Numerous aspects of healthcare have been altered by cloud-based computing. Scalability of required service as well as ability to upscale or downsize data storage, as well as the collaboration between AI and machine learning, are main benefits of cloud computing in healthcare. Current paper looked at a number of different research studies to find out how intelligent techniques can be used in health systems. The main focus was on security and privacy concerns with the current technologies. This study proposes a novel method for cloud service device-to-device communication using feature selection and classification for data analysis in an e-health system. Through a comprehensive requirement analysis as well as user study, the purpose of this research is to investigate viability of incorporating cloud as well as distributed computing into e-healthcare. After that, the smart healthcare system and conventional database-centric healthcare methods will be compared, and a prototype system will be created as well as put into use based on results. Convolutional adversarial neural networks with transfer perceptron are used to analyze the cloud-based e-health data that has been collected. Proposed technique attained training accuracy 98%, validation accuracy 93%, PSNR 66%, MSE 68%, precision 72%, QoS 63%, Latency 58%.
A deep learning approach is gaining popularity day by day in image data classification. The process of classification of graphical data considering training network is managed by conventional neural network. Such types of networks allow automatic classification by making use of CNN approach. But the issues that are faced during forensic investigation are slow in performance and lack accuracy. The major objective of work is to consider the CNN approach that is processing graphic data in order to perform cyber forensic investigation.
Vehicle ad hoc networks have made intelligent transportation systems that significantly increase road safety as well as management possible. Vehicles can now communicate and share information about the road using this new technology. However, malicious users might inject fake emergency alerts into VANET, making it impossible for nodes to access accurate road information. In vehicular ad hoc networks, assessing credibility of nodes has become a crucial task to ensure reliability as well as trustworthiness of data. Using machine learning methods, this study proposes a novel security technique that improves communication and intruder detection in VANET for smart transportation. Ciphertext-policy game theory encryption analysis for smart transportation is used here to improve the security of the VANET. Fuzzy rule-based encoder perceptron neural networks are utilized in the detection of the VANET intruder. For a variety of network datasets, the experimental analysis is conducted in terms of throughput, QoS, latency, computational cost, and data transmission rate.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.