Support Vector Machine Methodology Research Articles

An rs-fMRI based neuroimaging marker for adult absence epilepsy

ObjectiveApproximately 20–30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study aimed to investigate the clinical diagnostic utility of regional homogeneity (ReHo) assessed through the support vector machine (SVM) approach for identifying AE. MethodsThis research involved 102 healthy individuals and 93 AE patients. Resting-state functional magnetic resonance imaging was employed for data acquisition in all participants. ReHo analysis, coupled with SVM methodology, was utilized for data processing. ResultsCompared to healthy control individuals, AE patients demonstrated significantly elevated ReHo values in the bilateral putamen, accompanied by decreased ReHo in the bilateral thalamus. SVM was used to differentiate patients with AE from healthy control individuals based on rs-fMRI data. A composite assessment of altered ReHo in the left putamen and left thalamus yielded the highest accuracy at 81.64 %, with a sensitivity of 95.41 % and a specificity of 69.23 %. SignificanceAccording to the results, altered ReHo values in the bilateral putamen and thalamus could serve as neuroimaging markers for AE, offering objective guidance for its diagnosis.

Variability of Extreme Climate Events and Prediction of Land Cover Change and Future Climate Change Effects on the Streamflow in Southeast Queensland, Australia

The severity and frequency of extremes are changing; thus, it is becoming necessary to evaluate the impacts of land cover changes and urbanisation along with climate change. A framework of the Generalised Extreme Value (GEV) method, Google Earth Engine (GEE), and land cover patterns’ classification including Random Forest (RF) and Support Vector Machine (SVM) can be useful for streamflow impact analysis. For this study, we developed a unique framework consisting of a hydrological model in line with the Process-informed Nonstationary Extreme Value Analysis (ProNEVA) GEV model and an ensemble of General Circulation Models (GCMs), mapping land cover patterns using classification methods within the GEE platform. We applied these methods in Southeast Queensland (SEQ) to analyse the maximum instantaneous floods in non-stationary catchment conditions, considering the physical system in terms of cause and effect. Independent variables (DEM, population, slope, roads, and distance from roads) and an integrated RF, SVM methodology were utilised as spatial maps to predict their influences on land cover changes for the near and far future. The results indicated that physical factors significantly influence the layout of landscapes. First, the values of projected evapotranspiration and rainfall were extracted from the multi-model ensemble to investigate the eight GCMs under two climate change scenarios (RCP4.5 and RCP8.5). The AWBM hydrological model was calibrated with daily streamflow and applied to generate historical runoff for 1990–2010. Runoff was projected under two scenarios for eight GCMs and by incorporating the percentage of each land cover into the hydrological model for two horizons (2020–2065 and 2066–2085). Following that, the ProNEVA model was used to calculate the frequency and magnitude of runoff extremes across the parameter space. The maximum peak flood differences under the RCP4.5 and RCP8.5 scenarios were 16.90% and 15.18%, respectively. The outcomes of this study suggested that neglecting the non-stationary assumption in flood frequency can lead to underestimating the amounts that can lead to more risks for the related hydraulic structures. This framework is adaptable to various geographical regions to estimate extreme conditions, offering valuable insights for infrastructure design, planning, risk assessment, and the sustainable management of future water resources in the context of long-term water management plans.

CLASSIFICATION OF SMS SPAM WITH N-GRAM AND PEARSON CORRELATION BASED USING MACHINE LEARNING TECHNIQUES

The Short Message Service (SMS) has garnered widespread popularity due to its simplicity, reliability, and ubiquitous accessibility.This study aims to enhance the efficacy of SMS classification by refining the classification process itself. Specifically, it strives to streamline the process by diminishing feature dimensions and eliminating inconsequential attributes. The textual data undergoes preprocessing, which involves employing the N-Gram technique for feature representation, followed by meticulous feature selection utilizing Pearson Correlation. The study employs 5 of classification algorithms. Notably, the findings underscore that the optimal outcomes emerge from the fusion of the N-Gram methodology with feature selection through Pearson Correlation. Among these, the Support Vector Machine methodology stands out, exhibiting a remarkable 91.41% enhancement in accuracy without feature selection, a further improvement to 91.96% through N-Gram utilization, and a final performance of 70.80% following the inclusion of weighted correlation. However, it is imperative to acknowledge the limitations inherent in the model's generalizability, primarily stemming from the utilization of a relatively modest dataset. Despite the efficacy of Pearson correlation and N-gram-based feature selection in curbing data dimensionality and enhancing processing efficiency, certain pertinent features may have been overlooked, or the chosen attributes might not be optimally suited for specific classifications.

