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.
Table of Contents:
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
About the Author :
Akib Mohi Ud Din Khanday
Akib received the master’s degree in Information Technology from Islamic University of Science and Technology, Awantipora, Jammu and Kashmir, India and the Ph.D. degree in Computer Sciences from the Baba Ghulam Shah Badshah University, Rajouri, Jammu and Kashmir, India, in 2022. He has worked as Assistant Professor in the department of Information Technology, S.P. College, Cluster University, Srinagar, J&K, India and in the Department of Computer Science and Applications, Sharda University, India. He has worked as a Post Doctoral Research fellow in Department of Computer Science and Software Engineering-CIT, United Arab Emirates University, Al Ain from May 2023 to August 2024. He has worked as Assistant Professor in Department of Computer Science, Samarkand International University of Technology, Uzbekistan (September 2024 to May 2025). Currently, He is working as Assistant Professor in Information Technology, Cluster University of Srinagar, India.
His research interests are Computational Social Sciences, NLP and Machine/Deep Learning. He has authored many research articles in the reputed journals and conferences. He has served as a reviewer in reputed journals over the years.
Salah Bouktif
Salah Bouktif (Member, IEEE) received the degree in engineering and the master’s degree in industrial computing from the School of Computer Science, University of Tunis, Tunisia, and the Ph.D. degree (Hons.) in computer science from the University of Montreal, in 2015. He has been a Senior Software Engineer with Tunisian Railway Company (Known as SNCF), for three years. He is currently working as Full Professor with the College of Information Technology (CIT), United Arab Emirates University. Before joining CIT, in 2007, he was a Postdoctoral Fellow with the Department of Computer Engineering, Polytechnic School of Engineering, Montreal. He has published many papers in highly ranked journals and conferences, such as IEEE ICSM, IEEE ASE, ACM GECCO, ACM/IEEE ASONAM, IEEE ICWS, Information and Software Technology (Elsevier), ACM Transactions on IMS, and PLOS One. His research interests include energy prediction, data mining, big data analytics, search-based software engineering, and software quality assurance.
Mohd Anas Wajid
Mohd Anas received a Bachelor’s, Master’s, and a PhD degree in Computer Science and applications from Aligarh Muslim University, India. He was awarded with the MITACS-
SICI Globalink Research Award for doing a part of research at the University of Athabasca,
Edmonton, Alberta, Canada. He was also awarded a Diploma from the Neutrosophic Science International Association (NSIA), University of New Mexico, USA. He was a recipient of the Maulana Azad National Fellowship, Senior Research Fellow (SRF), and UGC fellowship from the Government of India. He was also a recipient of the ACM India Anveshan Setu Fellowship, ACM India Council. He qualified for various prestigious national exams such as UGC-NET and GATE multiple times. He has a keen research interest in the fields of Soft Computing, Machine Learning, Data Science, Information Retrieval, Neutrosophy, and Digital Twin. He has academic and industrial experience. His other favored tool is LaTeX, which he likes to use for all academic writings and presentations. He has published more than 20 research papers in international journals and conferences and published 10 books. He holds two patents for his research.
Syed Tanzeel Rabani
Syed Tanzeel Rabani currently serves as an Assistant Professor in the Department of Artificial Intelligence at Samarkand International University of Technology, Uzbekistan. His research expertise spans Artificial Intelligence, Machine Learning, Natural Language Processing (NLP), and Social Network Analysis, with a particular focus on mental health analytics, including the detection of suicidal ideation and hate speech on social media. He holds a Ph.D. in Computer Science along with an M.Phil(Gold Medalist), MCA, BCA, and has qualified UGC NET (June 2024) with 96.97 percentile. His work involves advanced techniques such as hybrid feature engineering, ensemble learning, and big data analytics applied to social media platforms like Twitter and Reddit. Rabani has authored 20 research papers published in high-indexed international journals and has made significant contributions to the development of intelligent systems for public health monitoring and ethical AI. His work continues to drive innovation in AI applications for real-world societal challenges.
