This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering.
The key features of this book are
Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence
Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications
Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals
Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields
Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence
Author(s): Priyanka Harjule, Azizur Rahman, Basant Agarwal and Vinita Tiwari
Publisher: CRC Press
Year: 2023
Language: English
Pages: 389
Cover
Half Title
Series Information
Title Page
Copyright Page
Dedication
Table of Contents
Preface
About the Editors
List of Contributors
Theme 1 Statistics and AI Methods With Applications
1 A Review of Computational Statistics and Artificial Intelligence Methodologies
1.1 Introduction
1.2 Current Methodologies
1.2.1 Related Work On Computational Statistics
1.2.2 Related Work in AI
1.2.3 A Comparison of CS and AI
1.3 Discussion and Conclusion
References
2 An Improved Random Forest for Classification and Regression Using Dynamic Weighted Scheme
2.1 Introduction
2.1.1 Proposed Work
2.2 Random Forest
2.2.1 Random Forest as Classifier
2.2.2 Random Forest as Regressor
2.3 Proposed Method
2.3.1 Dynamic Weight Score Computation
2.4 Experimental Results
2.4.1 Hyperspectral Image Classification
2.4.1.1 Implementation Details and Performance Analysis
2.4.2 Regression Application: Soil Moisture Prediction in Hyperspectral Dataset
2.4.2.1 Implementation Details and Performance Analysis
2.4.3 Object and Digit Classification
2.4.3.1 Implementation Details and Performance Analysis
2.5 Conclusion
Conflict of Interest
Note
References
3 Study of Computational Statistical Methodologies for Modelling the Evolution of COVID-19 in India During the Second Wave
3.1 Introduction
3.2 Related Work
3.3 Methodology
3.3.1 Preliminaries
3.3.2 Deterministic Approach
3.3.2.1 Proposed Model 1
3.3.2.2 Calculation of Basic Reproduction Number (R0)
3.3.2.3 Stability Analysis
3.3.2.4 Data and Implementation
3.3.3 Stochastic Approach
3.3.3.1 Proposed Model 2
3.3.3.2 Data and Implementation
3.3.3.3 Results and Discussion
3.4 Results
3.4.1 Deterministic Approach
3.4.2 Stochastic Approach
3.5 Discussion
3.5.1 Deterministic Approach
3.5.2 Stochastic Approach
3.5.3 Comparison of Both Approaches
3.6 Conclusion
References
Theme 2 Machine Learning-Adopted Models
4 Distracted Driver Detection Using Image Segmentation and Transfer Learning
4.1 Introduction
4.2 Related Works
4.3 System Model
4.3.1 Image Preprocessing
4.3.2 Classification Function
4.3.3 Training Algorithm
4.4 Dataset and Exploratory Analysis
4.5 Result and Discussion
4.6 Conclusion
References
5 Review Analysis of Ride-Sharing Applications Using Machine Learning Approaches: Bangladesh Perspective
5.1 Introduction
5.2 Related Work
5.3 Methodology
5.3.1 Data Description
5.3.2 Data Pre-Processing
5.3.3 Proposed Model Working Procedure
5.3.3.1 Machine Learning Models
5.3.3.2 Performance Measurement Unit
5.3.3.3 Accuracy
5.3.3.4 Precision
5.3.3.5 Recall
5.3.3.6 F1-Score
5.4 Result
5.4.1 Cross-Validation
5.5 Discussion
5.6 Conclusions
Acknowledgments
References
6 Nowcasting of Selected Imports and Exports of Bangladesh: Comparison Among Traditional Time Series Model and Machine ...
6.1 Introduction
6.2 Methodology
6.2.1 Data and Variables
6.2.2 Methods
6.2.2.1 ARIMA Model
6.2.2.2 Artificial Neural Network Procedure
6.2.2.3 Support Vector Regression Model
6.2.3 Evaluating Model Performance
6.3 Results
6.4 Conclusion
Availability of Data
Conflicting Interests
Funding
References
Theme 3 Development of the Forecasting Component to the Decision Support Tools
7 An Intelligent Interview Bot for Candidate Assessment By Using Facial Expression Recognition and Speech Recognition System
7.1 Introduction
7.2 Related Work
7.3 Proposed Artificial Intelligence Chatbot
7.3.1 Facial Recognition Module
7.3.2 Automatic Speech Recognition
7.4 Results and Experimentation
7.5 Conclusion
Note
References
8 Analysis of Oversampling and Ensemble Learning Methods for Credit Card Fraud Detection
8.1 Introduction
8.2 Related Work
8.3 Proposed Approach
8.3.1 Ensemble Learning
8.4 Experiment Results
8.4.1 Dataset and Preprocessing
8.4.2 Evaluation Metrics Ensemble Learning
8.4.2.1 Ensemble of Logistic Regression and Random Forest
8.4.2.2 Ensemble of Logistic Regression and KNN
8.4.2.3 Ensemble of Logistic Regression, Random Forest, and KNN
8.4.2.4 Ensemble of Logistic Regression, Random Forest, KNN, and SVM
8.4.2.5 Ensemble of Logistic Regression, Random Forest, KNN, and Naïve Bayes
8.4.2.6 Ensemble of Logistic Regression, Random Forest, KNN, Naïve Bayes, and SVM
8.5 Conclusion
Acknowledgements
References
9 Combining News With Time Series for Stock Trend Prediction
9.1 Introduction
9.2 Related Work
9.3 Methodology
9.3.1 Time Series Prediction
9.3.2 Text Mining and Prediction
9.3.3 Ensembling Prediction Models
9.4 Experiment and Results
9.4.1 Inference From Graphs
9.5 Conclusion
9.6 Future Work
References
10 Influencing Project Success Outcomes By Utilising Advanced Statistical Techniques and AI During the Project Initiating ...
