Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors
Volume-3 | Issue-2
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
A Review on Data Securing Techniques using Internet of Medical Things
Volume-3 | Issue-3
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks
Volume-3 | Issue-2
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING
Volume-1 | Issue-1
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
Volume-3 | Issue-2
Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
Volume-3 | Issue-2
Construction of a Framework for Selecting an Effective Learning Procedure in the School-Level Sector of Online Teaching Informatics
Volume-3 | Issue-4
Machine Learning Algorithms Performance Analysis for VLSI IC Design
Volume-3 | Issue-2
Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Characterizing WDT subsystem of a Wi-Fi controller in an Automobile based on MIPS32 CPU platform across PVT
Volume-2 | Issue-4
Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
Volume-3 | Issue-4
Design of Data Mining Techniques for Online Blood Bank Management by CNN Model
Volume-3 | Issue-3
Ethereum and IOTA based Battery Management System with Internet of Vehicles
Volume-3 | Issue-3
Volume - 2 | Issue - 1 | march 2020
Published
09 March, 2020
Nowadays to increase the efficiency, consistency and the quality of the organizations and to further extend the business world wide the digitization is followed in processing, storing and conveying the information. This in turn has also caused huge set of data flow paving way for the data recovery services. The cloud computing with the massive storage capabilities have become a predominantly used paradigm for data storage and recovery due to its on demand network access, elasticity, flexibility and pay as you go. Moreover to secure the information that is stored the information's are fragmented and stored. However this fragmentation process often occurs in the form of dispersed and scattered packages lacking proper order heightening the time and minimizing the efficiency of the recovery and information collection. To bring down the restoration time and enhance its efficiency the proposed method in the paper tries to reduce the fragmentation by minimizing the number of dispersed and scattered packages for this the paper utilizes the Hybridized Historical aware algorithm (HHAR) along with the cache aware filter to gather the historical information's associated with the back-up system and the identify the out of order containers respectively. Further the every data package is protected applying the advanced encryption standard by producing a key to authenticate the access of the data. The proposed model is simulated using the network simulator-II and the results obtained shows that the recovery time is enhanced by 95% and the restore performance is improved by 94.3%.
KeywordsDigitization Data Fragmentation Back up Storage HHAR Cache Aware Filter AES Encryption
Full Article PDF Download Article PDF