scholarly journals Modelling the Effects of Nanomaterial Addition on the Permeability of the Compacted Clay Soil Using Machine Learning-Based Flow Resistance Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 186
Author(s):  
Mehmet Şükrü Özçoban ◽  
Muhammed Erdem Isenkul ◽  
Selçuk Sevgen ◽  
Seren Acarer ◽  
Mertol Tüfekci
Keyword(s):  
Flow Resistance ◽  
Clay Soil ◽  
Oxygen Demand ◽  
Support Vector ◽  
Compacted Clay ◽  
Svm Model ◽  
Highly Correlated ◽  
Kjeldahl Nitrogen ◽  
Soil Changes ◽  
Standard Compaction

Impermeable base layers that are made of materials with low permeability, such as clay soil, are necessary to prevent leachate in landfills from harming the environment. However, over time, the permeability of the clay soil changes. Therefore, to reduce and minimize the risk, the permeability-related characteristics of the base layers must be improved. Thus, this study aims to serve this purpose by experimentally investigating the effects of nanomaterial addition (aluminum oxide, iron oxide) into kaolin samples. The obtained samples are prepared by applying standard compaction, and the permeability of the soil sample is experimentally investigated by passing leachate from the reactors, in which these samples are placed. Therefore, Flow Resistance (FR) analysis is conducted and the obtained results show that the Al additives are more successful than the Fe additive in reducing leachate permeability. Besides, the concentration values of some polluting parameters (Chemical Oxygen Demand (COD), Total Kjeldahl Nitrogen (TKN), and Total Phosphorus (TP)) at the inlet and outlet of the reactors are analyzed. Three different models (Artificial Neural Networks (ANN), Multiple Linear Regression (MLR), Support Vector Machine (SVM)) are applied to the data obtained from the experimental study. The results have shown that polluting parameters produce high FR regression similarity rates (>75%), TKN, TP, and COD features are highly correlated with the FR value (>60%) and the most successful method is found to be the SVM model.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

New Multi-View Classification Method with Uncertain Data

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Bo Liu ◽  
Haowen Zhong ◽  
Yanshan Xiao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Uncertain Data ◽  
Real Life ◽  
Support Vector ◽  
Classification Methods ◽  
Complementary Information ◽  
Novel Approach ◽  
Svm Model ◽  
Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

scholarly journals A Cyanobacteria-Based Biofilm System for Advanced Brewery Wastewater Treatment

2020 ◽  
Vol 11 (1) ◽  
pp. 174
Author(s):  
Konstantinos P. Papadopoulos ◽  
Christina N. Economou ◽  
Athanasia G. Tekerlekopoulou ◽  
Dimitris V. Vayenas
Keyword(s):  
Wastewater Treatment ◽  
Biomass Production ◽  
Low Cost ◽  
Oxygen Demand ◽  
Dry Weight ◽  
Surface Hydrophobicity ◽  
Reactor Performance ◽  
Brewery Wastewater ◽  
Kjeldahl Nitrogen ◽  
Supporting Materials

Algal/cyanobacterial biofilm photobioreactors provide an alternative technology to conventional photosynthetic systems for wastewater treatment based on high biomass production and easy biomass harvesting at low cost. This study introduces a novel cyanobacteria-based biofilm photobioreactor and assesses its performance in post-treatment of brewery wastewater and biomass production. Two different supporting materials (glass/polyurethane) were tested to investigate the effect of surface hydrophobicity on biomass attachment and overall reactor performance. The reactor exhibited high removal efficiency (over 65%) of the wastewater’s pollutants (chemical oxygen demand, nitrate, nitrite, ammonium, orthophosphate, and total Kjeldahl nitrogen), while biomass per reactor surface reached 13.1 and 12.8 g·m−2 corresponding to 406 and 392 mg·L−1 for glass and polyurethane, respectively, after 15 days of cultivation. The hydrophilic glass surface favored initial biomass adhesion, although eventually both materials yielded complete biomass attachment, highlighting that cell-to-cell interactions are the dominant adhesion mechanism in mature biofilms. It was also found that the biofilm accumulated up to 61% of its dry weight in carbohydrates at the end of cultivation, thus making the produced biomass a suitable feedstock for bioethanol production.

scholarly journals Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Three Dimensional ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Support Vector ◽  
Characterization Model ◽  
Svm Model ◽  
Input Variables ◽  
Learning Machine ◽  
A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

Research on Predicting the Stability of Partially Stabilized Zirconia Based on SVM

2010 ◽  
Vol 146-147 ◽  
pp. 460-465 ◽  
Author(s):  
Sheng Hui Guo ◽  
Dong Bo Li ◽  
Li Jun Liu ◽  
Jin Hui Peng ◽  
Li Bo Zhang ◽  
...  
Keyword(s):  
Relative Error ◽  
Influence Factors ◽  
Commercial Application ◽  
Support Vector ◽  
Stabilized Zirconia ◽  
Partially Stabilized Zirconia ◽  
Typical Data ◽  
Svm Model ◽  
Commercial Process ◽  
The Stability

The stability is one most important product performance index, which can directly determine the quality of the partially stabilized zirconia (PSZ), and the stability of PSZ is always fluctuating in the commercial process, so how to accurately, quickly and easily predict the stability of PSZ in the preparation process is very important. In the present paper, a new mathematical model to predict the stability of PSZ was proposed, based on statistical theory (SLT) and support vector machine (SVM) theory, which relates the stability of PSZ and the influence factors, such as the holding temperature, rising rate of temperature, holding time, decreasing rate of temperature and hardening temperature. Typical data collected from commercial process were collected for the training samples and test samples. Then testing and analyzing was done. The results showed that the max relative error was 1.80%, the least relative error was 0%, and the average relative error was 0.58%. It is accurate and reliable to predict the stability of PSZ by SVM model. Besides, multiple influence factors can be comprehensively considered in the SVM model, thus a new highly effective method for predicting the stability of PSZ is provided for commercial application.

scholarly journals Speech Synthesis for Gender Classification

2017 ◽  
Vol 5 (1) ◽  
pp. 67
Author(s):  
Kawthar AlDhlan
Keyword(s):  
Speech Synthesis ◽  
Support Vector ◽  
Levenshtein Distance ◽  
Identification System ◽  
Gender Identification ◽  
Accuracy Rate ◽  
Kingdom Of Saudi Arabia ◽  
Svm Model ◽  
Health Related ◽  
Test Flow

<p class="0abstract">This paper presents a gender identification system to be used for call forwarding in health related communications. The system listens to the caller then using speech synthesis, image processing, and linear support vector machine SVM identifies either he or she is a male or a female. This solution is imperative in a conservative country such as the Kingdom of Saudi Arabia in order to forward the call to a male or female practitioner. The originality of the approach is that no transcription is used to learn SVM models. To identify the gender of the caller, the trained SVM model of the reference pieces are compared to transcripts of the audio frequency record and are using the Levenshtein distance. For the identification of gender, we obtain an accuracy rate of 94% on a test flow containing 449 pieces of speech clips.</p>

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