Shakil Mahmud Shuvo

Suicide has recently emerged as a leading cause of death worldwide, underlining the importance of effective preventative measures. Online social media posts can provide valuable insights into people who are suicidal and assist in preventing unfortunate outcomes. However, the problem lies in the complexities of such posts, as people may be hesitant to share their distressing thoughts due to different psychological and societal constraints. This study examined the utilization of Bidirectional GRU, a type of recurrent neural network that analyzes data sequences in both forward and backward directions, to improve text classification using language models. This study improves textual analysis by incorporating Bidirectional GRU (Bi-GRU) layers with popular pre-trained language models like BERT, RoBERTa, DistilBERT, DistilRoBERTa, and ELECTRA-Small. The dataset used for this research is sourced from Reddit. The BERT-BiGRU and DistilBERT-BiGRU models demonstrated notable effectiveness, achieving accuracies of 95.8% and 95.2% respectively. These models also exhibited remarkably low false negative rates, with BERT-BiGRU at 4.17% and DistilBERT-BiGRU at 2.80%. These findings show that the combination of Bi-GRU and pre-trained language models considerably improves early detection of suicidal ideation, paving the path for prompt and perhaps lifesaving interventions.

Millions of people suffer from retinal defects over the world. Early discovery and treatment of these anomalies could halt further progression, saving many people from preventable blindness. Disease detection through manual methods is a tedious procedure that is both time-consuming and cannot produce consistent results. Building on the groundwork laid by Deep Convolutional Neural Networks (DCNNs) in computer-aided diagnosis (CAD), there have been initiatives to automate the detection of retinal diseases. This study presented a hybrid framework that utilizes pre-trained models (DenseNet121, ResNet50, VGG16, Xception, and EfficientB1) incorporating the Convolutional Block Attention Module (CBAM) and skip connections for accurate retinal disease classification. The CBAM block improved retinal disease classification by focusing on important features in OCT images. Skip connections were implemented to facilitate the direct transfer of feature information between layers. This study utilized the OCT-C8 dataset, which comprises seven disease classes and one healthy class. The DenseNet-CBAM-Skip and Xception-CBAM-Skip architectures were particularly noteworthy for their outstanding performance, with DenseNet obtaining a high accuracy of 96.28% and Xception 96.11%. They also acquired a significant F1-score of 96.31% and 96.11% respectively, making it promising for real-time application to help ophthalmologists.