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Artificial Intelligence based Solutions for Industrial Applications
Artificial Intelligence based Solutions for Industrial Applications aims to examine the utilization of Artificial Intelligence (AI) technologies to tackle difficult industrial issues and offers readers a thorough understanding of how these technologies are being employed to address intricate industrial challenges and to stimulate innovation. This book explores the fundamental principles of Artificial Intelligence (AI) and its practical use in industrial environments. This book improves understanding of core concepts, the present state of the art and real-time implementation of AI in many industrial applications.
This book describes the detailed implementation of AI in the industrial sector as well as related case studies for in-depth understanding. Basic concepts, related work reviews, illustrations, empirical results, and tables are integrated within each chapter to give the readers the opportunity to gain maximum knowledge and to easily understand the methodology and results presented.
This book introduces a variety of smart algorithms to help in filtering important information and to solve problems in the application domains. Application of Machine Learning and Deep Learning in the industry demonstrates the capabilities by which it may be used to solve practical problems in the 'Fourth Industrial Revolution', and it equips readers with the necessary knowledge and tools to design solutions by themselves with the help of theory and practical examples dealt with. The fourth industrial revolution and its consequences on society and organizations are discussed in this book.
Artificial Intelligence (AI) subfields, known as Deep Learning, use training algorithms to discover patterns and make predictions from big datasets. Deep Learning and Machine Learning can be applied in industrial settings to solve difficult issues and increase productivity in a variety of industries, including manufacturing, healthcare, finance, and transportation. Machine Learning algorithms use statistical techniques to analyze data and learn patterns that can be used to make predictions or classifications. These algorithms can be supervised, unsupervised, or semi?supervised, depending on whether they use labeled or unlabeled data to learn from neural networks, a subset of Machine Learning that mimics the structure and operation of the human brain and is used in Deep Learning. Layers of connected nodes that analyze information and recognize patterns make up neural networks. Deep Learning algorithms can handle complex and unstructured data, such as images, speech, and natural language, making them useful for various industrial applications. In industrial settings, Machine Learning and Deep Learning can be used for predictive maintenance, quality control, anomaly detection, demand forecasting, and optimization. For instance, both Deep Learning and Machine Learning can be used in manufacturing to improve production processes and find product flaws. Machine Learning and Deep Learning are used in the healthcare industry for the diagnosis of diseases, the discovery of new drugs, and personalized treatment. Deep Learning can be used, for instance, to analyze medical images, spot irregularities, or forecast patient outcomes. In finance, Machine Learning and Deep Learning can be used for fraud detection, risk assessment, and portfolio optimization.
Features
Detailed understanding of the industrial application of AI.
Discussion of core concepts of different Machine Learning and Deep Learning techniques such as artificial neural networks, support vector machines, K –nearest neighbour, decision tree, logistic regression, and many more.
Detailed study on various industrial applications of Machine Learning and Deep Learning in healthcare, education, entertainment, share market, manufacturing, and many more.
Case studies on industrial application of AI
Summataion of the fourth industrial revolution and its consquences on society and organizations.
This book is primarily written for graduate students, engineers, and academic researchers, industrial practitioners, and anyone who wants to optimize production processes, explore AI technology, or stay ahead in the industrial field. It covers the complexities of AI in industrial contexts from core basic understanding to complex implementation.
Preface
Scope of this book
Introduction
1 Understanding the fundamentals of deep learning for industrial applications
2 Application of machine learning/deep learning in healthcare
3 Implementing machine learning technology for smart agriculture based on data collection systems and predictive analytics use cases
4 Empowering learners through generative AI: Human‑centric design and machine learning in transforming education
5 Machine learning/deep learning use cases in education
6 Digital dark arts: Combating trolls with AI‑based solutions
7 Artificial intelligence for smart manufacturing using digital twin technology
8 Machine learning and deep learning in cyber security
9 Artificial intelligence and media industry: Exploring new avenues and future
10 Machine learning feature selection method for anatomic brain MRI in dementia: A comparative study
11 Study of bio‑nanofluid flow via vertical surface: A machine learning approach
12 Detection of bearing fault using vibration signal through multiple feature extraction‑based machine learning algorithms
13 Artificial neural network‑based stock price prediction using Levenberg‑Marquardt algorithm
14 Technological transformation in learning patterns in media and entertainment
15 Intelligent heart disease prediction using machine learning