The use of internet search data has been demonstrated to be effective at predicting influenza incidence. This approach may be more successful for dengue which has large variation in annual incidence and a more distinctive clinical presentation and mode of transmission.

Breast cancer remains a leading cause of mortality among women globally, with late-stage diagnosis posing a significant challenge in Bangladesh due to limited diagnostic resources, insufficient awareness, and a shortage of trained healthcare professionals. My research aims to harness the potential of Artificial Intelligence (AI) to bridge these gaps by improving early detection, accurate diagnosis, and personalized treatment planning for breast cancer. 

My research focuses on the application of artificial intelligence (AI) to neuro-imaging techniques, with the goal of advancing our understanding of brain function and improving healthcare outcomes. By utilizing state-of-the-art machine learning and deep learning algorithms, I analyze complex neuro-imaging data, including MRI, fMRI, and PET scans. This work aims to enhance the accuracy of diagnostics and develop personalized treatment approaches for neurological disorders. Through the integration of computational methodologies and clinical applications, my research contributes to innovations in early disease detection, monitoring of disease progression, and evaluation of therapeutic responses. 

The research focuses on applying deep learning techniques to analyze multi-omics big data, with the goal of addressing the biological complexity of cancer and neurodegenerative diseases. By integrating diverse datasets, including genomics, transcriptomics, proteomics, and epigenomics, it aims to uncover key molecular mechanisms driving these conditions. This approach facilitates the identification of biomarkers, the prediction of disease trajectories, and the discovery of potential therapeutic targets. Through the intersection of computational methods and biomedical research, the work aspires to advance early detection, personalized treatment strategies, and improved clinical outcomes in oncology and neurology.