provide a complete insight on Cognitive Services containers. In this chapter, we will highlight the key feature by creating an application. The application will deploy using Docker. No of pages: 22 Sub - Topics 1. Getting started with Cognitive Services containers 2. Understanding deployment and how to deploy and run a container on an Azure container instance 3. Understand Docker compose and use it to deploy multiple containers 4. Understanding Azure Kubernetes Service and how to deploy an application to Azure Kubernetes Service Chapter 9: Azure Bot Service Chapter Goal: This chapter will provide insight on Bot Service by creating the COVID-19 Bot. No of pages: 24 Sub - Topics 1. Understanding Azure Bot services 2. Create a COVID-19 Bot using Azure Bot Service 3. Using the Azure Bot Builder SDK. Reference: https://docs.microsoft.com/en-us/azure/bot-service/dotnet/bot-builder-dotnet-sdk-quickstart?view=azure-bot-service-4.0 Chapter 10: Azure Machine Learning Chapter Goal: This chapter will lead the reader to fully understand Azure Machine Learning and how to use it. You can train your application to learn without being explicitly programmed. We will include forecasts and predictions. The chapter will cover a preview feature – Azure Machine Learning designer. No of pages: 22 Sub - Topics 1. Building models with no-code, using the Azure Machine Learning designer 2. Publishing to Jupyter notebooks 3. Building ML models in Python or R 4. The ML Visual Studio Code extension 5. Commanding the ML CLI 6. Summary of ML

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Book SynopsisLearn how to harness modern deep-learning methods in many contexts. Packed with intuitive theory, practical implementation methods, and deep-learning case studies, this book reveals how to acquire the tools you need to design and implement like a deep-learning architect. It covers tools deep learning engineers can use in a wide range of fields, from biology to computer vision to business. With nine in-depth case studies, this book will ground you in creative, real-world deep learning thinking.You'll begin with a structured guide to using Keras, with helpful tips and best practices for making the most of the framework. Next, you'll learn how to train models effectively with transfer learning and self-supervised pre-training. You will then learn how to use a variety of model compressions for practical usage. Lastly, you will learn how to design successful neural network architectures and creatively reframe difficult problems into solvable ones. You'll learn notonly to understand and applTable of ContentsChapter 1: “A Deep Dive Into Keras”Chapter Goal: To give a structured yet deep overview of Keras and to lay the groundwork for implementations in future chapters.Number of Pages: ~30Subtopics1. Why Keras? Versatility and simplicity.2. Steps needed to create a Keras model: define architecture, compile, fit.a. Compile: discuss TensorFlow optimizers, losses, and metrics.b. Fit: discuss callbacks.3. Sequential model + example.4. Functional model + example.5. Visualizing Keras models.6. Data: using NumPy arrays, Keras Image Data Generator, and TensorFlow datasets.7. Hardware: using and accessing CPU, GPU, and TPU.Chapter 2: Pre-training Strategies and Transfer LearningChapter Goal: To understand the importance of transfer learning and to use a variety of transfer learning methods to solve deep learning problems efficiently.Number of Pages: ~30Subtopics1. Transfer learning theory, practical tips and tricks.2. Accessing and using Keras and TensorFlow pretrained models.a. Bonus: converting PyTorch models (PyTorch has a wider variety) into Keras models for greater access to pretrained networks.3. Manipulating pretrained models with other network elements.4. Layer freezing.5. Self-supervised learning methods.Chapter 3: “The Versatility of Autoencoders”Chapter Goal: To understand the versatility of autoencoders and to be able to use them in a wide variety of problem scenarios.Number of Pages: ~30Subtopics1. Autoencoder theory.2. One-dimensional data autoencoder implementation, tips and tricks.3. Convolutional autoencoder implementation, tips and tricks, special concerns.4. Using autoencoders for pretraining.a. Example case study: TabNet.5. Using autoencoders for feature reduction.6. Variational autoencoders for data generation.Chapter 4: “Model Compression for Practical Deployment”Chapter Goal: To understand pruning theory, implement pruning for effective model compression, and to recognize the important role of pruning in modern deep learning research.Number of Pages: ~20Subtopics1. Pruning theory.2. Pruning Keras models with TensorFlow.3. Exciting implications of pruning – the Lottery Ticket Hypothesis.a. Example case-study: no-training neural networks.b. Example case-study: extreme learning machines.Chapter 5: “Automating Model Design with Meta-Optimization”Chapter Goal: To understand what meta-optimization is and to be able to use it to effectively automate the design of neural networks.Number of Pages: ~20Subtopics1. Meta-optimization theory.2. Demonstration of meta-optimization using HyperOpt on Keras.3. Demonstration of Auto-ML and Neural Architecture Search.Chapter 6: “Successful Neural Network Architecture Design”Chapter Goal: To gain an understanding of principles in successful neural network architecture design through three case studies.Number of Pages: ~25Subtopics1. Diversity of neural network designs and the need to design specific architectures for particular problems.2. Theory and implementation of block/cell/module design and considerations.a. Example case study: Inception model.3. Theory and implementation of “Normal” and “extreme” usages of skip connections.a. Parallel towers and cardinalityb. Example case study: UMAP model.4. Neural network scaling.a. Example case study: EfficientNet.Chapter 7: “Reframing Difficult Deep Learning Problems”Chapter Goal: To explore how hard problems can be reframed to be solved by deep learning with three case studies.Number of Pages: ~30Subtopics1. The diversity of problems deep learning is being used to solve.2. Example case study: Siamese networks – experimenting with architecture.3. Example case study: DeepInsight – experimenting with data representation.4. Example case study: Semi-supervised generative adversarial networks – experimenting with data availability.

