Bim-Enabled Cognitive Computing for Smart Built Environment
The book brings together the Building Information Model (BIM)-enabled cognitive computing methods for smart built environment, and focuses on the potential, requirements and implementation of cognitive Internet of Things (CIoT) paradigm to buildings.
Causal Inference and Discovery in Python
Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental dataPurchase of the print or Kindle book includes a free PDF eBookKey Features- Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more- Discover modern causal inference techniques for average and heterogenous treatment effect estimation- Explore and leverage traditional and modern causal discovery methodsBook DescriptionCausal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality.You'll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you'll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you'll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You'll further explore the mechanics of how "causes leave traces" and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.What you will learn- Master the fundamental concepts of causal inference- Decipher the mysteries of structural causal models- Unleash the power of the 4-step causal inference process in Python- Explore advanced uplift modeling techniques- Unlock the secrets of modern causal discovery using Python- Use causal inference for social impact and community benefitWho this book is forThis book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who've worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.Table of Contents- Causality - Hey, We Have Machine Learning, So Why Even Bother?- Judea Pearl and the Ladder of Causation- Regression, Observations, and Interventions- Graphical Models- Forks, Chains, and Immoralities- Nodes, Edges, and Statistical (In)dependence- The Four-Step Process of Causal Inference- Causal Models - Assumptions and Challenges- Causal Inference and Machine Learning - from Matching to Meta- Learners- Causal Inference and Machine Learning - Advanced Estimators, Experiments, Evaluations, and More- Causal Inference and Machine Learning - Deep Learning, NLP, and Beyond- Can I Have a Causal Graph, Please?(N.B. Please use the Read Sample option to see further chapters)
Decision Support System and Automated Negotiations
This text discusses the techniques to convert agricultural knowledge in the context of ontology and assist grape growers by providing this knowledge through decision support system. It presents the design & development of an ontology-based decision support system for integrated crop management.
Artificial Intelligence and Deep Learning for Computer Network
This reference text aims to systematically collect quality research spanning AI, ML and Deep Learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more insights on the applicability of the theoretical similitudes.
Integration of Cloud Computing with Emerging Technologies
This book gives a complete overview of cloud computing, its importance, its trends, innovations, and its amalgamation with other technologies. It provides content for reference.
Multimedia Data Processing and Computing
This book focuses on different applications of multimedia with supervised and unsupervised data engineering in the modern world. It includes AI-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, manufacturing, data science, automation in electronics industries, and many more relevant fields.Multimedia Data Processing and Computing provides a complete introduction to machine learning concepts, as well as practical guidance on how to use machine learning tools and techniques in real-world data engineering situations. It is divided into three sections. In this book on multimedia data engineering and machine learning, the reader will learn how to prepare inputs, interpret outputs, appraise discoveries, and employ algorithmic strategies that are at the heart of successful data mining. The chapters focus on the use of various machine learning algorithms, neural net- work algorithms, evolutionary techniques, fuzzy logic techniques, and deep learning techniques through projects, so that the reader can easily understand not only the concept of different algorithms but also the real-world implementation of the algorithms using IoT devices. The authors bring together concepts, ideas, paradigms, tools, methodologies, and strategies that span both supervised and unsupervised engineering, with a particular emphasis on multimedia data engineering. The authors also emphasize the need for developing a foundation of machine learning expertise in order to deal with a variety of real-world case studies in a variety of sectors such as biological communication systems, healthcare, security, finance, and economics, among others. Finally, the book also presents real-world case studies from machine learning ecosystems to demonstrate the necessary machine learning skills to become a successful practitioner.The primary users for the book include undergraduate and postgraduate students, researchers, academicians, specialists, and practitioners in computer science and engineering.
Data Science for Civil Engineering
This book explains use of data science-based techniques for modelling and providing optimal solutions to complex problems in civil engineering. It deals with the basics of data science and essential mathematics and covers pertinent applications in structural and environmental engineering, construction management, and transportation.