An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology.

In the rapidly evolving landscape of agricultural technology, image processing has emerged as a powerful tool for addressing critical agricultural challenges, with a particular focus on the identification and management of crop diseases. This study is motivated by the imperative need to enhance agricultural sustainability and productivity through precise plant health monitoring. Our primary objective is to propose an innovative approach combining support vector machine (SVM) with advanced image processing techniques to achieve precise detection and classification of fig leaf diseases. Our methodology encompasses a step-by-step process, beginning with the acquisition of digital color images of diseased leaves, followed by denoising using the mean function and enhancement through Contrast-limited adaptive histogram equalization. The subsequent stages involve segmentation through the Fuzzy C Means algorithm, feature extraction via Principal Component Analysis, and disease classification, employing Particle Swarm Optimization (PSO) in conjunction with SVM, Backpropagation Neural Network, and Random Forest algorithms. The results of our study showcase the exceptional performance of the PSO SVM algorithm in accurately classifying and detecting fig leaf disease, demonstrating its potential for practical implementation in agriculture. This innovative approach not only underscores the significance of advanced image processing techniques but also highlights their substantial contributions to sustainable agriculture and plant disease mitigation. In conclusion, the integration of image processing and SVM-based classification offers a promising avenue for advancing crop disease management, ultimately bolstering agricultural productivity and global food security.

Diameter distributions in Pinus sylvestris L. stands: evaluating modelling approaches including a machine learning technique

The diameter distribution of trees in a stand provides the basis for determining the stand’s ecological and economic value, its structure and stability and appropriate management practices. Scots pine (Pinus sylvestris L.) is one of the most common and important conifers in Turkey, so a well-planned management schedule is critical. Diameter distribution models to accurately describe the stand structure help improve management strategies, but developing reliable models requires a deep understanding of the growth, output and constraints of the forests. The most important information derived by diameter distribution models is primary data on horizontal stand structure for each diameter class of trees: basal area and volume per unit area. These predictions are required to estimate the range of products and predicted volume and yield from a forest stand. Here, to construct an accurate, reliable diameter distribution model for natural Scots pine stands in the Türkmen Mountain region, we used Johnson’s SB distribution to represent the empirical diameter distributions of the stands using ground-based measurements from 55 sample plots that included 1219 trees in natural distribution zones of the forests. As an alternative, nonparametric approach, which does not require any predefined function, an artificial intelligence model was constructed based on support vector machine methodology. An error index was calculated to evaluate the results. Overall, both Johnson’s SB probability density function with a three-parameter recovery approach and the support vector regression methodology provided reliable estimates of the diameter distribution of these stands.

Modelling the removal efficiency of hydrogen sulfide from biogas in a biofilter using multiple linear regression and support vector machines

The main goal of this study is to create a statistical and intelligent model that can forecast the removal efficiency of hydrogen sulfide (H2S) in a fixed bed biofilter. The majority of conventional models employ mathematical principles that necessitate highly technical infrastructure, extremely detailed information on the biofiltration mechanism, and estimation of model input parameters. However, in this study, Multiple Linear Regression and Support Vector Machine methodology were used to reduce the reliance on experimental data. Experimental research was done to determine the effects of three factors on H2S removal efficiency, including the moisture content of the compost used as the biofilter bed, the empty bed residence time in the reactor, and the H2S concentration in the biogas stream input to the biofilter. Experimental findings demonstrated that the biofilter with a moisture content of 6%, and empty bed residence time of 60 s was capable of removing up to 75% of H2S from a biogas stream with H2S inlet concentration of 70 ppm(v). With a Coefficient of Determination between 0.98 and 0.99, the proposed models show excellent ability to predict the H2S removal efficiency. Compared to a Multiple Linear Regression based model, test results from a Support Vector Machine based model showed better agreement with experimental data.

