A Novel Optimized Perturbation-Based Machine Learning for Preserving Privacy in Medical Data.

In recent times, providing privacy to the medical dataset has been the biggest issue in medical applications. Since, in hospitals, the patient's data are stored in files, the files must be secured properly. Thus, different machine learning models were developed to overcome data privacy issues. But, those models faced some problems in providing privacy to medical data. Therefore, a novel model named Honey pot-based Modular Neural System (HbMNS) was designed in this paper. Here, the performance of the proposed design is validated with disease classification. Also, the perturbation function and the verification module are incorporated into the designed HbMNS model to provide data privacy. The presented model is implemented in a python environment. Moreover, the system outcomes are estimated before and after fixing the perturbation function. A DoS attack is launched in the system to validate the method. At last, a comparative assessment is made between executed models with other models. From the comparison, it is verified that the presented model achieved better outcomes than others.

Prevention of Covid-19 affected patient using multi robot cooperation and Q-learning approach: a solution.

Combat with the novel corona virus (COVID-19) has become challenging for all the frontline warriors like, medic people, police and other service provider. Many technology and intelligent algorithms have been developed to set the boundary in its incremental growth. This paper proposed a concept to set the boundary on spreading of this disease among the medic people, who are directly exposed to the COVID-19 patient. To reduce their risk to be infected, we have designed the theoretical model of the medic robot to provide medical services to the confirmed case patient. This paper explains the deployment and execution of assigned work of medic robot for patient carrying, delivering food, medications and handling the emergency health services. The medic robots are divided into various group based on their works. The COVID-19 area is considered as a multi-robot environment, where multiple medic robots will work simultaneously. To achieve the multi-robot cooperation and collision avoidance we have implemented the simplest reinforcement learning approach i.e. the Q-learning approach. We have compared the result with respect to the improved-Q-learning approach. A comparative analysis based on parameters like simplicity, objective, deployed robot category and cooperation has been done with some other approaches mentioned in the literature. For simplicity as well as the time and space complexity purpose the results reveal that Q-learning approach is a better consideration. The proposed approach reduces the mortality rate by 2%.