Advancement of Deep Learning and Its Applications in Object Detection and Recognition
Object detection is a basic visual identification problem in computer vision that has been explored extensively over the years. Visual object detection seeks to discover objects of specific target classes in a given image with pinpoint accuracy and apply a class label to each object instance. Object recognition strategies based on deep learning have been intensively investigated in recent years as a result of the remarkable success of deep learning-based image categorization. In this book, we go through in detail detector architectures, feature learning, proposal generation, sampling strategies, and other issues that affect detection performance. The book describes every newly proposed novel solution but skips through the fundamentals so that readers can see the field's cutting edge more rapidly. Moreover, unlike prior object detection publications, this project analyses deep learning-based object identification methods systematically and exhaustively, and also gives the most recent detection solutions and a collection of noteworthy research trends. The book focuses primarily on step-by-step discussion, an extensive literature review, detailed analysis and discussion, and rigorous experimentation results. Furthermore, a practical approach is displayed and encouraged.
Multi-Sensor and Multi-Temporal Remote Sensing
This book brings consolidated information in the form of fuzzy machine and deep learning models for single class mapping from multi-sensor multi-temporal remote sensing images at one place. It provides information about capabilities of multi-spectral and hyperspectral images, fuzzy machine learning models supported by case studies.
SAP GRC AC For Beginners
The book is useful and very helpful for the SAP techies working in SAP security area and wants to enhance their knowledge in SAP GRC AC .One should have a basic knowledge of SAP security and then with the help of the content mentioned here, reader can have an good overview you of SAP GRC and he/she would be able to start his career in SAP Grc Access Control area.
Modeling and Analysis of Communicating Systems
Rigorous theory and real-world applications for modeling and analysis of the behavior of complex communicating computer systems. Complex communicating computer systems--computers connected by data networks and in constant communication with their environments--do not always behave as expected. This book introduces behavioral modeling, a rigorous approach to behavioral specification and verification of concurrent and distributed systems. It is among the very few techniques capable of modeling systems interaction at a level of abstraction sufficient for the interaction to be understood and analyzed. Offering both a mathematically grounded theory and real-world applications, the book is suitable for classroom use and as a reference for system architects. The book covers the foundation of behavioral modeling using process algebra, transition systems, abstract data types, and modal logics. Exercises and examples augment the theoretical discussion. The book introduces a modeling language, mCRL2, that enables concise descriptions of even the most intricate distributed algorithms and protocols. Using behavioral axioms and such proof methods as confluence, cones, and foci, readers will learn how to prove such algorithms equal to their specifications. Specifications in mCRL2 can be simulated, visualized, or verified against their requirements. An extensive mCRL2 toolset for mechanically verifying the requirements is freely available online; this toolset has been successfully used to design and analyze industrial software that ranges from healthcare applications to particle accelerators at CERN. Appendixes offer material on equations and notation as well as exercise solutions.
Hands-On Graph Neural Networks Using Python
Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBookKey Features: Implement state-of-the-art graph neural network architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook Description: Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery.Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps.By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What You Will Learn: Understand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problemsWho this book is for: This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.
Sensing and Artificial Intelligence Solutions for Food Manufacturing
This book gives readers a practical introduction into machine learning and sensing techniques, their design and ultimately specific applications that could improve food production. It shows how these sensing and computing systems are suitable for process implementation in food factories.This book starts by giving the reader an overview of the historic structures of food manufacturing standards and how they defined today's manufacturing. It is followed by a topical introduction for professionals in the food industries in topics such as AI, machine learning, and neural networks. It also includes an explanation of the different sensor systems and their basic principles. It shows how these sensing and computing systems are suitable for process implementation in food factories and what types of sensing systems have already been proven to deliver benefit to the food manufacturing industries. The authors also discuss issues around food safety, labelling, and traceability and how sensing and AI can help to resolve issues. They also use case studies and specific examples that can show the benefit of such technologies compared to current approaches. This book is a practical introduction and handbook for students, food engineers, technologists and process engineers on the benefits and challenges around modern manufacturing systems following Industry 4.0 approaches.
Deep Learning for Crack-Like Object Detection
Accurately detecting crack localization is not an easy task. This book addresses important issues in detecting crack-like objects and provides a practical smart pavement surface inspection system using deep learning.
Advances in Soft Computing Applications
This book discusses the most recent soft computing and fuzzy logic-based applications and innovations in industrial advancements, supply chain and logistics, system optimization, decision-making, artificial intelligence, smart systems, and other rapidly evolving technologies.
