Institutional support and quality education are linked in a significant way. During Covid-19, institutional support is critical to closing the huge academic gap that has emerged as physical academic practices have been moved to a virtual education system using technology. This research aims to assess institutional support for online education in Bangladesh during the Covid-19 pandemic. This analysis is focused on the three main elements of the Adapted Model of Institutional Support (AMIS) of Bond et al, 2007, and the Institutional Support Model (ISM) of Valverde and Rodriguez, 2002, namely Financial Support (FS), Technical Support (TS), and Mentoring Support (MS). According to the findings, a few universities in Bangladesh have provided average support for continuing online education, while others have just started taking online classes. Several problems have been discovered, such as the lack of dedicated software for conducting online academic activities, lack of training and grooming, lack of mentoring, poor internet access, lack of smartphones, high internet package rates, and so on. This study concludes with some policy recommendations for a smooth online education system in Bangladesh.

Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. Controlling and curing diabetes in its early stages is the most significant technique to avoid its effects of diabetes. However, lack of awareness and expensive clinical tests are the primary reasons to skip clinical diagnosis and take preventive methods in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, the study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes. Because clinical features are not always known to all people from lower-income countries. To find the best fit supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. The study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for growing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.