Somiya Rani, Ph.D

A novel architecture CCheXR-Attention is proposed to extract the clinical features from the radiological reports and classify each report into normal and abnormal categories based on the extracted information. We have proposed a modified Mogrifier long short-term memory model and integrated a multihead attention method to extract the more relevant features. Experimental outcomes on two benchmark datasets demonstrated that the proposed model surpassed state-of-the-art models.

A novel model called Multi-task Learning based Dual Bidirectional LSTM Model (MLDBM) for ABSA of drug reviews. The MLDBM leverages BERT and incorporates a multi-head self-attention mechanism to produce aspect-specific representations which are further processed through the dual BiLSTM model. This enables the model to capture and analyze sentiments related to different aspects discussed in the reviews. We also introduce various modifications to the MLDBM to identify the constraints of the proposed model. The proposed model outperforms state-of-the-art models by achieving a performance gain of 8% to 12% on two benchmark datasets, demonstrating its effectiveness when compared to various baseline models. ABSA applied to drug reviews contributes to enhancing healthcare quality by considering different aspects of drugs as shared by consumers.

This review aims to survey the text processing pipeline with deep learning methods such as CNN, RNN, LSTM, and GRU in the healthcare domain and discuss various applications such as clinical concept detection and extraction, medically aware dialogue systems, sentiment analysis of drug reviews shared online, clinical trial matching, and pharmacovigilance. In addition, we highlighted the major challenges in deploying text processing with deep learning to clinical textual data and identified the scope of research in this domain. Furthermore, we have discussed various resources that can be used in the future to optimize the healthcare domain by amalgamating text processing and deep learning.

In this work, a novel architecture namely a deep fusion model (DFM) with a meta-learning ensemble method is proposed for sentiment analysis of public opinions on COVID-19 vaccines and omicron variant on Twitter. The proposed model employed using natural language processing with deep learning models such as LSTM, GRU, CNN, and their various combinations. The purpose of this study is to understand the public opinion around COVID-19 vaccines and omicron variant through the proposed model. In addition, the experiment demonstrated effectiveness with an accuracy of up to 88% in comparison with state-of-the-art models.

The present study shows the scientometric analysis of the publications on the fuzzy logic controller in autonomous mobile robot navigation during the period 2000 to 2018. The data is collected using Web of Science core collection database and analyzed at various levels such as Web of Science categories, publication years, document types, funding agencies, authors, research areas, countries or region, control terms, and organization to evaluate the research patterns. An extensive study is done to find the research trends in this area.