Zobrazeno 1 - 6
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pro vyhledávání: '"Islam, Muhammad Ifte"'
Autor:
Hossain, Tanvir, Saifuddin, Khaled Mohammed, Islam, Muhammad Ifte Khairul, Tanvir, Farhan, Akbas, Esra
Graph Neural Network (GNN) achieves great success for node-level and graph-level tasks via encoding meaningful topological structures of networks in various domains, ranging from social to biological networks. However, repeated aggregation operations
Externí odkaz:
http://arxiv.org/abs/2407.11928
Persistent homology is a mathematical tool used for studying the shape of data by extracting its topological features. It has gained popularity in network science due to its applicability in various network mining problems, including clustering, grap
Externí odkaz:
http://arxiv.org/abs/2306.11484
Graph Neural networks (GNNs) have recently become a powerful technique for many graph-related tasks including graph classification. Current GNN models apply different graph pooling methods that reduce the number of nodes and edges to learn the higher
Externí odkaz:
http://arxiv.org/abs/2303.03654
Autor:
Saifuddin, Khaled Mohammed, May, Corey, Tanvir, Farhan, Islam, Muhammad Ifte Khairul, Akbas, Esra
Sequence classification has a wide range of real-world applications in different domains, such as genome classification in health and anomaly detection in business. However, the lack of explicit features in sequence data makes it difficult for machin
Externí odkaz:
http://arxiv.org/abs/2303.02393
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics; September 2024, Vol. 21 Issue: 5 p1168-1179, 12p
Autor:
Islam MI; Department of Computer Science, Oklahoma State University, Stillwater, OK, United States., Tanvir F; Department of Computer Science, Oklahoma State University, Stillwater, OK, United States., Johnson G; Department of Computer Science, University of Tulsa, Tulsa, OK, United States., Akbas E; Department of Computer Science, Oklahoma State University, Stillwater, OK, United States., Aktas ME; Department of Mathematics and Statistics, University of Central Oklahoma, Edmond, OK, United States.
Publikováno v:
Frontiers in big data [Front Big Data] 2021 Jan 26; Vol. 3, pp. 608043. Date of Electronic Publication: 2021 Jan 26 (Print Publication: 2020).