Innovative Engineering with AI Applications

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 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 to do 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.

Table of Contents:
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 5.1 Introduction 84 5.2 Artificial Intelligence in Agriculture 86 5.2.1 AI Startups in Agriculture 88 5.2.2 Challenges in AI Adoption 89 5.2.3 Stunning Discoveries of AI 89 5.2.3.1 Precision Technology to Sow Seeds 89 5.2.3.2 Robots for Harvesting 89 5.2.3.3 Field Inspection Using Drones 90 5.2.3.4 “See and Spray” Model for Pest and Weed Control 90 5.3 Scope of Artificial Intelligence in Agriculture 91 5.3.1 Reactive Machines 92 5.3.2 Limited Memory 92 5.3.3 Theory of Mind 92 5.3.4 Self-Awareness 93 5.4 Applications of Artificial Intelligence in Agriculture 93 5.4.1 Agricultural Robots 93 5.4.2 Soil Analysis and Monitoring 94 5.4.3 Predictive Analysis 94 5.4.4 Agricultural Industry 94 5.4.5 Blue River Technology – Weed Control 94 5.4.6 Crop Harvesting 95 5.4.7 Plantix App 95 5.4.8 Drones 95 5.4.9 Driverless Tractors 95 5.4.10 Precise Farming 96 5.4.11 Return on Investment (RoI) 96 5.5 Advantages of AI in Agriculture 96 5.6 Disadvantages of AI in Agriculture 97 5.7 Conclusion 97 References 98 6 The Potential of Artificial Intelligence in the Healthcare System 101 Meena Gupta and Ruchika Kalra 6.1 Introduction 102 6.2 Machine Learning 103 6.3 Neural Networks 105 6.4 Expert Systems 106 6.5 Robots 107 6.6 Fuzzy Logic 108 6.7 Natural Language Processing 109 6.8 Sensor Network Technology in Artificial Intelligence 110 6.9 Sensory Devices in Healthcare 112 6.9.1 Wearable Devices 112 6.9.2 Implantable Devices 112 6.10 Neural Interface for Sensors 113 6.10.1 Intrusion Devices in Artificial Intelligence 113 6.11 Artificial Intelligence in Healthcare 115 6.11.1 Role of Artificial Intelligence in Medicine 115 6.11.2 Role of Artificial Intelligence in Surgery 116 6.11.3 Role of Artificial Intelligence in Rehabilitation 116 6.12 Why Artificial Intelligence in Healthcare 117 6.13 Advancements of Artificial Intelligence in Healthcare 117 6.14 Future Challenges 118 6.15 Discussion 118 6.16 Conclusion 119 References 119 7 Improvement of Computer Vision-Based Elephant Intrusion Detection System (EIDS) with Deep Learning Models 131 Jothibasu M., Sowmiya M., Harsha R., Naveen K. S. and Suriyaprakash T. B. 7.1 Introduction 132 7.2 Elephant Intrusion Detection System (EIDS) 133 7.2.1 Existing Approaches 133 7.2.2 Challenges 134 7.3 Theoretical Framework 134 7.3.1 Deep Learning Models for EIDS 134 7.3.1.1 Fast RCNN 135 7.3.1.2 Faster RCNN 135 7.3.1.3 Single-Shot Multibox Detector (SSD) 137 7.3.1.4 You Only Look Once (YOLO) 139 7.3.2 Hardware Specifications 141 7.3.2.1 Raspberry-Pi 3 Model B 141 7.3.2.2 Night Vision OV5647 Camera Module 141 7.3.2.3 PIR Sensor 142 7.3.2.4 GSM Module 142 7.3.3 Proposed Work 142 7.4 Experimental Results 144 7.4.1 Dataset Preparation 144 7.4.2 Performance Analysis of DL Algorithms 146 7.5 Conclusion 152 References 152 8 A Study of WSN Privacy Through AI Technique 155 Piyush Raja 8.1 Introduction 156 8.2 Review of Literature 159 8.3 ml in WSNs 160 8.3.1 Supervised Learning 161 8.3.2 Unsupervised Learning 164 8.3.3 Reinforcement Learning 166 8.4 Conclusion 169 References 169 9 Introduction to AI Technique and Analysis of Time Series Data Using Facebook Prophet Model 171 S. Sivaramakrishnan, C.R. Rathish, S. Premalatha and Niranjana C. 9.1 Introduction 172 9.2 What is AI? 172 9.2.1 Process of Thoughts – Human Approach 173 9.3 Main Frameworks of Artificial Intelligence 174 9.3.1 Feature Engineering 174 9.3.2 Artificial Neural Networks 175 9.3.3 Deep