Numerical assessment of rectangular tunnels configurations using support vector machine (SVM) and gene expression programming (GEP)

2021 ◽  
Author(s):  
Jun Zhang ◽  
Ruoli Shi ◽  
Shaohua Shi ◽  
A. K. Alzo’ubi ◽  
Angel Roco-Videla ◽  
...  
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Gene Expression Programming ◽  
Support Vector ◽  
Numerical Assessment

scholarly journals Prediction of the number of consumed disc cutters of tunnel boring machine using intelligent methods

2021 ◽  
Vol 15 (4) ◽  
pp. 68-74
Author(s):  
Alireza Afradi ◽  
Arash Ebrahimabadi ◽  
Tahereh Hallajian
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Nonlinear Regression ◽  
Gene Expression Programming ◽  
Regression Method ◽  
Support Vector ◽  
Tunnel Boring ◽  
Mechanized Tunneling ◽  
Disc Cutters

Purpose. Disc cutters are the main cutting tools for the Tunnel Boring Machines (TBMs). Prediction of the number of consumed disc cutters of TBMs is one of the most significant factors in the tunneling projects. Choosing the right model for predicting the number of consumed disc cutters in mechanized tunneling projects has been the most important mechanized tunneling topics in recent years. Methods. In this research, the prediction of the number of consumed disc cutters considering machine and ground conditions such as Power (KW), Revolutions per minute (RPM) (Cycle/Min), Thrust per Cutter (KN), Geological Strength Index (GSI) in the Sabzkooh water conveyance tunnel has been conducted by multiple linear regression analysis and multiple nonlinear regression, Gene Expression Programming (GEP) method and Support Vector Machine (SVM) approaches. Findings. Results showed that the number of consumed disc cutters for linear regression method is R2 = 0.95 and RMSE = 0.83, nonlinear regression method is – R2 = 0.95 and RMSE = 0.84, Gene Expression Programming (GEP) method is – R2 = 0.94 and RMSE = 0.95, Support Vector Machine (SVM) method is – R2 = 0.98 and RMSE = 0.45. Originality. During the analyses, in order to evaluate the accuracy and efficiency of predictive models, the coefficient of determination (R2) and root mean square error (RMSE) have been used. Practical implications. Results demonstrated that all four methods are effective and have high accuracy but the method of support vector machine has a special superiority over other methods.

scholarly journals Application of Support Vector Machine and Gene Expression Programming on Tropospheric ozone Prognosticating for Tehran Metropolitan

2017 ◽  
Vol 3 (8) ◽  
pp. 557 ◽  
Author(s):  
Vahid Mehdipour ◽  
Mahsa Memarianfard
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Tropospheric Ozone ◽  
Meteorological Parameters ◽  
Gene Expression Programming ◽  
Input Parameter ◽  
Developed Countries ◽  
Support Vector ◽  
Effective Parameters ◽  
Measurement Tools

Air pollution became fatal issue for humanity and all environment and developed countries unanimously allocated vast investments on monitoring and researches about air pollutants. Soft computing as a novel way for pollutants prediction can be used for measurement tools calibration which can coincidently decrease the expenditures and enhance their ability to adapt quickly. In this paper support vector machine (SVM) and gene expression programming (GEP) as two powerful approaches with reliable results in previous studies, used to predict tropospheric ozone in Tehran metropolitan by using the photochemical precursors and meteorological parameters as predictors. In a comparison between the two approaches, the best model of SVM gave superior results as it depicted the RMSE= 0.0774 and R= 0.8459 while these results of gene expression programming, respectively, are 0.0883 and 0.7938. Sensitivity of O3 against photochemical precursors and meteorological parameters and also for every input parameter, has been analysed discreetly and the gained results imply that PM2.5, PM10, temperature, CO and NO2 are the most effective parameters for O3 values tolerances. For SVM, several kernel tricks used and the best appropriate kernel selected due to its result. Nonetheless, gamma and sin2 values varied for every kernel and in the last radial basis function kernel opted as the best trick in this study. Finally, the best model of both applications revealed, and the resulted models evaluated as reliable and acceptable.

