scholarly journals Forecasting failure rate of water pipes

2018 ◽  
Vol 19 (1) ◽  
pp. 264-273 ◽  
Author(s):  
M. Kutyłowska
Keyword(s):  
Failure Rate ◽  
Kernel Functions ◽  
Support Vector ◽  
Maximum Relative Error ◽  
Failure Rates ◽  
Data Set ◽  
Independent Variables ◽  
Svm Model ◽  
Testing Stage ◽  
Training Subset

Abstract This paper presents the results of failure rate prediction by means of support vector machines (SVM) – a non-parametric regression method. A hyperplane is used to divide the whole area in such a way that objects of different affiliation are separated from one another. The number of support vectors determines the complexity of the relations between dependent and independent variables. The calculations were performed using Statistical 12.0. Operational data for one selected zone of the water supply system for the period 2008–2014 were used for forecasting. The whole data set (in which data on distribution pipes were distinguished from those on house connections) for the years 2008–2014 was randomly divided into two subsets: a training subset – 75% (5 years) and a testing subset – 25% (2 years). Dependent variables (λr for the distribution pipes and λp for the house connections) were forecast using independent variables (the total length – Lr and Lp and number of failures – Nr and Np of the distribution pipes and the house connections, respectively). Four kinds of kernel functions (linear, polynomial, sigmoidal and radial basis functions) were applied. The SVM model based on the linear kernel function was found to be optimal for predicting the failure rate of each kind of water conduit. This model's maximum relative error of predicting failure rates λr and λp during the testing stage amounted to about 4% and 14%, respectively. The average experimental failure rates in the whole analysed period amounted to 0.18, 0.44, 0.17 and 0.24 fail./(km·year) for the distribution pipes, the house connections and the distribution pipes made of respectively PVC and cast iron.

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.

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.

scholarly journals Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation

2021 ◽  
Vol 11 (6) ◽  
pp. 701
Author(s):  
Cheng-Hsuan Chen ◽  
Kuo-Kai Shyu ◽  
Cheng-Kai Lu ◽  
Chi-Wen Jao ◽  
Po-Lei Lee
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Kernel Functions ◽  
Olfactory Stimulation ◽  
Support Vector ◽  
Hemodynamic Response Function ◽  
Functional Near Infrared Spectroscopy ◽  
Data Set

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

Water quality prediction using machine learning methods

2018 ◽  
Vol 53 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Amir Hamzeh Haghiabi ◽  
Ali Heidar Nasrolahi ◽  
Abbas Parsaie
Keyword(s):  
Water Quality ◽  
Kernel Functions ◽  
Support Vector ◽  
Group Method ◽  
Water Quality Prediction ◽  
Machine Learning Methods ◽  
Svm Model ◽  
Different Types ◽  
Artificial Neural Network Ann ◽  
Southwest Of Iran

Abstract This study investigates the performance of artificial intelligence techniques including artificial neural network (ANN), group method of data handling (GMDH) and support vector machine (SVM) for predicting water quality components of Tireh River located in the southwest of Iran. To develop the ANN and SVM, different types of transfer and kernel functions were tested, respectively. Reviewing the results of ANN and SVM indicated that both models have suitable performance for predicting water quality components. During the process of development of ANN and SVM, it was found that tansig and RBF as transfer and kernel functions have the best performance among the tested functions. Comparison of outcomes of GMDH model with other applied models shows that although this model has acceptable performance for predicting the components of water quality, its accuracy is slightly less than ANN and SVM. The evaluation of the accuracy of the applied models according to the error indexes declared that SVM was the most accurate model. Examining the results of the models showed that all of them had some over-estimation properties. By evaluating the results of the models based on the DDR index, it was found that the lowest DDR value was related to the performance of the SVM model.

scholarly journals A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Jiang Wu ◽  
Yanju Ji ◽  
Ling Zhao ◽  
Mengying Ji ◽  
Zhuang Ye ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Detection ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Analysis Method ◽  
Data Set ◽  
Specificity And Sensitivity ◽  
Svm Model ◽  
Tof Ms

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data.Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity.Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively.Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.

