دانلود کتاب AI-Centric Modeling and Analytics; Concepts, Technologies, and Applications

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Cover
Half Title
Title
Copyright
Contents
Preface
Acknowledgments
Editors
Contributors
Chapter 1 Artificial Intelligence-Based Model and Applications in Business Decision-Making
1.1 Introduction
1.2 Literature Survey
1.3 Artificial Intelligence-Centric Business Models
1.4 Tools for Supporting Business Decision-Making
1.4.1 Artificial Intelligence-Centric Tools for Supporting Business Decision-Making
1.4.2 ChatGPT for Business Decision-Making
1.4.3 Example (Case Study) for Business Decision-Making
1.5 Conclusion
References
Chapter 2 Exploration of Machine Learning Models for Business Ecosystem
2.1 Introduction
2.2 Machine Learning in Industry 4.0
2.2.1 Machine Learning Models
2.2.2 Technology Features of Machine Learning for Industry 4.0
2.2.3 Challenges in Industry 4.0 Using Machine Learning
2.3 Literature Survey
2.4 Machine Learning Framework for Business Ecosystem
2.5 Performance Analysis of Machine Learning Models
2.6 Conclusion
References
Chapter 3 The Role of Big Data and Data Analysis Tools in Business and Production
3.1 Introduction
3.2 Definition of Big Data
3.2.1 Managing Big Data
3.2.2 Data Use Cases
3.3 Big Data Process Life Cycle
3.3.1 Data Ingestion
3.3.2 Data Storage
3.3.3 Data Processing
3.3.4 Data Analysis
3.3.5 Big Data Analytics
3.3.6 Data Visualization
3.4 Conclusion
References
Chapter 4 Revolutionized Teaching by Incorporating Artificial Intelligence Chatbot for Higher Education Ecosystem
4.1 Introduction
4.2 Related Work
4.2.1 Student Engagement
4.2.2 Chatbots and Language Learning
4.3 Methods
4.4 Results and Discussion
4.4.1 Student Engagement in Incorporating Artificial Intelligence Chatbots
4.4.2 Discussion
4.5 Conclusion
References
Chapter 5 Application of Artificial Intelligence in AgroWeb
5.1 Introduction
5.2 Related Work
5.3 Pre-Harvesting
5.3.1 Crop Prediction
5.3.2 Model Selection for Crop Prediction
5.3.3 Seed Prediction
5.3.4 Crop Disease Prediction
5.3.5 Irrigation System
5.3.6 Irrigation System Model
5.4 Conclusion
5.5 Recommendation
References
Chapter 6 Natural Language Processing: A Study of State of the Art
6.1 Introduction
6.2 Text Pre-Processing and Vector-Based Models
6.3 Text Pre-Processing Techniques
6.3.1 Term Frequency–Inverse Document Frequency
6.3.2 Term Frequency Matrix
6.4 Natural Language Processing: Text Similarity and Semantic Analysis
6.4.1 Euclidian Distance
6.4.2 Dot Product
6.4.3 Cosine Similarity
6.5 Semantic Analysis
6.6 Probability Models in Natural Language Processing
6.6.1 Hidden Markov Model
6.6.2 Language Models
6.7 Machine Learning Methods for Natural Language Processing
6.7.1 Spam Detection–Naive Bayes
6.7.2 Sentiment Analysis–Logistic Regression
6.7.3 Latent Semantic Analysis–Singular Value Decomposition
6.7.4 Topic Modeling–Latent Dirichlet Allocation
6.8 Deep Learning Methods
6.8.1 Multilayer Perceptron
6.8.2 Convolutional Neural Network
6.8.3 Recurrent Neural Network