Logic Learning Machine and standard supervised methods for Hodgkin’s lymphoma prognosis using gene expression data and clinical variables

2016 ◽  
Vol 24 (1) ◽  
pp. 54-65 ◽  
Author(s):  
Stefano Parodi ◽  
Chiara Manneschi ◽  
Damiano Verda ◽  
Enrico Ferrari ◽  
Marco Muselli
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Hodgkin’S Lymphoma ◽  
Hodgkin's Lymphoma ◽  
Support Vector ◽  
Expression Data ◽  
Clinical Variables ◽  
Learning Machine

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin’s lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin’s lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin’s lymphoma patients.

ITERATIVE FEATURE PERTURBATION AS A GENE SELECTOR FOR MICROARRAY DATA

2012 ◽  
Vol 26 (05) ◽  
pp. 1260003 ◽  
Author(s):  
JUANA CANUL-REICH ◽  
LAWRENCE O. HALL ◽  
DMITRY B. GOLDGOF ◽  
JOHN N. KORECKI ◽  
STEVEN ESCHRICH
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Support Vector Machine ◽  
Performance Improvement ◽  
Microarray Data ◽  
T Test ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Gene Sets ◽  
Microarray Datasets

Gene-expression microarray datasets often consist of a limited number of samples with a large number of gene-expression measurements, usually on the order of thousands. Therefore, dimensionality reduction is critical prior to any classification task. In this work, the iterative feature perturbation method (IFP), an embedded gene selector, is introduced and applied to four microarray cancer datasets: colon cancer, leukemia, Moffitt colon cancer, and lung cancer. We compare results obtained by IFP to those of support vector machine-recursive feature elimination (SVM-RFE) and the t-test as a feature filter using a linear support vector machine as the base classifier. Analysis of the intersection of gene sets selected by the three methods across the four datasets was done. Additional experiments included an initial pre-selection of the top 200 genes based on their p values. IFP and SVM-RFE were then applied on the reduced feature sets. These results showed up to 3.32% average performance improvement for IFP across the four datasets. A statistical analysis (using the Friedman/Holm test) for both scenarios showed the highest accuracies came from the t-test as a filter on experiments without gene pre-selection. IFP and SVM-RFE had greater classification accuracy after gene pre-selection. Analysis showed the t-test is a good gene selector for microarray data. IFP and SVM-RFE showed performance improvement on a reduced by t-test dataset. The IFP approach resulted in comparable or superior average class accuracy when compared to SVM-RFE on three of the four datasets. The same or similar accuracies can be obtained with different sets of genes.

scholarly journals An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification using Support Vector Machine Kernels

2021 ◽  
Vol 18 (17) ◽  
Author(s):  
Micheal Olaolu AROWOLO ◽  
Marion Olubunmi ADEBIYI ◽  
Chiebuka Timothy NNODIM ◽  
Sulaiman Olaniyi ABDULSALAM ◽  
Ayodele Ariyo ADEBIYI
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Malaria Vector ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Rna Seq ◽  
Gene Expressions

As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique results in related performance had 98.3 and 96.7 % accuracy rates, respectively. HIGHLIGHTS Dimensionality reduction method based of feature selection Classification using Support vector machine Classification of malaria vector dataset using an adaptive GA-RFE-SVM GRAPHICAL ABSTRACT

A Hybrid Kernel Extreme Learning Machine and Improved Cat Swarm Optimization for Microarray Medical Data Classification

2020 ◽  
pp. 779-814
Author(s):  
S. Chakravarty ◽  
R. Bisoi ◽  
P. K. Dash
Keyword(s):  
Gene Expression ◽  
Support Vector Machine ◽  
Extreme Learning Machine ◽  
Medical Data ◽  
Support Vector ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
Small Sample Sizes

This paper presents the pattern classification of the binary microarray gene expression based medical data using extreme learning machine (ELM) and its variants like on-line sequential ELM (OSELM) and kernel based extreme learning machine (KELM). In the KELM category two variants namely the wavelet based kernel (WKELM) extreme learning machine and radial basis kernel extreme learning machine (RKELM) along with support vector machine (SVMRBF) and support vector machine polynomial (SVMPoly) are used to classify microarray medical datasets. Further to reduce the high dimensionality of Microarray medical datasets giving rise to high number of gene expression and small sample sizes, a modified evolutionary cat swarm optimization (MCSO) technique is adopted. The efficiency of the proposed algorithm is verified using a set of performance metrics for four binary medical datasets belonging to breast cancer, prostate cancer, colon tumor, and leukemia, respectively.

