Computational Statistical Methodologies and Modeling for Artificial Intelligence

Computational Statistical Methodologies and Modeling for Artificial Intelligence

by Priyanka Harjule, Azizur Rahman, Basant Agarwal, Vinita Tiwari
Released March 2023
Publisher(s): CRC Press
ISBN: 9781000831092

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Book description

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.

Table of contents

  1. Cover
  2. Half-Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Dedication
  7. Table of Contents
  8. Preface
  9. Acknowledgments
  10. About the Editors
  11. List of Contributors
  12. Theme 1 Statistics and AI Methods with Applications
  13. 1 A Review of Computational Statistics and Artificial Intelligence Methodologies
    1. 1.1 Introduction
    2. 1.2 Current Methodologies
      1. 1.2.1 Related Work on Computational Statistics
      2. 1.2.2 Related Work in AI
      3. 1.2.3 A Comparison of CS and AI
    3. 1.3 Discussion and Conclusion
    4. References
  14. 2 An Improved Random Forest for Classification and Regression Using Dynamic Weighted Scheme
    1. 2.1 Introduction
      1. 2.1.1 Proposed Work
    2. 2.2 Random Forest
      1. 2.2.1 Random Forest as Classifier
      2. 2.2.2 Random forest as Regressor
    3. 2.3 Proposed Method
      1. 2.3.1 Dynamic Weight Score Computation
    4. 2.4 Experimental Results
      1. 2.4.1 Hyperspectral Image Classification
      2. 2.4.2 Regression Application: Soil Moisture Prediction in Hyperspectral Dataset
      3. 2.4.3 Object and Digit Classification
    5. 2.5 Conclusion
    6. Conflict of Interest
    7. Note
    8. References
  15. 3 Study of Computational Statistical Methodologies for Modelling the Evolution of COVID-19 in India during the Second Wave
    1. 3.1 Introduction
    2. 3.2 Related Work
    3. 3.3 Methodology
      1. 3.3.1 Preliminaries
      2. 3.3.2 Deterministic Approach
      3. 3.3.3 Stochastic Approach
    4. 3.4 Results
      1. 3.4.1 Deterministic approach
      2. 3.4.2 Stochastic approach
    5. 3.5 Discussion
      1. 3.5.1 Deterministic approach
      2. 3.5.2 Stochastic approach
      3. 3.5.3 Comparison of both approaches
    6. 3.6 Conclusion
    7. References
  16. Theme 2 Machine Learning-adopted Models
  17. 4 Distracted Driver Detection Using Image Segmentation and Transfer Learning
    1. 4.1 Introduction
    2. 4.2 Related Works
    3. 4.3 System Model
      1. 4.3.1 Image Preprocessing
      2. 4.3.2 Classification Function
      3. 4.3.3 Training Algorithm
    4. 4.4 Dataset and Exploratory Analysis
    5. 4.5 Result and Discussion
    6. 4.6 Conclusion
    7. References
  18. 5 Review Analysis of Ride-Sharing Applications Using Machine Learning Approaches: Bangladesh Perspective
    1. 5.1 Introduction
    2. 5.2 Related Work
    3. 5.3 Methodology
      1. 5.3.1 Data Description
      2. 5.3.2 Data Pre-Processing
      3. 5.3.3 Proposed Model Working Procedure
    4. 5.4 Result
      1. 5.4.1 Cross-Validation
    5. 5.5 Discussion
    6. 5.6 Conclusions
    7. Acknowledgments
    8. References
  19. 6 Nowcasting of Selected Imports and Exports of Bangladesh: Comparison among Traditional Time Series Model and Machine Learning Models
    1. 6.1 Introduction
    2. 6.2 Methodology
      1. 6.2.1 Data and Variables
      2. 6.2.2 Methods
      3. 6.2.3 Evaluating Model Performance
    3. 6.3 Results
    4. 6.4 Conclusion
    5. Availability of data
    6. Conflicting Interests
    7. Funding
    8. References
  20. Theme 3 Development of the Forecasting Component to the Decision Support Tools
  21. 7 An Intelligent Interview Bot for Candidate Assessment by Using Facial Expression Recognition and Speech Recognition System
    1. 7.1 Introduction
    2. 7.2 Related Work
    3. 7.3 Proposed Artificial Intelligence Chatbot
      1. 7.3.1 Facial Recognition Module
      2. 7.3.2 Automatic Speech Recognition
    4. 7.4 Results and Experimentation
    5. 7.5 Conclusion
    6. Note
    7. References
  22. 8 Analysis of Oversampling and Ensemble Learning Methods for Credit Card Fraud Detection
    1. 8.1 Introduction
    2. 8.2 Related Work
    3. 8.3 Proposed Approach
      1. 8.3.1 Ensemble Learning
    4. 8.4 Experiment Results
      1. 8.4.1 Dataset and Preprocessing
      2. 8.4.2 Evaluation Metrics Ensemble Learning
    5. 8.5 Conclusion
    6. Acknowledgements
    7. References
  23. 9 Combining News with Time Series for Stock Trend Prediction
    1. 9.1 Introduction
    2. 9.2 Related Work
    3. 9.3 Methodology
      1. 9.3.1 Time Series Prediction
      2. 9.3.2 Text Mining and Prediction
      3. 9.3.3 Ensembling Prediction Models
