Helping Academically Talented STEM Students with Financial Need Succeed

This Research to Practice Full Paper presents the experiences and lessons learned from five programs that provide financial awards and a holistic student support structure to lowincome, academically talented students in Science, Technology, Engineering, and Mathematics (STEM). This report synthesizes the experiences of a diverse set of institutions, both public and private, that vary in size and geographic location. We have experience supporting students from a range of disciplines with an emphasis on students studying Computer Science. The goals of this work are to (1) outline the decisions that must be considered when designing a financial award program;(2) describe the interventions we have implemented and underline the institutional contexts that have led to their success;(3) describe the unique challenges posed by the COVID pandemic;and (4) highlight key elements necessary for successful program implementation. We specifically discuss the challenges we have encountered when implementing existing best practices. We report observations and results, some of which buttress those reported in the literature. Our work is intended to serve as a guide for educators who wish to implement programs to support students from financially disadvantaged and/or historically marginalized groups. By sharing our experiences and pain points, we hope to make it easier for them to design and implement effective programs adapted to their institutional needs and contexts.

Corona disease prediction using traditional machine learning methods

Corona virus disease (COVID-19) is a disease caused by a newly discovered corona virus that is contagious. This paper provides continuous monitoring of developments in COVID-19 as a supplement to the monitoring of reported incidents. The SIR (Susceptible-Infected-Recovered) model is regressed with data from different countries to estimate the curves of the pandemic life cycle and to predict when the pandemic will end with codes from Our World in Data in respective countries and the world. The predictive monitors are updated daily with the latest data, given the rapidly changing circumstances. © 2021 Author(s).

Convolutional neural network use chest radiography images for identification of COVID-19.

The epic Covid sickness 2019 (COVID-19) has turned into the significant danger to humankind in year 2020. The pandemic COVID-19 flare-up has influenced more than 2.7 million individuals and caused around 187 thousand fatalities worldwide [1] inside scarcely any months of its first appearance in Wuhan city of China and the number is developing quickly in various pieces of world. As researcher everywhere on the world are battling to discover the fix and treatment for COVID-19, the urgent advance fighting against COVID-19 is the screening of immense number of associated cases for disconnection and isolate with the patients. One of the key methodologies in screening of COVID-19 can be chest radiological imaging. The early investigations on the patients influenced by COVID-19 shows the attributes variations from the norm in chest radiography pictures. This introduced a chance to utilize distinctive counterfeit clever (AI) frameworks dependent on profound picking up utilizing chest radiology pictures for the recognition of COVID-19 and numerous such framework were proposed indicating promising outcomes. In this paper, we proposed a profound learning based convolution neural organization to characterize COVID-19, Pneumonia and Normal cases from chest radiology pictures. The proposed convolution neural organization (CNN) grouping model had the option to accomplish exactness of 94.85% on test dataset. The trial was completed utilizing the subset of information accessible in GitHub and Kaggle.