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.
This book presents a study on current developments, trends, and the future usage of Artificial Intelligence (AI). The impending research on AI applications—like improvements in agricultural systems, security systems, web services, etc.—has shown the usefulness of AI in engineering, as well as in Deep Learning tools and models.
Engineering advancements combined with Artificial Intelligence, 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.
This book takes foundational steps toward analyzing stress among teaching professionals using deep learning algorithms. Various deep learning models are discussed, with a practical approach that employs the MNIST dataset. These models help to solve various complex problems in the domains of computer vision and natural language processing (NLP). Also introduced are some emerging and interdisciplinary domains that are associated with Deep Learning and AI. The book touches upon the core concept of deep learning technology, which is fruitful for a beginner in this area. Furthermore, it compiles the various applications of AI in agriculture, such as irrigation, weeding, spraying with sensors, and other means that are facilitated by robots and drones. This book covers most of the popular and important deep-learning neural network models.
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.
Author(s): Anamika Ahirwar; Piyush Kumar Shukla; Manish Shrivastava; Priti Maheshwary; Bhupesh Gour
Publisher: Wiley-Scrivener
Year: 2023
Language: English
Pages: 288
Cover
Series Page
Title Page
Copyright Page
Preface
1 Introduction of AI in Innovative Engineering
1.1 Introduction to Innovation Engineering
1.2 Flow for Innovation Engineering
1.3 Guiding Principles for Innovation Engineering
1.4 Introduction to Artificial Intelligence
1.5 Types of Learning
1.6 Categories of AI
1.7 Branches of Artificial Intelligence
1.8 Conclusion
References
2 An Analytical Review of Deep Learning Algorithms for Stress Prediction in Teaching Professionals
2.1 Introduction
2.2 Literature Review
2.3 Dataset Pre-Processing
2.4 Machine Learning Techniques Used
2.5 Performance Parameter
2.6 Proposed Methodology
2.7 Result and Experiment
2.8 Comparison of Six Different Approaches For Stress Detection
2.9 Conclusions
2.10 Future Scope
References
3 Deep Learning: Tools and Models
3.1 Introduction
3.2 Deep Learning Models
3.3 Research Perspective of Deep Learning
3.4 Conclusion
References
4 Web Service Composition Using an AI Planning Technique
4.1 Introduction
4.2 Background
4.3 Proposed Methodology for AI Planning-Based Composition of Web Services
4.4 Implementation Details
4.5 Conclusions and Future Directions
References
5 Artificial Intelligence in Agricultural Engineering
5.1 Introduction
5.2 Artificial Intelligence in Agriculture
5.3 Scope of Artificial Intelligence in Agriculture
5.4 Applications of Artificial Intelligence in Agriculture
5.5 Advantages of AI in Agriculture
5.6 Disadvantages of AI in Agriculture
5.7 Conclusion
References
6 The Potential of Artificial Intelligence in the Healthcare System
6.1 Introduction
6.2 Machine Learning
6.3 Neural Networks
6.4 Expert Systems
6.5 Robots
6.6 Fuzzy Logic
6.7 Natural Language Processing
6.8 Sensor Network Technology in Artificial Intelligence
6.9 Sensory Devices in Healthcare
6.10 Neural Interface for Sensors
6.11 Artificial Intelligence in Healthcare
6.12 Why Artificial Intelligence in Healthcare
6.13 Advancements of Artificial Intelligence in Healthcare
6.14 Future Challenges
6.15 Discussion
6.16 Conclusion
References
7 Improvement of Computer Vision-Based Elephant Intrusion Detection System (EIDS) with Deep Learning Models
7.1 Introduction
7.2 Elephant Intrusion Detection System (EIDS)
7.3 Theoretical Framework
7.4 Experimental Results
7.5 Conclusion
References
8 A Study of WSN Privacy Through AI Technique
8.1 Introduction
8.2 Review of Literature
8.3 ML in WSNs
8.4 Conclusion
References
9 Introduction to AI Technique and Analysis of Time Series Data Using Facebook Prophet Model
9.1 Introduction
9.2 What is AI?
9.3 Main Frameworks of Artificial Intelligence
9.4 Techniques of AI
9.5 Application of AI in Various Fields
9.6 Time Series Analysis Using Facebook Prophet Model
9.7 Feature Scope of AI
9.8 Conclusion
References
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
10. 1 Introduction
10.2 Related Previous Work
10.3 Methodology Adopted in Research
10.4 Results and Discussion
10.5 Novelties in the Work
10.6 Future Research Directions
10.7 Limitations
10.8 Conclusions
Acknowledgements
Key Terms and Definitions
Additional Readings
References
11 Eye-Based Cursor Control and Eye Coding Using Hog Algorithm and Neural Network
11.1 Introduction
11.2 Related Work
11.3 Methodology
11.4 Experimental Analysis
11.5 Observation and Results
11.6 Conclusion
11.7 Future Scope
References
12 Role of Artificial Intelligence in the Agricultural System
12.1 Introduction
12.2 Artificial Intelligence Effect on Farming
12.3 Applications of Artificial Intelligence in Agriculture
12.4 Robots in Agriculture
12.5 Drones for Agriculture
12.6 Advantage of AI Implementation in Farming
12.7 Research, Challenges, and Scope for the Future
12.8 Conclusion
References
13 Improving Wireless Sensor Networks Effectiveness with Artificial Intelligence
13.1 Introduction
13.2 Wireless Sensor Network (WSNs)
13.3 AI and Multi-Agent Systems
13.4 WSN and AI
13.5 Multi-Agent Constructed Simulation
13.6 Multi-Agent Model Plan
13.7 Simulation Models on Behalf of Wireless Sensor Network
13.8 Model Plan
13.9 Conclusion
References
Index
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