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
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
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
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 2 | Issue - 2 | june 2020
Published
22 May, 2020
The existing applications that are associated with the internet produce enormous amount of data according to the requirements of diverse circumstances prevailing. This causes multitudes of challenges in examining the data and as well as in the operation of the system that relies on the cloud. To simply process and manage the execution of the tasks properly with respect to time the workflow scheduling was devised in the cloud. To further enhance the process of scheduling the named entity recognition is used. The NER-named entity recognition is an important chore of more general discipline of internet explorer application. Since the NER- problem is highly challenging in cloud paradigm. An innovative frame work termed as the MC-SVM (Multi ClassSupport Vector Machine) is laid out in the paper to devise the scheduling of the workflow in the cloud paradigm. The scheduling of the tasks in the cloud delivers an arrangement setting up the work flows with the named entity recognition using the MC-SVM. The algorithm developed enhances the resource allocation process, by performing a simultaneous and dynamic allocation/reallocation of named entities to the resources of the cloud satisfying the demands in the performance and cost. The results observed on validating the proposed algorithm proves the capability of the system to manage the resources in the cloud effectively optimizing the make span and the cost.
KeywordsMC-SVM Work-Flow Scheduling NER Dynamic Allocation Cloud Computing
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