Makespan and cost are the major factors in the current scenario and they generally upgrade the results to optimize the upcoming task processing by implementing the scheduling within the specified cloud platform. A new proposed agenda is being considered for a health care system to make the world healthier. The paper is designed to identify prior work as a health evaluator for the end user and act accordingly. It also satisfies the end user by providing demanded results and can establish trust between the system and the customer within a global era. By analyzing the COVID-pandemic situation in the digital world, things should be implemented in conjunction with healthcare and technology to serve things better. The proposed algorithm works on priority basis by analyzing the current patient condition and then implemented in CloudSim Toolkit. As per the results, the proposed steps are performing 50-70% better in terms of makespan and cost. Notable optimization has been accomplished by the proposed healthcare evaluator.

Online banking transaction fraud occurs when fraudulent activity is initiated by a criminal. Such as seizing accounts and hacking points to execute online fund transfer mechanisms. This scenario is a main challenge for the upcoming data processing that is traveling only online. In today’s scenario, most of the work has become digital. In such cases, machine learning algorithms must be ex-traordinary to take stringent security measures to transfer such funds over a public channel. The paper analyzes some machine learning techniques such as naive Bayes and support vector machines to prevent loss. The machine has been used based on real data and can reduce losses manifold. After processing, the optimization reached 95 percent in terms of accuracy. The implementation can improve business operations to move the funds online while reducing overall risk.