Learning 175 9.4 Techniques of AI 177 9.4.1 Machine Learning 177 9.4.1.1 Supervised Learning 179 9.4.1.2 Unsupervised Learning 179 9.4.1.3 Reinforcement Learning 179 9.4.2 Natural Language Processing (NLP) 180 9.4.3 Automation and Robotics 181 9.4.4 Machine Vision 181 9.5 Application of AI in Various Fields 182 9.6 Time Series Analysis Using Facebook Prophet Model 183 9.7 Feature Scope of AI 186 9.8 Conclusion 186 References 187 10 A Comparative Intelligent Environmental Analysis of Air-Pollution in COVID: Application of IoT and AI Using ML in a Study Conducted at the North Indian Zone 189 Rohit Rastogi, Abhishek Goyal, Akshit Rajan Rastogi and Neha Gupta 10.1 Introduction 190 10.1.1 Intelligent Environment Systems 190 10.1.2 Types of Pollution 190 10.1.3 Components in Pollution Particles 191 10.1.4 Research Problem Introduction and Motivation 191 10.2 Related Previous Work 191 10.2.1 Machine Learning Models 192 10.2.2 Regression Techniques Applications 192 10.3 Methodology Adopted in Research 193 10.3.1 Data Source 193 10.3.2 Data Pre-Processing 195 10.3.3 Calculating AQI 195 10.3.4 Computing AQI 195 10.3.5 Data Pre-Processing 196 10.3.6 Feature Selection 198 10.4 Results and Discussion 199 10.4.1 Collective Analysis 199 10.4.2 Applying Various Repressors 200 10.4.3 Comparison with Existing State-of-the-Art Technologies 201 10.5 Novelties in the Work 202 10.6 Future Research Directions 203 10.7 Limitations 203 10.8 Conclusions 203 Acknowledgements 204 Key Terms and Definitions 204 Additional Readings 205 References 206 11 Eye-Based Cursor Control and Eye Coding Using Hog Algorithm and Neural Network 209 S. Sivaramakrishnan, Vasuprada G., V. R. Harika, Vishnupriya P. and Supriya Castelino 11.1 Introduction 210 11.2 Related Work 210 11.3 Methodology 212 11.3.1 Eye Blink Detection 213 11.3.2 Hog Algorithm 214 11.3.3 Eye Gaze Detection 215 11.3.3.1 Deep Learning and CNN 215 11.3.3.2 Hog Algorithm for Gaze Determination 216 11.3.4 GUI Automation 216 11.4 Experimental Analysis 217 11.4.1 Eye-Based Cursor Control 217 11.4.2 Eye Coding 217 11.5 Observation and Results 220 11.6 Conclusion 223 11.7 Future Scope 224 References 224 12 Role of Artificial Intelligence in the Agricultural System 227 Nilesh Kunhare, Rajeev Kumar Gupta and Yatendra Sahu 12.1 Introduction 228 12.2 Artificial Intelligence Effect on Farming 229 12.2.1 Agriculture Lifecycle 229 12.2.2 Problems with Traditional Methods of Farming 230 12.3 Applications of Artificial Intelligence in Agriculture 231 12.3.1 Forecasting Weather Details 231 12.3.2 Crop and Soil Quality Surveillance 231 12.3.3 Pesticide Use Reduction 233 12.3.4 AI Farming Bots 233 12.3.5 AI-Based Monitoring Systems 233 12.3.6 AI-Based Irrigation System 234 12.4 Robots in Agriculture 235 12.5 Drones for Agriculture 236 12.6 Advantage of AI Implementation in Farming 237 12.6.1 Intelligent Agriculture Cloud Platform 238 12.6.1.1 Remote Control and Administration in Real Time 238 12.6.1.2 Consultation of Remote Experts 238 12.7 Research, Challenges, and Scope for the Future 239 12.8 Conclusion 240 References 240 13 Improving Wireless Sensor Networks Effectiveness with Artificial Intelligence 243 Piyush Raja, Santosh Kumar, Digvijay Singh and Taresh Singh 13.1 Introduction 244 13.2 Wireless Sensor Network (WSNs) 245 13.3 AI and Multi-Agent Systems 246 13.4 WSN and AI 247 13.5 Multi-Agent Constructed Simulation 248 13.6 Multi-Agent Model Plan 249 13.7 Simulation Models on Behalf of Wireless Sensor Network 250 13.8 Model Plan 251 13.8.1 Hardware Layer 251 13.8.2 Middle Layer 252 13.8.3 Application Layer 253 13.9 Conclusion 253 References 254 Index 257

About the Author :
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.