6.9 Conclusion
References
Chapter 7 Application of Artificial Intelligence in Healthcare System Management with Dynamic Modeling of COVID-19 Diagnosis
7.1 Introduction
7.2 Nomenclature
7.3 Mathematical Assumptions
7.4 Mathematical Model (SEIQR)
7.4.1 Model Analysis for Boundedness and Positivity
7.4.2 Calculation of Basic Reproduction Number
7.5 Existence of Equilibrium Points of the System of Equations
7.5.1 Coronavirus-Free Equilibrium Point
7.5.2 Stability Analysis of the Endemic Equilibrium
7.5.3 Global Stability of the Endemic Equilibrium
7.6 Results and Discussion
7.7 Conclusion
References
Chapter 8 Breast Cancer Prediction Using Voting Classifier Model
8.1 Introduction
8.1.1 Problem
8.1.2 Requirement for Our System
8.2 Literature Review
8.3 Methodology
8.3.1 Implementing Environment
8.3.2 Dataset Analysis and Pre-Processing
8.4 Proposed Work
8.5 Analysis and Comparison
8.6 Conclusion
References
Chapter 9 Privacy Protection for Internet of Medical Things Data Using Effective Outsourced Support Vector Machine Approach
9.1 Introduction
9.1.1 Big Data
9.1.2 Privacy-Preserving Data
9.1.3 Internet of Medical Things
9.1.4 Machine Learning in Internet of Medical Things
9.2 Literature Review
9.2.1 Data Mining
9.2.2 Machine Learning
9.3 System Design
9.4 Results and Discussion
9.5 Conclusion
References
Chapter 10 Robotics in Real-Time Applications Using Bayesian Hyper-Tuned Artificial Neural Network
10.1 Introduction
10.2 Problem Statement
10.3 Proposed Work
10.3.1 Data Collection
10.3.2 Pre-Processing Using Min-Max Normalization
10.3.3 Feature Extraction Using Kernel Linear Discriminant Analysis
10.3.4 Feature Detection Using Bayesian Hyper-Tuned Artificial Neural Network
10.4 Performance Evaluation
10.4.1 Security
10.4.2 Sensing Level
10.4.3 Robustness
10.4.4 Implementation Cost
10.5 Conclusion
References
Chapter 11 Quantitative Study on Variation of Glaucoma Eye Images Using Various EfficientNetV2 Models
11.1 Introduction
11.2 Literature Survey
11.3 Technical Approach
11.3.1 Dataset Description
11.3.2 EfficientNetV2 Models
11.4 Why EfficientNetV2?
11.4.1 EfficientNetV2B0
11.4.2 EfficientNetV2B1
11.4.3 EfficientNetV2B2
11.4.4 EfficientNetV2B3
11.4.5 EfficientNetV2S
11.4.6 EfficientNetV2M
11.4.7 EfficientNetV2L
11.4.8 EfficientNetV2XL
11.5 Hyper-Parameters Used
11.5.1 Adam (Optimizer)
11.5.2 Sigmoid (Activation Function)
11.6 Proposed Methodology
11.7 Implementation and Results
11.7.1 Evaluation of a Model Using Classification Metrics
11.7.2 Evaluation of Models Using Confusion Matrix
11.8 Conclusion
References
Chapter 12 Disaster Management System for Forest Fire Prediction: Fog and Cloud Data-Driven Analytical Compatible Model
12.1 Introduction
12.2 Related Work
12.3 Discussion and Results
12.3.1 Data Acquisition Layer
12.3.2 Fog Layer
12.3.3 Cloud Layer
12.4 Performance Analysis and Results
12.5 Implementation
12.6 Conclusion
References
Chapter 13 Hybrid Particle Swarm Optimization with Random Forest Algorithm Used in Job Scheduling to Improve Business and Production
