Dataset on early-age strength of ambient-cured geopolymer mortars from waste concrete and bricks with different alkaline activators.

The dataset presented here emanates from preliminary studies that compared the early-age compressive strengths of geopolymer mortars produced from construction and demolition wastes (CDW) commonly found in Qatar using different alkaline activators. Waste concrete, waste bricks and steel slag were used as aluminosilicate sources for the geopolymer mortars. Waste concrete was used as fine aggregate (75 µm to 4 mm), while solid or hollow red clay bricks were used together with steel slag as aluminosilicate powders.

An enhanced diabetes prediction amidst COVID-19 using ensemble models.

In the contemporary landscape of healthcare, the early and accurate prediction of diabetes has garnered paramount importance, especially in the wake of the COVID-19 pandemic where individuals with diabetes exhibit increased vulnerability. This research embarked on a mission to enhance diabetes prediction by employing state-of-the-art machine learning techniques. Initial evaluations highlighted the Support Vector Machines (SVM) classifier as a promising candidate with an accuracy of 76.

Public mental health through social media in the post COVID-19 era.

Social media is a powerful communication tool and a reflection of our digital environment. Social media acted as an augmenter and influencer during and after COVID-19. Many of the people sharing social media posts were not actually aware of their mental health status.

3D indoor modeling and game theory based navigation for pre and post COVID-19 situation.

The COVID-19 pandemic has greatly affected human behavior, creating a need for individuals to be more cautious about health and safety protocols. People are becoming more aware of their surroundings and the importance of minimizing the risk of exposure to potential sources of infection. This shift in mindset is particularly important in indoor environments, especially hospitals, where there is a greater risk of virus transmission.

A deep transfer learning approach for COVID-19 detection and exploring a sense of belonging with Diabetes.

COVID-19 is an epidemic disease that results in death and significantly affects the older adult and those afflicted with chronic medical conditions. Diabetes medication and high blood glucose levels are significant predictors of COVID-19-related death or disease severity. Diabetic individuals, particularly those with preexisting comorbidities or geriatric patients, are at a higher risk of COVID-19 infection, including hospitalization, ICU admission, and death, than those without Diabetes.