Your selections:
A Comparative Study of Machine Learning and Deep Learning Techniques for Fake News Detection
- Alghamdi, Jawaher, Lin, Yuqing, Luo, Suhuai
Towards Fake News Detection on Social Media
- Alghamdi, Jawaher, Lin, Yuqing, Luo, Suhuai
- Alghamdi, Jawaher, Lin, Yuqing, Luo, Suhuai
Modeling Fake News Detection Using BERT-CNN-BiLSTM Architecture
- Alghamdi, Jawaher, Lin, Yuqing, Luo, Suhuai
A comprehensive survey on machine learning approaches for fake news detection
- Alghamdi, Jawaher, Luo, Suhuai, Lin, Yuqing
Fake news detection in low-resource languages: A novel hybrid summarization approach
- Alghamdi, Jawaher, Lin, Yuqing, Luo, Suhuai
Super-vertex-antimagic total labelings of disconnected graphs
- Ali, Gohar, Bača, Martin, Lin, Yuqing, Semaničová-Feňovčíková, Andrea
- Almansour, Hussain, Luo, Suhuai, Lin, Yuqing
A survey of face-antimagic evaluations of graphs
- Baca, Martin, Baskoro, Edy, Brankovic, Ljiljana, Jendrol', Stanislav, Lin, Yuqing, Phanalasy, Oudone, Ryan, Joe, Semanicova-Fenovcikova, Andrea, Slamin, Sugeng, Kiki A.
Superconnectivity of regular graphs with small diameter
- Balbuena, Camino, Tang, Jianmin, Marshall, Kim, Lin, Yuqing
Diameter-sufficient conditions for a graph to be super-restricted connected
- Balbuena, Camino, Lin, Yuqing, Miller, Mirka
Note on super antimagicness of disconnected graphs
- Bača, Martin, Lin, Yuqing, Semaničová-Feňovčíková, Andrea
Edge-antimagic labelings of forests
- Bača, Martin, Lin, Yuqing, Muntaner-Batle, Francesc A.
A note on even disjoint union of paths
- Bača, Martin, Lin, Yuqing, Muntaner-Batle, Francesc A.
Normalized embedding of path-like trees
- Bača, Martin, Lin, Yuqing, Muntaner-Batle, Francesc A.
RTCSS: a framework for developing real-time peer-to-peer web applications
- Bille, Ross J., Lin, Yuqing, Chalup, Stephan K.
Some properties of the multiset dimension of graphs
On distance-irregular labelings of cycles and wheels
- Bong, Novi H., Lin, Yuqing, Slamin
- Cheers, Hayden, Lin, Yuqing, Smith, Shamus P.
Reverse engineering UML sequence diagrams for program comprehension activities
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