Computational Techniques for Text Summarization based on Cognitive Intelligence
The book is concerned with contemporary methodologies used for automatic text summarization. It proposes interesting approaches to solve well-known problems on text-summarization using computational intelligence (CI) techniques including cognitive approaches.
Smart Urban Computing Applications
This book reports research advances in the area of deep learning, IoT and urban computing, and describes new insights based on deep learning and IoT for urban computing.
Soft Computing
This book explores Soft Computing techniques in a systematic manner starting from their initial stage to recent developments in this area. The book presents a survey of the existing knowledge and also the current state-of-the-art development through cutting-edge original new contributions from the researchers.
A Developer's Guide to Cloud Apps Using Microsoft Azure
Build and deploy modern and secure applications on Microsoft Azure by implementing best practices, patterns, and new technologies with this easy-to-follow guidePurchase of the print or Kindle book includes a free PDF eBookKey Features: Learn various methods to migrate legacy applications to cloud using different Azure servicesImplement continuous integration and deployment as a best practice for DevOps and agile developmentGet started with building cloud-based applications using containers and orchestrators in different scenariosBook Description: Companies face several challenges during cloud adoption, with developers and architects needing to migrate legacy applications and build cloud-oriented applications using Azure-based technologies in different environments. A Developer's Guide to Cloud Apps Using Microsoft Azure helps you learn how to migrate old apps to Azure using the Cloud Adoption Framework and presents use cases, as well as build market-ready secure and reliable applications.The book begins by introducing you to the benefits of moving legacy apps to the cloud and modernizing existing ones using a set of new technologies and approaches. You'll then learn how to use technologies and patterns to build cloud-oriented applications. This app development book takes you on a journey through three major services in Azure, namely Azure Container Registry, Azure Container Instances, and Azure Kubernetes Service, which will help you build and deploy an application based on microservices. Finally, you'll be able to implement continuous integration and deployment in Azure to fully automate the software delivery process, including the build and release processes.By the end of this book, you'll be able to perform application migration assessment and planning, select the right Azure services, and create and implement a new cloud-oriented application using Azure containers and orchestrators.What You Will Learn: Get to grips with new patterns and technologies used for cloud-native applicationsMigrate old applications and databases to Azure with easeWork with containers and orchestrators to automate app deploymentSelect the right Azure service for deployment as per the use casesSet up CI/CD pipelines to deploy apps and services on Azure DevOpsLeverage Azure App Service to deploy your first applicationBuild a containerized app using Docker and Azure Container RegistryWho this book is for: This book is for cloud developers, software architects, system administrators, developers, and computer science students looking to understand the new role of the software architect or developer in the cloud world. Professionals looking to enhance their cloud and cloud-native programming concepts will also find this book useful. A sound background in C#, ASP.NET Core, and Visual Studio (any recent version) and basic knowledge of cloud computing will be helpful.
Machine Learning Methods for Engineering Application Development
This book is a quick review of machine learning methods for engineeringapplications. It provides an introduction to the principles of machine learningand common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field.Next, it offers some guidelines on applying machine learning methods to softwareengineering tasks. Finally, it gives an outlook into some of the futuredevelopments and possibly new research areas of machine learning and artificialintelligence in general.Techniques highlighted in the book include: Bayesian models, supportvector machines, decision tree induction, regression analysis, and recurrent andconvolutional neural network. Finally, it also intends to be a reference book. Key Features: Describes real-world problems that can be solved using machine learningExplains methods for directly applying machine learning techniques to concrete real-world problemsExplains concepts used in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT). It does not require prior knowledge of the machine learning This book is meantto be an introduction to artificial intelligence (AI), machine earning, and itsapplications in Industry 4.0. It explains the basic mathematical principlesbut is intended to be understandable for readers who do not have a backgroundin advanced mathematics.
Smart Trajectories
This book highlights the developments, discoveries, and practical and advanced experiences related to responsive distributed computing and how it can support the deployment of trajectory-based applications in smart systems.