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Book SynopsisChapter 1:  Introduction to AI & the AI Ecosystem.- Chapter 2:  AI Best Practise & DataOps.- Chapter 3: Data Ingestion for AI.- Chapter 4: Machine Learning on Cloud.- Chapter 5: Neural Networks and Deep Learning.- Chapter 6: The Employer's Dream: AutoML, AutoAI and the rise of NoLo UIs.- Chapter 7: AI Full Stack: Application Development.- Chapter 8: AI Case Studies.- Chapter 9: Deploying an AI Solution (Productionizing & Containerization).- Chapter 10: Natural Language Processing.- Postscript.Table of ContentsChapter 1: Introduction to AI & the AI EcosystemChapter Goal: Embracing the hype and the pitfalls, introduces the reader to current and emerging trends in AI and how many businesses and organisations are struggling to get machine and deep learning operationalizedNo of pages: 30Sub -Topics1. The AI ecosystem2. Applications of AI3. AI pipelines4. Machine learning5. Neural networks & deep learning6. Productionizing AIChapter 2: AI Best Practise & DataOpsChapter Goal: Help the reader understand the wider context for AI, key stakeholders, the importance of collaboration, adaptability and re-use as well as DataOps best practice in delivering high-performance solutionsNo of pages: 20Sub - Topics 1. Introduction to DataOps and MLOps 2. Agile development3. Collaboration and adaptability4. Code repositories5. Module 4: Data pipeline orchestration6. CI / CD7. Testing, performance evaluation & monitoringChapter 3: Data Ingestion for AIChapter Goal: Inform on best practice and the right (cloud) data architectures and orchestration requirements to ensure the successful delivery of an AI project.No of pages : 20Sub - Topics: 1. Introduction to data ingestion2. Data stores for AI3. Data lakes, warehousing & streaming4. Data pipeline orchestrationChapter 4: Machine Learning on CloudChapter Goal: Top-down ML model building from design thinking, through high level process, data wrangling, unsupervised clustering techniques, supervised classification, regression and time series approaches before interpreting results and algorithmic performance No of pages: 20Sub - Topics: 1. ML fundamentals2. EDA & data wrangling3. Supervised & unsupervised machine learning4. Python Implementation5. Unsupervised clustering, pattern & anomaly detection6. Supervised classification & regression case studies: churn & retention modelling, risk engines, social media sentiment analysis7. Time series forecasting and comparison with fbprophetChapter 5: Neural Networks and Deep LearningChapter Goal: Help the reader establish the right artificial neural network architecture, data orchestration and infrastructure for deep learning with TensorFlow, Keras and PyTorch on CloudNo of pages: 40Sub - Topics: 1. An introduction to deep learning2. Stochastic processes for deep learning3. Artificial neural networks4. Deep learning tools & frameworks5. Implementing a deep learning model6. Tuning a deep learning model7. Advanced topics in deep learningChapter 6: The Employer’s Dream: AutoML, AutoAI and the rise of NoLo UIsChapter Goal: Building on acquired ML and DL skills, learn to leverage the growing ecosystem of AutoML, AutoAI and No/Low code user interfacesNo of pages: 20Sub - Topics: 1. AutoML2. Optimizing the AI pipeline3. Python-based libraries for automation4. Case Studies in Insurance, HR, FinTech & Trading, Cybersecurity and Healthcare5. Tools for AutoAI: IBM Cloud Pak for Data, Azure Machine Learning, Google Teachable MachinesChapter 7: AI Full Stack: Application Development Chapter Goal: Starting from key business/organizational needs for AI, identify the correct solution and technologies to develop and deliver “Full Stack AI”No of pages: 20Sub - Topics: 6. Introduction to AI application development7. Software for AI development8. Key Business applications of AI:• ML Apps• NLP Apps• DL Apps4. Designing & building an AI applicationChapter 8: AI Case StudiesChapter Goal: A comprehensive (multi-sector, multi-functional) look at the main AI use uses in 2022No of pages: 20Sub - Topics: 1. Industry case studies2. Telco solutions3. Retail solutions4. Banking & financial services / fintech solutions5. Oil & gas / energy & utilities solutions6. Supply chain solutions7. HR solutions8. Healthcare solutions9. Other case studiesChapter 9: Deploying an AI Solution (Productionizing & Containerization)Chapter Goal: A practical look at “joining the dots” with full-stack deployment of Enterprise AI on CloudNo of pages: 20Sub - Topics: 1. Productionizing an AI application2. AutoML / AutoML3. Storage & Compute4. Containerization5. The final frontier…

