AN INTELLIGENT CRYPTO-LOCKER RANSOMWARE DETECTION TECHNIQUE USING SUPPORT VECTOR MACHINE CLASSIFICATION AND GREY WOLF OPTIMIZATION ALGORITHMS

2019 ◽  
Vol 13 (3) ◽  
pp. 15
Author(s):  
MOHAMMED MAIGIDA ABDULLAHI ◽  
MUHAMMAD ABDULHAMID SHAFI'I ◽  
OLALERE MORUFU ◽  
ISMAILA IDRIS ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Optimization Algorithms ◽  
Detection Technique ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Support Vector Machine Classification

Fog Enabled Cloud Based Intelligent Resource Management Approach Using Improved Grey Wolf Optimization Strategy and Kernel Support Vector Machine

2021 ◽  
Vol 18 (4) ◽  
pp. 1275-1281
Author(s):  
R. Sudha ◽  
G. Indirani ◽  
S. Selvamuthukumaran
Keyword(s):  
Support Vector Machine ◽  
Resource Management ◽  
Support Vector ◽  
Management Approach ◽  
Optimization Strategy ◽  
Grey Wolf ◽  
Processing Load ◽  
Grey Wolf Optimization ◽  
Kernel Support Vector Machine ◽  
Service Response Time

Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.

scholarly journals Forecasting of Commodity Future Index using a Hybrid Regression Model based on Support Vector Machine and Grey Wolf Optimization Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 2856-2862
Keyword(s):  
Support Vector Machine ◽  
Time Series Data ◽  
Optimization Problems ◽  
Series Data ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
The Everyday ◽  
Hybrid Forecasting ◽  
Commodity Future

The forecasting and investigation of finance time series data are hard, and are the most confounded works pertained with investor decision. In this paper, an economic derivative instrument for Multi Commodity Exchange (MCX) index of CRUDEOIL is estimated by utilizing forecasting models based on recently formulated artificial intelligence (AI) approaches. These approaches have been appeared to perform astoundingly well in different optimization problems. Specifically, a novel hybrid forecasting model is designed by combining the support vector machine (SVM) and grey wolf optimization (GWO) and it is named as hybrid SVM-GWO. The presented hybrid SVM-GWO model eliminates the user determined control parameter, which is needed for other AI techniques. The practicality and proficiency of the presented SVM-GWO regression method is evaluated by predicting the everyday close price of CRUDEOIL index traded in the MCX of India Limited. The exploratory outcomes depicts that the present hybrid SVM-GWO technique is viable and outperforms superior to the conventional SVM, hybrid SVM-TLBO and SVM-PSO regression models

scholarly journals A Hybrid Framework for Brain Tumor Classification using Grey Wolf Optimization and Multi-Class Support Vector Machine

2019 ◽  
Vol 8 (3) ◽  
pp. 7746-7752 ◽  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Medical Image ◽  
Medical Image Processing ◽  
Support Vector ◽  
Human Beings ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Nature Inspired Algorithm

Medical image processing has a vital role in the detection of diseases in human beings. The accuracy for disease detection using any medical image is highly dependent on the image processing methods. Features extraction and reduction are the two key steps during the medical image processing for disease classification. To develop an effective and efficient mechanism with high accuracy for classification of malignant brain tumor from Magnetic Resonance Imaging (MRI) is the objective of the present research. To achieve this, a nature inspired algorithm; namely, Grey Wolf Optimization (GWO) along with a classification method, multiclass Support Vector Machine (MSVM) is used. Further, Results for the classification accuracy obtained from GWO are compared with other two well-known optimization algorithms such as Particle Swarm Optimization (PSO) and Firefly Algorithm (FA).

scholarly journals Sensor Fault Diagnosis Method Based on α -Grey Wolf Optimization-Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xuezhen Cheng ◽  
Dafei Wang ◽  
Chuannuo Xu ◽  
Jiming Li
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Similar Data ◽  
Grey Wolf ◽  
Sensor Fault ◽  
Grey Wolf Optimization ◽  
Diagnosis Method

Aimed to address the low diagnostic accuracy caused by the similar data distribution of sensor partial faults, a sensor fault diagnosis method is proposed on the basis of α Grey Wolf Optimization Support Vector Machine (α-GWO-SVM) in this paper. Firstly, a fusion with Kernel Principal Component Analysis (KPCA) and time-domain parameters is performed to carry out the feature extraction and dimensionality reduction for fault data. Then, an improved Grey Wolf Optimization (GWO) algorithm is applied to enhance its global search capability while speeding up the convergence, for the purpose of further optimizing the parameters of SVM. Finally, the experimental results are obtained to suggest that the proposed method performs better in optimization than the other intelligent diagnosis algorithms based on SVM, which improves the accuracy of fault diagnosis effectively.

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