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Submit a ProposalInnovative Engineering with AI Applications
 | Edited by Anamika Ahirwar, Piyush Kumar Shukla, Manish Shrivastava, Priti Maheshwary and Bhupesh Gour
Copyright: 2023 | Status: Published ISBN: 9781119791638 | Hardcover | 282 pages | 146 illustrations Price: $195 USD  |
One Line Description
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.
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.
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.
Description
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.
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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.
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Author / Editor Details
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.
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.
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Table of Contents
Preface
1. Introduction of AI in Innovative Engineering
Anamika Ahirwar
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
1.4.1 History of Artificial Intelligence
1.4.2 Need for Artificial Intelligence
1.4.3 Applications of AI
1.4.4 Comprised Elements of Intelligence
1.4.5 AI Tools
1.4.6 AI Future in 2035
1.4.7 Humanoid Robot and AI
1.4.8 The Explosive Growth of AI
1.5 Types of Learning
1.6 Categories of AI
1.7 Branches of Artificial Intelligence
1.8 Conclusion
Bibliography
2. An Analytical Review of Deep Learning Algorithms for Stress Prediction in Teaching Professionals
Ruby Bhatt
2.1 Introduction
2.2 Literature Review
2.3 Dataset and 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
Brijesh K. Soni and Akhilesh A. Waoo
3.1 Introduction
3.1.1 Definition
3.1.2 Elements of Neural Networks
3.1.3 Tool: Keras
3.2 Deep Learning Models
3.2.1 Deep Belief Network [DBN]
3.2.1.1 Fundamental Architecture of DBN
3.2.1.2 Implementing DBN Using MNIST Dataset
3.2.2 Recurrent Neural Network [RNN]
3.2.2.1 Fundamental Architecture of RNN
3.2.2.2 Implementing RNN Using MNIST Dataset
3.2.3 Convolutional Neural Network [CNN]
3.2.3.1 Fundamental Architecture of CNN
3.2.3.2 Implementing CNN Using MNIST Dataset
3.2.4 Gradient Adversarial Network [GAN]
3.2.4.1 Fundamental Architecture of GAN
3.2.4.2 Implementing GAN Using MNIST Dataset
3.3 Research Perspective of Deep Learning
3.3.1 Multi-Agent System: Argumentation
3.3.2 Image Processor: Phenotyping
3.3.3 Saliency-Map: Visualization
3.4 Conclusion
References
4. Web Service Composition Using an AI Planning Technique
Lalit Purohit and Satyendra Singh Chouhan
4.1 Introduction
4.2 Background
4.2.1 Introduction to AI
4.2.2 AI Planning
4.2.3 AI Planning for Effective Composition of Web Services
4.3 Proposed Methodology for AI Planning-Based Composition of Web Services
4.3.1 Clustering Web Services
4.3.2 OWL-S: Semantic Markup for Web Services (For Composition Request)
4.3.3 PDDL: Planning Domain Description Language
4.3.4 AI Planner
4.3.5 Flowchart of Proposed Approach
4.4 Implementation Details
4.4.1 Domain Used
4.4.2 Case Studies on AI Planning
4.4.2.1 Experiments and Results on Case 1 and Case 2
4.5 Conclusions and Future Directions
References
5. Artificial Intelligence in Agricultural Engineering
Ashwini A. Waoo, Jyoti Pandey and Akhilesh A. Waoo
5.1 Introduction
5.2 Artificial Intelligence in Agriculture
5.2.1 AI Startups in Agriculture
5.2.2 Challenges in AI Adoption
5.2.3 Stunning Discoveries of AI
5.2.3.1 Precision Technology to Sow Seeds
5.2.3.2 Robots for Harvesting
5.2.3.3 Field Inspection Using Drones
5.2.3.4 “See and Spray” Model for Pest and Weed Control
5.3 Scope of Artificial Intelligence in Agriculture
5.3.1 Reactive Machines
5.3.2 Limited Memory
5.3.3 Theory of Mind
5.3.4 Self-Awareness
5.4 Applications of Artificial Intelligence in Agriculture
5.4.1 Agricultural Robots
5.4.2 Soil Analysis and Monitoring
5.4.3 Predictive Analysis
5.4.4 Agricultural Industry
5.4.5 Blue River Technology – Weed Control
5.4.6 Crop Harvesting
5.4.7 Plantix App
5.4.8 Drones
5.4.9 Driverless Tractors
5.4.10 Precise Farming
5.4.11 Return on Investment (RoI)
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
Meena Gupta and Ruchika Kalra
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
6.9.1 Wearable Devices
6.9.2 Implantable Devices
6.10 Neural Interface for Sensors
6.10.1 Intrusion Devices in Artificial Intelligence
6.11 Artificial Intelligence in Healthcare
6.11.1 Role of Artificial Intelligence in Medicine
6.11.2 Role of Artificial Intelligence in Surgery
6.11.3 Role of Artificial Intelligence in Rehabilitation
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
Jothibasu M., Sowmiya M., Harsha R., Naveen K. S. and Suriyaprakash T. B.
