Handbook of Artificial Intelligence

Table of Contents

Chapter 1 Machine Learning Techniques and Their Applications:

• Survey
• P. Karthik, K. Chandra Sekhar and D. Latha

1. Introduction
1.1. History of Ai & Ml
1.2. Applications of Ml
1.2.1. Speech Recognition [19]
1.2.2. Predictive Analytics [19]
1.2.3. Product Recommendation [20]
1.2.4. Image Recognition [19]
1.2.5. Video Surveillance [20]
1.2.6. Extraction [19]
1.2.7. Traffic Alerts [20]
1.2.8. Medical Diagnosis [19]
1.2.9. Sentiment Analysis [20]
1.2.10. Google Translate [20]
1.2.11. Virtual Personal Assistants [20]
1.3. Difference Between Traditional Programming Concepts and Ml Concepts [23]
1.3.1. Why Must We Learn Ml?
1.3.2. Difference Between Ai & Ml [23]
1.4. Steps to Learn Ml
1.4.1. Data Gathering [24]
1.4.2. Data Preparation [24]
1.4.3. Selecting the Model [24]
1.4.4. Training the Model [24]
1.4.5. Evaluate the Model [24]
1.4.6. Parameter Tuning [24]
1.4.7. Make Patterns [24]
1.5. Types of Ml
1.6. Basic Ml Methods
1.7. Ml in Agriculture
1.8. Ml in Sentiment Analysis
1.9. Ml in Stock Prediction
1.10. Ml in Disease Prediction
1.11. Ml in Data Mining
1.12. Ml in Covid-19
1.13. Ml in Cyber Security
1.14. Ml in Cloud Computing
1.15. Ml in Big Data Analytics (Bda)
1.16. Ml in Recommendation System
1.17. Future Experiments on Real Time Problems Using Ml

• Conclusion
• References

Chapter 2 Applications of Machine Learning

• K. Sudheer Babu, Ch. M. Reddy, A. Swapna and D. Abdus Subhahan

1. Introduction
1.1. Machine Learning
1.2. Machine Learning Vs. Traditional Programming
1.2.1. Machine Learning Work
2. Machine Learning Algorithms
2.1. Introduction
2.2. Supervised Learning
2.2.1. Classification
2.2.2. Regression
2.3. Unsupervised Learning
3. Application of Machine Learning
3.1. Augmentation
3.2. Automation
3.3. Finance Industry
3.4. Government Organization
3.5. Healthcare Industry 2
3.6. Marketing
3.7. Traffic Snarl
3.8. Image Recognition
3.9. Video Reconnaissance
3.10. Analysis of Sentiment
3.11. Recommendation of a Product
3.12. Online Support Using Chatbots
3.13. Google Translate
3.14. Online Video Streaming Applications
3.15. Virtual Professional Assistants
3.16. Usage of Ml in Social Media
3.17. Stock Market Signals Utilising Ml
3.18. Cars That Are Auto-Driven
3.19. Dynamic Pricing- Real-Time

• Conclusion
• References

Chapter 3 Prediction Using Machine Learning

• Adluri Vijaya Lakshmi, Sowmya Gudipati Sri, Ponnuru Sowjanya and K. Vedavathi

1. Introduction to Machine Learning
2. Classification of Machine-Learning
2.1. Supervised Learning
2.2. Unsupervised Learning
2.3. Reinforcement Learning
3. Breast Cancer Prediction Using Ml Techniques
3.1. Introduction
3.2. Related Works
4. Heart Disease Prediction Using Machine Learning Techniques
4.1. Introduction
4.2. Existing System
5. Predicting Ipl Results Using Ml Techniques
5.1. Introduction
5.2. Related Work
6. Prediction of Software Bug Utilising Ml Technique
6.1. Introduction
6.2. Related Work
7. Prediction of Rainfall Using Machine Learning Techniques
7.1. Introduction
7.2. Related Work
8. Weather Prediction Using Machine Learning Techniques
8.1. Machine Learning
8.2. Use of Algorithms

• Conclusion
• References

Chapter 4 Machine Learning Algorithms for Health Care Data

• Analytics Handling Imbalanced Datasets
• T. Sajana and K.V.S.N. Rama Rao

1. Introduction
2. Machine Learning- An Intelligent Automated System
3. Types of Datasets-By Nature
3.1. Balanced Datasets
3.2. Imbalanced Datasets
4. Issues With Imbalanced Datasets
4.1. Class Imbalance Problem
4.2. Classifiers Learning on Imbalanced Datasets
4.3. Taxonomy of Various Techniques on Imbalanced Datasets
5. Application of Conventional Data Mining & Machine Learning

• Techniques for Handling Class Imbalance Problem

6. Application of Data Level Methods for Handling Class

• Imbalance Problem

6.1. Undersampling
6.2. Oversampling
7. Application of Algorithmic Level Methods for Handling Class

• Imbalance Problem

7.1. Cost-Sensitive Classifiers
7.2. Ensemble Techniques

• Conclusion
• References

Chapter 5 Ai for Crop Improvement

• S.V. Vasantha

1. Introduction
2. Genomics for Agriculture
3. Ai for Agriculture
4. Ai Techniques for Crop Improvement
5. Ai-Based Crop Improvement Model (Ai-Cim)
5.1. Automation of Modern Crop Improvement Process
5.2. Ai Model for Enhanced Crop Breeding
5.2.1. Automated Selective Breeding System
5.2.2. Automated Plant Health Monitoring System

• Conclusion
• References

Chapter 6 Real-Time Object Detection and Localization For

• Autonomous Driving
• Swathi Gowroju, V. Swathi, J. Narasimha Murthy and D. Sai Kamesh

1. Introduction
2. Literature Survey
3. Proposed Method
3.1. Proposed Architecture
4. Implementation
4.1. Bounding Boxes
4.2. Anchor Boxes
4.3. Non-Max Suppression
5. Relu Activation
6. Loss Function
7. Training Parameters
8. Results

• Conclusion
• Acknowledgement
• References

Chapter 7 Machine Learning Techniques in Image Segmentation

• Narmada Kari, Sanjay Kumar Singh and Dumpala Shanthi

1. Introduction
2. Literature Review
3. Methodology
3.1. Collection of Data
3.2. Pre-Processing of Images
3.3. Training Options
3.4. Define Label Ids
3.5. Feature Extraction
3.6. Feature Reduction/Selection
3.7. Feature Classification
3.8. Machine Learning
3.8.1. Supervised Learning
3.8.2. Unsupervised Learning
3.8.3. Reinforcement Learning
3.8.4. Deep Learning
3.8.5. Deep Reinforcement Learning

• Conclusion
• References

Chapter 8 Optimal Page Ranking Technique for Webpage

• Personalization Using Semantic Classifier
• P. Pranitha, A. Manjula, G. Narsimha and K. Vaishali

1. Introduction
2. Literature Survey
2.1. Challenges
2.2. Motivation of Research
2.3. Proposed Methodology
2.4. Generation of Web Pages
3. Pre-Processing
3.1. Feature Extraction and Web Page Ranking
3.2. Enn-Based Semantic Features
4. Re-Ranking of Web Pages
4.1. Grass Hopper Optimization (Gho)
4.1.1. Social Interaction Calculation
4.1.2. Solution Updating

Author

• Dumpala Shanthi
• B. Madhuravani
• Ashwani Kumar