ACSL Contests 2020-2021
This book contains the 2020-2021 contests organized by the American Computer Science League. ACSL is divided into multiple divisions to appeal to different abilities of students. Each competition in the Senior, Intermediate, and Junior divisions consist of two parts: Theory Problems that cover fundamental concepts in computer science and a Coding Problem that students solve by writing a computer program in a language of their choice. The Elementary and Classroom divisions consist of theory problems only. The 2020-2021 school year was ACSL's 43rd year of continuous operation! About 5,000 students in the United States, Canada, Europe, and Asia participated. We welcome the reader to learn more about the American Computer Science League on its website, www.acsl.org.
Diagnosis of Neurological Disorders Based on Deep Learning Techniques
This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders.
Introduction to Spacecraft Control Centers
This book covers the topic of satellite control centers. We'll take a look at the historical development of satellite control centers, from the earliest efforts of the iconic Apollo Mission Control at Houston. The primary focus will be NASA efforts, but similar facilities for other nation's spaceflight efforts will also be presented. This book is intended as an introduction to the subject. We'll look at the evolution of satellite control centers to understand how we got to where we are, and we'll look at evolving technology to see where we can go.As technology advances, we have a better basis for control centers, as well as cheaper yet more capable hardware, and better and more available software. With the proliferation of inexpensive Cubesat projects, colleges and universities, high school, and even individuals are getting their Cubesats launched. They all need control centers. For lower cost missions, these can be shared facilities. Communicating with and operating a spacecraft in orbit or on another planet is challenging, but is an extension of operating any remote system. We have communications and bandwidth issues, speed-of-light communication limitations, and complexity. Remote debugging is a always a challenge. The satellite control center is part of what is termed the Ground Segment, which also includes the communication uplink and downlink. The control center generates uplink data (commands) to the spacecraft, and receives, processes, and archives downlink (telemetry) data. Now, we can implement control-center-as-a service, and there are global colloborative networks for command and control.
AI and Digital Technology for Oil and Gas Fields
The book essentially covers the growing role of AI in oil and gas industry, including digital technology in exploration phase to customer sales service along with a cloud-based digital storage of reservoir simulation data for modeling. It initiates with the description of AI system and its role towards oil and gas business.
Iot in Healthcare Systems
Implementing new information technologies into the healthcare sector can provide alternatives to managing patients' health records, systems, and improving the quality of care received. This book provides an overview of IoT technologies related to the healthcare field and covers the main advantages and disadvantages along with case studies.
B C, Before Computers
The idea that the digital age has revolutionized our day-to-day experience of the world is nothing new, and has been amply recognized by cultural historians. In contrast, Stephen Robertson's BC: Before Computers is a work which questions the idea that the mid-twentieth century saw a single moment of rupture. It is about all the things that we had to learn, invent, and understand - all the ways we had to evolve our thinking - before we could enter the information technology revolution of the second half of the twentieth century. Its focus ranges from the beginnings of data processing, right back to such originary forms of human technology as the development of writing systems, gathering a whole history of revolutionary moments in the development of information technologies into a single, although not linear narrative. Treading the line between philosophy and technical history, Robertson draws on his extensive technical knowledge to produce a text which is both thought-provoking and accessible to a wide range of readers. The book is wide in scope, exploring the development of technologies in such diverse areas as cryptography, visual art and music, and the postal system. Through all this, it does not simply aim to tell the story of computer developments but to show that those developments rely on a long history of humans creating technologies for increasingly sophisticated methods of manipulating information.Through a clear structure and engaging style, it brings together a wealth of informative and conceptual explorations into the history of human technologies, and avoids assumptions about any prior knowledge on the part of the reader. As such, it has the potential to be of interest to the expert and the general reader alike.
B C, Before Computers
The idea that the digital age has revolutionized our day-to-day experience of the world is nothing new, and has been amply recognized by cultural historians. In contrast, Stephen Robertson's BC: Before Computers is a work which questions the idea that the mid-twentieth century saw a single moment of rupture. It is about all the things that we had to learn, invent, and understand - all the ways we had to evolve our thinking - before we could enter the information technology revolution of the second half of the twentieth century. Its focus ranges from the beginnings of data processing, right back to such originary forms of human technology as the development of writing systems, gathering a whole history of revolutionary moments in the development of information technologies into a single, although not linear narrative. Treading the line between philosophy and technical history, Robertson draws on his extensive technical knowledge to produce a text which is both thought-provoking and accessible to a wide range of readers. The book is wide in scope, exploring the development of technologies in such diverse areas as cryptography, visual art and music, and the postal system. Through all this, it does not simply aim to tell the story of computer developments but to show that those developments rely on a long history of humans creating technologies for increasingly sophisticated methods of manipulating information.Through a clear structure and engaging style, it brings together a wealth of informative and conceptual explorations into the history of human technologies, and avoids assumptions about any prior knowledge on the part of the reader. As such, it has the potential to be of interest to the expert and the general reader alike.
