Applied Learning Algorithms for Intelligent IoT

图书简介

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 devicesCyber physical systems (CPS)The Internet of Things (IoT) and industrial use casesIndustry;4.0 for smarter manufacturingPredictive and prescriptive insights for smarter systemsMachine vision and intelligenceNatural interfacesK-means clustering algorithmSupport vector machine (SVM) algorithmA priori algorithmsLinear 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 a

1. Convolutional Neural Network in Computer Vision Aishwarya D and R.I.Minu 2. Trends and Transition in the Machine Learning (ML) Space Sruthi Anand, N Susila, and S Usha 3. Deep Learning: Algorithms, Platforms, Applications, and Research Trends in IoT R. Ranjana, T. Sheela, B. Narendra Kumar Rao, and T. Subha 4. The Next-Generation IoT Use Cases across Industry Verticals using Machine and Deep Learning Algorithms Kalaiarasan T R, Sruthi Anand, V Anandkuma, and Ratheeshkumar A.M. 5. A Panoramic View of Cyber Attack Detection and Prevention Using Machine Learning and Deep Learning Approaches Esther Daniel, N. Susila, and S. Durga 6. Regression Algorithms in Machine Learning S. Usha, Neha Singhal, Pethuru Raj, and Ashwini R Malipatil 7. Machine Learning Based Industrial Internet of Things (IIoT) and Its Applications M. Sureshkumar, Rachel S, and Kaviselvan M V 8. Employee Turnover Prediction Using Single Voting Model R. Valarmathi, M. Umadevi, and T. Sheela 9. A Novel Implementation of Sentiment Analysis towards Data Science Vijayalakshmi Saravanan, Ishpreet Singh, Emanuel Szarek, Jereon Hak, and Anju S Pillai 10. Conspectus of K-Means Clustering Algorithm S.Usha, Jyothi A P, N Susila, and T. Sheela 11. Systematic Approach to Deal with Internal Fragmentation and Enhancing Memory Space during COVID-19 Aparna Mohan, Maheswari R, and Thomas Abraham J. V. 12. IoT Automated Spy Drone to Detect and Alert Illegal Drug Plants for Law Enforcement Gotluru Arun Kumar, PuluguYamini, and Maheswari R 13. Expounding K-Means-inspired Network Partitioning Algorithm for SDN Controller Placement Pushpa J. and Pethuru Raj 14. An Intelligent Deep Learning Based Wireless Underground Sensor System for IoT Based Agricultural Application Priscilla Rajadurai and G. Jaspher W. Kathrine 15. Predicting Effectiveness of Solar Pond Heat Exchanger with LTES Containing CUO Nanoparticle Using Machine Learning K Karunamurthy, G Suganya, M Ananthi, and T Subha

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Applied Learning Algorithms for Intelligent IoT 智能物联网应用学习算法 计算机科学技术基础学科

Applied Learning Algorithms for Intelligent IoT

智能物联网应用学习算法
计算机科学技术基础学科