    4. 9.4 Experiment and Results
      1. 9.4.1 Inference from Graphs
    5. 9.5 Conclusion
    6. 9.6 Future Work
    7. References
  24. 10 Influencing Project Success Outcomes by Utilising Advanced Statistical Techniques and AI during the Project Initiating Process
    1. 10.1 Introduction: Background and Driving Forces
    2. 10.2 Data Collection
      1. 10.2.1 Quantitative Data Collection
      2. 10.2.2 Stratified Random Sampling
      3. 10.2.3 Qualitative Data Collection
    3. 10.3 Proposed Method
      1. 10.3.1 Stage One – Factor Analysis
      2. 10.3.2 Stage Two – Cluster Analysis
      3. 10.3.3 Stage Three – Alignment to Cynefin Framework
    4. 10.4 Cynefin and the Qualitative Dataset
    5. 10.5 Cynefin and the Quantitative Dataset
    6. 10.6 Complexity and Decision Assessment Matrix
    7. 10.7 Robotic Process Automation (RPA)
    8. 10.8 Limitations and Restrictions of the Proposal
    9. 10.9 Conclusion
    10. References
  25. Theme 4 Socio-economic and Environmental Modelling
  26. 11 Computational Statistical Methods for Uncertainty Assessment in Geoscience
    1. 11.1 Introduction
    2. 11.2 Methods
      1. 11.2.1 Case Study Description
      2. 11.2.2 Bayesian Approximation of Interpretation Uncertainty
      3. 11.2.3 Conditional Indicator Simulation
      4. 11.2.4 Comparison of Intervals
    3. 11.3 Results
      1. 11.3.1 Uncertainty Assessment Using Bayesian Approximation
      2. 11.3.2 Uncertainty Assessment Using SIS
      3. 11.3.3 Comparison of Interpretation and Spatial Uncertainty
      4. 11.3.4 Discussion
    4. Conflict of Interest
    5. Acknowledgments
    6. Note
    7. References
  27. 12 A Comparison of Geocomputational Models for Validating Geospatial Distribution of Water Quality Index
    1. 12.1 Introduction
    2. 12.2 Application Domain: A Case Study in Cork Harbour
    3. 12.3 Methods and Materials
      1. 12.3.1 Data Obtaining Process
      2. 12.3.2 WQI Calculation
      3. 12.3.3 Prediction Techniques
      4. 12.3.4 Model Performance Analysis
    4. 12.4 Results and Discussion
      1. 12.4.1 Descriptive Assessment of Water Quality
      2. 12.4.2 Assessing Water Quality Using WQI Models
      3. 12.4.3 Comparison of Geostatistical Perdition Models
      4. 12.4.4 Evaluation of Uncertainty of Geocomputational-Interpolation Models
      5. 12.4.5 Comparison of Model Suitability for the Prediction of WQIs
    5. 12.5 Conclusion
    6. Declaration of Competing Interest
    7. Acknowledgments
    8. Funding information
    9. References
  28. 13 Mathematical Modeling for Socio-economic Development: A Case from Palestine
    1. 13.1 Introduction: Background and Driving Forces
    2. 13.2 Methodology
    3. 13.3 Fixed Points and Stability for the System
    4. 13.4 Numerical Solution and Bifurcation
    5. 13.5 Conclusion
    6. References
  29. Theme 5 Healthcare and Mental Disorder Detection with AIs
  30. 14 A Computational Study Based on Tensor Decomposition Models Applied to Screen Autistic Children: High-order SVD, Orthogonal Iteration and Discriminant Analysis Algorithms
    1. 14.1 Introduction
    2. 14.2 Experimental Design
    3. 14.3 Methodology
      1. 14.3.1 Feature Extraction, Feature Ranking, and Classification
    4. 14.4 Experimental Results and Discussion
      1. 14.4.1 Results
    5. 14.5 Concluding Remarks
    6. References
  31. 15 Stress-Level Detection Using Smartphone Sensors
    1. 15.1 Introduction
    2. 15.2 Related Work
    3. 15.3 Proposed Methodology
      1. 15.3.1 Data Collection
      2. 15.3.2 Data Extraction
      3. 15.3.3 Exploratory Data Analysis
      4. 15.3.4 Proposed Model
    4. 15.4 Results and Discussion
    5. 15.5 Challenges and Future Directions
    6. 15.6 Conclusion
    7. Acknowledgments
    8. References
  32. 16 Antecedents and Inhibitors for Use of Primary Healthcare: A Case Study of Mohalla Clinics in Delhi
    1. 16.1 Introduction
    2. 16.2 Background
    3. 16.3 Related Work
    4. 16.4 Research Gap
    5. 16.5 Objectives
    6. 16.6 Methodology
      1. 16.6.1 Design of the study
      2. 16.6.2 Sampling
      3. 16.6.3 Instrument Deployed
      4. 16.6.4 Statistical Analysis
    7. 16.7 Results and Discussion
      1. 16.7.1 Exploratory Factor Analysis
      2. 16.7.2 Chi-square Testing for Association
    8. 16.8 Conclusion
    9. Acknowledgments
    10. Notes
    11. References
  33. Index

Product information

  • Title: Computational Statistical Methodologies and Modeling for Artificial Intelligence
  • Author(s): Priyanka Harjule, Azizur Rahman, Basant Agarwal, Vinita Tiwari
  • Release date: March 2023
  • Publisher(s): CRC Press
  • ISBN: 9781000831092