Intersection of Machine Learning and Computational Social Sciences (Future Generation Information Systems)

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Intersection of Machine Learning and Computational Social Sciences (Future Generation Information Systems)

Khanday, Akib Mohi Ud Din (EDT)/ Bouktif, Salah (EDT)/ Wajid, Mohd Anas (EDT)

ウェブストア価格 ¥51,947（本体¥47,225）
CRC Press（2026/06発売）
外貨定価 US$ 235.00
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ポイント 2,360pt

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製本 Hardcover:ハードカバー版／ページ数 440 p.
言語 ENG
商品コード 9781032821177

Full Description

The text employs computational techniques and large-scale data analysis to study complex social phenomena and human behavior. It discusses diverse methodologies, including agent-based modeling, network analysis, natural language processing, and machine learning, to gain insights into topics ranging from social network dynamics and opinion formation to economic trends and public health crises.

Discusses the theoretical background of each algorithm in detail and presents the applications of each method.
Presents artificial intelligence implications, sustainable artificial intelligence, and the importance of artificial intelligence in agriculture, and energy.
Explains the use of predictive modeling in computational social science and applications of computational social science.
Showcases the framework for social network analysis, application program interface, data collection methods, and data preprocessing.
Covers topics such as density-based spatial clustering of applications with noise, the role of clustering in computational social science, and clustering in network structure.

The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

1. Leveraging Artificial Intelligence for Educational Transformation: A Critical Study of the Indian Context 2. A Study on Artificial Intelligence and its Role in Medical Image Analysis 3. An Overview of Machine Learning: Concepts, Algorithms and Applications 4. Natural Language Processing: Food Habits Based Disease Prediction Using Large Language Models) 5. Convolutional Neural Network-Based Plant Leaf Disease Classification: Implications for Society and Agriculture 6. Classifying the Social World: Algorithms and Applications in Computational Social Science 7. Social Network Analysis: Need, Data Collection, API's, Data Preprocessing, Feature Engineering Techniques. 8. Feature Selection in DNA Microarray Data: Insights for Healthcare and Social Science Applications through Machine Learning 9. Self-supervised Learning for Pathological Speech Detection 10. Analyzing the Social Consequences of Lung Cancer Risk Prediction with Lifestyle Data: A Comparative Study of Machine Learning Techniques 11. Adversarial Learning for Enhancing Security in Cobot-Driven Industries: A Machine Learning Approach to Risk Mitigation 12. Cybersecurity Challenges in Energy Harvesting Systems: A Machine Learning Approach to Safeguarding Industrial IoT Networks 13. Integrating Transfer Learning Techniques for Automated Recognition of Medicinal Plant Leaves in Computational Social Science 14. Deep Learning Based approach for Combating Fake news