Description
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.
Table of Contents
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