7.1 Introduction
7.2 Elephant Intrusion Detection System (EIDS)
7.2.1 Existing Approaches
7.2.2 Challenges
7.3 Theoretical Framework
7.3.1 Deep Learning Models for EIDS
7.3.1.1 Fast RCNN
7.3.1.2 Faster RCNN
7.3.1.3 Single-Shot Multibox Detector (SSD)
7.3.1.4 You Only Look Once (YOLO)
7.3.2 Hardware Specifications
7.3.2.1 Raspberry-Pi 3 Model B
7.3.2.2 Night Vision OV5647 Camera Module
7.3.2.3 PIR Sensor
7.3.2.4 GSM Module
7.3.3 Proposed Work
7.4 Experimental Results
7.4.1 Dataset Preparation
7.4.2 Performance Analysis of DL Algorithms
7.5 Conclusion
References
8. A Study of WSN Privacy Through AI Technique
Piyush Raja
8.1 Introduction
8.2 Review of Literature
8.3 ML in WSNs
8.3.1 Supervised Learning
8.3.2 Unsupervised Learning
8.3.3 Reinforcement Learning
8.4 Conclusion
References
9. Introduction to AI Technique and Analysis of Time Series Data Using Facebook Prophet Model
S. Sivaramakrishnan, C.R. Rathish, S. Premalatha and Niranjana C.
9.1 Introduction
9.2 What is AI?
9.2.1 Process of Thoughts – Human Approach
9.3 Main Frameworks of Artificial Intelligence
9.3.1 Feature Engineering
9.3.2 Artificial Neural Networks
9.3.3 Deep Learning
9.4 Techniques of AI
9.4.1 Machine Learning
9.4.1.1 Supervised Learning
9.4.1.2 Unsupervised Learning
9.4.1.3 Reinforcement Learning
9.4.2 Natural Language Processing (NLP)
9.4.3 Automation and Robotics
9.4.4 Machine Vision
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
Rohit Rastogi, Abhishek Goyal, Akshit Rajan Rastogi and Neha Gupta
10.1 Introduction
10.1.1 Intelligent Environment Systems
10.1.2 Types of Pollution
10.1.3 Components in Pollution Particles
10.1.4 Research Problem Introduction and Motivation
10.2 Related Previous Work
10.2.1 Machine Learning Models
10.2.2 Regression Techniques Applications
10.3 Methodology Adopted in Research
10.3.1 Data Source
10.3.2 Data Pre-Processing
10.3.3 Calculating AQI
10.3.4 Computing AQI
10.3.5 Data Pre-Processing
10.3.6 Feature Selection
10.4 Results and Discussion
10.4.1 Collective Analysis
10.4.2 Applying Various Repressors
10.4.3 Comparison with Existing State-of-the-Art Technologies
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
S. Sivaramakrishnan, Vasuprada G., V. R. Harika, Vishnupriya P. and Supriya Castelino
11.1 Introduction
11.2 Related Work
11.3 Methodology
11.3.1 Eye Blink Detection
11.3.2 Hog Algorithm
11.3.3 Eye Gaze Detection
11.3.3.1 Deep Learning and CNN
11.3.3.2 Hog Algorithm for Gaze Determination
11.3.4 GUI Automation
11.4 Experimental Analysis
11.4.1 Eye-Based Cursor Control
11.4.2 Eye Coding
11.5 Observation and Results
11.6 Conclusion
11.7 Future Scope
References
12. Role of Artificial Intelligence in the Agricultural System
Nilesh Kunhare, Rajeev Kumar Gupta and Yatendra Sahu
12.1 Introduction
12.2 Artificial Intelligence Effect on Farming
12.2.1 Agriculture Lifecycle
12.2.2 Problems with Traditional Methods of Farming
12.3 Applications of Artificial Intelligence in Agriculture
12.3.1 Forecasting Weather Details
12.3.2 Crop and Soil Quality Surveillance
12.3.3 Pesticide Use Reduction
12.3.4 AI Farming Bots
12.3.5 AI-Based Monitoring Systems
12.3.6 AI-Based Irrigation System
12.4 Robots in Agriculture
12.5 Drones for Agriculture
12.6 Advantage of AI Implementation in Farming
12.6.1 Intelligent Agriculture Cloud Platform
12.6.1.1 Remote Control and Administration in Real Time
12.6.1.2 Consultation of Remote Experts
12.7 Research, Challenges, and Scope for the Future
12.8 Conclusion
References
13. Improving Wireless Sensor Networks Effectiveness with Artificial Intelligence
Piyush Raja, Santosh Kumar, Digvijay Singh and Taresh Singh
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
13.8.1 Hardware Layer
13.8.2 Middle Layer
13.8.3 Application Layer
13.9 Conclusion
References