Machine vision-based gradient-boosted tree and support vector regression for tool life prediction in turning

One of the essential elements of automated and intelligent machining processes is accurately predicting tool life. It also helps in achieving the goal of producing quality products with reduced production costs. This work proposes a computer vision-based tool wear monitoring and tool life prediction system using machine learning methods. Gradient-boosted trees and support vector machine (SVM) techniques are used to predict tool life. The experimental investigation on the CNC machine is conducted to study the applicability of the proposed tool wear monitoring system. Experiments are performed using workpiece material made of alloy steel and PVD-coated cutting inserts, and flank wear is monitored. An imaging system consisting of an industrial camera, lens, and LED ring light is mounted on the machine to capture tool wear zone images. Images are then processed by algorithms developed in MATLAB®. Boosted tree methods and the SVM methodology have 96% and 97% prediction accuracy, respectively. Validation tests are carried out to determine the accuracy of proposed models. It is observed that the prediction accuracy of boosted three and SVM is good, with a maximum error of 5.89% and 7.56%, respectively. The outcome of the study established that the developed system can monitor the tool wear with good accuracy and can be adopted in industries to optimize the utilization of tool inserts.

The use of a genomic relationship matrix for breed assignment of cattle breeds: comparison and combination with a machine learning method.

To develop a breed assignment model, three main steps are generally followed: 1) The selection of breed informative single nucleotide polymorphism (SNP); 2) The training of a model, based on a reference population, that allows to classify animals to their breed of origin; and 3) The validation of the developed model on external animals i.e., that were not used in previous steps. However, there is no consensus in the literature about which methodology to follow for the first step, nor about the number of SNP to be selected. This can raise many questions when developing the model and lead to the use of sophisticated methodologies for selecting SNP (e.g., with iterative algorithms, partitions of SNP, or combination of several methods). Therefore, it may be of interest to avoid the first step by the use of all the available SNP. For this purpose, we propose the use of a genomic relationship matrix (GRM), combined or not with a machine learning method, for breed assignment. We compared it with a previously developed model based on selected informative SNP. Four methodologies were investigated: 1) The PLS_NSC methodology: selection of SNP based on a partial least square-discriminant analysis (PLS-DA) and breed assignment by classification based on the nearest shrunken centroids (NSC) method; 2) Breed assignment based on the highest mean relatedness of an animal to the reference populations of each breed (referred to mean_GRM); 3) Breed assignment based on the highest SD of the relatedness of an animal to the reference populations of each breed (referred to SD_GRM) and 4) The GRM_SVM methodology: the use of means and SD of the relatedness defined in mean_GRM and SD_GRM methodologies combined with the linear support vector machine (SVM), a machine learning method used for classification. Regarding mean global accuracies, results showed that the use of mean_GRM or GRM_SVM was not significantly different (Bonferroni corrected P > 0.0083) than the model based on a reduced SNP panel (PLS_NSC). Moreover, the mean_GRM and GRM_SVM methodology were more efficient than PLS_NSC as it was faster to compute. Therefore, it is possible to bypass the selection of SNP and, by the use of a GRM, to develop an efficient breed assignment model. In routine, we recommend the use of GRM_SVM over mean_GRM as it gave a slightly increased global accuracy, which can help endangered breeds to be maintained. The script to execute the different methodologies can be accessed on: https://github.com/hwilmot675/Breed_assignment.

