Machine Learning Techniques and Industry Applications

✍ Scribed by Srivastava Pramod

• Publisher: Engineering Science Reference
• Year: 2024
• Tongue: English
• Leaves: 327
• Category: Library

✦ Synopsis

In today's rapidly evolving world, the exponential growth of data poses a significant challenge. As data volumes increase, traditional methods of analysis and decision-making become inadequate. This surge in data complexity calls for innovative solutions that efficiently extract meaningful insights. Machine learning has emerged as a powerful tool to address this challenge, offering algorithms and techniques to analyze large datasets and uncover hidden patterns, trends, and correlations.

Machine Learning Techniques and Industry Applications demystifies machine learning through detailed explanations, examples, and case studies, making it accessible to a broad audience. Whether you're a student, researcher, or practitioner, this book equips you with the knowledge and skills needed to harness the power of machine learning to address diverse challenges. From e-government to healthcare, cyber-physical systems to agriculture, this book explores how machine learning can drive innovation and sustainable development.

✦ Table of Contents

Cover
Title Page
Copyright Page
Book Series
Mission
Coverage
Preface
Chapter 1: A Novel Study on IoT and Machine Learning-Based Transportation
ABSTRACT
INTRODUCTION
MACHINE LEARNING AND IOT BASED TRANSPORTATION
CONCLUSION
REFERENCES
Chapter 2: A Review on Application of Artificial Intelligence in Mechanical Engineering
ABSTRACT
INTRODUCTION
REFERENCES
Chapter 3: An Anticipatory Framework for Categorizing Nigerian Supreme Court Rulings
ABSTRACT
INTRODUCTION
SUPPLIES AND PROCEDURES
CONCLUSION
REFERENCES
Chapter 4: Analysis Model at Sentence Level for Phishing Detection
ABSTRACT
INTRODUCTION
PHISHING DETECTION FEATURES AT THE SENTENCE LEVEL
RESEARCH PURPOSE
MODELS FOR DETECTING PHISHING
TECHNIQUES
OUTCOMES
RESULTS AND FUTURE WORK
REFERENCES
Chapter 5: Cancer Prediction Using Graph Database
ABSTRACT
INTRODUCTION
METHODOLOGY OF THE PROJECT
CONCLUSION
REFERENCES
Chapter 6: Change Detection Based on Binary Mask Enhancement
ABSTRACT
INTRODUCTION
FILTERING METHODS
BACKGROUND AND LITERATURE REVIEW
MAIN FOCUS OF THE CHAPTER
PROPOSED METHODOLOGY
EXPERIMENTAL RESULTS
FUTURE RESEARCH DIRECTIONS
CONCLUSION
REFERENCES
Chapter 7: Computer Vision and Its Intelligence in Industry 4.0
ABSTRACT
INTRODUCTION
FUNDAMENTALS OF COMPUTER VISION
APPLICATIONS OF COMPUTER VISION IN INDUSTRY 4.0
ADVANCED TECHNIQUES AND TECHNOLOGIES
CHALLENGES AND LIMITATIONS
FUTURE DIRECTIONS AND TRENDS
CONCLUSION AND FUTURE DIRECTIONS
REFERENCES
Chapter 8: Detection of Pepper Plant Leaf Disease Detection Using Tom and Jerry Algorithm With MSTNet
ABSTRACT
INTRODUCTION
RELATED WORKS
PROPOSED METHODOLOGY
RESULTS AND DISCUSSIONS
CONCLUSION
REFERENCES
Chapter 9: Fractional Order Epidemiological Model of Fake Information Mitigation in OSNs With PINN, TFC, and ELM
ABSTRACT
INTRODUCTION
LEARNING STRATEGY AND NEURAL NETWORK
SOLUTION BY PINN METHOD USING THEORY OF FUNCTIONAL CONNECTION WITH EXTREME LEARNING MACHINES
MODELLING OF THE PROBLEM
NUMERICAL SOLUTION OF THE PROPOSED MODEL AND ANALYSIS
APPLICATION OF THE PINN METHOD WITH TFC AND ELM FOR THE SEIVR MODEL
CONCLUSION
REFERENCES
Chapter 10: Recent Trends in Pattern Recognition
ABSTRACT
INTRODUCTION
OPTICAL CHARACTER RECOGNITION
VARIOUS SECTORS OF PATTERN RECOGNITION
APPLICATIONS OF NATURAL LANGUAGE PROCESSING
CONCLUSION
REFERENCES
Chapter 11: Review on Machine Learning as a Key Technology Enabler for Sustainable Biodiesel Production
ABSTRACT
INTRODUCTION
CONCLUSION
ACKNOWLEGEMENT
REFERENCES
Chapter 12: The Impact of Data Science and Participated Geographic Metadata on Improving Government Service Deliveries
ABSTRACT
INTRODUCTION
DATA SCIENCE
CONCLUSION
REFERENCES
Compilation of References
About the Contributors