Index
Back to Top
Preface
1. Introduction of AI in Innovative Engineering
Anamika Ahirwar
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
1.4.1 History of Artificial Intelligence
1.4.2 Need for Artificial Intelligence
1.4.3 Applications of AI
1.4.4 Comprised Elements of Intelligence
1.4.5 AI Tools
1.4.6 AI Future in 2035
1.4.7 Humanoid Robot and AI
1.4.8 The Explosive Growth of AI
1.5 Types of Learning
1.6 Categories of AI
1.7 Branches of Artificial Intelligence
1.8 Conclusion
Bibliography
2. An Analytical Review of Deep Learning Algorithms for Stress Prediction in Teaching Professionals
Ruby Bhatt
2.1 Introduction
2.2 Literature Review
2.3 Dataset and 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
Brijesh K. Soni and Akhilesh A. Waoo
3.1 Introduction
3.1.1 Definition
3.1.2 Elements of Neural Networks
3.1.3 Tool: Keras
3.2 Deep Learning Models
3.2.1 Deep Belief Network [DBN]
3.2.1.1 Fundamental Architecture of DBN
3.2.1.2 Implementing DBN Using MNIST Dataset
3.2.2 Recurrent Neural Network [RNN]
3.2.2.1 Fundamental Architecture of RNN
3.2.2.2 Implementing RNN Using MNIST Dataset
3.2.3 Convolutional Neural Network [CNN]
3.2.3.1 Fundamental Architecture of CNN
3.2.3.2 Implementing CNN Using MNIST Dataset
3.2.4 Gradient Adversarial Network [GAN]
3.2.4.1 Fundamental Architecture of GAN
3.2.4.2 Implementing GAN Using MNIST Dataset
3.3 Research Perspective of Deep Learning
3.3.1 Multi-Agent System: Argumentation
3.3.2 Image Processor: Phenotyping
3.3.3 Saliency-Map: Visualization
3.4 Conclusion
References
4. Web Service Composition Using an AI Planning Technique
Lalit Purohit and Satyendra Singh Chouhan
4.1 Introduction
4.2 Background
4.2.1 Introduction to AI
4.2.2 AI Planning
4.2.3 AI Planning for Effective Composition of Web Services
4.3 Proposed Methodology for AI Planning-Based Composition of Web Services
4.3.1 Clustering Web Services
4.3.2 OWL-S: Semantic Markup for Web Services (For Composition Request)
4.3.3 PDDL: Planning Domain Description Language
4.3.4 AI Planner
4.3.5 Flowchart of Proposed Approach
4.4 Implementation Details
4.4.1 Domain Used
4.4.2 Case Studies on AI Planning
4.4.2.1 Experiments and Results on Case 1 and Case 2
4.5 Conclusions and Future Directions
References
5. Artificial Intelligence in Agricultural Engineering
Ashwini A. Waoo, Jyoti Pandey and Akhilesh A. Waoo
5.1 Introduction
5.2 Artificial Intelligence in Agriculture
5.2.1 AI Startups in Agriculture
5.2.2 Challenges in AI Adoption
5.2.3 Stunning Discoveries of AI
5.2.3.1 Precision Technology to Sow Seeds
5.2.3.2 Robots for Harvesting
5.2.3.3 Field Inspection Using Drones
5.2.3.4 “See and Spray” Model for Pest and Weed Control
5.3 Scope of Artificial Intelligence in Agriculture
5.3.1 Reactive Machines
5.3.2 Limited Memory
5.3.3 Theory of Mind
5.3.4 Self-Awareness
5.4 Applications of Artificial Intelligence in Agriculture
5.4.1 Agricultural Robots
5.4.2 Soil Analysis and Monitoring
5.4.3 Predictive Analysis
5.4.4 Agricultural Industry
5.4.5 Blue River Technology – Weed Control
5.4.6 Crop Harvesting
5.4.7 Plantix App
5.4.8 Drones
5.4.9 Driverless Tractors
5.4.10 Precise Farming
5.4.11 Return on Investment (RoI)
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
Meena Gupta and Ruchika Kalra
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
6.9.1 Wearable Devices
6.9.2 Implantable Devices
6.10 Neural Interface for Sensors
6.10.1 Intrusion Devices in Artificial Intelligence
6.11 Artificial Intelligence in Healthcare
6.11.1 Role of Artificial Intelligence in Medicine
6.11.2 Role of Artificial Intelligence in Surgery
6.11.3 Role of Artificial Intelligence in Rehabilitation
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
Jothibasu M., Sowmiya M., Harsha R., Naveen K. S. and Suriyaprakash T. B.
