Chinese Science Bulletin

Contagious lung infections are a matter of serious concern as they spread rapidly from person to person through aerosols and small droplets. Identifying the type of pulmonary infection is crucial due to similar clinical manifestations and an overlap of CT imaging characteristics of infections such as COVID-19, pneumonia and tuberculosis. Clinicians have relied on deep learning-based automated systems as a supplementary diagnostics tool. In recent times, federated learning has been extensively used in COVID-19 diagnosis to protect privacy of patient data sourced from multiple medical facilities

This study investigates the effectiveness of ensemble learning approaches in enhancing collaborative filtering-based recommendation systems in educational settings. Significant improvements in performance indicators were obtained after merging predictions from three different models - Singular Value Decomposition (SVD), Non-negative Matrix Factorization (NMF), and CoClustering - using ensemble learning approaches. Bagging, boosting, and stacking techniques led in significant reductions in Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as compared to individual models. Specifically

Over the past few years the higher education sector has expanded rapidly. Several new universities have arisen from both the public and private sectors that deliver a range of courses for undergraduate and postgraduate students. University education enrolment rates have also risen but not so much as the number of higher establishments is rising. This is a challenge for today's education system and this gap needs to be discussed and presented adequately to the learning community. The quantity of data stored in an educational database is rapidly increasing. Such databases provide secret informat

Over the past few years, the spread of COVID-19 has significantly impacted people’s lives globally, necessitating rapid diagnosis to interrupt its spread and reduce infection risk. CT imaging has been recognized as a vital tool for detailed insights into the disease’s manifestations, with deep learning proving highly effective in improving diagnostic accuracy and efficiency. However, despite achieving high accuracy, previous works have faced challenges related to high computational costs, reliance on scarce labeled data, and difficulty generalizing to diverse datasets. To address these iss

The objectives of this study were to determine the ability of ongole grade cattle (OGC) and Buffaloes (B) in intake and digestibility of Calliandra calothyrsus (CC), Gliricidia sp. (G) Corn stover (CS). The study used 5 females (OGC) and 5 females (B). This research was carried out in two periods, namely the adaptation period 14 days and the collection period 15 days. The variables observed were voluntary intake of neutral detergent fiber (VINDF), digestibility of neutral detergent fiber (DNDF), digestibility of acid detergent fiber (DADF), digestibility of crude protein (DCP). Treatment is de