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Book SynopsisThis book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You''ll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. EaTable of ContentsChapter 1: Introduction to Recommender SystemsChapter Goal: Introduction of recommender systems, along with a high-level overview of how recommender systems work, what are the different existing types, and how to leverage basic and advanced machine learning techniques to build these systems.No of pages: 25Sub - Topics: 1. Intro to recommender system 2. How it works3. Types and how they worka. Association rule miningb. Content basedc. Collaborative filtering d. Hybrid systemse. ML Clustering basedf. ML Classification basedg. Deep learning and NLP basedh. Graph basedChapter 2: Association Rule MiningChapter Goal: Building one of the simplest recommender systems from scratch, using association rule mining; also called market basket analysis.No of pages: 20Sub - Topics 1 APRIORI2 FP GROWTH3 Advantages and DisadvantagesChapter 3: Content and Knowledge-Based Recommender SystemChapter Goal: Building the content and knowledge-based recommender system from scratch using both product content and demographicsNo of pages: 25Sub - Topics 1 TF-IDF2 BOW3 Transformer based4 Advantages and disadvantagesChapter 4: Collaborative Filtering using KNNChapter Goal: Building the collaborative filtering using KNN from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics: 1 KNN – item based2 KNN – user based3 Advantages and disadvantagesChapter 5: Collaborative Filtering Using Matrix Factorization, SVD and ALS.Chapter Goal: Building the collaborative filtering using SVM from scratch, both item-item and user-user basedNo of pages: 25Sub - Topics: 1 Latent factors2 SVD3 ALS4 Advantages and disadvantagesChapter 6: Hybrid Recommender SystemChapter Goal: Building the hybrid recommender system (Using both content and collaborative methods) which is widely used in the industryNo of pages: 25Sub - Topics: 1 Weighted: a different weight given to the recommenders of each technique used to favor some of them.2 Mixed: a single set of recommenders, without favorites.3 Augmented: suggestions from one system are used as input for the next, and so on until the last one.4 Switching: Choosing a random method5 Advantages and disadvantagesChapter 7: Clustering Algorithm-Based Recommender SystemChapter Goal: Building the clustering model for recommender systems.No of pages: 25Sub - Topics: 1 K means clustering2 Hierarchal clustering 3 Advantages and disadvantagesChapter 8: Classification Algorithm-Based Recommender SystemChapter Goal: Building the classification model for recommender systems.No of pages: 25Sub - Topics: 1 Buying propensity model2 Logistic regression3 Random forest4 SVM5 Advantages and disadvantagesChapter 9: Deep Learning and NLP Based Recommender SystemChapter Goal: Building state of art recommender system using advanced topics like Deep learning along with NLP (Natural Language processing).No of pages: 25Sub - Topics: 1 Word embedding’s2 Deep neural networks3 Advantages and disadvantagesChapter 10: Graph-Based Recommender SystemChapter Goal: Implementing graph-based recommender system using Python for computation performanceNo of pages: 25Sub - Topics: 1 Generating nodes and edges2 Building algorithm3 Advantages and disadvantagesChapter 11: Emerging Areas and Techniques in Recommender System Chapter Goal: To get an overview of the new and emerging techniques and the areas of research in Recommender systemsNo of pages: 15Sub - Topics: 1 Personalized recommendation engine2 Context-based search engine3 Multi-objective recommendations4 Summary

