Developed Support Vector Machine Model Research Articles

Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater

The current work demonstrates the support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) modeling to assess the removal efficiency of Kjeldahl Nitrogen of a full-scale aerobic biological wastewater treatment plant. The influent variables such as pH, chemical oxygen demand, total solids (TS), free ammonia, ammonia nitrogen and Kjeldahl Nitrogen are used as input variables during modeling. Model development focused on postulating an adaptive, functional, real-time and alternative approach for modeling the removal efficiency of Kjeldahl Nitrogen. The input variables used for modeling were daily time series data recorded at wastewater treatment plant (WWTP) located in Mangalore during the period June 2014–September 2014. The performance of ANFIS model developed using Gbell and trapezoidal membership functions (MFs) and SVM are assessed using different statistical indices like root mean square error, correlation coefficients (CC) and Nash Sutcliff error (NSE). The errors related to the prediction of effluent Kjeldahl Nitrogen concentration by the SVM modeling appeared to be reasonable when compared to that of ANFIS models with Gbell and trapezoidal MF. From the performance evaluation of the developed SVM model, it is observed that the approach is capable to define the inter-relationship between various wastewater quality variables and thus SVM can be potentially applied for evaluating the efficiency of aerobic biological processes in WWTP.

Prediction of autogenous shrinkage of concretes by support vector machine

Support vector machine (SVM) is firmly based on learning theory and uses regression technique by introducing accuracy insensitive loss function. In this paper, a SVM model for the autogenous shrinkage of concrete mixtures was proposed. The model chose water-to-cementitious material ratio (w/cm), cement content, silica fume percentage, fly ash percentage, total aggregate content, curing temperature, high-range water-reducing admixture (HRWRA) content, and hydration age as input parameters, and the autogenous shrinkage of concrete as the model output. The data set used for training and testing of the SVM model covers the experimental data presented in the existing literature. The developed SVM model was validated using experimental work. The SVM model was compared with the ANN prediction model, the SVM model shows comparable prediction accuracy and could easily be established. In short, the proposed SVM model exhibited excellent capability in predicting the autogenous shrinkage of concrete mixtures.

Prediction of performance of Stirling engine using least squares support machine technique

Stirling engine is an environmental friendly heat engine which could reduce CO2 emission through combustion process. Output power, shaft torque and brake specific fuel consumption represent the efficiency and robustness of the Stirling engines. The present research tries to determine the three aforementioned parameters with high accuracy and low uncertainty. In this research a new type of intelligent models named “least square support vector machine (LSSVM) was employed to predict output power, shaft torque and brake specific fuel consumption. Furthermore, high accurate actual values of the required parameters from previous studies were implemented to develop the robust intelligent model. A great advantage of LSSVM model over ANN is that in the present model over fitting does not happen. Expected statistical parameters of the suggested intelligent model have been indicated and validate the high efficiency of the suggested LSSVM model. Good agreement between LSSVM results and actual values was observed. Solutions obtained from the developed support vector machine model could help us in exact designing of Stirling engine with low uncertainty.

Application of teaching learning based optimization procedure for the development of SVM learned EDM process and its pseudo Pareto optimization

Manufacturing processes could be well characterized by both the quantitative and the qualitative measurements of their performances. In case of conflicting type performance measures, it is necessary to get possible optimum values of all performances simultaneously, like higher material removal rate (MRR) with lower average surface roughness (ASR) in electric discharge machining (EDM) process. EDM itself is a stochastic process and predictions of responses – MRR and ASR are still difficult. Advanced structural risk minimization based learning system – support vector machine (SVM) is, therefore, applied to capture the random variations in EDM responses in a robust way. Internal parameters of SVM – C, ɛ and σ are tuned by modified teaching learning based optimization (TLBO) procedure. Subsequently, using the developed SVM model as a virtual data generator of EDM process, responses are generated at the different points in the experimental space and power law models are fitted to the estimated data. Varying the weight factors, different weighted combinations of the inverse of MRR and the ASR are minimized by modified TLBO. Pseudo Pareto front passing through the optimum results, thus obtained, gives a guideline for selection of optimum achievable value of ASR for a specific demand of MRR. Further, inverse solution procedure is elaborated to find the near-optimum setting of process parameters in EDM machine to obtain the specific need based MRR-ASR combination.

