Comparison of support vector machine and artificial neural network systems for drug/nondrug classification.

Internal combustion engines are frequently used in transportation, power plants, and in many other applications for industrial purposes. For this reason, it is very important that the maintenance is done systematically and that the faults are detected correctly. In this study, two different methods were used for the detection of the healthy internal combustion engine (H) and faulty internal combustion engines (single-cylinder misfire-F1, two-cylinder misfire-F2). In the first method, classical signal features were extracted from engine vibration measurements and used in the training of artificial neural networks (ANNs) and support vector machine (SVM). In the second method, convolutional neural networks (CNNs), a deep learning method in which features are extracted automatically, are used. Spectrograms of engine vibration signals were used to train pre-trained CNNs with different structures. Spectrograms were obtained by applying short-time Fourier transform (STFT) to vibration signals. The results of GoogleNet and ResNet-50 models trained with spectrograms were compared with the results obtained from models based on ANNs and SVM.

This study is about the selection of classifiers in handwritten character recognition. The aim of the study is to determine the most appropriate classifier type for a given handwritten character feature vector. PCA based features were classified by both multilayer artificial neural networks (ANN) and support vector machines (SVM), and then the recognition results were compared. We selected error backpropagation, resilient backpropagation and scaled conjugate gradients as ANN training methods, while the SVM kernel types selected were linear, RBF and polynomial. The experimental results show that the SVM has better training and test performance than ANN.

The paper explores the hybrid of SVM and system of Artificial Neural Network as the finest binary classification system for calculating the diabetic nature of people in comparison to Support Vector Machine (SVM). In this research, the sets of all the parameters describing the diabetic nature of a person are taken from the laboratories. This approach is chosen so that a better learning method can be used for various problems. The testing results were found to be in accordance with the accepted results that resemble with the direct diagnosis of a physician. The results of this research shows that this hybrid SVM and Artificial Neural Network (ANN) model is more precise than the SVM model. These results of the hybrid SVM and ANN model suggest that it is very effective for the classification of Diabetic and Non Diabetic nature of a person. This paper highlights the concept of Support Vector Machines and its integration with Artificial Neural Network, the two key characteristics with one being the generalization theory of the SVM model that best describes how to select a hypothesis and functions given by Kernel that introduces the idea of non-linearity without the inclusion of the actual algorithm.

This research delves into the classification of cardiovascular disease (CVD) utilizing state-of-the-art machine learning algorithms, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). Before model training, extensive data preprocessing techniques were implemented, including data cleaning, feature scaling, encoding, Feature selection, handling imbalanced data, normalization, and cross-validation. After data preparation, an extensive evaluation of performance was carried out against various parameters like accuracy, precision, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odd ratio (DOR). The comparison of SVM and ANN techniques indicates that the SVM has a better sensitivity in detecting positive cases while ANNs have more accuracy in the classification. This paper not only documents the use of new methods but also highlights the advantages and disadvantages of SVM and ANN models, and therefore helps to improve the use of machine learning applications in making health care decisions on CVD diagnosis.

This paper presents an optimized support vector machine (SVM) training processor employing the alternative direction method of multipliers (ADMM) optimizer. Low-rank approximation is exploited to reduce the dimension of the kernel matrix by employing the Nyström method. Verified in four datasets, the proposed ADMM-based training processor with rank approximation reduces 32× of matrix dimension with only 2% drop in inference accuracy. Compared to the conventional sequential minimal optimization (SMO) algorithm, the ADMM-based training algorithm is able to achieve a 9.8×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> shorter latency for training 2048 samples. Hardware optimization techniques, including pre-computation and memory sharing, are proposed to reduce the computational complexity by 62% and the memory usage by 60%. As a proof of concept, an epileptic seizure detector is designed to demonstrate the effectiveness of the proposed optimization techniques. The chip achieves a 153,310× higher energy efficiency and a 364× higher throughput-to-area ratio for SVM training than a high-end CPU. This work provides a promising solution for edge devices which require low-power and real-time training.

&lt;abstract&gt;&lt;p&gt;The training of artificial neural networks (ANNs) with rectified linear unit (ReLU) activation via gradient descent (GD) type optimization schemes is nowadays a common industrially relevant procedure. GD type optimization schemes can be regarded as temporal discretization methods for the gradient flow (GF) differential equations associated to the considered optimization problem and, in view of this, it seems to be a natural direction of research to &lt;italic&gt;first aim to develop a mathematical convergence theory for time-continuous GF differential equations&lt;/italic&gt; and, thereafter, to aim to extend such a time-continuous convergence theory to implementable time-discrete GD type optimization methods. In this article we establish two basic results for GF differential equations in the training of fully-connected feedforward ANNs with one hidden layer and ReLU activation. In the first main result of this article we establish in the training of such ANNs under the assumption that the probability distribution of the input data of the considered supervised learning problem is absolutely continuous with a bounded density function that every GF differential equation admits for every initial value a solution which is also unique among a suitable class of solutions. In the second main result of this article we prove in the training of such ANNs under the assumption that the target function and the density function of the probability distribution of the input data are piecewise polynomial that every non-divergent GF trajectory converges with an appropriate rate of convergence to a critical point and that the risk of the non-divergent GF trajectory converges with rate 1 to the risk of the critical point. We establish this result by proving that the considered risk function is &lt;italic&gt;semialgebraic&lt;/italic&gt; and, consequently, satisfies the &lt;italic&gt;Kurdyka-Łojasiewicz inequality&lt;/italic&gt;, which allows us to show convergence of every non-divergent GF trajectory.&lt;/p&gt;&lt;/abstract&gt;

Support vector machine is a new and promising technique for pattern classification and regression estimation. The training of support vector machine is characterized by convex optimization problem, up to the determination of a few additional tuning parameters. Moreover, the model complexity follows from this convex optimization problem. In this paper we introduce sequential support vector machine for regression estimation. The support vector machine is trained by the particle filter. By the method of training support vector machine we can design the controller based on support vector machine. Afterwards we discuss feedforward control based on support vector machine. Support vector machine builds the inverse model of plant in the feedforward control based on support vector machine. In order to demonstrate the availability of this new controller designing method, we give a simulation of the simple nonlinear system for output tracking problem. The results of simulation demonstrate this method of new controller designing is very effective.

