Efficient Trust Management Technique Using Neural Network in Cloud Computing

Worldwide human health and economic has been affected due to the ongoing pandemic of corona virus (COVID-19). The major COVID-19 challenges are prevention, monitoring and FDA approved vaccines. IOT and cloud computing play vital role in epidemic prevention and blocking COVID-19 transmission. Mostly lungs and hearts are affected. Other than lungs many parts are affected which are not considered as prominent conversational cue. In this paper, we have proposed smart system that is effective through detection of pancreas, kidney and intestine. It detects acute pancreatitis, protein leak, microscopic blood leak, post infectious dysmotility and gastrointestinal bleeding. The data from the edge devices are collected and mapped into the cloud layer. The cloud consists of COVID-19 patients medical records which compare the user data with the existing patient records. Once the data matches it sends warning message to the user regarding the result of affected parts. Based on the result from KPI system, it analyzes with all data and using deep Convolutional Neural Network (CNN) it classifies whether the pancreas, kidney and intestine are affected or not due to COVID-19.

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Underground Disease Detection Based on Cloud Computing and Attention Region Neural Network

Journal on Artificial Intelligence ◽

10.32604/jai.2019.06157 ◽

2019 ◽

Vol 1 (1) ◽

pp. 9-18

Author(s):

Pinjie Xu ◽

Ce Li ◽

Liguo Zhang ◽

Feng Yang ◽

Jing Zheng ◽

...

Keyword(s):

Neural Network ◽

Cloud Computing ◽

Disease Detection

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RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

Scientific Programming ◽

10.1155/2016/5635673 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 13

Author(s):

Yao Lu ◽

John Panneerselvam ◽

Lu Liu ◽

Yan Wu

Keyword(s):

Neural Network ◽

Cloud Computing ◽

Prediction Model ◽

Prediction Accuracy ◽

Service Providers ◽

Cloud Service ◽

Backpropagation Neural Network ◽

Environmental Implications ◽

Cloud Service Providers ◽

Cloud Workloads

Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.

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Frequency Detection and Variation with Smart-Sensor Data Analysis Using Artificial Neural Network and Cloud Computing

Machine Learning for Predictive Analysis - Lecture Notes in Networks and Systems ◽

10.1007/978-981-15-7106-0_14 ◽

2020 ◽

pp. 143-152

Author(s):

Arabinda Rath ◽

Dillip K. Mishra ◽

S. Q. Baig ◽

Gayatri Devi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Cloud Computing ◽

Data Analysis ◽

Sensor Data ◽

Smart Sensor ◽

Frequency Detection ◽

Artificial Neural

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