Developing a Dual-Stage Vision Transformer Model for Lung Disease Classification

Lung diseases have become a prevalent problem throughout the United States, affecting over 34 million people. Accurate and timely diagnosis of the different types of lung diseases is critical, and Artificial Intelligence (AI) methods could speed up these processes. A dual-stage vision transformer is built throughout this research by integrating a Vision Transformer (ViT) and a Swin Transformer to classify 14 different lung diseases from X-ray scans of patients with these diseases. The proposed model achieved an accuracy of 92.06\% when making predictions on an unseen testing subset of the dataset after data preprocessing and training the neural network. The model showed promise for accurately classifying lung diseases and diagnosing patients who suffer from these harmful diseases.

Deep Learning for Polycystic Kidney Disease: Utilizing Neural Networks for Accurate and Early Detection through Gene Expression Analysis

With Polycystic Kidney Disease (PKD) potentially leading to fatal complications in patients due to the formation of cysts in the kidneys, early detection of PKD is crucial for effective management of the condition. However, the various patient-specific factors that play a role in the diagnosis make it an intricate puzzle for clinicians to solve. Therefore, in this study, we aim to utilize a deep learning-based approach for early disease detection. The devised neural network can achieve accurate and robust predictions for possible PKD in patients by analyzing patient gene expressions.

Towards Sustainable Development: A Novel Integrated Machine Learning Model for Holistic Environmental Health Monitoring

Urbanization enables economic growth but also harms the environment through degradation. Traditional methods of detecting environmental issues have proven inefficient. Machine learning has emerged as a promising tool for tracking environmental deterioration by identifying key predictive features. Recent research focused on developing a predictive model using pollutant levels and particulate matter as indicators of environmental state in order to outline challenges. Machine learning was employed to identify patterns linking areas with worse conditions. This research aims to assist governments in identifying intervention points, improving planning and conservation efforts, and ultimately contributing to sustainable development.