Data-Driven Intelligence in Wireless Networks
This book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and AI.
Production-Ready Applied Deep Learning
Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud servicesKey Features: Understand how to execute a deep learning project effectively using various tools availableLearn how to develop PyTorch and TensorFlow models at scale using Amazon Web Services Explore effective solutions to various difficulties that arise from model deploymentBook Description: Machine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives.First, you will learn how to construct complex deep learning models in PyTorch and TensorFlow. Next, you will acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce. The book also provides concrete implementations and associated methodologies that will help you apply the knowledge you gain right away. You will get hands-on experience with commonly used deep learning frameworks and popular cloud services designed for data analytics at scale. Additionally, you will get to grips with the authors' collective knowledge of deploying hundreds of AI-based services at a large scale.By the end of this book, you will have understood how to convert a model developed for proof of concept into a production-ready application optimized for a particular production setting.What You Will Learn: Understand how to develop a deep learning model using PyTorch and TensorFlowConvert a proof-of-concept model into a production-ready applicationDiscover how to set up a deep learning pipeline in an efficient way using AWSExplore different ways to compress a model for various deployment requirementsDevelop Android and iOS applications that run deep learning on mobile devicesMonitor a system with a deep learning model in productionChoose the right system architecture for developing and deploying a modelWho this book is for: Machine learning engineers, deep learning specialists, and data scientists will find this book helpful in closing the gap between the theory and application with detailed examples. Beginner-level knowledge in machine learning or software engineering will help you grasp the concepts covered in this book easily.
Telepresence: Actual and Virtual
1. EARLY HISTORY OF ROBOTIC TELEPRESENCE AND VIRTUAL REALITY. 2. ELEMENTS OF THE TECHNOLOGY. 3. WHAT IS TELEPRESENCE? WHAT IS REALITY?. 4. APPLICATIONS. 5. CHALLENGES FOR ROBOTIC TELEPRESENCE AND VIRTUAL REALITY. APPENDIX 1. RESPONSES TO POLLING. APPENDIX 2. BIBLIOGRAPHY.
Cognitive IoT
This book contains different research area of Cognitive IoT and explains how Machine learning algorithms can be applied for Cognitive IoT with applications (Covid-19), student performance evaluation, human healthcare for chronic disease prediction, wearable sensors energy optimization, and in farming.
Intelligent Internet of Things for Smart Healthcare Systems
The book focuses on developments in AI and IoT integration for smart healthcare, with an emphasis on current methodologies and frameworks for the design, growth, implementation, and creative use of such convergence technologies to provide insight into smart healthcare service demands, including connectivity and intelligent networks.
Bioinformatics Tools and Big Data Analytics for Patient Care
This book describe the applications of bioinformatics, data management and computational techniques in clinical studies and drug discovery for patient care. It gives details about the recent developments in the fields of artificial intelligence, cloud computing and data analytics for improved patient care.
Cyber Security Threats and Challenges Facing Human Life
This book proposes to provide a comprehensive view of the issues, threats, and challenges that are faced in the cyber security domain with a detailed analysis of effective countermeasures and mitigations. It is primarily aimed at undergraduate students, graduate students, researchers, academicians, and professionals working in the area.
