This book is a quick review of machine learning methods for engineering applications. It provides an introduction to the principles of machine learning and common algorithms in the first section. The proceeding chapters summarize and analyze the existing scholarly work and discuss some general issues in this field. Next, it offers some guidelines on applying machine learning methods to software-engineering tasks. Finally, it gives an outlook into some of the future developments and possibly new research areas of machine learning and artificial intelligence in general. Techniques highlighted in the book include Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural network.
Finally, it also intends to be a reference book. Key Features: Describes real-world problems that can be solved using machine learning explains methods for directly applying machine learning techniques to concrete real-world problems explains 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 machine learning.
This book is meant to be an introduction to artificial intelligence (AI), machine earning, and its applications in Industry 4.0. It explains the basic mathematical principles but is intended to be understandable for readers who do not have a background in advanced mathematics.
Table of Contents
Chapter 1 Cutting Edge Techniques of Adaptive Machine Learning for Image Processing and Computer Vision
- Introduction
- Techniques for Improvising Images
- Spatial-Domain Method
- Frequency-Domain Method
- Transforms: Image Improvement
- Wavelet-Transform Oriented Image Improvement
- Scaling and Translation
- Image Improvement With Filters
- Denoising of Images
- Frontward Transform
- Image Improvement With Principal Component Pca for 2D
- Implementing 2D-Pca
- Selection and Extraction of Features
- Criteria for Selecting Features
- Linear Criteria for Extracting Features
- Discontinuity Handling
- Integration Part: Limitations
- Alteration of Smoothness Terminology
- Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgement
- References
Chapter 2 Algorithm for Intelligent Systems
- Introduction
- Reinforcement Learning
- Q-Learning
- Game Theory
- Machine Learning
- Decision Tree
- Logistic Regression
- K-Means Clustering
- Artificial Neural Network (Ann)
- Swarm Intelligence
- Swarm Robots
- Swarm Intelligence in Decision Making Algorithm
- Natural Language Processing
- Conclusion
- Future Scope
- Consent for Publication
- Conflict of Interest
- Acknowledgements
- References
Chapter 3 Clinical Decision Support System for Early Prediction of Congenital Heart Disease Using Machine Learning Techniques
- Introduction
- Related Work
- Proposed Methodology and Dataset
- Steps for Training and Testing the Dataset
- Machine Learning Algorithms for Prediction
- Support Vector Machine
- Random Forest
- Multilayer Perceptron
- Input Layer
- Hidden Layer
- Output Layer
- K- Nearest Neighbor (K-Nn)
- Experiments and Results
- Comparison Results
- Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgements
- References
Chapter 4 a Review on Covid-19 Pandemic and Role of Multilingual Information Retrieval and Machine Translation for Managing Its Effect
- Introduction
- Related Work
- Outbreak Stage of Covid 19
- Travel History From Infected Countries
- Local Transmission
- Geographical Cluster of Cases
- Community Transmission
- Current Situation in India
- Treatment
- Illness Severity
- Antibody and Plasma Therapy
- Vaccine
- Preventive Measure
- Myths
- Emerging Technology for Mitigating the Effect of the Covid-19 Pandemic
- Infodemic and Natural Language Processing
- Arogya Setu App
- Issues of Languages All Over the World and Machine Translation
- Difficulties in Accessing Data in the Native Language
- Information Retrieval System for Covid-19
- New Information Retrieval System for Covid-19: Trec Covid
- Co-Search: Covid-19 Information Retrieval
- Covid-19 Dataset Search System
- Role of Cross-Lingual and Multilingual Information Retrieval in Covid-19 Pandemic
- Challenges in Machine Translation, Information Retrieval and Mlir System
- Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgement
- References
Chapter 5 An Empirical View of Genetic Machine Learning Based on Evolutionary Learning Computations
- Introduction
- Preamble of Evolutionary Algorithms (Ea)
- Contextual Parameters of Ea
- Classification of Evolutionary Algorithms
- The Family of Evolutionary Algorithms
- Fitness Function & Probability
- Short-Term Memory Thresholding (Stm)
- Inclusion of Probabilistic and Stochastic Processes (Psp) in Ea
- Optimizing Eas
- Imitation
- Innovation
- Functionality of Ga
- Sample Code of Ea To Find Optimal Result of a Test
- Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgement
- References
Chapter 6 High-Performance Computing for Satellite Image Processing Using Apache Spark
- Introduction
- Parallel Computing
- Distributed Computing
- Virtual Machine Software (Vmware Workstation Pro)
- Apache Spark
- Features of Apache Spark
- Speed
- Supports Multiple Languages
- Reusability
- Components of Spark
- Apache Spark Core
- Spark Sql
- Spark Streaming
- Mllib (Machine Learning Library)
- Graphx
- Spark Architecture Overview
- Resilient Distributed Dataset (Rdd)
- Methodology
- Ndvi (Normalized Difference Vegetation Index)
- Proposed Plan Work
- Result
- Conclusion
- Consent for Publication
- Conflict of Interest
- Background
- Clinical Features
- Transmission Mechanism
- Organization
- Other Related Papers
- Effect of the Covid-19 Pandemic on the Global Economy
- Effects on the Lives of People
- Effects on Employment
- Employment Misfortune
- Treatment and Vaccine Development
- Vaccine Development
- Moderna's Mrna-1273
- Pittcovacc
- Vaccine From Johnson & Johnson
- Cepi Multiple Efforts
- Potential Drugs
- Preventive Measures
- Emerging Technologies To Mitigate the Covid-19 Pandemic Effect 103 Artificial Intelligence (Ai) and Covid-19
- Applications of Ai in Covid-19 Pandemic
- Early Detection and Diagnosis of the Infection
- Monitoring the Treatment
- Contact Tracing of Sars Cov-2 Individual
- Development of Drugs and Vaccines
- Reducing the Workload of Healthcare Workers
- Prevention of the Disease
- Summary of Ai Applications for Covid-19
- Future Scope of the Study and Conclusion
- Consent for Publication
- Conflict of Interest
- Acknowledgement
- References
Chapter 7 Intelligent Personalized E-Learning Platform Using Machine Learning Algorithms
Author
- Prasad Lokulwar
- Basant Verma
- N. Thillaiarasu
- Kailash Kumar
