High Efficient System for Automatic Classification of the Electrocardiogram Beats

Application of the PSO–SVM model for recognition of control chart patterns

This article presents improvements in the segmentation module, feature extraction module, and the classification module of a low-cost automated visual inspection (AVI) system for wood defect classification. One of the major drawbacks in the low-cost AVI system was the erroneous segmentation of clear wood regions as defects, which then introduces confusion in the classification module. To reduce this problem, we use the fuzzy min–max neural network for image segmentation (FMMIS). The FMMIS method grows boxes from a set of seed pixels, yielding ideally the minimum bounded rectangle for each defect present in the wood board image. Additional features with texture information are considered for the feature extraction module, and multi-class support vector machines are compared with multilayer perceptron neural networks in the classification module. Results using the FMMIS, additional features, and a pairwise classification support vector machine on a 550 test wood image set containing 11 defect categories show 91% of correct classification, which is significantly better than the original 75% of the low-cost AVI system. The use of computational intelligence techniques improved significantly the overall performance of the proposed automated visual inspection system for wood boards.

Background Machine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management. Purpose To develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors. Methods We constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables. Results DL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below). Conclusions In the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 TIMI performance on validation setDL performance on validation set

Deep convolutional cross-connected kernel mapping support vector machine based on SelectDropout

Support vector machine (SVM) and artificial neural network (ANN) systems were applied to a drug/nondrug classification problem as an example of binary decision problems in early-phase virtual compound filtering and screening. The results indicate that solutions obtained by SVM training seem to be more robust with a smaller standard error compared to ANN training. Generally, the SVM classifier yielded slightly higher prediction accuracy than ANN, irrespective of the type of descriptors used for molecule encoding, the size of the training data sets, and the algorithm employed for neural network training. The performance was compared using various different descriptor sets and descriptor combinations based on the 120 standard Ghose-Crippen fragment descriptors, a wide range of 180 different properties and physicochemical descriptors from the Molecular Operating Environment (MOE) package, and 225 topological pharmacophore (CATS) descriptors. For the complete set of 525 descriptors cross-validated classification by SVM yielded 82% correct predictions (Matthews cc = 0.63), whereas ANN reached 80% correct predictions (Matthews cc = 0.58). Although SVM outperformed the ANN classifiers with regard to overall prediction accuracy, both methods were shown to complement each other, as the sets of true positives, false positives (overprediction), true negatives, and false negatives (underprediction) produced by the two classifiers were not identical. The theory of SVM and ANN training is briefly reviewed.

BackgroundMachine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management.PurposeTo develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors.MethodsWe constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables.ResultsDL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below).ConclusionsIn the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting.Funding AcknowledgementType of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1TIMI performance on validation setDL performance on validation set

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.

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.

Since the 21st century, with the rapid development of China’s domestic economy and the continuous improvement of people’s living standards, the development and construction of urban infrastructure are also faster and faster, and the requirements for the stability of foundations such as buildings, roads and bridges, subway, and high-speed railway are also higher and higher. In the southwest of China, due to the influence of terrain conditions, there are a large number of soil rock mixtures with uneven particle size change and complex particle composition. Understanding the mechanical properties of soil rock mixture and effectively measuring the strength and deformation parameters of soil rock mixture are one of the most important research topics. At the same time, in order to identify the soil rock mixture, the conventional Gauss Euclidean distance calculation method cannot fully reflect the structure of soil rock mixture, so an improved Gauss kernel function multiclass support vector machine (MSVM) method is proposed. On the basis of Gaussian radial, Gaussian kernel function is used to replace Euclidean distance in ranging. The attitude kernel function of Gaussian kernel function is established according to ranging. Many types of support vector machines (SVM) are constructed by a binary tree method to complete classification. The experimental results show that, based on the improved Gaussian kernel function, this paper discusses the high-intensity interval training, so as to achieve a good recognition effect, whether it has a promoting effect on improving the physical quality of college boys, and designs the training scheme to carry out experimental intervention on college boys, so as to provide a new training theoretical reference for school physical education teaching. It also provides a training method for college boys to improve their physical fitness.

Since the 21st century, with the rapid development of China’s domestic economy and the continuous improvement of people’s living standards, the development and construction of urban infrastructure are also faster and faster, and the requirements for the stability of foundations such as buildings, roads and bridges, subway, and high-speed railway are also higher and higher. In the southwest of China, due to the influence of terrain conditions, there are a large number of soil rock mixtures with uneven particle size change and complex particle composition. Understanding the mechanical properties of soil rock mixture and effectively measuring the strength and deformation parameters of soil rock mixture are one of the most important research topics. At the same time, in order to identify the soil rock mixture, the conventional Gauss Euclidean distance calculation method cannot fully reflect the structure of soil rock mixture, so an improved Gauss kernel function multiclass support vector machine (MSVM) method is proposed. On the basis of Gaussian radial, Gaussian kernel function is used to replace Euclidean distance in ranging. The attitude kernel function of Gaussian kernel function is established according to ranging. Many types of support vector machines (SVM) are constructed by a binary tree method to complete classification. The experimental results show that, based on the improved Gaussian kernel function, this paper discusses the high-intensity interval training, so as to achieve a good recognition effect, whether it has a promoting effect on improving the physical quality of college boys, and designs the training scheme to carry out experimental intervention on college boys, so as to provide a new training theoretical reference for school physical education teaching. It also provides a training method for college boys to improve their physical fitness.

