The presence of a large number of genes in gene expression profiles poses a computational challenge for cancer classification. To address the high-dimensional feature space, this project proposes a set of frameworks that incorporate both filter-based and metaheuristic swarm-based optimization algorithms, including the binary whale optimization algorithm (BWOA), to effectively identify the most important genes.
This project aims to understand how journal ranking metrics, including impact factors and citation scores, are related to their editorial boards. By applying a set of data mining and regression techniques, we attempt to reveal the association between these two entities.
This project aims to explore various feature selection approaches for malware classification at different granularity levels.
Related Publications:
[C3] Sazzed, S., Feature selection in gene expression profile employing relevancy and redundancy measures and binary whale optimization algorithm (BWOA)., In International Conference on Advanced Data Mining and Applications (ADMA), 2021.
[C2] Sazzed, S., An investigation of the performances of simple gene selection methodologies for cancer classification, In 21st IEEE International Conference on BioInformatics and BioEngineering (BIBE), 2021.
[C1] Sazzed, S., ANOVA-SRC-BPSO: a hybrid filter and swarm optimization-based method for gene selection and cancer classification using gene expression profiles, In Canadian Conference on Artificial Intelligence (Canadian AI), 2021.
Related Publications:
[C1] Sazzed, S., Analyzing Scientometric Indicators of Journals and Chief Editors: A Case Study in Artificial Intelligence (AI) Domain., In International Conference on Computational Data and Social Networks (CSoNet), 2022.
[J1] Sazzed, S., Association between the Rankings of Top Bioinformatics and Medical Informatics Journals and the Scholarly Reputations of Chief Editors., In Publications, 2021.
Impact Factor: 3.8
Related Publications:
[C1] Sazzed, S. and Ullah, S., The Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection, In International Conference on Machine Learning and Applications (ICMLA), 2023.