Innovative Engineering with AI Applications Innovative Engineering with AI Applications demonstrates how we can innovate in different engineering domains as well as how to make most business problems simpler by applying AI to them. Engineering advancements combined with artificial intelligence (AI), have resulted in a hyper-connected society in which smart devices are not only used to exchange data but also have increased capabilities. These devices are becoming more context-aware and smarter by the day. This timely book shows how organizations, who want to innovate and adapt, can enter…mehr
Innovative Engineering with AI Applications demonstrates how we can innovate in different engineering domains as well as how to make most business problems simpler by applying AI to them.
Engineering advancements combined with artificial intelligence (AI), have resulted in a hyper-connected society in which smart devices are not only used to exchange data but also have increased capabilities. These devices are becoming more context-aware and smarter by the day. This timely book shows how organizations, who want to innovate and adapt, can enter new markets using expertise in various emerging technologies (e.g. data, AI, system architecture, blockchain), and can build technology-based business models, a culture of innovation, and high-performing networks. The book specifies an approach that anyone can use to better architect, design, and more effectively build things that are technically novel, useful, and valuable, and todo so efficiently, on-time, and repeatable.
Audience
The book is essential to AI product developers, business leaders in all industries and organizational domains. Researchers, academicians, and students in the AI field will also benefit from reading this book.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Anamika Ahirwar, PhD, is an associate professor at the Compucom Institute of Information Technology & Management, Jaipur, India. She has about 20 years of experience in teaching and research and has published more than 45 research papers in reputed national/international journals and conferences, authored several books as well as five patents. Piyush Kumar Shukla, PhD, is an associate professor in the Department of Computer Science & Engineering, University Institute of Technology, Bhopal, India. He has about 15 years of experience in teaching and research, is the author of 3 books, more than 50 articles and book chapters in international publications, as well as 15 Indian patents. Manish Shrivastava, PhD, is the Principal of the Chameli Devi Institute of Technology & Management, Indore, India. He has published more than 100 articles in international journals and spent 7 years as a software engineer. Priti Maheshwary, PhD, is a professor in the Department of CSE and Head of the Centre for Excellence in Internet of Things and Advance Computing Lab, Rabindranath Tagore University, Bhopal, India. Bhupesh Gour, PhD, is a professor in the Department of Computer Science and Engineering at Lakshmi Narain College of Technology in Bhopal, India. He has 22 years of experience in academia as well as the software industry. He has published more than 50 articles in national and international journals, as well as four patents.
Inhaltsangabe
Preface xiii
1 Introduction of AI in Innovative Engineering 1 Anamika Ahirwar
1.1 Introduction to Innovation Engineering 2
1.2 Flow for Innovation Engineering 3
1.3 Guiding Principles for Innovation Engineering 4
1.4 Introduction to Artificial Intelligence 7
1.4.1 History of Artificial Intelligence 8
1.4.2 Need for Artificial Intelligence 8
1.4.3 Applications of AI 8
1.4.4 Comprised Elements of Intelligence 12
1.4.5 AI Tools 14
1.4.6 AI Future in 2035 15
1.4.7 Humanoid Robot and AI 15
1.4.8 The Explosive Growth of AI 15
1.5 Types of Learning 16
1.6 Categories of AI 17
1.7 Branches of Artificial Intelligence 18
1.8 Conclusion 21
Bibliography 22
2 An Analytical Review of Deep Learning Algorithms for Stress Prediction in Teaching Professionals 23 Ruby Bhatt
2.1 Introduction 24
2.2 Literature Review 26
2.3 Dataset and Pre-Processing 27
2.4 Machine Learning Techniques Used 28
2.5 Performance Parameter 30
2.6 Proposed Methodology 31
2.7 Result and Experiment 34
2.8 Comparison of Six Different Approaches For Stress Detection 37
2.9 Conclusions 38
2.10 Future Scope 38
References 38
3 Deep Learning: Tools and Models 41 Brijesh K. Soni and Akhilesh A. Waoo
3.1 Introduction 41
3.1.1 Definition 42
3.1.2 Elements of Neural Networks 43
3.1.3 Tool: Keras 44
3.2 Deep Learning Models 47
3.2.1 Deep Belief Network [DBN] 48
3.2.1.1 Fundamental Architecture of DBN 48
3.2.1.2 Implementing DBN Using MNIST Dataset 49
3.2.2 Recurrent Neural Network [RNN] 50
3.2.2.1 Fundamental Architecture of RNN 50
3.2.2.2 Implementing RNN Using MNIST Dataset 51
3.2.3 Convolutional Neural Network [CNN] 52
3.2.3.1 Fundamental Architecture of CNN 52
3.2.3.2 Implementing CNN Using MNIST Dataset 53
3.2.4 Gradient Adversarial Network [GAN] 57
3.2.4.1 Fundamental Architecture of GAN 57
3.2.4.2 Implementing GAN Using MNIST Dataset 57
3.3 Research Perspective of Deep Learning 61
3.3.1 Multi-Agent System: Argumentation 61
3.3.2 Image Processor: Phenotyping 61
3.3.3 Saliency-Map: Visualization 61
3.4 Conclusion 61
References 62
4 Web Service Composition Using an AI Planning Technique 65 Lalit Purohit and Satyendra Singh Chouhan
4.1 Introduction 66
4.2 Background 69
4.2.1 Introduction to AI 69
4.2.2 AI Planning 70
4.2.3 AI Planning for Effective Composition of Web Services 70
4.3 Proposed Methodology for AI Planning-Based Composition of Web Services 71
4.3.1 Clustering Web Services 71
4.3.2 OWL-S: Semantic Markup for Web Services(For Composition Request) 72
4.3.3 PDDL: Planning Domain Description Language 73
4.3.4 AI Planner 75
4.3.5 Flowchart of Proposed Approach 75
4.4 Implementation Details 76
4.4.1 Domain Used 76
4.4.2 Case Studies on AI Planning 77
4.4.2.1 Experiments and Results on Case 1 and Case 2 78
4.5 Conclusions and Future Directions 80
References 80
5 Artificial Intelligence in Agricultural Engineering 83 Ashwini A. Waoo, Jyoti Pandey and Akhilesh A. Waoo
