Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers.
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Babita Pandey is an Associate Professor in Department of Computer Application at Lovely Professional University of India. Her research interests include Cognitive Neuroscience, Artificial intelligence and Neural networks, Machine learning, Pattern Recognition and Cognitive Neuropsychology. She received a doctorate degree from Banaras Hindu University, Varanasi of India.
Aditya Khamparia is serving as academician and research person from past five years. Currently, He is working as Assistant Professor of Computer Science at Lovely Professional University, Punjab, India. He was awarded PhD in Computer Science from the Lovely Professional University, India. His research area is Machine Learning, Soft Computing, Educational Technologies, IoT, Semantic Web and Ontologies. He has published more than 35 scientific research publications in reputed International/National Journals and Conferences, which are indexed in various international databases. Invited as a Faculty Resource Person/Session Chair/Reviewer/TPC member in different FDP, conferences and journals. Dr. Aditya received research excellence award in 2016, 2017 and 2018 at Lovely Professional University for his research contribution during the academic year. He is member of CSI, IET, ISTE, IAENG, ACM and IACSIT. He is also acting as reviewer and member of various renowned national and international conferences/journals.
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers. Seller Inventory # 9781522590965
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