Forecasting Violent Extremist Cyber Recruitment

The Internet’s increasing use as a means of communication has led to the formation of cyber communities, which have become appealing to violent extremist (VE) groups. This paper presents research on forecasting the daily level of cyber-recruitment activity of VE groups. We used a previously developed support vector machine model to identify recruitment posts within a Western jihadist discussion forum. We analyzed the textual content of this data set with latent Dirichlet allocation (LDA), and we fed these analyses into a variety of time series models to forecast cyber-recruitment activity within the forum. Quantitative evaluations showed that employing LDA-based topics as predictors within time series models reduces forecast error compared with naive (random-walk), autoregressive integrated moving average, and exponential smoothing baselines. To the best of our knowledge, this is the first result reported on this forecasting task. This research could ultimately help assist with efficient allocation of intelligence analysts in response to predicted levels of cyber-recruitment activity.

Automatic Prediction Model of Ground Vibration for High-Speed Trains

This paper presents an automatic prediction model for ground vibration induced by Taiwan high-speed trains on embankment structures. The prediction model is developed using different field-measured ground vibration data. The main characteristics that affect the overall vibration level are established based on the database of measurement results. The influence factors include train speed, ground condition, measurement distance, and supported structure. Support vector machine (SVM) algorithm, a widely used prediction model, is adopted to predict the vibration level induced by high-speed trains on embankments. The measured and predicted vibration levels are compared to verify the reliability of the prediction model. Analysis results show that the developed SVM model can reasonably predict vibration level with an accuracy rate of 72% to 84% for four types of vibration level, including overall, low, middle, and high frequency ranges. The methodology in developing the automatic prediction system for ground vibration level is also presented in this paper.

Development of quantitative interspecies toxicity relationship modeling of chemicals to fish

In this work, quantitative interspecies-toxicity relationship methodologies were used to improve the prediction power of interspecies toxicity model. The most relevant descriptors selected by stepwise multiple linear regressions and toxicity of chemical to Daphnia magna were used to predict the toxicities of chemicals to fish. Modeling methods that were used for developing linear and nonlinear models were multiple linear regression (MLR), random forest (RF), artificial neural network (ANN) and support vector machine (SVM). The obtained results indicate the superiority of SVM model over other models. Robustness and reliability of the constructed SVM model were evaluated by using the leave-one-out cross-validation method (Q2=0.69, SPRESS=0.822) and Y-randomization test (R2=0.268 for 30 trail). Furthermore, the chemical applicability domains of these models were determined via leverage approach. The developed SVM model was used for the prediction of toxicity of 46 compounds that their experimental toxicities to a fish were not being reported earlier from their toxicities to D. magna and relevant molecular descriptors.

Quantitative Structure Activity Relationship Prediction of Oral Bioavailabilities Using Support Vector Machine

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of QSVM 2 = 0.603 and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

Developing a Support Vector Machine Based QSPR Model for Prediction of Atmospheric Lifetime of Some Halocarbons

Abstract In this work, the atmospheric lifetime of 60 halocarbons was estimated from their theoretical derived molecular descriptors by applying quantitative structure–property relationship (QSPR) methodology. The most relevant descriptors selected by stepwise multiple linear regression were used for developing linear and nonlinear models by using multiple linear regression and support vector machine, respectively. Here we show that the support vector machine model is finely capable for predicting the lifetime of halocarbons. The built support vector machine model was assessed by leave one out cross-validation (Q2 = 0.928, SPRESS = 0.479) and Y-randomization test (R2 = 0.222 for 25 trail) as well as external validation test. The developed support vector machine model was used for prediction of the atmospheric lifetime of some halocarbons with lifetimes not reported experimentally.

Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models – A case study

Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash–Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.

