Author Description

<p>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.</p><p>Features: </p><ul> <li>Discusses the theoretical background of each algorithm in detail and presents the applications of each method.</li> <li>Presents artificial intelligence implications, sustainable artificial intelligence, and the importance of artificial intelligence in agriculture, and energy.</li> <li>Explains the use of predictive modeling in computational social science and applications of computational social science.</li> <li>Showcases the framework for social network analysis, application program interface, data collection methods, and data preprocessing.</li> <li>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.</li> </ul><p>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.</p>