scholarly journals Flood forecasting using support vector machines

2007 ◽  
Vol 9 (4) ◽  
pp. 267-276 ◽  
Author(s):  
D. Han ◽  
L. Chan ◽  
N. Zhu
Keyword(s):  
Network Models ◽  
Flood Forecasting ◽  
Kernel Functions ◽  
Support Vector ◽  
Data Series ◽  
Neural Network Models ◽  
Optimum Selection ◽  
Svm Model ◽  
Vector Machines ◽  
Artificial Neural Network Models

This paper describes an application of SVM over the Bird Creek catchment and addresses some important issues in developing and applying SVM in flood forecasting. It has been found that, like artificial neural network models, SVM also suffers from over-fitting and under-fitting problems and the over-fitting is more damaging than under-fitting. This paper illustrates that an optimum selection among a large number of various input combinations and parameters is a real challenge for any modellers in using SVMs. A comparison with some benchmarking models has been made, i.e. Transfer Function, Trend and Naive models. It demonstrates that SVM is able to surpass all of them in the test data series, at the expense of a huge amount of time and effort. Unlike previous published results, this paper shows that linear and nonlinear kernel functions (i.e. RBF) can yield superior performances against each other under different circumstances in the same catchment. The study also shows an interesting result in the SVM response to different rainfall inputs, where lighter rainfalls would generate very different responses to heavier ones, which is a very useful way to reveal the behaviour of a SVM model.

scholarly journals Support Vector Machine Based Classification Model for Screening Plasmodium falciparum Proliferation Inhibitors and Non-Inhibitors

2011 ◽  
Vol 3 ◽  
pp. BECB.S7503 ◽  
Author(s):  
Sangeetha Subramaniam ◽  
Monica Mehrotra ◽  
Dinesh Gupta
Keyword(s):  
Support Vector Machine ◽  
High Throughput Screening ◽  
Disease Burden ◽  
Classification Model ◽  
Rapid Screening ◽  
Support Vector ◽  
Data Set ◽  
Chemical Libraries ◽  
Average Accuracy ◽  
Svm Model

There is an urgent need to develop novel anti-malarials in view of the increasing disease burden and growing resistance of the currently used drugs against the malarial parasites. Proliferation inhibitors targeting P. falciparum intraerythrocytic cycle are one of the important classes of compounds being explored for its potential to be novel antimalarials. Support Vector Machine (SVM) based model developed by us can facilitate rapid screening of large and diverse chemical libraries by reducing false hits and prioritising compounds before setting up expensive High Throughput Screening experiment. The SVM model, trained with molecular descriptors of proliferation inhibitors and non-inhibitors, displayed a satisfactory performance on cross validations and independent data set, with an average accuracy of 83% and AUC of 0.88. Intriguingly, the method displayed remarkable accuracy for the recently submitted P. falciparum whole cell screening datasets. The method also predicted several inhibitors in the National Cancer Institute diversity set, mostly similar to the known inhibitors.

scholarly journals Early Detection of Mold-Contaminated Peanuts Using Machine Learning and Deep Features Based on Optical Coherence Tomography

2021 ◽  
Vol 3 (3) ◽  
pp. 703-715
Author(s):  
Edwin Manhando ◽  
Yang Zhou ◽  
Fenglin Wang
Keyword(s):  
Optical Coherence Tomography ◽  
Early Stage ◽  
Support Vector ◽  
Optical Coherence ◽  
Data Set ◽  
New Approach ◽  
Svm Model ◽  
Incubation Periods ◽  
Output Code ◽  
Oct Imaging

Fungal infection is a pre-harvest and post-harvest crisis for farmers of peanuts. In environments with temperatures around 28 °C to 30 °C or relative humidity of approximately 90%, mold-contaminated peanuts have a considerable likelihood to be infected with Aflatoxins. Aflatoxins are known to be highly carcinogenic, posing danger to humans and livestock. In this work, we proposed a new approach for detection of mold-contaminated peanuts at an early stage. The approach employs the optical coherence tomography (OCT) imaging technique and an error-correcting output code (ECOC) based Support Vector Machine (SVM) trained on features extracted using a pre-trained Deep Convolutional Neural Network (DCNN). To this end, mold-contaminated and uncontaminated peanuts were scanned to create a data set of OCT images used for training and evaluation of the ECOC-SVM model. Results showed that the proposed approach is capable of detecting mold-contaminated peanuts with respective accuracies of approximately 85% and 96% after incubation periods of 48 and 96 h.

