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Book Cover
E-book
Author Khang, Alex

Title AI-Centric Modeling and Analytics Concepts, Technologies, and Applications
Published Milton : Taylor & Francis Group, 2023

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Description 1 online resource (396 p.)
Contents 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
Notes Description based upon print version of record
6.7.3 Latent Semantic Analysis-Singular Value Decomposition
Form Electronic book
Author Abdullayev, Vugar
Jadhav, Babasaheb
Gupta, Shashi Kant
Morris, Gilbert
ISBN 9781003815389
1003815383