Learning Algorithms For Internet Of Things
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Learning Algorithms for Internet of Things
This book describes learning algorithms that can be applied to IoT-based, real-time applications and improve the utilization of data collected and the overall performance of the system. The advent of Internet of Things (IoT) has paved the way for sensing the environment and smartly responding. This can be further improved by enabling intelligence to the system with the support of machine learning and deep learning techniques. Many societal challenges and problems can be resolved using a better amalgamation of IoT and learning algorithms. “Smartness” is the buzzword that is realized only with the help of learning algorithms. This book provides readers with an easier way to understand the purpose and application of learning algorithms on IoT. In addition, it supports researchers with code snippets that focus on the implementation and performance of learning algorithms on IoT based applications such as healthcare, agriculture, transportation, etc. What you’lllearn Machine learning, deep learning, and genetic learning algorithms for IoT. Python packages for learning algorithms, such as Scipy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch and more. Supervised algorithms such as Regression and Classification. Unsupervised algorithms, like K-means clustering, KNN, hierarchical clustering, principal component analysis, and more. Artificial neural networks for IoT (architecture, feedback, feed-forward, unsupervised). Convolutional neural networks for IoT (general, LeNet, AlexNet, VGGNet, GoogLeNet, etc.). Optimization methods, such as gradient descent, stochastic gradient descent, Adagrad, AdaDelta, and IoT optimization. Who This Book Is For The audience includes students interested in learning algorithms and their implementations, as well as researchers in IoT looking to extend their work with learning algorithms.
Applied Learning Algorithms for Intelligent IoT
This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry 4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interfaces K-means clustering algorithm Support vector machine (SVM) algorithm A priori algorithms Linear and logistic regression Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights. This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book’s detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.
Internet of Things
Author: Aurora González-Vidal
language: en
Publisher: Springer Nature
Release Date: 2023-01-01
This book constitutes revised selected papers from the refereed proceedings of the 5th The Global IoT Summit, GIoTS 2022, which took place in Dublin, Ireland, in June 20–23, 2022. The 33 full papers included in this book were carefully reviewed andselected from 75 submissions. They were organized in topical sections as follows: ioT enabling technologies; ioT applications, services and real implementations; ioT security, privacy and data protection; and ioT pilots, testbeds and experimentation results.