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Design of Distribution Transformer Health Management System using IoT Sensors
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Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
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Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
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Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
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Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
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Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
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A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
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An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
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An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
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Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
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Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
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Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
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Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
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Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 3 | Issue - 3 | september 2021
Published
27 September, 2021
In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.
KeywordsWorkload Time Series Logarithmic Operation Deep Learning Min-Max Scalar
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