Hubness weighted svm ensemble for prediction of breast cancer subtypes

2021 ◽  
pp. 1-14
Author(s):  
S. Raja Sree ◽  
A. Kunthavai
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Support Vector Machine ◽  
Breast Cancer Subtype ◽  
Breast Cancer Subtypes ◽  
High Dimensional ◽  
Support Vector ◽  
Gene Expressions ◽  
Cancer Subtypes ◽  
Cancer Subtype

BACKGROUND: Breast cancer is a major disease causing panic among women worldwide. Since gene mutations are the root cause for cancer development, analyzing gene expressions can give more insights into various phenotype of cancer treatments. Breast Cancer subtype prediction from gene expression data can provide more information for cancer treatment decisions. OBJECTIVE: Gene expressions are complex for analysis due to its high dimensional nature. Machine learning algorithms such as k-Nearest Neighbors, Support Vector Machine (SVM) and Random Forest are used with selection of features for prediction of breast cancer subtypes. Prediction accuracy of the existing methods are affected due to high dimensional nature of gene expressions. The objective of the work is to propose an efficient algorithm for the prediction of breast cancer subtypes from gene expression. METHODS: For subtype prediction, a novel Hubness Weighted Support Vector machine algorithm (HWSVM) using bad hubness score as a weight measure to handle the outliers in the data has been proposed. Based on the various subtypes, features are projected into seven different feature sets and Ensemble based Hubness Aware Weighted Support Vector Machine (HWSVMEns) is implemented for breast cancer subtype prediction. RESULTS: The proposed algorithms have been compared with the classical SVM and other traditional algorithms such as Random Forest, k-Nearest Neighbor algorithms and also with various gene selection methods. CONCLUSIONS: Experimental results show that the proposed HWSVM outperforms other algorithms in terms of accuracy, precision, recall and F1 score due to the hubness weightage scheme and the ensemble approach. The experiments have shown an average accuracy of 92% across various gene expression datasets.

Gene Expression Programming and Machine Learning Methods for Bushfire Susceptibility Mapping in New South Wales, Australia

2021 ◽  
Author(s):  
Maryamsadat Hosseini ◽  
Samsung Lim
Keyword(s):  
Gene Expression ◽  
New South ◽  
Gene Expression Programming ◽  
Single Gene ◽  
New South Wales ◽  
Susceptibility Mapping ◽  
Support Vector ◽  
Ensemble Techniques ◽  
South Wales ◽  
Susceptibility Maps

Abstract Australia is one of the most bushfire-prone countries. Prediction and management of bushfires in bushfire-susceptible areas can reduce the negative impacts of bushfires. The generation of bushfire susceptibility maps can help improve the prediction of bushfires. The main aim of this study was to use single gene expression programming (GEP) and ensemble of GEP with well-known statistical methods to generate bushfire susceptibility maps for New South Wales, Australia as a case study. We used eight methods for bushfire susceptibility mapping: GEP, random forest (RF), support vector machine (SVM), frequency ratio (FR), ensemble techniques of GEP and FR (GEPFR), RF and FR (RFFR), SVM and FR (SVMFR), and LR and FR (LRFR). Areas under the curve (AUCs) of the receiver operating characteristic were used to evaluate the proposed methods. GEPFR exhibited the best performance for bushfire susceptibility mapping based on the AUC (0.890), while RFFR had the highest accuracy (94.70%) among the proposed methods. GEPFR is an ensemble method that uses features from the evolutionary algorithm and the statistical FR method, which results in a better AUC for the bushfire susceptibility maps. The ensemble methods had better performances than those of the single methods.

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