The increasing sophistication of cyber threats poses significant challenges to network security. This makes effective intrusion detection system (IDS) more important than ever before. Conventional IDS methods, which often rely on signatures or rules it will struggle to keep up with its complex attacks and evolution. This thesis evaluates and analyze the performance of DL algorithms. They include convolutional neural networks (CNN), recurrent neural networks (RNN), deep belief networks (DBN), and Auto-encoder. Using the models, these models are trained and tested only on the NSL-set. KDD data, which is a widely accepted benchmark for evaluating IDS performance. Results show that the proposed deep learning approach significantly outperforms traditional methods, has a higher detection rate, reduce the false positive rate and the ability to identify both known and unknown intrusions. They leverage the strengths of CNN, RNN, DBN, and autoencoders. Doing this research Advances IDS capabilities by providing a robust and adaptable solution to enhance network security.

Mahshar Yahan, Dr. Mohammad Islam. Proceedings of the Fifth Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP). 2025.

Mahshar Yahan, Dr. Mohammad Islam. Proceedings of the Fifth Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP). 2025.

Background: Cervical cancer (CC) and ovarian cancer (OC) are among the most prevalent and lethal gynecological malignancies in women, necessitating the identification of reliable biomarkers for early diagnosis and prognosis. Methods: This study integrates bioinformatics and Healthcare 4.0 to identify key biomarkers associated with these cancers. Differentially expressed genes (DEGs) were identified from two microarray datasets. mRMR followed by SVM-RFE was applied to the identified DEGs to extract the most significant ML-based DEGs (MDEGs). The predictive ability of the selected gene subsets was further evaluated via multiple classifiers, where attention-based long short-term memory (AttLSTM) consistently achieved the best performance across both datasets. In parallel, WGCNA was conducted to identify coexpression-associated genes (CAGs) from significant modules in each dataset. A PPI network (PPIN) was constructed using the genes common to MDEGs and CAGs and was analyzed via Cytoscape. Results: Four hub genes, MCM3, FOXM1, SH3BP5, and PAPSS2, were identified via the degree method. mRNA expression analysis revealed that FOXM1 and MCM3 were upregulated, whereas SH3BP5 and PAPSS2 were downregulated in cancer tissues compared with normal tissues. ROC curve analysis demonstrated the high prognostic significance of these hub genes, with substantial AUC scores indicating strong discriminatory power. Furthermore, molecular docking analysis with an FDA-approved drug compound confirmed the significant binding affinity between these genes and the drug molecules. Conclusions: These findings suggest that FOXM1, MCM3, SH3BP5, and PAPSS2 could serve as biomarkers for early prognosis, diagnosis, and targeted therapy in patients with cervical and ovarian cancer.