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Social media platforms are playing increasingly critical roles in disaster response and rescue operations. During emergencies, users can post rescue requests along with their addresses on social media, while volunteers can search for those messages and send help. However, efficiently leveraging social media in rescue operations remains challenging because of the lack of tools to identify rescue request messages on social media automatically and rapidly. Analyzing social media data, such as Twitter data, relies heavily on Natural Language Processing (NLP) algorithms to extract information from texts. The introduction of bidirectional transformers models, such as the Bidirectional Encoder Representations from Transformers (BERT) model, has significantly outperformed previous NLP models in numerous text analysis tasks, providing new opportunities to precisely understand and classify social media data for diverse applications. This study developed and compared ten VictimFinder models for identifying rescue request tweets, three based on milestone NLP algorithms and seven BERT-based. A total of 3191 manually labeled disaster-related tweets posted during 2017 Hurricane Harvey were used as the training and testing datasets. We evaluated the performance of each model by classification accuracy, computation cost, and model stability. Experiment results show that all BERT-based models have significantly increased the accuracy of categorizing rescue-related tweets. The best model for identifying rescue request tweets is a customized BERT-based model with a Convolutional Neural Network (CNN) classifier. Its F1-score is 0.919, which outperforms the baseline model by 10.6%. The developed models can promote social media use for rescue operations in future disaster events. 

Most of the cancers are curable if they are detected at early stages. The early stage detection of cancers can significantly improve the patient treatment outcomes and thus helps to decrease the. To achieve the early detection of specific cancer, the biochip is incorporated with an innovative sensing mechanism and surface treated microchannels. The sensing mechanism employed in the Point of Care (POC) biochip is designed to be highly specific and sensitive. The surface treated microchannel helps to control the self-driven flow of the blood sample. Cancer antibodies with enhanced specificity and affinity are immobilized on the surface of the nano circuit in the microchannel. When the blood sample flows in the microchannel over the cancer antibodies, the corresponding cancer antigens from the blood form the antigen-antibody complex. These antigen-antibody interactions are captured with the variation in the electrical properties of the gold nano circuit using the sensing mechanism in the biochip. The point of care (POC) micro biochip is designed as an in-situ standalone device to diagnose ovarian cancer at the early stages by sensing the cancer antigens in the blood sample drawn from a finger prick. The POC biochip can help to diagnose, the existence of cancer and also its severity using the qualitative and the quantitative results of the sensing mechanism in the biochip.