Kernel function based regression approaches for estimating the oxygen transfer performance of plunging hollow jet aerator

2019 ◽  
Vol 2 (95) ◽  
pp. 74-84
Author(s):  
M. Kumar ◽  
N.K. Tiwari ◽  
S. Ranjan
Keyword(s):  
Transfer Coefficient ◽  
Kernel Function ◽  
Nonlinear Regression ◽  
Oxygen Transfer ◽  
Regression Models ◽  
Kernel Functions ◽  
Support Vector ◽  
Data Set ◽  
Oxygen Transfer Coefficient ◽  
Volumetric Oxygen Transfer Coefficient

Purpose: To evaluate the capability of various kernels employed with support vector regression (SVR) and Gaussian process regression (GPR) techniques in estimating the volumetric oxygen transfer coefficient of plunging hollow jets. Design/methodology/approach: In this study, a data set of 81 observations is acquired from laboratory experiments of hollow jets plunging on the surface of water in the tank. The jet variables: jet velocity, jet thickness, jet length, and water depth are varied accordingly and the values of volumetric oxygen transfer coefficient is computed. An empirical relationship expressing the oxygenation performance of plunging hollow jet aerator in terms of jet variables is formulated using multiple nonlinear regression. The performance of this nonlinear relationship is compared with various kernel function based SVR and GPR models. Models developed with the training data set (51 observations) are checked on testing data set (24 observations) for performance comparison. Sensitivity analysis is carried out to examine the influence of jet variables in effecting the oxygen transfer capabilities of plunging hollow jet aerator. Findings: The overall comparison of kernels yielded good estimation performance of Radial Basis Function kernel (RBF) and Pearson VII Function kernel (PUK) using the SVR technique which is followed by nonlinear regression, and other kernel function based regression models. Research limitations/implications: The results of the study pertaining to the performance of kernels are based on the current experimental conditions and the estimation potential of the regression models may fluctuate beyond the selection of current data range due to datadependant learning of the soft computing models. Practical implications: Volumetric oxygen transfer coefficient of plunging hollow jets can be predicted precisely using SVR model by employing RBF as kernel function as compared to empirical correlation and other kernel function based regression models. Originality/value: The comparative analysis of kernel functions is conducted in this study. In previous studies, the predictive modelling approaches are implemented in simulating the aeration properties of cylindrical solid jets only, while this paper simulates the volumetric oxygen transfer coefficient of diverging hollow jets with the jet variables by utilizing polynomial, normalized polynomial, PUK, and RBF kernels in SVR and GPR.

scholarly journals Mitigation of Nonlinear Impairments by Using Support Vector Machine and Nonlinear Volterra Equalizer

2019 ◽  
Vol 9 (18) ◽  
pp. 3800
Author(s):  
Rebekka Weixer ◽  
Jonas Koch ◽  
Patrick Plany ◽  
Simon Ohlendorf ◽  
Stephan Pachnicke
Keyword(s):  
Support Vector Machine ◽  
Phase Noise ◽  
Nonlinear Effects ◽  
Single Mode ◽  
Single Mode Fiber ◽  
Kernel Functions ◽  
Training Data ◽  
Support Vector ◽  
Data Set ◽  
Nonlinear Phase

A support vector machine (SVM) based detection is applied to different equalization schemes for a data center interconnect link using coherent 64 GBd 64-QAM over 100 km standard single mode fiber (SSMF). Without any prior knowledge or heuristic assumptions, the SVM is able to learn and capture the transmission characteristics from only a short training data set. We show that, with the use of suitable kernel functions, the SVM can create nonlinear decision thresholds and reduce the errors caused by nonlinear phase noise (NLPN), laser phase noise, I/Q imbalances and so forth. In order to apply the SVM to 64-QAM we introduce a binary coding SVM, which provides a binary multiclass classification with reduced complexity. We investigate the performance of this SVM and show how it can improve the bit-error rate (BER) of the entire system. After 100 km the fiber-induced nonlinear penalty is reduced by 2 dB at a BER of 3.7 × 10 − 3 . Furthermore, we apply a nonlinear Volterra equalizer (NLVE), which is based on the nonlinear Volterra theory, as another method for mitigating nonlinear effects. The combination of SVM and NLVE reduces the large computational complexity of the NLVE and allows more accurate compensation of nonlinear transmission impairments.

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