The Support Vector Machine (SVM) is an effective classification tool. Though extremely effective, SVMs are not a panacea. SVM training and testing is computationally expensive. Also, tuning the kernel parameters is a complicated procedure. On the other hand, the Nearest Neighbor (KNN) classifier is computationally efficient. In order to achieve the classification efficiency of an SVM and the computational efficiency of a KNN classifier, it has been shown previously that, rather than training a single global SVM, a separate SVM can be trained for the neighbourhood of each query point. In this work, we have extended this Local SVM (LSVM) formulation. Our Local Adaptive SVM (LASVM) formulation trains a local SVM in a modified neighborhood space of a query point. The main contributions of the paper are twofold: First, we present a novel LASVM algorithm to train a local SVM. Second, we discuss in detail the motivations behind the LSVM and LASVM formulations and its possible impacts on tuning the kernel parameters of an SVM. We found that training an SVM in a local adaptive neighborhood can result in significant classification performance gain. Experiments have been conducted on a selection of the UCIML, face, object, and digit databases.

This paper proposes a fuzzy rule extraction method from support vector machines. Support vector machines (SVM) are learning systems based on statistical learning theory that have been successfully applied to a wide variety of application. However, SVM are "black box" models, that is, they generate a solution with linear combination of kernel functions which has a quite difficult interpretation. Methods for rule extraction from trained SVM have already been proposed, however, the rules generated by these methods have, in their antecedents, intervals or functions. This format decreases the interpretability of the generated rules and jeopardizes the knowledge extraction capability. Hence, to increase the linguistic interpretability of the generated rules, we propose in this paper a methodology for extracting fuzzy rules from a trained SVM, where the rule's antecedents are associated with fuzzy sets.

I335 the training of Support Vector Machine (SVM) classification model, some problems such as over-fitting, under-fitting, slow training speed and prediction affected by penalty and kernel function parameters are exposed. A fault classification method of rolling bearing based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Grey Wolf Optimization (GWO) -SVM is proposed. Firstly, the acquire signals are adaptively decomposed into a plurality of Intrinsic Mode Function(IMF) component by CEEMDAN, and the energy entropy of each IMF component is extracted to form a set of high dimensional feature vector. Then, the processed feature vectors are introduced into the diagnosis network of GWO-SVM algorithm to build a fault classification model of motor rolling bearing. The classification results show that the CEEMDAN and GWO-SVM fault classification network of motor rolling bearing has higher accuracy and shorter diagnosis time than Particle Swarm Optimization (PSO) -SVM and Cross Validation (CV) -SVM.

In this article, a new strategy is proposed for soft urban land-cover extraction based on mixed training data and Support Vector Machines (SVMs). The strategy is applied to soft classification of urban water cover using Landsat 8 multispectral data in Beijing. The results are validated with extensive manual mapping data and are compared with the results derived from a well-trained SVM and the linear spectral unmixing method using only pure samples. Our experimental results indicate that the proposed strategy works effectively in extracting the water cover in the urban image. The results are better than those obtained by SVM and linear spectral unmixing with only pure samples. Our findings demonstrate that the combination of image-based mixed training data and SVM enhances the strengths of kernel-based approaches for soft mapping urban land cover from imaging data. Therefore, the proposed workflow constitutes a new flexible and extendable approach to soft mapping urban land cover. Future work includes the construction of a comprehensive image-based spectral library for urban areas, and the testing of the proposed strategy on additional land-cover types in urban areas such as grass, tree, and bare soil.

Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest

Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network

The amount of negative content circulating on the internet can damage people's morale so that social conflicts arise in society that threaten national sovereignty. Detecting negative content can help identify and prevent harmful events before they occur. This can lead to a safer and more positive online environment. Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm for Detection of Negative Content on Websites. The research contributions are 1) detect negative content on the internet with random forest and SVM, 2) comparing SVM and RF algorithms for detecting negative content on websites, 3) detection of negative content based on text focusing on the categories of fraud, gambling, pornography and Whitelist. The stages of this research are preparing a text content dataset on a website that has been labeled, preprocessing (duplicated data, text cleansing, case folding, stopward, tokenize, label encoding, data splitting, and determine the TF-IDF), finally performing the classification process with SVM and Random Forest. The dataset used in this study is a structured dataset in the form of text obtained from emails that have been registered on the TrustPositive website as negative content. Negative content includes fraud, pornography and gambling. The results show the accuracy of the SVM is 97%, Precision 90% and Recall 91%, while for Accuracy in Random Forest is 92%, Precision 71%, and Recall 86%. The value obtained is the result of testing using 526 website URLs. The test results show that the Support Vector Machine is better than the Random Forest in this study.

Evaluation of artificial intelligence tool performance and uncertainty for predicting sewer structural condition