scholarly journals Study of Dementia Severity Prognosis in Mr Images Using Grey Wolf Optimization Based Dual Deep Learning Technique

2021 ◽  
Author(s):  
Ahana priynaka ◽  
Kavitha Ganesan
Keyword(s):  
Deep Learning ◽  
Symmetry Analysis ◽  
Support Vector ◽  
Grey Wolf ◽  
Mr Images ◽  
Grey Wolf Optimization ◽  
Feature Vectors ◽  
Dementia Severity ◽  
Improved Performance ◽  
Multiclass Svm

Abstract Prognosis of in a dementia disorder is a tedious task in preclinical stage. Ventricle pathology changes in dementia appear to be overlapped for neuro degeneration in brain. Identification of these overlaps among the groups severity helps to understand the pathogenesis of this disorder. In this work impact of changes in ventricle region on severity stages of dementia is observed using dual deep learning techniques (DDLT). Alzheimer's Disease Neuroimaging Initiative (ADNI) database that contains 1169 MR images are used in this study. Segmentation of ventricle region is carried out using multilevel threshold based Grey Wolf Optimization (GWO) technique. The feature vectors obtained from combined AlexNet and ResNet are analysed. The fused feature vectors are given to support vector machine (SVM) to observe the severity changes. Consequently, symmetry analysis of ventricle is carried out to perceive the distinctive changes in progression. The obtained results show that ventricle region is accurately delineated from other region with optimized thresholds. The segmented ventricle shows better correlation for all considered classes (> 0.9). It is observed that DDLT with multiclass SVM provides an improved accuracy of about 79.87% compared to individual transfer learning such as AlexNet (74%) and ResNet (76.53%). Further, symmetry analysis shows that left side ventricle with DDLT features shows an improved performance than right side for onset stages. Further, clinical correlation of left ventricle seems to be statically significant (p<0.0001) which prominently differentiate dementia severity variations. This framework is more prominent and clinically useful to identify the distinct ventricle region variation in dementia.

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 Dam Deformation Prediction Based on Modified Grey Wolf Optimization Algorithm and Support Vector Machine

2021 ◽  
Vol 2005 (1) ◽  
pp. 012084
Author(s):  
Li Mingjun ◽  
Wang junxing ◽  
Pan Jiangyang ◽  
Peng Cheng ◽  
Li Songzhang
Keyword(s):  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Deformation Prediction ◽  
Dam Deformation

Evolving support vector regression using Grey Wolf optimization; forecasting the geomechanical properties of rock

2020 ◽  
Author(s):  
Chuanhua Xu ◽  
Menad Nait Amar ◽  
Mohammed Abdelfetah Ghriga ◽  
Hocine Ouaer ◽  
Xiliang Zhang ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Geomechanical Properties

scholarly journals Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Binghui Xu ◽  
Tzu-Chia Chen ◽  
Danial Ahangari ◽  
S. M. Alizadeh ◽  
Marischa Elveny ◽  
...  
Keyword(s):  
High Performance ◽  
Laboratory Data ◽  
Optimization Method ◽  
Good Alternative ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Bio Oil ◽  
Learning Machine

This paper deals with modeling hydrogen contents of bio-oil (H-BO) as a function of pyrolysis conditions and biomass compositions of feedstock. The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. Comprehensive data for this purpose were aggregated from previous sources and reports. The results of various analyses showed that this algorithm has a high ability to predict actual results. The calculated values of R2, MRE (%), MSE, and RMSE were obtained as 0.973, 1.98, 0.0568, and 0.241, respectively. According to the results of various analyses, the high performance of this model in predicting the output values was proved. Also, by comparing this model with the previously proposed models in terms of accuracy, it was observed that this model had a better performance. This algorithm can be a good alternative to costly and time-consuming laboratory data.