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Book SynopsisTable of Contents​Chapter 1. Introduction to SteamlitChapter Goal: Introduce the reader to the Streamlit libraryNo of pages - 10Sub -Topics1. A brief introduction to Streamlit2. Pre-requisites and installation guide for Streamlit3. Creating our first Streamlit application Chapter 2. Texts & Table ElementsChapter Goal: The text is one of the important features that will be discussed in this chapter.No of pages - 10Sub -Topics1. Write title, header, sub-header, markdown and a caption.2. Code text, latex and default text in an application.3. json, table, metric and dataframe in the application.Chapter 3. Charts / Visualization Chapter Goal: Visualization is one of the important aspects in data science and machine learning. The visualizing techniques helps to understand the data more appropriately. In this chapter, we will implement different visualizing techniques that are available in python for data science and machine learning developers. No of pages - 20Sub -Topics1. Implementing simple charts 2. Visualizing data using interactive charts in the application.3. Implementing data into the maps. Chapter 4. Data and Media ElementsChapter Goal: In this chapter, we will learn how media elements can be used in the streamlit application. No of pages - 20Sub -Topics1. We will first try to implement simple charts to start with and display them on the application.2. Next, we will visualize data using interactive charts in the application.3. At last, we will see how we can use data into the maps. Chapter 5. ButtonsChapter Goal: One more important feature from Streamlit are buttons. These buttons are used to select the required data to process or visualize in the application developed. No of pages - 20Sub -Topics1. Introduction to buttons2. Discuss various buttons like download button, checkbox, radio buttons and multiselect.3. Sliders to select specific range of data.Chapter 6. FormsChapter Goal: This chapter mainly focusses on data that will be provided by the user to process data in the application. We will discuss user data in terms forms. No of pages - 20Sub -Topics1. Discuss various types input data like numbers and text.2. Discuss advanced input data like date, time, file uploads and color picker. 3. Getting live image data from webcamChapter 7. NavigationsChapter Goal: This chapter discusses about navigation on the application to be developed. The primary aim is to learn how to switch between multiple pages in an application using navigation. No of pages - 20Sub -Topics1. Discuss on navigation. 2. Discuss the complex layouts associated with it.3. Discuss on containers that can be used to hold multiple elements in it.Chapter 8. Control Flow and StatusChapter Goal: We will discuss on custom handling of application using control flow in this chapter. We will also learn on status elements provided by streamlit. No of pages - 20Sub -Topics1. Handling functionality of the application using control flow. 2. Flow control of application can be changed from its default flow.3. We will also check on the what are status elements? and their types available in Streamlit.Chapter 9. Advanced FeaturesChapter Goal: In this chapter, we will discuss on huge data handling, mutating data and optimizing performance of the Streamlit application. No of pages - 20Sub -Topics1. Handling huge data in the Streamlit Application developed for data science and machine learning.2. Implementing various optimizing techniques to improve performance of the application. 3. How to mutate data in live application.Chapter 10. Project BuildChapter Goal: Finally, we will discuss to build and run complete application on various platforms. No of pages - 10Sub -Topics1. Discuss various application platforms available.2. Pre-requisites to implement developed application on these platforms.3. Implement and run the project.4. Test application on deployment and create requirement files for it.Chapter 11. Use case: NLP Project PrototypeChapter Goal: This chapter discusses about navigation on the application to be developed. The primary aim is to learn how to switch between multiple pages in an application using navigation. No of pages - 10Sub -Topics1. Pre-requisites.2. NLP module that will be implemented in our application.3. Test application after deployment.Chapter 12. Use case: Computer Vision Project PrototypeChapter Goal: We will develop a complete streamlit application on Computer Vision from scratch. We will see how all the features we have seen in the above chapters will be implemented in this application No of pages - 10Sub -Topics1. Pre-requisite.2. Computer Vision techniques that needs to implemented.3. Test all functions implemented on our deployed application.

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Book SynopsisBeginning user levelTable of ContentsChapter 1: Introduction to Precision Health and Artificial IntelligenceChapter Goal: An introduction to precision health, the concepts of AI-wearables, health data and health tech and how they transform the health industry No of pages: 15Chapter 2: Foundations of Precision HealthChapter Goal: A deep dive into precision health including key principles and processes.No of pages: 25 Chapter 3: DataChapter Goal: Data has been the beginning of many great products, services or ventures in health tech — explore types of data, and how they can be used.No of pages: 25Sub - Topics: 1. Little and big data2. Types of data3. Wearables and IoT, genomics4. Using data to enable precision health Chapter 4: Artificial Intelligence in Precision HealthChapter Goal: Concepts and ideas in artificial intelligence (AI) and machine learning -- including statistical approaches, visualization, human-computer interactions and evaluating health AI.Pages: 251. Statistical approaches2. Visualization3. Human computer interaction4. Evaluations of AIChapter 5: Ethics and RegulatoryChapter Goal: An in-depth study of legal, ethical, and regulatory concepts in precision health.No of pages: 35Sub - Topics:1.Ethics2.Legal3.Regulatory concerns Chapter 6: Case Studies: The Application of Artificial Intelligence in Precision Healthcare and MedicineChapter Goal: Applications of AI techniques and software tools. This will primarily involve exploring recent examples of AI and Machine Learning tools being specifically used to aid in clinical practice.Pages: 251. Best case examples of AI to aid clinical practice

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Book SynopsisReimagine different generative AI as useful creative prototyping tools that can be used to augment your own creative process and projects. Gain a deeper understanding of how generative AI can elevate your creative future. You will acquire a comprehensive understanding of how AI works, uncover tools that can enhance your AI interactions, learn how to extract maximum potential from AI-produced content, and experiment with methods for assessing, refining, and boosting the content to transform your creative projects. You'll also explore how creative professionals from varied disciplines are employing generative AI in their workflows to produce distinctive contributions to the world. Each chapter provides examples of how designers and other creative individuals can utilize these technological wonders, adopting various prototyping techniques to fast-track and optimize design processes and workflows. Creators from all disciplines can tap into the vast capabilities and benefits of generatiTable of Contents Rapid Prototyping with Generative AI​1. Introduction: Your AI Best Friend2. AI as a Creative Muse3. Prototyping with AI4. Creative Tools and Processes5. AI Structures6. The Master of Mash-Up: Leveraging AI for Prototyping7. Uncanny by Nature8. Layering AI Generation9. The Art of the Prompt10. Five Dilemmas Using AI11. AI Curator for Hire

