ABSTRACT
In recent years, there has been a surge in the use of deep learning systems for e-healthcare applications. While these systems can provide significant benefits regarding improved diagnosis and treatment, they also pose substantial privacy risks to patients' sensitive data. Privacy is a crucial issue in e-healthcare, and it is essential to keep patient information secure. A new approach based on multi-agent-based privacy metrics for e-healthcare deep learning systems has been proposed to address this issue. This approach uses a combination of deep learning and multi-agent systems to provide a more robust and secure method for e-healthcare applications. The multi-agent system is designed to monitor and control the access to patients' data by different agents in the system. Each agent is assigned a specific role and has specific data access permissions. The system employs a set of privacy metrics to a substantial privacy level of the data accessed by each agent. These metrics include confidentiality, integrity, and availability, evaluated in real-time and used to identify potential privacy violations. In addition to the multi-agent system, the deep learning component is also integrated into the system to improve the accuracy of diagnoses and treatment plans. The deep learning model is trained on a large dataset of medical records and can accurately predict the diagnosis and treatment plan based on the patient's symptoms and medical history. The multi-agent-based privacy metrics for the e-healthcare deep learning system approach have several advantages. It provides a more secure system for e-healthcare applications by ensuring only authorized agents can access patients' data. Privacy metrics enable the system to identify potential privacy violations in real-time, thereby reducing the risk of data breaches. Finally, integrating deep learning improves the accuracy of diagnoses and treatment plans, leading to better patient outcomes.
ABSTRACT
COVID-19 data analysis and prediction from patient data repository collected from hospitals and health organizations. Users' credentials and personal information are at risk; it could be an unrecoverable issue worldwide. A Homomorphic identification of possible breaches could be more appropriate for minimizing the risk factors in preventing personal data. Individual user privacy preservation is a must-needed research focus in various fields. Health data generated and collected information from multiple scenarios increasing the complexity involved in maintaining secret patient information. A homomorphic-based systematic approach with a deep learning process could reduce depicts and illegal functionality of unknown organizations trying to have relation to the environment and physical and social relations. This article addresses the homomorphic standard system functionality, which refers to all the functional aspects of deep learning system requirements in COVID-19 health management. Moreover, this paper spotlights the metric privacy incorporation for improving the Deep Learning System (DPLS) approaches for solving the healthcare system's complex issues. It is absorbed from the result analysis Homomorphic-based privacy observation metric gradually improves the effectiveness of the deep learning process in COVID-19-health care management.
ABSTRACT
Technical growth in the field of communication and information is an important aspect in the development and innovation of industrial automation and in the recent advances in the field of communications. The recent development of mobile communications has led to worldwide ubiquitous information sharing and has rehabilitated human lifestyles. This communication revolution is now introducing effective information sharing into the automotive industry. The current technology is extending this field of applications for vehicle safety, improving the efficiency in traffic management, offering reliable assistance for drivers and supporting the modern field of vehicle design. With these advances, the vehicular network concept has grabbed worldwide attention. In this article, a novel sampling-based estimation scheme (SES), to initiate the involvements and increase the probabilistic contacts of vehicle communication. The scheme is divided into a few segments, for ease of operations with a perfect sample. The contact duration between two vehicles moving in opposite directions on their overlapped road is lower, but their contact probability is higher. By contrast, the duration of the contact between two vehicles moving in the same direction on their overlapped road is higher, but their contact probability is lower. SES can easily obtain efficient routing by considering the above-mentioned stochastic contacts. Furthermore, we investigate the content transmission among the probabilistic contacts, by using the flow model with probabilistic capacities. The performance of the proposed SES is experimentally validated with the probabilistic contacts in VANETs.