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 Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4890
Author(s):  
Mengran Zhou ◽  
Tianyu Hu ◽  
Kai Bian ◽  
Wenhao Lai ◽  
Feng Hu ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Support Vector ◽  
Electric Load ◽  
Variational Mode Decomposition ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Short Term ◽  
Mode Decomposition ◽  
The Impact ◽  
Electric Load Forecasting

Short-term electric load forecasting plays a significant role in the safe and stable operation of the LO system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated into the grid. This has posed a serious challenge to short-term electric load forecasting. Focusing on load series with non-linear and time-varying characteristics, an approach to short-term electric load forecasting using a “decomposition and ensemble” framework is proposed in this paper. The method is verified using hourly load data from Oslo and the surrounding areas of Norway. First, the load series is decomposed into five components by variational mode decomposition (VMD). Second, a support vector regression (SVR) forecasting model is established for the five components to predict the electric load components, and the grey wolf optimization (GWO) algorithm is used to optimize the cost and gamma parameters of SVR. Finally, the predicted values of the five components are superimposed to obtain the final electric load forecasting results. In this paper, the proposed method is compared with GWO-SVR without modal decomposition and using empirical mode decomposition (EMD) to test the impact of VMD on prediction. This paper also compares the proposed method with the SVR model using VMD and other optimization algorithms. The four evaluation indexes of the proposed method are optimal: MAE is 71.65 MW, MAPE is 1.41%, MSE is 10,461.32, and R2 is 0.9834. This indicates that the proposed method has a good application prospect for short-term electric load forecasting.

Grey wolf optimization based parameter selection for support vector machines

2016 ◽  
Vol 35 (5) ◽  
pp. 1513-1523 ◽  
Author(s):  
Sathish Eswaramoorthy ◽  
N. Sivakumaran ◽  
Sankaranarayanan Sekaran
Keyword(s):  
Design Methodology ◽  
Support Vector ◽  
Svm Classifier ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Vector Machines ◽  
Human Perceptions ◽  
Selection For

Purpose The purpose of this paper is to tune support vector machine (SVM) classifier using grey wolf optimizer (GWO). Design/methodology/approach The schema of the work aims at extracting the features from the collected data followed by a SVM classifier and metaheuristic optimization to tune the classifier parameters. Findings The optimal tuning of classifier parameters lowers errors due to manual elucidation and decreases the risk in human perceptions and repeated visual dignosis. Originality/value A novel, GWO based tuning algorithm is used for SVM classifier, which is implemented in classifying the complex and nonlinear biomedical signals like intracranial electroencephalogram.

scholarly journals An Improved Grey Wolf Optimization Strategy Enhanced SVM and Its Application in Predicting the Second Major

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Wei ◽  
Ni Ni ◽  
Dayou Liu ◽  
Huiling Chen ◽  
Mingjing Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Sensitivity And Specificity ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Support Vector ◽  
Final Decision ◽  
Feature Subset ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization

In order to develop a new and effective prediction system, the full potential of support vector machine (SVM) was explored by using an improved grey wolf optimization (GWO) strategy in this study. An improved GWO, IGWO, was first proposed to identify the most discriminative features for major prediction. In the proposed approach, particle swarm optimization (PSO) was firstly adopted to generate the diversified initial positions, and then GWO was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on SVM. The resultant methodology, IGWO-SVM, is rigorously examined based on the real-life data which includes a series of factors that influence the students’ final decision to choose the specific major. To validate the proposed method, other metaheuristic based SVM methods including GWO based SVM, genetic algorithm based SVM, and particle swarm optimization-based SVM were used for comparison in terms of classification accuracy, AUC (the area under the receiver operating characteristic (ROC) curve), sensitivity, and specificity. The experimental results demonstrate that the proposed approach can be regarded as a promising success with the excellent classification accuracy, AUC, sensitivity, and specificity of 87.36%, 0.8735, 85.37%, and 89.33%, respectively. Promisingly, the proposed methodology might serve as a new candidate of powerful tools for second major selection.

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