Intersection of Machine Learning and Computational Social Sciences

Features

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.