Liquefaction prediction using support vector machine model based on cone penetration data

A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance (qc) and cyclic stress ratio (CSR), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters (qc and maximum horizontal acceleration amax), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor (C) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

A Support Vector Machine based method to distinguish proteobacterial proteins from eukaryotic plant proteins

BackgroundMembers of the phylum Proteobacteria are most prominent among bacteria causing plant diseases that result in a diminution of the quantity and quality of food produced by agriculture. To ameliorate these losses, there is a need to identify infections in early stages. Recent developments in next generation nucleic acid sequencing and mass spectrometry open the door to screening plants by the sequences of their macromolecules. Such an approach requires the ability to recognize the organismal origin of unknown DNA or peptide fragments. There are many ways to approach this problem but none have emerged as the best protocol. Here we attempt a systematic way to determine organismal origins of peptides by using a machine learning algorithm. The algorithm that we implement is a Support Vector Machine (SVM).ResultThe amino acid compositions of proteobacterial proteins were found to be different from those of plant proteins. We developed an SVM model based on amino acid and dipeptide compositions to distinguish between a proteobacterial protein and a plant protein. The amino acid composition (AAC) based SVM model had an accuracy of 92.44% with 0.85 Matthews correlation coefficient (MCC) while the dipeptide composition (DC) based SVM model had a maximum accuracy of 94.67% and 0.89 MCC. We also developed SVM models based on a hybrid approach (AAC and DC), which gave a maximum accuracy 94.86% and a 0.90 MCC. The models were tested on unseen or untrained datasets to assess their validity.ConclusionThe results indicate that the SVM based on the AAC and DC hybrid approach can be used to distinguish proteobacterial from plant protein sequences.

In silico identification of human pregnane X receptor activators from molecular descriptors by machine learning approaches

In the current study, computational models for hPXR activators and hPXR non-activators were developed using support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural networks (ANN) algorithms. 73 molecular descriptors used for hPXR activator and hPXR non-activator prediction were selected from a pool of 548 descriptors by using a multi-step hybrid feature selection method combining Fischer's score and Monte Carlo simulated annealing method. The y-scrambling method was used to test if there is a chance correlation in the developed SVM model. In the meantime, five-fold cross validation of these machine learning methods results in the prediction accuracies of 87.2–92.5% for hPXR activators and 73.8–87.8% for hPXR non-activators, and the prediction accuracies for external test set are 93.8–95.8% for hPXR activators and 86.7–92.8% for hPXR non-activators. Our study suggested that the tested machine learning methods are potentially useful for hPXR activators identification.

Abstract 1733: Development of a novel blood plasma protein test for diagnosis of lung cancer

Abstract Background: A blood test for detection of lung cancer (LC) could provide survival benefits in high-risk populations. We conducted a retrospective study as the next step of our effort to develop a test for diagnosis of non-small cell lung carcinoma (NSCLC), the most common form of LC, by measuring levels of protein biomarkers in plasma. Materials and Methods: We quantified levels of 106 proteinaceous biomarkers using multiplexed immunoassays of subjects a) diagnosed with stage I/II NSCLC, b) normal non-smoking controls, and c) normal heavy smoking controls (> 10 p.y.). All groups consisted of Caucasian men and women (93 subjects each, ages range 45-87, Ave 59). The specimens were randomized and tested in a blinded manner. A support vector machine (SVM) algorithm, developed and tested in a pilot study, was used to identify differentially expressed biomarkers in the disease and control groups taking into account patients’ gender. Pathway analysis was applied to characterize pathology- and gender-specific patterns of biomarker expression. Results: In a recent pilot study we developed SVM models that classified subjects to NSCLC or normal using 59 plasma markers or subsets thereof, for both genders combined or separately. When data for both genders were analyzed together, SVM classified subjects to NSCLC or normal and identified 4 differentially expressed markers: EGF, sCD40 ligand, IL-8 and MMP-8; sensitivity (SE) 0.93 (95% CI: 0.88-0.96), specificity (SP) 0.87 (95% CI: 0.80-0.92). Considering the sexes separately, the differentially expressed best subset of markers for men (EGF, IL-8, sFAS, MMP-9 and PAI-1) had both SE and SP of 1.0. For women, the best subset of markers (EGF, sCD40 ligand and IL-8) also yielded SE and SP of 1.0. The present study has further confirmed and refined our earlier findings and expanded panels of diagnostic biomarkers for NSCLC with comparable sensitivities and specificities. Analysis of cellular pathways represented by the discriminatory biomarker signatures suggested a role of sex hormone-driven differences of NSCLC plasma biomarkers. Conclusions: The set of diagnostic biomarkers for NSCLC identified in this study holds significant promise for use in the early diagnosis of lung cancer and warrants validation of findings in other ethnic groups and for potentially confounding diseases in clinical studies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1733. doi:1538-7445.AM2012-1733