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Book Synopsis'Compelling and innovative... Barry takes a story that has been done countless times before and makes it seem original' - Daily MailShe is the ultimate weapon. She once served us. Now she's got her own plans. Once we approached the aliens in peace... and they annihilated us. Now mankind has developed the ultimate killing machine, the Providence class of spaceship. With the ships' frightening speed, frightening intelligence and frightening weaponry, it's now the salamanders' turn to be annihilated... in their millions. The mismatched quartet of Talia, Gilly, Jolene and Anders are the crew on one of these destroyers. But with the ship's computers designed to outperform human decision-making in practically all areas, they are virtual bystanders. The Providence will take them to where the enemy are and she will dictate the strategy in any battle. The crew's only job role is to publicise their glorious war to a sceptical Earth. Social media and video clips are THEIR weapons in an endless charm offensive. THEIR chief enemy is not the space reptiles but each other, and boredom. But then everything changes. A message comes from base: the Providence is going into the Violet Zone, where there are no beacons and no communications with Earth. It is the heart of the enemy empire - and now the crew are left to wonder whether this is a mission of ultimate destruction or, more sinisterly, of ultimate self-destruction...PROVIDENCE is a dazzling, inventive, and thought-provoking new novel from the author of Lexicon.Trade ReviewPraise for Providence * : *Providence is such a blast you almost overlook how clever it is. Like Starship Troopers and 2001: A Sapce Odyssey, it's about the limit of human intelligence, the nature of humanity and the price we're prepared to pay for the survival of the species * Daily Mail *Providence is a light-hearted thriller with a superbly dark, existential sting in its tail * The Times *A quirky, character-driven commentary on the mechanisation of conflict and the sheer perversity of human nature * Financial Times *Compelling and innovative... Barry takes a story that has been done countless times before and makes it seem original * Daily Mail *[A] terrific sci-fi thriller * Publishers Weekly *PROVIDENCE is an absolute treat. Pulls the trick of being both irrepressibly old-school sci-fi and creepily relevant to the data-driven future * Austin Grossman, author of Soon I Will Be Invincible *An astonishing novel! PROVIDENCE is Philip K. Dick and William Gibson fueled by pure adrenaline (with a bit of Spielberg and Ridley Scott thrown in). The brilliant, unstoppable imagination of Max Barry glows on every page of this action-filled, yet emotionally resonant, tale. It will keep you riveted from first page till last. I read in one sitting and I guarantee you'll do the same * Jeffery Deaver, author of The Never Game *Action-packed and unsettling, PROVIDENCE is a sleekly-written parable about the absurdity of war--and the deeply human urge to destroy everything we don't understand, whether it comes from a distant planet or right here on Earth * Annalee Newitz, author of Autonomous and The Future of Another Timeline *I could not put PROVIDENCE down until I'd finished it in one thrilling sitting. This is science fiction at its best-a ship so believably alive and characters so determined, flawed, and compelling that you'll forget you're not also part of the crew * Peng Shepherd, author of The Book of M *PROVIDENCE'S greatest strength - apart from being terrifically entertaining - is its characters. (...) It's Barry's empathy for his characters, his willingness to dig deep into their foibles, that distinguishes PROVIDENCE from most of the golden-age science fiction that inspired it. * Locus *The meat of this is in its human interactions * SciFi Bulletin *Max Barry's excellent novel Providence * New Scientist *Providence is an extremely smart take on an entirely different genre ... page-turning anticipation which pays off beautifully in the final third * SFX *Providence is a meditation ... on the difference between programmed intelligence and the human version. Barry tells a fun and funny story that finds the perfect moments to break your heart in the best ways * Locus *Like Starship Troopers with added brain, this is such a blast you can overlook just how clever and thought-provoking it is * Daily Mail *