10.1 Introduction: Background and Driving Forces
10.2 Data Collection
10.2.1 Quantitative Data Collection
10.2.2 Stratified Random Sampling
10.2.3 Qualitative Data Collection
10.3 Proposed Method
10.3.1 Stage One – Factor Analysis
10.3.2 Stage Two – Cluster Analysis
10.3.3 Stage Three – Alignment to Cynefin Framework
10.4 Cynefin and the Qualitative Dataset
10.5 Cynefin and the Quantitative Dataset
10.6 Complexity and Decision Assessment Matrix
10.7 Robotic Process Automation (RPA)
10.8 Limitations and Restrictions of the Proposal
10.9 Conclusion
References
Theme 4 Socio-Economic and Environmental Modelling
11 Computational Statistical Methods for Uncertainty Assessment in Geoscience
11.1 Introduction
11.2 Methods
11.2.1 Case Study Description
11.2.2 Bayesian Approximation of Interpretation Uncertainty
11.2.2.1 Selection of Important Variables
11.2.2.2 Bayesian Approximation
11.2.3 Conditional Indicator Simulation
11.2.3.1 Variogram Parameters
11.2.3.2 Simulation
11.2.4 Comparison of Intervals
11.3 Results
11.3.1 Uncertainty Assessment Using Bayesian Approximation
11.3.2 Uncertainty Assessment Using SIS
11.3.3 Comparison of Interpretation and Spatial Uncertainty
11.3.4 Discussion
Conflict of Interest
Acknowledgments
Note
References
12 A Comparison of Geocomputational Models for Validating Geospatial Distribution of Water Quality Index
12.1 Introduction
12.2 Application Domain: A Case Study in Cork Harbour
12.3 Methods and Materials
12.3.1 Data Obtaining Process
12.3.2 WQI Calculation
12.3.3 Prediction Techniques
12.3.3.1 Spatial Computational Methods
12.3.4 Model Performance Analysis
12.3.4.1 Cross-Validation (CV) Approaches
12.3.4.2 Prediction Uncertainty Analysis
12.3.4.3 Model Suitability Analysis
12.4 Results and Discussion
12.4.1 Descriptive Assessment of Water Quality
12.4.2 Assessing Water Quality Using WQI Models
12.4.3 Comparison of Geostatistical Perdition Models
12.4.4 Evaluation of Uncertainty of Geocomputational-Interpolation Models
12.4.5 Comparison of Model Suitability for the Prediction of WQIs
12.5 Conclusion
Declaration of Competing Interest
Acknowledgments
Funding Information
References
13 Mathematical Modeling for Socio-Economic Development: A Case From Palestine
13.1 Introduction: Background and Driving Forces
13.2 Methodology
13.3 Fixed Points and Stability for the System
13.4 Numerical Solution and Bifurcation
13.5 Conclusion
References
Theme 5 Healthcare and Mental Disorder Detection With AIs
14 A Computational Study Based On Tensor Decomposition Models Applied to Screen Autistic Children: High-Order SVD, …
14.1 Introduction
14.2 Experimental Design
14.3 Methodology
14.3.1 Feature Extraction, Feature Ranking, and Classification
14.4 Experimental Results and Discussion
14.4.1 Results
14.5 Concluding Remarks
References
15 Stress-Level Detection Using Smartphone Sensors
15.1 Introduction
15.2 Related Work
15.3 Proposed Methodology
15.3.1 Data Collection
15.3.2 Data Extraction
15.3.3 Exploratory Data Analysis
15.3.4 Proposed Model
15.4 Results and Discussion
15.5 Challenges and Future Directions
15.6 Conclusion
Acknowledgments
References
16 Antecedents and Inhibitors for Use of Primary Healthcare: A Case Study of Mohalla Clinics in Delhi
16.1 Introduction
16.2 Background
16.3 Related Work
16.4 Research Gap
16.5 Objectives
16.6 Methodology
16.6.1 Design of the Study
16.6.2 Sampling
16.6.3 Instrument Deployed
16.6.4 Statistical Analysis
16.7 Results and Discussion
16.7.1 Exploratory Factor Analysis
16.7.2 Chi-Square Testing for Association
16.8 Conclusion
Acknowledgments
Notes
References
Index
