The expert intelligent system is introduced to develop hardware fault detection for any computer system. Different hardware system or electronics devices usually face application fault as well as hardware fault. This application oriented formal reasoning applies to determine the problem in computer system. This paper emphasizes an automated system that accepts the defects of any system and then after consulting with an intelligent database, diagnoses and advises for probable rectification. This is the extensions of the already published work by the authors implemented by Turbo Prolog programming language. This paper extends that concept exploring the boundary of Expert System targeting the implementation using Object Oriented web based programming approach. It proposes lot of new technological changes and in
Machine learning is a popular approach in the field of healthcare. Healthcare is an important industry that provides service to millions of people and as well as at the same time becoming top revenue earners in many countries. Machine learning in healthcare helps to analyze thousands of different data points and suggest outcomes, provide timely risk factors, optimize resource allocation. Machine learning is playing a critical role in patient care, billing processing to set the target to marketing and sales team, and medical records for patient monitoring and readmission, etc. Machine learning is allowing healthcare specialists to develop alternate staffing models, intellectual property management, and using the most effective way to capitalize on developed intellectual property assets. Machine learning approaches provide smart healthcare and reduce administrative and supply costs. Today healthcare industry is committed to deliver quality, value, and satisfactory outcomes.
Data aggregation in wireless sensor network is implemented to reduce the communication overhead and to reduce bandwidth utilization. Data confidentiality requires the sensor node to transmit the data in a secure manner so that the adversary is unable to read the data or transmit false data even if it compromises some of the sensor nodes or aggregation node. In this paper a distributed aggregation protocol using homomorphic trapdoor permutation is proposed. This protocol distributes the responsibility of key generation , aggregation and verification to different nodes to reduce the overall power consumption of the sensor network. The peer verification scheme is also proposed as a part of the protocol. Peer verification ensures the authentication of the data and sender node in the network, by at least k peer nodes. Security of the proposed protocol is analyzed against passive and active adversary model.
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