Anant Agrawal

Aadit Nilay, Bhavesh Thapar, Anant Agrawal et al.

The COVID-19 pandemic exposed critical limitations in diagnostic workflows: RT-PCR tests suffer from slow turnaround times and high false-negative rates, while CT-based screening offers faster complementary diagnosis but requires expert radiological interpretation. Deploying automated CT analysis across multiple hospital centres introduces further challenges, as differences in scanner hardware, acquisition protocols, and patient populations cause substantial domain shift that degrades single-model performance. To address these challenges, we present a heterogeneous ensemble of nine models spanning three inference paradigms: (1) a self-supervised DINOv2 Vision Transformer with slice-level sigmoid aggregation, (2) a RadImageNet-pretrained DenseNet-121 with slice-level sigmoid averaging, and (3) seven Gated Attention Multiple Instance Learning models using EfficientNet-B3, ConvNeXt-Tiny, and EfficientNetV2-S backbones with scan-level softmax classification. Ensemble diversity is further enhanced through random-seed variation and Stochastic Weight Averaging. We address severe overfitting, reducing the validation-to-training loss ratio from 35x to less than 3x, through a combination of Focal Loss, embedding-level Mixup, and domain-aware augmentation. Model outputs are fused via score-weighted probability averaging and calibrated with per-source threshold optimization. The final ensemble achieves an average macro F1 of 0.9280 across four hospital centres, outperforming the best single model (F1=0.8969) by +0.031, demonstrating that heterogeneous architectures combined with source-aware calibration are essential for robust multi-site medical image classification.

Jitendra Tembhurne, Anant Agrawal, Kirtan Lakhotia

In the contemporary era, social media platforms amass an extensive volume of social data contributed by their users. In order to promptly grasp the opinions and emotional inclinations of individuals regarding a product or event, it becomes imperative to perform sentiment analysis on the user-generated content. Microblog comments often encompass both lengthy and concise text entries, presenting a complex scenario. This complexity is particularly pronounced in extensive textual content due to its rich content and intricate word interrelations compared to shorter text entries. Sentiment analysis of public opinion shared on social networking websites such as Facebook or Twitter has evolved and found diverse applications. However, several challenges remain to be tackled in this field. The hybrid methodologies have emerged as promising models for mitigating sentiment analysis errors, particularly when dealing with progressively intricate training data. In this article, to investigate the hesitancy of COVID-19 vaccination, we propose eight different hybrid deep learning models for sentiment classification with an aim of improving overall accuracy of the model. The sentiment prediction is achieved using embedding, deep learning model and grid search algorithm on Twitter COVID-19 dataset. According to the study, public sentiment towards COVID-19 immunization appears to be improving with time, as evidenced by the gradual decline in vaccine reluctance. Through extensive evaluation, proposed model reported an increased accuracy of 98.86%, outperforming other models. Specifically, the combination of BERT, CNN and GS yield the highest accuracy, while the combination of GloVe, BiLSTM, CNN and GS follows closely behind with an accuracy of 98.17%. In addition, increase in accuracy in the range of 2.11% to 14.46% is reported by the proposed model in comparisons with existing works.