PurposeThe purpose of this paper is to present a novel model for inhabitant prediction in smart houses based on daily life activities. It uses data provided by non intrusive sensors and devices to predict the house occupant. The authors' model, named Behavior Classification Model (BCM), applies Support Vector Machines (SVM) classifier to learn the users' habits when they perform activities, and then predicts the user. BCM was tested using real data and compared with a frequency based approach. In this paper the authors present also their approach to improve the accuracy of BCM using SVM feature selection algorithm.Design/methodology/approachThe model, named Behavior Classification Model (BCM), applies Support Vector Machines (SVM) classifier to learn the users' habits when they perform activities, and then predicts the user.FindingsBCM was tested using real data and compared with a frequency based approach. In this paper the authors' also present their approach to improve the accuracy of BCM using SVM feature selection algorithm.Originality/valueThe paper is based on blind user recognition in smart homes.

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 paper, we present an evaluation of face recognition algorithm in video surveillance system using multi-class Support Vector Machine (SVM). We also show how the variation of sizes (due to distance) and intensity of training images (due to camera hardware) can improve the face recognition rate. The system starts by detecting the face regions using AdaBoost training algorithm. Holistic approach is chosen to extract features from the detected face regions as this approach is the simplest. After the feature vectors are generated, multi-class SVM is used for classification step. SVM is chosen as the classifier as it is a low samples, high dimensional classifier. As the training and test set, we use SCface database in which the images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Based on our study using only linear kernel multi-class SVM, it is shown that distance of approximately 2.6m gives the best results, and the training and test images have to be from similar intensity cameras although surveillance camera will have different quality of images.

Automatic recognition of skin symptom plays an importance role in the skin diagnosis and treatment. Feature selection is to increase the classification performance of skin symptom. In this paper, the effects of feature selection on the classification of 4-class skin symptoms (chloasma, blackhead, freckled and comedone) are analyzed. Support vector machine (SVM) is employed to construct classifier, the parameters setting is very crucial to the learning results and generalization ability of SVM, and many irrelevant and redundant features degrade the performance of classification, furthermore, the choice of SVM parameters is influenced by the feature subset taken into account and vice versa. So a joint optimization method for selecting features and SVM parameters is proposed. Genetic algorithms, which have a lot of improvements about fitness, crossover and mutation in comparison with simple genetic algorithms, is to achieve a balance between the classification accuracy and the size of the feature subsets selected and have a fast convergence rate. Furthermore, ‘one-against-one’ approach is used to solve multi-class classification problems and a comparison of the model performances is made among the results of the joint optimization and the single optimization of features or SVM parameters in the experiments. The simulation result shows that our approach can find subsets of features for 4-class skin symptoms classification with higher average cross validation accuracy and a higher rate of convergence compared with single optimization of features or SVM parameters.

Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution and can be used to discover a unique pattern of students' academic performance. Secondary schools are increasing rapidly from past years and progress of the institution can be measured based on student's success and failure rates. Failure rates can be measured in terms of a core subject such as mathematics which has been considered in this proposed system. Real data was collected using school reports and questionnaire method by the Portugal school which has been used for the project. The students will be classified according to the grades assigned for the range of marks scored by them. This involves classifying the students into five levels of grading system starting from grade ‘A’ to grade ‘F’ where grade ‘A’ represents the student getting the highest marks, grade ‘B’ being the second highest, grade ‘C’ being third, grade ‘D’ being the fourth and the grade ‘F’ which implies that the student has failed. To carry out this type of classification many machine learning algorithms can be used to implement it. Comparison was made between the algorithms like Multiclass Support Vector Machine and Neural Networks using the Weka tool. Based on the analysis carried out Multiclass Support Vector Machine showed prominent accuracy. The Multiclass Support Vector Machine is implemented on the basis of one-to-rest strategy use of class labels which is primarily an extension of linear Support Vector Machine. In order to get appropriate results in terms of the accuracy of the model, parameters like ‘C’ and ‘Gamma’ is tuned while implementing Multiclass Support Vector Machine. The result gives a good predictive accuracy based on the grades that are provided by the school. The accuracy of prediction made by Support Vector Machine Classifier is determined by using K-fold cross validation, according to which the accuracy is 89%.