13.1 Introduction
13.1.1 Job Scheduling
13.1.2 Data Cleaning
13.1.3 Machine Learning
13.1.4 Big Data Analysis
13.1.5 Particle Swarm Optimization
13.1.6 Random Forest
13.2 Literature Review
13.3 System Design
13.3.1 Particle Swarm Optimization for Data in Feature Selection
13.3.2 Training Data
13.3.3 Testing Data
13.3.4 Verification Step
13.3.5 Particle Swarm Optimization Algorithm
13.3.6 RF in Classification Algorithm
13.4 Results and Discussion
13.5 Conclusion
References
Chapter 14 Robotic Process Automation Applications in Data Management
14.1 Introduction
14.1.1 Robotic Process Automation Tools and Techniques
14.1.2 Relation of Robotic Process Automation with Artificial Intelligence Processes
14.2 Robotic Process Automation in Data Management
14.2.1 Robotic Process Automation Application in Data Cleansing
14.2.2 Robotic Process Automation Application in Data Normalization
14.2.3 How Is Robotic Process Automation Used in Data Wrangling?
14.2.4 Robotic Process Automation Application Management of Metadata
14.2.5 Variations of Robotic Process Automation
14.3 What Makes Robotic Process Automation So Special?
14.3.1 Business Process Automation
14.3.2 Business Process Management
14.3.3 Business Process Outsourcing
14.3.4 Advantages of Robotic Process Automation
14.4 Robotic Process Automation Learning with Cloud
14.4.1 Public Cloud
14.4.2 Private Cloud
14.4.3 Hybrid Cloud
14.5 Generally Used Robotic Process Automation Tools
14.5.1 Blue Prism
14.5.2 UiPath
14.5.3 Automation Anywhere
14.5.4 Kofax Kapow
14.5.5 Neptune Intelligence Computer Engineering
14.5.6 Key Differences between the Best Robotic Process Automation Tools
14.6 Robotic Process Automation in Business Data Management
14.7 Robotic Process Automation Use Cases in Business Data Management
14.7.1 How Customer Support Management Use Robotic Process Automation
14.7.2 Natural Language Use in Business Data Processing with Robotic Process Automation
14.8 Conclusion
References
Chapter 15 Artificial Intelligence-Enabled Bibliometric Analysis in Tourism and Hospitality Using Biblioshiny and VOSviewer Software
15.1 Introduction
15.2 Artificial Intelligence/Machine Learning
15.3 Methodology
15.4 Related Work
15.4.1 Analysis and Visualization of Data
15.4.2 Main Information about the Study
15.4.3 Emergence of Sources and Citation Analysis
15.4.4 Keyword Analysis
15.4.5 Analysis Based on Structures of Knowledge
15.5 Results and Discussion
15.5.1 Objective 1: To Investigate the Research Trend, Cluster Research, and the Evolution of Recent Research Domains in Tourism and the Hospitality Industry
15.5.2 Objective 2: To Investigate the Scientific Production by Countries
15.5.3 Objective 3: To Investigate the Scientific Production by Authors
15.5.4 Objective 4: To Investigate the Scientific Production by Institutions
15.5.5 Objective 5: To Investigate the Scientific Collaboration of the Countries
15.5.6 Objective 6: To Investigate Scientific Production by Sources and Dissemination by Sources
15.5.7 Objective 7: To Investigate the Content Based on the Author’s Keywords, KeyWords Plus, Titles, and Abstracts
15.5.8 Objective 8: To Investigate the Content Based on Citations (Most Cited References)
15.5.9 Objective 9: To Investigate the Less Researched Keywords Based on Centrality and Density
15.6 Conclusion
15.7 Limitations
15.8 Further Research
References
Chapter 16 Data-Centric Predictive Analytics for Solving Environmental Problems
16.1 Introduction
16.2 Materials and Methods
16.3 Discussion and Results
16.3.1. Exponential Decay and Logistic Models for Removal of Phenol
16.3.2. Results for the Logistic Model
16.4 Conclusion
References
Chapter 17 Phishing Attack and Defense: An Exploratory Data Analytics of Uniform Resource Locators for Cybersecurity
17.1 Introduction
17.1.1 Background
17.1.2 Methodology
17.1.3 Chapter Organization
17.2 Related Work
17.3 Proposed Methodology
17.3.1 Data Acquisition
17.3.2 Exploratory Data Analysis
17.3.3 Predictive Analytics of Uniform Resource Locators
17.4 Results
17.5 Conclusion
17.6 Recommendation
References
Chapter 18 Analysis of Deep Learning-Based Approaches for Spam Bots and Cyberbullying Detection in Online Social Networks
18.1 Introduction
18.1.1 SPAM
18.1.2 Spam Bot Detection Methods
18.1.3 Cyberbullying
18.1.4 Types of Cyberbullying
18.1.5 Cyberbullying Detection Methods
18.2 Proposed Methodology
18.2.1 Research Queries
18.2.2 Search Strategy
18.2.3 Study Choice
18.2.4 Quality Valuation Tools
18.3 Performance Metrics for Spam Bot and Cyberbullying Detection
18.3.1 Accuracy and Average Accuracy
18.3.2 Precision and Average Precision
18.3.3 Recall and Average Recall
18.3.4 F-Measure
18.3.5 G-Measure
18.3.6 Specificity and Average Specificity
18.4 Spam Bot Detection in Online Social Network Using Deep Learning
18.4.1 Social Media Bots
18.4.2 Spam Bot Detection in X/Twitter Using Deep Learning
18.4.3 Cyberbullying Detection in Online Social Network Using Deep Learning
18.5 Research Gap Analysis
18.6 Outcome of Literature Survey
18.7 Conclusion
18.8 Future Scope
References
Index