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Books in Artificial intelligence

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71-80 of 523 results in All results

Emerging Practices in Telehealth

  • 1st Edition
  • February 15, 2023
  • Andrew M. Freeman + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 5 9 8 0 - 0
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 5 9 8 1 - 7
Emerging Practices in Telehealth: Best Practices in a Rapidly Changing Field is an introduction to telehealth basics, best practices and implementation methods. The book guides the reader from start to finish through the workflow implementation of telehealth technology, including EMRs, clinical workflows, RPM, billing systems, and patient experience. It also explores how telehealth can increase healthcare access and decrease disparities across the globe. Practicing clinicians, medical fellows, allied healthcare professionals, hospital administrators, and hospital IT professionals will all benefit from this practical guidebook.

Intelligent Edge Computing for Cyber Physical Applications

  • 1st Edition
  • February 3, 2023
  • D. Jude Hemanth + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 9 4 1 2 - 5
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 9 4 3 3 - 0
Intelligent Edge Computing for Cyber Physical Applications introduces state-of-the-art research methodologies, tools and techniques, challenges, and solutions with further research opportunities in the area of edge-based cyber-physical systems. The book presents a comprehensive review of recent literature and analysis of different techniques for building edge-based CPS. In addition, it describes how edge-based CPS can be built to seamlessly interact with physical machines for optimal performance, covering various aspects of edge computing architectures for dynamic resource provisioning, mobile edge computing, energy saving scenarios, and different security issues. Sections feature practical use cases of edge-computing which will help readers understand the workings of edge-based systems in detail, taking into account the need to present intellectual challenges while appealing to a broad readership, including academic researchers, practicing engineers and managers, and graduate students.

Hamiltonian Monte Carlo Methods in Machine Learning

  • 1st Edition
  • February 3, 2023
  • Tshilidzi Marwala + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 3 - 1 9 0 3 5 - 3
  • eBook
    9 7 8 - 0 - 4 4 3 - 1 9 0 3 6 - 0
Hamiltonian Monte Carlo Methods in Machine Learning introduces methods for optimal tuning of HMC parameters, along with an introduction of Shadow and Non-canonical HMC methods with improvements and speedup. Lastly, the authors address the critical issues of variance reduction for parameter estimates of numerous HMC based samplers. The book offers a comprehensive introduction to Hamiltonian Monte Carlo methods and provides a cutting-edge exposition of the current pathologies of HMC-based methods in both tuning, scaling and sampling complex real-world posteriors. These are mainly in the scaling of inference (e.g., Deep Neural Networks), tuning of performance-sensitive sampling parameters and high sample autocorrelation. Other sections provide numerous solutions to potential pitfalls, presenting advanced HMC methods with applications in renewable energy, finance and image classification for biomedical applications. Readers will get acquainted with both HMC sampling theory and algorithm implementation.

Data Science, Analytics and Machine Learning with R

  • 1st Edition
  • January 23, 2023
  • Luiz Paulo Favero + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 4 2 7 1 - 1
  • eBook
    9 7 8 - 0 - 3 2 3 - 8 5 9 2 3 - 3
Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.

Blockchain Technology Solutions for the Security of IoT-Based Healthcare Systems

  • 1st Edition
  • January 10, 2023
  • Bharat Bhushan + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 9 1 9 9 - 5
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 9 3 9 1 - 3
Blockchain Technology Solutions for the Security of IoT-Based Healthcare Systems explores the various benefits and challenges associated with the integration of blockchain with IoT healthcare systems, focusing on designing cognitive-embedded data technologies to aid better decision-making, processing and analysis of large amounts of data collected through IoT. This book series targets the adaptation of decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures, as well as big data and Internet of Things (IoT) problems can be handled in practice. Current Internet of Things (IoT) based healthcare systems are incapable of sharing data between platforms in an efficient manner and holding them securely at the logical and physical level. To this end, blockchain technology guarantees a fully autonomous and secure ecosystem by exploiting the combined advantages of smart contracts and global consensus. However, incorporating blockchain technology in IoT healthcare systems is not easy. Centralized networks in their current capacity will be incapable to meet the data storage demands of the incoming surge of IoT based healthcare wearables.

Intelligent Environments

  • 2nd Edition
  • December 5, 2022
  • P. Droege
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 2 0 2 4 7 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 0 2 4 8 - 7
The promises and realities of digital innovation have come to suffuse everything from city regions to astronomy, government to finance, art to medicine, politics to warfare, and from genetics to reality itself. Digital systems augmenting physical space, buildings, and communities occupy a special place in the evolutionary discourse about advanced technology. The two Intelligent Environments books edited by Peter Droege span a quarter of a century across this genre. The second volume, Intelligent Environments: Advanced Systems for a Healthy Planet, asks: how does civilization approach thinking systems, intelligent spatial models, design methods, and support structures designed for sustainability, in ways that could counteract challenges to terrestrial habitability? This book examines a range of baseline and benchmark practices but also unusual and even sublime endeavors across regions, currencies, infrastructure, architecture, transactive electricity, geodesign, net-positive planning, remote work, integrated transport, and artificial intelligence in understanding the most immediate spatial setting: the human body. The result of this quest is both highly informative and useful, but also critical. It opens windows on what must fast become a central and overarching existential focus in the face of anthropogenic planetary heating and other threats—and raises concomitant questions about direction, scope, and speed of that change.

Digital Twin for Healthcare

  • 1st Edition
  • November 21, 2022
  • Abdulmotaleb El Saddik
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 9 1 6 3 - 6
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 5 0 9 5 - 4
Digital Twins for Healthcare: Design, Challenges and Solutions establishes the state-of-art in the specification, design, creation, deployment and exploitation of digital twins' technologies for healthcare and wellbeing. A digital twin is a digital replication of a living or non-living physical entity. When data is transmitted seamlessly, it bridges the physical and virtual worlds, thus allowing the virtual entity to exist simultaneously with the physical entity. A digital twin facilitates the means to understand, monitor, and optimize the functions of the physical entity and provide continuous feedback. It can be used to improve citizens' quality of life and wellbeing in smart cities and the virtualization of industrial processes.

Meta-Learning

  • 1st Edition
  • November 5, 2022
  • Lan Zou
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 8 9 9 3 1 - 4
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 0 3 7 0 - 7
Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications.

Data Analytics for Social Microblogging Platforms

  • 1st Edition
  • November 4, 2022
  • Soumi Dutta + 3 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 1 7 8 5 - 8
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 7 2 3 0 - 7
Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

Statistical Modeling in Machine Learning

  • 1st Edition
  • October 29, 2022
  • Tilottama Goswami + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 3 2 3 - 9 1 7 7 6 - 6
  • eBook
    9 7 8 - 0 - 3 2 3 - 9 7 2 5 2 - 9
Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more.

Related subjects

Artificial intelligence expert systems and knowledge based systems

Artificial intelligence general

Cognitive science

Computer human interaction

Evolutionary computation genetic algorithms programming

Fuzzy logic

Machine learning

Natural language and computational linguistics

Neural networks

Planning and scheduling

Reasoning

Robotics and autonomous systems