Survival Analysis
Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way.Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis.Features: Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model, Aalen's additive hazards model, etc. Information criteria to facilitate model selection including Akaike, Bayes, and Focused Penalized methods Survival trees and ensemble techniques of bagging, boosting, and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book
Practical Deep Learning at Scale with MLflow
Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflowKey Features: Focus on deep learning models and MLflow to develop practical business AI solutions at scaleShip deep learning pipelines from experimentation to production with provenance trackingLearn to train, run, tune and deploy deep learning pipelines with explainability and reproducibilityBook Description: The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas.From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox.By the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.What You Will Learn: Understand MLOps and deep learning life cycle developmentTrack deep learning models, code, data, parameters, and metricsBuild, deploy, and run deep learning model pipelines anywhereRun hyperparameter optimization at scale to tune deep learning modelsBuild production-grade multi-step deep learning inference pipelinesImplement scalable deep learning explainability as a serviceDeploy deep learning batch and streaming inference servicesShip practical NLP solutions from experimentation to productionWho this book is for: This book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.
Pastoral Virtues for Artificial Intelligence
Pastoral Virtues for Artificial Intelligence (AI) acknowledges that human destiny is intimately tied to artificial intelligence. AI already outperforms a person on most tasks. Our ever-deepening relationship with an AI that is increasingly autonomous mirrors our relationship to what is perceived as Sacred or Divine. Like God, AI awakens hope and fear in people, while giving life to some and taking livelihood, especially in the form of jobs, from others. AI, built around values of convenience, productivity, speed, efficiency, and cost reduction, serve humanity poorly, especially in moments that demand care and wisdom. This book explores the pastoral virtues of hope, patience, play, wisdom, and compassion as foundational to personal flourishing, communal thriving, and building a robust AI. Biases of determinism, speed, objectivity, ignorance, and apathy within AI's algorithms are identified. These biases can be minimized through the incorporation of pastoral virtues as values guiding AI.
Soft Computing in Materials Development and Its Sustainability in the Manufacturing Sector
This book focuses on the application of soft computing in materials and manufacturing sectors with the objective to offer an intelligent approach to improve the manufacturing process, material selection and characterization techniques for developing advanced new materials.
Green Internet of Things
This book focusses on both theoretical and implementation aspects in green computing, next generation networks or networks that can be utilized in providing green systems through IoT enabling technologies i.e. technology behind its architecture and building components. It also encompasses design concepts and related advanced computing.
Computational Intelligence for Wireless Sensor Networks
This book addresses the challenges for the development of Computational Intelligence based WSN for various applications in public management system. It presents theoretical, methodological, well-established and validated empirical work related to the subject.
Machine Learning for Edge Computing
This book divides edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). It focuses on providing optimal solutions to the key concerns in edge computing through effective AI technologies, and it also discusses how to build AI models, i.e., model training and inference, on edge.
Deep Learning in Visual Computing
This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning.
Deep Learning in Visual Computing
This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning.
Phishing Detection Using Content-Based Image Classification
Phishing Detection using Content Based Image Classification explores Phishing detection using computer vision through CNN, transfer learning and representation learning, utilizing ML & DL classifiers. This book is primarily aimed at researchers, professionals and students in field of Computer Vision and Cyber Security domain.
Practical Digital Design
The VHSIC Hardware Description Language (VHDL) is one of the two most popular languages used to design digital logic circuits. This book provides a comprehensive introduction to the syntax and the most commonly used features of VHDL. It also presents a formal digital design process and the best-case design practices that have been developed over more than twenty-five years of VHDL design experience by the author in military ground and satellite communication systems. Unlike other books on this subject, this real-world professional experience captures not only the what of VHDL, but also the how. Throughout the book, recommended methods for performing digital design are presented along with the common pitfalls and the techniques used to successfully avoid them. Written for students learning VHDL for the first time as well as professional development material for experienced engineers, this book's contents minimize design time while maximizing the probability of first-time design success.
Data Structures using C
The data structure is a set of specially organized data elements and functions, which are defined to store, retrieve, remove and search for individual data elements. Data Structures using C: A Practical Approach for Beginners covers all issues related to the amount of storage needed, the amount of time required to process the data, data representation of the primary memory and operations carried out with such data. Data Structures using C: A Practical Approach for Beginners book will help students learn data structure and algorithms in a focused way. Resolves linear and nonlinear data structures in C language using the algorithm, diagrammatically and its time and space complexity analysis Covers interview questions and MCQs on all topics of campus readiness Identifies possible solutions to each problem Includes real-life and computational applications of linear and nonlinear data structures This book is primarily aimed at undergraduates and graduates of computer science and information technology. Students of all engineering disciplines will also find this book useful.
