Histopathological image classification stands as a cornerstone in the pathological diagnosis workflow, yet it remains challenging due to the inherent complexity of histopathological images. Recently, transformers and convolutional neural network (CNN) based deep models have shown promising results in the automatic histopathology image classification. Transformers excel at capturing global dependencies within the image content, while CNNs effectively extract local features. In this study, we introduce the CViTS-Net model, a novel deep learning architecture that combines CNNs with vision transformer (ViT) models, enhanced by innovative skip connections. This fusion allows our model to capture both local and global dependencies within histopathological images. Extensive experiments were conducted, including holdout validation and cross-validation, comparing CViTS-Net with several state-of-the-art CNN, ViT, and attention-based methods on the Chaoyang histopathology dataset. Furthermore, we evaluated the model’s generalizability and robustness by testing it on large and diverse datasets such as Lymphoma dataset and invasive ductal carcinoma (IDC) dataset. Our model achieves remarkable classification accuracy of 96.06% on the Chaoyang dataset, 99.61% accuracy on the Lymphoma dataset and 95% accuracy on IDC dataset, surpassing state-of-the-art deep learning models while maintaining superior efficiency. The CViTS-Net model showcases outstanding classification performance, underscoring its potential to significantly aid pathologists in histopathological diagnosis.

Background: The human microbiome plays a critical role in maintaining human health. Due to the recent advances in high-throughput sequencing technologies, the microbiome profiles present in the human body have become publicly available. Hence, many works have been done to analyze human microbiome profiles. These works have identified that different microbiome profiles are present in healthy and sick individuals for different diseases. Recently, several computational methods have utilized the microbiome profiles to automatically diagnose and classify the host phenotype. Results: In this work, a novel deep learning framework based on boosting GraphSAGE is proposed for automatic prediction of diseases from metagenomic data. The proposed framework has two main components, (a). Metagenomic Disease graph (MD-graph) construction module, (b). Disease prediction Network (DP-Net) module. The graph construction module constructs a graph by considering each metagenomic sample as a node in the graph. The graph captures the relationship between the samples using a proximity measure. The DP-Net consists of a boosting GraphSAGE model which predicts the status of a sample as sick or healthy. The effectiveness of the proposed method is verified using real and synthetic datasets corresponding to diseases like inflammatory bowel disease and colorectal cancer. The proposed model achieved a highest AUC of 93%, Accuracy of 95%, F1-score of 95%, AUPRC of 95% for the real inflammatory bowel disease dataset and a best AUC of 90%, Accuracy of 91%, F1-score of 87% and AUPRC of 93% for the real colorectal cancer dataset. Conclusion: The proposed framework outperforms other machine learning and deep learning models in terms of classification accuracy, AUC, F1-score and AUPRC for both synthetic and real metagenomic data.

The tourism industry has experienced fast and sustainable growth over the years in the economic sector. The data available online on the ever-growing tourism sector must be given importance as it provides crucial economic insights, which can be helpful for consumers and governments. Natural language processing (NLP) techniques have traditionally been used to tackle the issues of structuring of unprocessed data, and the representation of the data in a knowledge-based system. NLP is able to capture the full richness of the text by extracting the entity and relationship from the processed data, which is gathered from various social media platforms, webpages, blogs, and other online sources, while successfully taking into consideration the semantics of the text. With the purpose of detecting connections between tourism and economy, the research aims to present a visual representation of the refined data using knowledge graphs. In this research, the data has been gathered from Twitter using keyword extraction techniques with an emphasis on tourism and economy. The research uses TextBlob to convert the tweets to numeric vector representations and further uses clustering techniques to group similar entities. A cluster-wise knowledge graph has been constructed, which comprises a large number of relationships among various factors, that visualize entities and their relationships connecting tourism and economy.