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),...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc
[2023]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009757933206719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright Page
- Contents
- Preface
- Chapter 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
- 1.4.1 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
- Chapter 2 An Analytical Review of Deep Learning Algorithms for Stress Prediction in Teaching Professionals
- 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
- Chapter 3 Deep Learning: Tools and Models
- 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
- Chapter 4 Web Service Composition Using an AI Planning Technique
- 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
- Chapter 5 Artificial Intelligence in Agricultural Engineering
- 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
- Chapter 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
- 6.9.1 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
- Chapter 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)
- 7.2.1 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
- Chapter 8 A Study of WSN Privacy Through AI Technique
- 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
- Chapter 9 Introduction to AI Technique and Analysis of Time Series Data Using Facebook Prophet Model
- 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
- Chapter 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
- 10.1.1 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.
- Chapter 11 Eye-Based Cursor Control and Eye Coding Using Hog Algorithm and Neural Network
- 11.1 Introduction
- 11.2 Related Work
- 11.3 Methodology
- 11.3.1 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
- Chapter 12 Role of Artificial Intelligence in the Agricultural System
- 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
- Chapter 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
- 13.8.1 Hardware Layer
- 13.8.2 Middle Layer
- 13.8.3 Application Layer
- 13.9 Conclusion
- References
- Index
- EULA.