A FINGERPRINT BASED GENDER DETECTOR SYSTEM USING FINGERPRINT PATTERN ANALYSIS

Humans have distinctive and unique traits which can be used to distinguish them thus, acting as a form of identification. Biometrics identifies people by measuring some aspect of individual’s anatomy or physiology such as hand geometry or fingerprint which consists of a pattern of interleaved ridges and valleys. The aim of this research is to analyse humans fingerprint texture in order to determine their gender, and correlation of RTVTR and Ridge Count on gender detection. The study is to analyze the effectiveness of physical biometrics (thumbprint) in order to determine gender in humans. Humans have distinctive and unique traits which can be used to distinguish them thus, acting as a form of identification. Biometrics identify people by measuring some aspect of individual’s anatomy or physiology such as hand geometry or fingerprint which consists of a pattern of interleaved ridges and valleys. This work developed a system that determines human gender using fingerprint analysis trained with SVM+CNN (for gender classification). To build an accurate fingerprint based model for gender detection system using fingerprint pattern analysis, there are certain steps that must be taken, which include; Data collection (in conducting research, the first step is collecting data in the form of a set of fingerprint image), Pre-processing Data (before entering the training data, pre-processing data is performed, which is resize the fingerprint image 96x96 pixels). Training Data (in this processing the dataset will be trained using the Convolutional neural network and Support vector machine methodology. This training data processing is a stage where CNN + SVM are trained to obtained high accuracy from the classification conducted). Result Verification (after doing all the above process, at this stage, we will display the results of gender prediction based on fingerprint images in the application that has been making). SOCOFing database is made up of 6,000 fingerprint images from 600 African subjects. It contains unique attributes such as labels for gender, hand and finger name as well as synthetically altered versions with three different levels of alteration for obliteration, central rotation, and z-cut. The values for accuracy, sensitivity and precision using the CNN classifier at threshold 0.25 were 96%, 97.8% and 96.92% respectively. At threshold 0.45 the values were 96.3%, 97.6% and 97.6% respectively. At threshold 0.75 the values were 96.5%, 97.3% and 97.9% respectively. In case of the SVM classifier, at threshold 0.25 were 94.3%, 96.6% and 95.8% respectively. At threshold 0.45 the values were 94.5%, 96.4% and 96.2% respectively. At threshold 0.75 the values were 94.8%, 97.3% and 96.8% respectively. From the 600 fingerprints classified, it was observed that a total of 450 fingerprints were detected for male and 150 for female. Results were obtained for gender accuracy, sensitivity and precision through several thresholds to compare the two classifiers. However, it should be verified that the results obtained showed that the CNN classification yielded better accuracy, sensitivity and precision than SVM.

Nonlinear optimization and support vector machines

Support vector machine (SVM) is one of the most important class of machine learning models and algorithms, and has been successfully applied in various fields. Nonlinear optimization plays a crucial role in SVM methodology, both in defining the machine learning models and in designing convergent and efficient algorithms for large-scale training problems. In this paper we present the convex programming problems underlying SVM focusing on supervised binary classification. We analyze the most important and used optimization methods for SVM training problems, and we discuss how the properties of these problems can be incorporated in designing useful algorithms.

Support vector machine versus convolutional neural network for hyperspectral image classification: A systematic review

AbstractVarious machine learning and deep learning techniques have been proposed for classification purposes in the case of hyperspectral imaging. Among all the machine learning techniques support vector machine (SVM) has been a promising classification algorithm in the case of remote sensing applications, particularly in the field of hyperspectral imaging. In the case of deep learning techniques, the convolutional neural network is gaining much attention from researchers for classification purposes and has given reliable results in this field, thus is being used widely by researchers. So, in this article, a systematic review about the two most used hyperspectral classification techniques that is, SVM and CNN is given. A total of 86 papers from the four well‐known journals belonging to the field of remote sensing have been reviewed where the methodologies used by different authors, datasets used, results acquired, contribution and shortcomings have been put forth. This meta‐analysis generally focuses on the recent SVM and CNN methodologies that are proposed by various authors. A summary of the best results obtained from SVM and CNN in terms of classification accuracies is also provided to help the researchers working in this area achieve better results.

Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery

The support vector machine (SVM) algorithm is one of the most widely used machine learning (ML) methods for predicting active compounds and molecular properties. In chemoinformatics and drug discovery, SVM has been a state-of-the-art ML approach for more than a decade. A unique attribute of SVM is that it operates in feature spaces of increasing dimensionality. Hence, SVM conceptually departs from the paradigm of low dimensionality that applies to many other methods for chemical space navigation. The SVM approach is applicable to compound classification, and ranking, multi-class predictions, and –in algorithmically modified form– regression modeling. In the emerging era of deep learning (DL), SVM retains its relevance as one of the premier ML methods in chemoinformatics, for reasons discussed herein. We describe the SVM methodology including strengths and weaknesses and discuss selected applications that have contributed to the evolution of SVM as a premier approach for compound classification, property predictions, and virtual compound screening.

Early Stage DRC Prediction Using Ensemble Machine Learning Algorithms

At leading technology nodes, the industry is facing a stiff challenge to make profitable integrated circuits (ICs). One of the primary issues is the design rule checking (DRC) violation. This research cohort with the DARPA IDEA program aims for “no-human-in-the-loop” and 24-h turnaround time to implement an IC from design specifications. In order to reduce human effort, this work introduces the ensemble random forest, gradient boosting, and Adaboost algorithms to predict DRC violations before detailed routing, which is considered the most time-consuming step in an IC design flow. In addition, this work identifies the features that critically impact DRC violations. The proposed algorithm has a 2% better F1-score compared to the existing support-vector machine (SVM) classifiers. The proposed ensemble approach has up to an area-under-the-curve–receiver operating characteristics (AUC–ROC) curve mean of 0.940 with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 0.011 standard deviation compared to the state-of-the-art SVM classifier with an AUC–ROC curve mean of 0.854 with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 0.01 standard deviation. The proposed ensemble approach exhibits up to 28.7% better DRC violation prediction rate compared to those using SVM algorithms on the test data. In addition, the proposed gradient boosting algorithm requires 37.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> lower average training time and 50 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> lower average testing time compared to the existing SVM methodologies.

Evaluation of Fingerprint Liveness Detection by Machine Learning Approach - A Systematic View

Recently, fake fingerprint detection is a challenging task in the cyber-crime sector in any developed country. Biometric authentication is growing in many sectors such as internet banking, secret file locker, etc. There spoof fingerprint detection is an essential element that is used to detect spot-on fingerprint analysis. This article focuses on the implementation and evaluation of suitable machine learning algorithms to detect fingerprint liveness. It also includes the comparative study between Ridge-let Transform (RT) and the Machine Learning (ML) approach. This article emphasis on research and analysis of the detection of the liveness spoof fingerprint and identifies the problems in different techniques and solutions. The support vector machine (SVM) classifiers work with indiscriminate loads and confined grayscale array values. This leads to a liveness report of fingerprints for detection purposes. The SVM methodology classifies the fingerprint images among more than 50K of real and spoof fingerprint image collections based on this logic. Our proposed method achieves an overall high accuracy of detection of liveness fingerprint analysis. The ensemble classifier approach model is proving an overall efficiency rate of 90.34 % accurately classifies samples than the image recognition method with RT. This recommended method demonstrates the decrement of 2.5% error rate when compared with existing methods. The augmentation of the dataset is used to improve the accuracy to detect. Besides, it gives fake fingerprint recognition and makes available future direction.

Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

Potato Plant Leaves Disease Detection and Classification using Machine Learning Methodologies

Agriculture is one of the essential sectors for the survival of humankind. At the same time, digitalization touching across all the fields that became easier to handle various difficult tasks. Adapting technology as well as digitalization is very crucial for the field of agriculture to benefit the farmer as well as the consumer. Due to adopting technology and regular monitoring, one can able to identify the diseases at the very initial stages and those can be eradicated to obtain a better yield of the crop. In this document, a methodology was proposed for the detection as well as the classification of diseases that occur for the potato plants. For this scenario, the openly accessible, standard, and reliable data set was considered which was popularly known as Plant Village Dataset. For the process of image segmentation, the K-means methodology was considered, for the feature extraction purpose, the gray level co-occurrence matrix concept was utilized, and for the classification purpose, the multi-class support vector machine methodology was utilized. The proposed methodology able to attain an accuracy of 95.99%.

Predicting NICU admissions in near-term and term infants with low illness acuity.