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Book SynopsisWe present conceptual foundations for artificial intelligence, expert systems, and knowledge engineering and management and discuss high quality in education. Following, we discuss the basics of our vision and prospective about higher education (HE): the battle for the future with digital transformation (DT). Next, we present our central Chapter 4 on DT: our dual model of knowledge and data, as befits a HE institution. Below, we present a succinct outline of our architectural model. The pillars of the architecture are funding, research, entrepreneurship and social projection (Chapter 8); recognizing from the start that knowledge has its ethos in the university; these pillars correspond to: Productive ecosystem of the DT DT that enhances knowledge and innovation in the universities for the habilitation of the digital capabilities A new economy that requires the U transformation as social projection. New DT human talent required by the new knowledge and intelligence industry. The student hyper-personalization by competences and skills is required. Ten Views of Our DT Model, given its complex process that takes place in long ways, a process required for the successful survival of an organization into the IV Industrial Revolution, with the final purpose of being very competitive, productive and of high quality (HQ). The DT views, namely: 1-The DT Ecosystem. 2-The structural vision or the pillars of the DT mentioned. 3-The DT strategic map, showing scenarios, actors and vision-mission. 4-The architectural components for DT: 8 architectures were developed and implemented, applying some intelligent constructs that we have developed and documented in the last 10 years at FESSANJOSE (U. San José), leading DT in postsecondary education (PSEd). 5-Digital 360° architecture of DT Academy and Administration LOCUS: this architecture is the digital portfolio that implies the organization of the subsystems to obtain better and/or new functionalities based on knowledge to obtain an intelligent behavior. The DT multilayer-architecture approach is a system of systems (SoS) one, which ensures compliance with government policies, norms and standards, in a highly complex social institution with digital assets; this approach describes the subsystems at a higher level, where a system is composed, and with the protocols by which the subsystems communicate. It provides a 360° business vision map and a planning framework for commercial and technological changes. 6-The computational-mathematical perspective of DT, identifying endogenous and exogenous variables and their interrelations. 7-The synthesis, the Matrix of End-Means (EMM) that summarizes in DT: Where the HE is. In addition, where can the HE go? 8-The MIR Matrix, which describes DT Objectives, impacts-indicators and results. 9-The dynamic model of the DT system, based on computational intelligence, representing the system information control of all the components to achieve the completion of the DT. The intelligent management information system (iMIS) for PSEd, shows the dynamics of DT, integrating several multilevel system hybrid architectures, as a space to respond to the solution of the HE problems, tending to the desired competitiveness, specifically pointing out the way that these modern technologies can be included for their adaptation and evolution in PSEd in post-modernity, making governability, and teaching and student productivity compatible with educational high quality, the purpose of DT. The interface Results of the iMIS includes: high quality metrics, digitization rate progress, indicators (an special appendix on KPI were included) and values of management, desertion, answers, and plans. The Input Interface includes data, information and knowledge acquisition, where the attributes, parameters.Table of ContentsList of FiguresList of TablesIntroduction: Digital Transformation Inside UniversitiesBasic and Conceptual Aspects for Digital TransformationThe Battle for the FuturePostsecondary Education Digital TransformationPortfolio AnalyticsSelf-Evaluation and High Quality Education AnalyticsIntelligent Architecture of Position Audit of Digital Transformation USANJOSE-Arch-Level-DTDigitalization of the Social Projection and Relationship with the External SectorConclusionAppendixReferencesAbout the AuthorsIndex.

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Book SynopsisArtificial Intelligence may be defined as a collection of several analytic tools that collectively attempt to imitate life and has matured to a set of analytic tools that facilitate solving problems which were previously difficult or impossible to solve. In this book, the authors present topical research in the study of the tools and applications of artificial intelligence. Topics discussed include the application of artificial intelligence in the oil and gas industry and in metal stamping die design; using artificial intelligence to predict embryo quality and in biomedical imaging techniques.

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Book SynopsisArtificial intelligence is a subfield of computer science concerned with understanding the nature of intelligence and constructing computer systems capable of intelligent action. Artificial intelligence can be applied to all systems and manufacturing processes. This book aims to provide the research and review studies on artificial intelligence in manufacturing. The present research book can be used as a text book for final undergraduate engineering course (for example, mechanical, manufacturing, systems, etc) or as a subject on artificial intelligence in manufacturing at the postgraduate level. Also, this book can serve as a useful reference for academics, manufacturing and computational sciences researchers, mechanical, systems and manufacturing engineers, professional in related industries with artificial intelligence and manufacturing.

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Book SynopsisThis current book provides new research on artificial neural networks (ANNs). Topics discussed include the application of ANNs in chemistry and chemical engineering fields; the application of ANNs in the prediction of biodiesel fuel properties from fatty acid constituents; the use of ANNs for solar radiation estimation; the use of in silico methods to design and evaluate skin UV filters; a practical model based on the multilayer perceptron neural network (MLP) approach to predict the milling tool flank wear in a regular cut, as well as entry cut and exit cut, of a milling tool; parameter extraction of small-signal and noise models of microwave transistors based on ANNs; and the application of ANNs to deep-learning and predictive analysis in semantic TCM telemedicine systems.

