Prof. Fei Hao
Shaanxi Normal University, China
Research area：Social Computing, Pervasive Computing, Big Data Analytics
Profile: Dr. Hao received his Ph.D. degree in Computer Science and Engineering from Soonchunhyang University, South Korea, in 2016. Since 2016, he has been with Shaanxi Normal University, Xi'an, China, where he is an Associate Professor. From 2020 to 2022, he was a Marie Curie Fellow with the University of Exeter, Exeter, United Kingdom. His research interests include social computing, soft computing, big data analytics, pervasive computing, and data mining.
Title：Concept-cognitive Learning for Social Network Analysis
Abstract: The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision, and noisy data from social networks. Thanks to the emerging soft computing techniques, unlike conventional hard computing, which are widely used for coping with the tolerance of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) which is the process of investigating social structures and relevant properties through the use of network and graph theories. In this talk, a novel concept-cognitive learning paradigm for social network analysis will be introduced. Specifically, the representation model of Social Networks using Formal Concept Analysis (FCA) is introduced first. Then, some of our latest research works on topological structures mining and analysis in Social Networks based on concept interestingness are presented. Finally, the relevant FCA-based SNA software packages are summarized.
Research Area: big data, computational intelligence, knowledge graph, deep learning
Title: Machine learning for Stock forecasting
Abstract: This talk mainly introduces different models for stock forecasting, one model combines features selected by multiple feature selection techniques to generate an optimal feature subset and then use a deep generative model to predict future stock movements. Another model is for a turning point prediction method of stock price based on RVFL-GMDH and chaotic time series analysis. The turning indicator of time series is computed firstly; then, by applying the RVFL-GMDH model for the turning point prediction of the stock price. Computation results are reported.
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