Objective:Describe NICU admission rate variation among hospitals in infants with birthweight ≥2500g and low illness acuity and describe factors that predict NICU admission.Study Design:Retrospective study from the Vizient Clinical Data Base/Resource Manager®. Support vector machine methodology was used to develop statistical models using 1) patient characteristics 2) only the indicator for the inborn hospital and 3) patient characteristics plus indicator for the inborn hospital.Results:NICU admission rates of 427,449 infants from 154 hospitals ranged from 0–28.6%. C-statistics for the patient characteristics model: 0.64 (Confidence Interval (CI) 0.62–0.65), hospital only model: 0.81 (CI, 0.81–0.82) and patient characteristic plus hospital variable model: 0.84 (CI, 0.83–0.84).Conclusion/Relevance:There is wide variation in NICU admission rates in infants with low acuity diagnoses. In all cohorts, birth hospital better predicted NICU admission than patient characteristics alone.

High Throughput Density Functional Investigations of the Stabilities, Electronic Structures of XYZ2 Compounds

The development of high-performance thermoelectric materials requires the reduction of the thermal conductivity as well as the enhancement of the power factor simultaneously, which may benefit from high-throughput materials simulations. Recently, we have revealed a new class of high-performance thermoelectric materials, namely XYZ2 (X,Y: rare earth or transition metals, Z: group VI element) based on high throughput simulations, among which TmAgTe2, YAgTe2, YCuTe2 have been successfully synthesized and proved to have extraordinary low thermal conductivity. But, similar to many other high-performance thermoelectric materials, e.g. PbTe, SnSe, XYZ2 thermoelectric materials could also have multiple phase transformations. Thus, it is important to systematically study the stability, electronic structure and transport property of different phases, and how these are related to the chemistry of the compound. The phase stabilities of XYZ2 compounds have been assessed by support vector machine methodology. We find that those compounds with larger X ionic radius have P3m1 space group with X ion in octahedral interstices, while smaller X ionic radius favors a tetrahedral site occupation with a space group of the compound of I-42d. Based on the gradient boosting algorithm, density of states effective mass mS* and fermi surface complexity factor NV*K* are identified as important descriptors to enhance zT values. Band structures and Crystal Orbital Hamilton Population analyses reveal that materials with larger mS* have a flat band to enhance Seebeck coefficient if the X-s (Y-d) orbitals have similar energy with Z-p orbital near the VBM. Cu and Ag based compounds with P3m1 space group are benefited from the Y-d orbital contribution to the high Fermi surface complexity factor. These intrinsic parameters including ionic size and molecular orbitals can alter stabilities and electronic properties, which provide us clues on how to design and improve the thermoelectric performance of XYZ2 compounds.

Robust Weighted Least Squares Support Vector Regression algorithm to estimate the nanofluid thermal properties of water/graphene Oxide–Silicon carbide mixture

A new optimization/statistical approach of “Robust Weighted Least Squares Support Vector Regression” algorithm (RWLS-SVR) is provided for the first time. The experimental achieved amounts of the thermal conductivity for a new hybrid nanofluid of water/Graphene Oxide–Silicon Carbide, are examined at different values of temperature and nanoparticles volume fraction. A Support Vector Regression is a supervised learning regression algorithm based on the Support Vector Machine methodology. However in the Least Squares Support Vector Machine method, the inequality constraints are converted to equality constraints and the sum squared error function is employed. Moreover a LS-SVR is applied to the problem in order to calculate the error variables. Afterwards, the weights computed based on the error variables are applied to the optimization problem in order to reduce the effects of outliers on the final results. As a result, the WLS-SVR method does not significantly increase the computational burden, but it provides sparseness and robustness.

Nonlinear optimization and support vector machines

Support Vector Machine (SVM) is one of the most important class of machine learning models and algorithms, and has been successfully applied in various fields. Nonlinear optimization plays a crucial role in SVM methodology, both in defining the machine learning models and in designing convergent and efficient algorithms for large-scale training problems. In this paper we present the convex programming problems underlying SVM focusing on supervised binary classification. We analyze the most important and used optimization methods for SVM training problems, and we discuss how the properties of these problems can be incorporated in designing useful algorithms.