Identification of DNA adduct formation of small molecules by molecular descriptors and machine learning methods

In this study, we developed new computational DNA adduct prediction models by using significantly more diverse training data-set of 217 DNA adducts and 1024 non-DNA adducts, and applying five machine learning methods which include support vector machine (SVM), k-nearest neighbour, artificial neural networks, logistic regression and continuous kernel discrimination. The molecular descriptors used for DNA adduct prediction were selected from a pool of 548 descriptors by using a multi-step hybrid feature selection method combining Fischer-score and Monte Carlo simulated annealing method. Some of the selected descriptors are consistent with the structural and physicochemical properties reported to be important for DNA adduct formation. The y-scrambling method was used to test whether there is a chance correlation in the developed SVM model. In the meantime, fivefold cross-validation of these machine learning methods results in the prediction accuracies of 64.1–82.5% for DNA adducts and 95.1–97.6% for non-DNA adducts, and the prediction accuracies for external test set are 78.2–100% for DNA adducts and 92.6–98.4% for non-DNA adducts. Our study suggested that the tested machine learning methods are potentially useful for DNA adducts identification.

A Support Vector Machine Approach for the Prediction of Drilling Fluid Density at High Temperature and High Pressure

A support vector machine (SVM) approach was presented for predicting the drilling fluid density at high temperature and high pressure (HTHP). It is a universal model for water-based, oil-based, and synthetic drilling fluids. Available experimental data in the literature were used to develop and test this SVM model. Good agreement between SVM predictions and measured drilling fluid density values confirmed that the developed SVM model had good predictive precision and extrapolative features. The SVM model was also compared with the most popular models such as the artificial neural network (ANN) model, empirical correlations, and analytical models. Results showed that the SVM approach outperformed the competing methods for the prediction of drilling fluid density at HTHP.

Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction

Summary In the research, the role of three input selection techniques is evaluated on support vector machine (SVM) performance for prediction of monthly stream flow. First, a SVM model is adapted to predict the next monthly flow as a function of 18 input variables including monthly rainfall (R), discharge (Q), sun radiation (Rad), and temperature {as minimum (Tmin), maximum (Tmax) and average (Tave)} with three temporal delays belong to t, t-1, and t-2. Subsequently, principal component analysis (PCA), Gamma test (GT), and forward selection (FS) techniques are used to reduce the number of input variables. Upon reducing 18 input variables to 5 (using PCA and GT) and 7 (using FS techniques), they are then fed to SVM model. In addition, a proper artificial neural network (ANN) model based on PCA technique is developed (PCA-ANN). Then, comparison among the developed SVM models (PCA-SVM and GT-SVM) and PCA-ANN model is carried out. Furthermore, the imperfections of the discrepancy ratio (DR) statistic are remedied and an appropriate DR statistic is developed. Finally, the error distribution during testing step of selected models (PCA-SVM, GT-SVM, and PCA-ANN) is computed using the developed DR statistic. Results indicated that preprocessing the input variables by means of PCA and GT techniques has improved the SVM model operation and the developed models (PCA-SVM and GT-SVM) are considerably better than original SVM model. Besides, PCA-SVM is superior to GT-SVM and PCA-ANN models. Determination coefficient (R2) for PCA-SVM model was equal to 0.92 and 0.88 in the training and testing steps, respectively.

