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Computational Statistical Methodologies and Modeling for Artificial Intelligence

Computational Statistical Methodologies and Modeling for Artificial Intelligence

 
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Kiadó: CRC Press
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Rövid leírás:

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.

Hosszú leírás:

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
Tartalomjegyzék:

1: A review of  computational statistical and Artificial Intelligence methodologies. 2: An improved random forest for classification and regression using dynamic weighted schem. 3: Study of Computational Statistical methodologies for modelling the evolution of COVID-19 in India during the second wave.  4: Distracted Driver Detection using Image Segmentation and Transfer Learning.5: Review Analysis of Ride-Sharing Application Using Machine Learning Approaches - Bangladesh Perspective. 6: Nowcasting of selected imports and exports of Bangladesh: Comparison among traditional time series model and machine learning models.7: An Intelligent Interview bot for candidate assessment by using facial expression recognition and speech recognition System. 8: Analysis of Oversampling and Ensemble Learning Methods for Credit Card Fraud Detection. 9: Combining News with Time Series for Stock Trend Prediction. 10: Influencing project success outcomes by utilising advanced statistical techniques and AI during the Project Initiating Process.11: Computational Statistical Methods For Uncertainty Assessment In Geoscience. 12: A comparison of geocomputational models for validating geospatial distribution of water quality index. 13: Mathematical Modeling for Socio-Economic Development: A Case from Palestine.14: A computational study based on Tensor decomposition models applied to screen of autistic children: High order SVD, orthogonal iteration and discriminant analysis algorithms.15: Stress Level Detection using Smartphone Sensors.16: Antecedents and inhibitors for use of primary healthcare: A case study of Mohalla clinics in Delhi