Srivastava P.K., Yadav A.K. Machine Learning Techniques and Industry Applications

IGI Global, 2024. — 327 p. — (Advances in Computational Intelligence and Robotics (ACIR) Book Series). — ISBN 979-8-3693-5271-7.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 demystify Machine Learning (ML) 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.Smarter apps and connected devices are now possible because of the proliferation of IoT, which has greatly improved the quality of life in today’s urban centers. ML and IoT approaches have been employed in the study of smart transportation, which has attracted a large number of researchers. Smart transportation is viewed as a catch-all word that encompasses a wide range of topics, including optimization of routes, parking, street lighting, accident detection, abnormalities on the road, and other infrastructure-related issues. The purpose of this chapter is to examine the state of Machine Learning (ML) and Internet of Things (IoT) applications for smart city transport to better comprehend recent advances in these fields and to spot any holes in coverage. From the existing publications, it’s clear that ML may be underrepresented in smart lighting and smart parking systems. Additionally, researchers’ favorite applications in terms of transportation system intelligence include the optimization of routes, smart parking management, and accident/collision detection.Advances in science and technology are making mechatronic systems more common in mechanical engineering. AI is involved in this transition. Artificial Intelligence (AI) is computer software that makes decisions on its own. Simulating professionals’ intelligent behavior boosts efficiency and product quality. Artificial Intelligence systems have advanced since their creation. These systems are widely used in mechanical and industrial fields for image processing, intelligent perception, pattern recognition, and virtual reality. Automation and AI are common in industry and mechanics. This study examined AI in mechanical disciplines such as heat transfer, production, design, and quality control. Research simulations show a variety of Artificial Intelligence systems that regulate mechanical manufacturing process components. ANNs, DCNN, CNN, fuzzy logic, and other AI are featured. Thus, new products and system development should be expedited. Mechanical engineering component rejections and faults may be eliminated using AI. Optimized systems can produce higher-quality, more expensive items.A Novel Study on IoT and Machine Learning-Based Transportation.
A Review on Application of Artificial Intelligence in Mechanical Engineering.
An Anticipatory Framework for Categorizing Nigerian Supreme Court Rulings.
Analysis Model at Sentence Level for Phishing Detection Cancer Prediction Using Graph Database.
Change Detection Based on Binary Mask Enhancement Computer Vision and Its Intelligence in Industry 4.0.
Detection of Pepper Plant Leaf Disease Detection Using Tom and Jerry Algorithm With MSTNet.
Fractional Order Epidemiological Model of Fake Information Mitigation in OSNs With PINN, TFC, and ELM.
Recent Trends in Pattern Recognition: Challenges and Opportunities.
Review on Machine Learning as a Key Technology Enabler for Sustainable Biodiesel Production.
The Impact of Data Science and Participated Geographic Metadata on Improving Government Service Deliveries: Prospects and Obstacles.
Compilation of References.