Support vector machine for evaluating seismic-liquefaction potential using shear wave velocity

The use of the shear wave velocity data as a field index for evaluating the liquefaction potential of sands is receiving increased attention because both shear wave velocity and liquefaction resistance are similarly influenced by many of the same factors such as void ratio , state of stress, stress history and geologic age. In this paper, the potential of support vector machine (SVM) based classification approach has been used to assess the liquefaction potential from actual shear wave velocity data. In this approach, an approximate implementation of a structural risk minimization (SRM) induction principle is done, which aims at minimizing a bound on the generalization error of a model rather than minimizing only the mean square error over the data set. Here SVM has been used as a classification tool to predict liquefaction potential of a soil based on shear wave velocity. The dataset consists the information of soil characteristics such as effective vertical stress (σ′ v0 ), soil type, shear wave velocity (V s ) and earthquake parameters such as peak horizontal acceleration (a max ) and earthquake magnitude (M). Out of the available 186 datasets, 130 are considered for training and remaining 56 are used for testing the model. The study indicated that SVM can successfully model the complex relationship between seismic parameters, soil parameters and the liquefaction potential. In the model based on soil characteristics, the input parameters used are σ′ v0 , soil type, V s , a max and M. In the other model based on shear wave velocity alone uses V s , a max and M as input parameters. In this paper, it has been demonstrated that Vs alone can be used to predict the liquefaction potential of a soil using a support vector machine model. ► The developed SVM successfully models the complex relationship between the seismic and soil parameters. ► User can use the developed equations for prediction of seismic liquefaction potential of soil. ► This study shows that the developed SVM model can be used as a practical tool for prediction of seismic liquefaction potential of soil based on V s data.

A Simple Approach for Predicting Protein-Protein Interactions

The availability of an increased number of fully sequenced genomes demands functional interpretation of the genomic information. Despite high throughput experimental techniques and in silico methods of predicting protein-protein interaction (PPI); the interactome of most organisms is far from completion. Thus, predicting the interactome of an organism is one of the major challenges in the post-genomic era. This manuscript describes Support Vector Machine (SVM) based models that have been developed for discriminating interacting and non-interacting pairs of proteins from their amino acid sequence. We have developed SVM models using various types of sequence compositions e.g. amino acid, dipeptide, biochemical property, split amino acid and pseudo amino acid composition. We also developed SVM models using evolutionary information in the form of Position Specific Scoring Matrix (PSSM) composition. We achieved maximum Matthews's correlation coefficient (MCC) of 1.00, 0.52 and 0.74 for Escherichia coli, Saccharomyces cerevisiae, and Helicobacter pylori, using dipeptide based SVM model at default threshold. It was observed that the performance of a prediction model depends on the dataset used for training and testing. In case of E. coli MCC decreased from 1.0 to 0.67 when evaluated on a new dataset. In order to understand PPI in different cellular environment, we developed species-specific and general models. It was observed that species-specific models are more accurate than general models. We conclude that the primary amino acid sequence based descriptors could be used to differentiate interacting from non-interacting protein pairs. Some amino acids tend to be favored in interacting pairs than non-interacting ones. Finally, a web server has been developed for predicting protein-protein interactions.

Support Vector Machines Approach to HMA Stiffness Prediction

The application of artificial intelligence (AI) techniques to engineering has increased tremendously over the last decade. Support vector machine (SVM) is one efficient AI technique based on statistical learning theory. This paper explores the SVM approach to model the mechanical behavior of hot-mix asphalt (HMA) owing to high degree of complexity and uncertainty inherent in HMA modeling. The dynamic modulus (|E*|), among HMA mechanical property parameters, not only is important for HMA pavement design but also in determining HMA pavement performance associated with pavement response. Previously employed approaches for development of the predictive |E*| models concentrated on multivariate regression analysis of database. In this paper, SVM-based |E*| prediction models were developed using the latest comprehensive |E*| database containing 7,400 data points from 346 HMA mixtures. The developed SVM models were compared with the existing multivariate regression-based |E*| model as well as the artificial neural networks (ANN) based |E*| models developed recently by the writers. The prediction performance of SVM model is better than multivariate regression-based model and comparable to the ANN. Fewer constraints in SVM compared to ANN can make it a promising alternative considering the availability of limited and nonrepresentative data frequently encountered in construction materials characterization.