Technology Management for Accelerated Recovery during COVID-19

Abstract

Objective- The research looks forward to extracting strategies for accelerated recovery during the ongoing Covid-19 pandemic. Design - Research design considers quantitative methodology and evaluates significant factors from 170 countries to deploy supervised and unsupervised Machine Learning techniques to generate non-trivial predictions. Findings - Findings presented by the research reflect on data-driven observation applicable at the macro level and provide healthcare-oriented insights for governing authorities. Policy Implications - Research provides interpretability of Machine Learning models regarding several aspects of the pandemic that can be leveraged for optimizing treatment protocols. Originality - Research makes use of curated near-time data to identify significant correlations keeping emerging economies at the center stage. Considering the current state of clinical trial research reflects on parallel non-clinical strategies to co-exist with the Coronavirus.

Reinforcing Positive Cognitive States with Machine Learning: An Experimental Modeling for Preventive Healthcare

“…The latter part of the study was used for ML modeling using a decision tree as a statistical technique. Considering a large number of data points and to derive realtime insights, the presented research opted for data science-driven techniques using ML-driven modeling [73].…”