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.

An Efficient Hybrid Grey Wolf Optimization Based KELM Approach for Prediction of the Discharge Coefficient of Submerged Radial Gates

2021 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Saman Shahnazi ◽  
Arman Alirezazadeh Sadaghiani
Keyword(s):  
Discharge Coefficient ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Experimental Database ◽  
Learning Machine ◽  
Downstream Flow

Abstract Radial gates are widely used hydraulic structures for flow control in irrigation canals. Accurately prediction of discharge coefficient through radial gates is considered as a challenging hydraulic subject, particularly under highly submerged flow conditions. Incurring the advantages of Kernel-depend Extreme Learning Machine (KELM), this study offers a Grey Wolf Optimization-based KELM (GWO-KELM) for effective prediction of discharge coefficient through submerged radial gates. Additionally, Support Vector Machine (SVM), and Gaussian Process Regression (GPR) methods are also presented for comparative purposes. To build prediction models using GWO-KELM, GPR, and SVM an extensive experimental database was established, consisting of 2125 data samples gathered by the US Bureau of Reclamation. From simulation results, it is observed that the proposed GWO-KELM approach with input parameters of the ratio of the downstream flow depth to the gate opening (y3/w) and submergence ratio (y1-y3/w) provides the best performance with the correlation coefficient (R) of 0.983, the Determination Coefficient (DC) of 0.966 and the Root Mean Squared Error (RMSE) of 0.027. Furthermore, the obtained results showed that the employed kernel-depend methods are capable of a statistically predicting the discharge coefficient under varied submergence conditions with satisfactory level of accuracy.

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 Planning for Distribution System with Grey Wolf Optimization Method

2020 ◽  
Vol 15 (4) ◽  
pp. 1485-1499
Author(s):  
Muhammad Mohsin Ansari ◽  
Chuangxin Guo ◽  
Muhammad Suhail Shaikh ◽  
Nitish Chopra ◽  
Inzamamul Haq ◽  
...  
Keyword(s):  
Distribution System ◽  
Optimization Method ◽  
Grey Wolf ◽  
Grey Wolf Optimization

scholarly journals Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang-Rui Ren ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Junliang Shang ◽  
Chun-Hou Zheng
Keyword(s):  
Extreme Learning Machine ◽  
High Performance ◽  
Structural Information ◽  
Optimization Method ◽  
Classification Performance ◽  
Weight Matrix ◽  
New Method ◽  
Classification Problems ◽  
Graph Regularization ◽  
Learning Machine

Abstract Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

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.

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