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Book SynopsisThis book presents the latest research in the field of machine learning, discussing the real-world application problems associated with new innovative renewable energy methodologies as well as cutting edge technologies in the transport industry. The requirements and demands of problem solving have been increasing exponentially, and new artificial intelligence and machine learning technologies have reduced the scope of data coverage worldwide. Recent advances in data technology (DT) have contributed to reducing the gaps in the coverage of domains around the globe. Attention to clean energy in recent decades has been growing exponentially. This is mainly due to a decrease in the cost of both installed capacity of converters and a decrease in the cost of generated energy. Such successes were achieved thanks to the improvement of modern technologies for the production of converters, an increase in the efficiency of using incoming energy, optimisation of the operation of converters and analysis of data obtained during the operation of systems with the possibility of planning production. The use of clean energy plays an important role in the transportation industry, where technologies are also being improved from year to year - the transportation industry is growing, and machinery and systems are becoming more autonomous and robotic, where it is no longer possible to do without complex intelligent computing, machine learning optimisation, planning and working with large amounts of data. The book is a valuable reference work for researchers in the fields of renewable energy, computer science and engineering with a particular focus on machine learning and intelligent optimization as well as for postgraduates, managers, economists and decision makers, policy makers, government officials, industrialists and practicing scientists and engineers as well compassionate global decision makers. Topics include: Machine learning, Quantum Optimization, Modern Technology in Transport Industry, Innovative Technologies in Transport Education, Systems Based on Renewable Energy Conversion, Business Process Models and Applications in Renewable Energy, Clean Energy, and Climate Change.Table of ContentsPreface; RES-based Multipurpose Plant for Hydrogen Production; Developing a Bayesian Network to Model Environmental, Organizational, and Human Risk Factors: A Case Study on Wind Turbines; Digital Technologies for the Implementation of Intelligent Diagnostics of the Insulation of Power Supply Systems with Insulated Neutral in Operating Mode; Irrigation System of Agricultural Fields with the Use of Solar Energy; Strategies Hybrid Simulation for Regional Market Development of Renewable Energy; RES-Based Power Plants Versus Polluting Power Plants: Pros and Cons; A Comprehensive Study of System Building Blocks for Radio Frequency Energy Harvesting; The Management of Community Participation in Rural Infrastructure Development in the Mekong River Delta, Vietnam; Warning System for Cracked Pipes in Autonomous Vehicles; Contribution of Machine Learning to Rail Transport Safety; The Power of Variable Freeing and Variable Sum Bounds in Solving the Linear Knapsack Problem; Index.

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Book SynopsisToday, many Americans are concerned about the impact that artificial intelligence and machine learning will have on jobs. This book examines the impact of these factors on the workforce, including issues related to worker displacement, retraining of the current workforce, and developing a skilled technical workforce of the future that can thrive in an economy in which AI increasingly plays a role.Table of ContentsPreface; Artificial Intelligence and the Future of Work; Machines, Artificial Intelligence, and the Workforce: Recovering and Readying Our Economy for the Future; Index.

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Book SynopsisThis book will focus on the involvement of data mining and intelligent computing methods for recent advances in Biomedical applications and algorithms of nature-inspired computing for Biomedical systems. The proposed meta heuristic or nature-inspired techniques should be an enhanced, hybrid, adaptive or improved version of basic algorithms in terms of performance and convergence metrics. In this exciting and emerging interdisciplinary area a wide range of theory and methodologies are being investigated and developed to tackle complex and challenging problems. Today, analysis and processing of data is one of big focuses among researchers community and information society. Due to evolution and knowledge discovery of natural computing, related meta heuristic or bio-inspired algorithms have gained increasing popularity in the recent decade because of their significant potential to tackle computationally intractable optimization dilemma in medical, engineering, military, space and industry fields. The main reason behind the success rate of nature inspired algorithms is their capability to solve problems. The nature inspired optimization techniques provide adaptive computational tools for the complex optimization problems and diversified engineering applications. Tentative Table of Contents/Topic Coverage:　 - Neural Computation - Evolutionary Computing Methods - Neuroscience driven AI Inspired Algorithms - Biological System based algorithms - Hybrid and Intelligent Computing Algorithms - Application of Natural Computing - Review and State of art analysis of Optimization algorithms - Molecular and Quantum computing applications - Swarm Intelligence - Population based algorithm and other optimizations

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Book SynopsisThis book brings together experts from research and practice. It includes the design of innovative Robot Process Automation (RPA) concepts, the discussion of related research fields (e.g., Artificial Intelligence, AI), the evaluation of existing software products, and findings from real-life implementation projects. Similar to the substitution of physical work in manufacturing (blue collar automation), Robotic Process Automation tries to substitute intellectual work in office and administration processes with software robots (white-collar automation). The starting point for the development of RPA was the observation that – despite the use of process-oriented enterprise systems (such as ERP, CRM and BPM systems) – additional manual activities are still indispensable today. In the RPA approach, these manual activities are learned and automated by software robots, either by defining rules or by observing manual activities. RPA is related to business process management, machine learning, and artificial intelligence. Tools for RPA originated from dedicated stand-alone software. Today, RPA functionalities are also integrated into elaborated process management suites. From a conceptual perspective, RPA can be structured into input components (sensors in the wide sense), an intelligence center, and output components (actuators in the wide sense). From a strategic perspective, the impact of RPA can be related to the support of existing tasks, the complete substitution of human activities, and the innovation of processes as well as business models. At present, high expectations are related to the use of RPA in the improvement of software-supported business processes. Manual activities are learned and automated by software robots that interact with existing applications via the presentation layer. In combination with artificial intelligence (AI) as well as innovative interfaces (e. g., voice recognition) RPA creates a novel level of automation for office and administration processes. Its benefit potential reaches a return on investment (ROI) up-to 800% that is documented in various case studies.