7.1 Introduction
7.2 Elephant Intrusion Detection System (EIDS)
7.2.1 Existing Approaches
7.2.2 Challenges
7.3 Theoretical Framework
7.3.1 Deep Learning Models for EIDS
7.3.1.1 Fast RCNN
7.3.1.2 Faster RCNN
7.3.1.3 Single-Shot Multibox Detector (SSD)
7.3.1.4 You Only Look Once (YOLO)
7.3.2 Hardware Specifications
7.3.2.1 Raspberry-Pi 3 Model B
7.3.2.2 Night Vision OV5647 Camera Module
7.3.2.3 PIR Sensor
7.3.2.4 GSM Module
7.3.3 Proposed Work
7.4 Experimental Results
7.4.1 Dataset Preparation
7.4.2 Performance Analysis of DL Algorithms
7.5 Conclusion
References
8. A Study of WSN Privacy Through AI Technique
Piyush Raja
8.1 Introduction
8.2 Review of Literature
8.3 ML in WSNs
8.3.1 Supervised Learning
8.3.2 Unsupervised Learning
8.3.3 Reinforcement Learning
8.4 Conclusion
References
9. Introduction to AI Technique and Analysis of Time Series Data Using Facebook Prophet Model
S. Sivaramakrishnan, C.R. Rathish, S. Premalatha and Niranjana C.
9.1 Introduction
9.2 What is AI?
9.2.1 Process of Thoughts – Human Approach
9.3 Main Frameworks of Artificial Intelligence
9.3.1 Feature Engineering
9.3.2 Artificial Neural Networks
9.3.3 Deep Learning
9.4 Techniques of AI
9.4.1 Machine Learning
9.4.1.1 Supervised Learning
9.4.1.2 Unsupervised Learning
9.4.1.3 Reinforcement Learning
9.4.2 Natural Language Processing (NLP)
9.4.3 Automation and Robotics
9.4.4 Machine Vision
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
Rohit Rastogi, Abhishek Goyal, Akshit Rajan Rastogi and Neha Gupta
10.1 Introduction
10.1.1 Intelligent Environment Systems
10.1.2 Types of Pollution
10.1.3 Components in Pollution Particles
10.1.4 Research Problem Introduction and Motivation
10.2 Related Previous Work
10.2.1 Machine Learning Models
10.2.2 Regression Techniques Applications
10.3 Methodology Adopted in Research
10.3.1 Data Source
10.3.2 Data Pre-Processing
10.3.3 Calculating AQI
10.3.4 Computing AQI
10.3.5 Data Pre-Processing
10.3.6 Feature Selection
10.4 Results and Discussion
10.4.1 Collective Analysis
10.4.2 Applying Various Repressors
10.4.3 Comparison with Existing State-of-the-Art Technologies
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
S. Sivaramakrishnan, Vasuprada G., V. R. Harika, Vishnupriya P. and Supriya Castelino
11.1 Introduction
11.2 Related Work
11.3 Methodology
11.3.1 Eye Blink Detection
11.3.2 Hog Algorithm
11.3.3 Eye Gaze Detection
11.3.3.1 Deep Learning and CNN
11.3.3.2 Hog Algorithm for Gaze Determination
11.3.4 GUI Automation
11.4 Experimental Analysis
11.4.1 Eye-Based Cursor Control
11.4.2 Eye Coding
11.5 Observation and Results
11.6 Conclusion
11.7 Future Scope
References
12. Role of Artificial Intelligence in the Agricultural System
Nilesh Kunhare, Rajeev Kumar Gupta and Yatendra Sahu
12.1 Introduction
12.2 Artificial Intelligence Effect on Farming
12.2.1 Agriculture Lifecycle
12.2.2 Problems with Traditional Methods of Farming
12.3 Applications of Artificial Intelligence in Agriculture
12.3.1 Forecasting Weather Details
12.3.2 Crop and Soil Quality Surveillance
12.3.3 Pesticide Use Reduction
12.3.4 AI Farming Bots
12.3.5 AI-Based Monitoring Systems
12.3.6 AI-Based Irrigation System
12.4 Robots in Agriculture
12.5 Drones for Agriculture
12.6 Advantage of AI Implementation in Farming
12.6.1 Intelligent Agriculture Cloud Platform
12.6.1.1 Remote Control and Administration in Real Time
12.6.1.2 Consultation of Remote Experts
12.7 Research, Challenges, and Scope for the Future
12.8 Conclusion
References
13. Improving Wireless Sensor Networks Effectiveness with Artificial Intelligence
Piyush Raja, Santosh Kumar, Digvijay Singh and Taresh Singh
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
13.8.1 Hardware Layer
13.8.2 Middle Layer
13.8.3 Application Layer
13.9 Conclusion
References
Index
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