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Book SynopsisAlgorithms play a central role both in the theory and in the practice of computing. The goal of the authors was to write a textbook that would not trivialize the subject but would still be readable by most students on their own. The book contains over 120 exercises. Some of them are drills; others make important points about the material covered in the text or introduce new algorithms not covered there. The book also provides programming projects. From the Table of Contents: Chapter 1: Basic knowledge of Mathematics, Relations, Recurrence relation and Solution techniques, Function and Growth of functions. Chapter 2: Different Sorting Techniques and their analysis. Chapter 3: Greedy approach, Dynamic Programming, Branch and Bound techniques, Backtracking and Problems, Amortized analysis, and Order Statics. Chapter 4: Graph algorithms, BFS, DFS, Spanning Tree, Flow Maximization Algorithms. Shortest Path Algorithms. Chapter 5: Binary search tree, Red black Tree, Binomial heap, B-Tree and Fibonacci Heap. Chapter 6: Approximation Algorithms, Sorting Networks, Matrix operations, Fast Fourier Transformation, Number theoretic Algorithm, Computational geometry Randomized Algorithms, String matching, NP-Hard, NP-Completeness, Cooks theorem.

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Book SynopsisThis book focuses on the use of AI/ML-based techniques to solve issues related to IoT-based environments, as well as their applications. It addresses, among others, signal detection, channel modeling, resource optimization, routing protocol design, transport layer optimization, user/application behavior prediction, software-defi ned networking, congestion control, communication network optimization, security, and anomaly detection.

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Book SynopsisThis book focuses on the latest developments in the fields of visual AI, image processing and computer vision. It shows research in basic techniques like image pre-processing, feature extraction, and enhancement, along with applications in biometrics, healthcare, neuroscience and forensics. The book highlights algorithms, processes, novel architectures and results underlying machine intelligence with detailed execution flow of models.

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Book SynopsisThis book introduces the most recent research and innovative developments regarding the new strains of COVID-19. While medical and natural sciences have been working instantly on deriving solutions and trying to protect humankind against such virus types, there is also a great focus on technological developments for improving the mechanism – momentum of science for effective and efficient solutions. At this point, computational intelligence is the most powerful tools for researchers to fight against COVID-19. Thanks to instant data-analyze and predictive techniques by computational intelligence, it is possible to get positive results and introduce revolutionary solutions against related medical diseases. By running capabilities – resources for rising the computational intelligence, technological fields like Artificial Intelligence (with Machine / Deep Learning), Data Mining, Applied Mathematics are essential components for processing data, recognizing patterns, modelling new techniques and improving the advantages of the computational intelligence more. Nowadays, there is a great interest in the application potentials of computational intelligence to be an effective approach for taking humankind more step away, after COVID-19 and before pandemics similar to the COVID-19 many appear.

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Book SynopsisThis book provides research on the state-of-the-art methods for data management in the fourth industrial revolution, with particular focus on cloud.based data analytics for digital manufacturing infrastructures. Innovative techniques and methods for secure, flexible and profi table cloud manufacturing will be gathered to present advanced and specialized research in the selected area.

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Book SynopsisThis book shows some contributions presented in the 2010 International Conference on Robotic Welding, Intelligence and Automation (RWIA’2010), Oct. 14-16, 2010, Shanghai, China. Welding handicraft is one of the most primordial and traditional techniques, mainly by manpower and human experiences. Weld quality and efficiency are, therefore, straightly limited by the welder’s skill. In the modern manufacturing, automatic and robotic welding is becoming an inevitable trend. In recent years, the intelligentized techniques for robotic welding have a great development. The current teaching play-back welding robot is not with real-time functions for sensing and adaptive control of weld process. Generally, the key technologies on Intelligentized welding robot and robotic welding process include computer visual and other information sensing, monitoring and real-time feedback control of weld penetration and pool shape and welding quality. Seam tracking is another key technology for welding robot system. Some applications on intelligentized robotic welding technology is also described in this book, it shows a great potential and promising prospect of artificial intelligent technologies in the welding manufacturing.Table of ContentsPart I: Intelligent Techniques for Robotic Welding.- Part II: Sensing of Arc Welding Processing.- Part III: Modeling and Intelligent Control of Welding Processing.- Part IV: Welding Technics and Automations.- Part V: Special Robot Technology and Systems.- Part VI: Intelligent Control and its Applications in Engineering.

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