序贯最小二乘支持向量机的结构系统识别

提出一种用于结构系统识别的序贯最小二乘支持向量机（SLS-SVM）方法,通过对训练数据的序列进入和数据缩减,分别采用增量算法和减缩修剪算法有效地改进了LS-SVM.这种方法克服了标准LS-SVM算法的稀疏性缺失的缺点,并使LS-SVM的序贯训练成为可能.对非线性滞迟结构的在线参数识别显示了所提出方法的鲁棒性和高效率,同时也表明SLS-SVM算法的速度比批处理SVM算法要快.     

基于LS-SVM的立体视觉摄像机标定

利用最小二乘支持向量机来直接学习图像信息与三维信息之间的关系,不需确定摄像机具体的内部参数和外部参数.在双目视觉的情况下,两摄像机的位置关系不需具体求出,而是隐含在映射关系中.根据最小二乘支持向量机与摄像机标定的特点,提出了基于最小二乘支持向量机的双目立体摄像机标定方法.将摄像头采集到的图像的像素坐标作为输入,将世界坐标作为输出,用最小二乘支持向量机使网络实现给定的输入输出映射关系.该方法同BP神经网络预测结果对比表明:基于最小二乘支持向量机的双目视觉标定方法速度快,实时性好,能有效提高标定精度.     

Performance analysis of EM,SVD, and SVM classifiers in classification of carcinogenic regions of medical images

In this article, the performance analysis of Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM) classifiers for classification of carcinogenic regions from various medical images is carried out. Cancer detection is one of the critical issues where excessive care needs to be taken for better diagnosis. Any classifier needs to detect the cancer with respect to the efficiency in time of detection and performance. Due to these, three classifiers are selected: Expectation Maximization (EM), Singular Value Decomposition (SVD), and Support Vector Machines (SVM). EM classifier performs as the optimizer and SVD classifier performs as the dual class classifier. SVM classifier is used as both optimizer and classifier for multiclass classification procedure and for wide stage cancer detection procedures. The performance analysis of all the three classifiers are analyzed for a group of 100 cancer patients based on the benchmark parameter such as Performance Measures and Quality Metrics. From the experimental results it is evident, that the SVM classifier significantly outperforms other classifiers in the classification of carcinogenic regions of medical images.     

Failure and reliability prediction by support vector machines regression of time series data

Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques.     

基于边缘识别的虹膜定位算法

为了提高虹膜定位算法的抗干扰能力,本文提出了一种基于虹膜边缘识别的虹膜定位算法.该算法应用图像几何矩函数提取虹膜内外边缘的特征,通过由支持向量机(SVM)训练的分类器进行虹膜边缘点的识别.最后由Hough变换对识别的结果进行参数求解并实现虹膜的定位.本算法经过了CASIA虹膜图形库的实验验证,仿真实验数据表明所提算法不仅具有较快的定位速度和较高的定位成功率,而且性能稳定.     

Adaptive accuracy trust region: using cross-validation in the optimization process

This article presents an adaptive accuracy trust region (AATR) optimization strategy where cross-validation is used by the trust region to reduce the number of sample points needed to construct metamodels for each step of the optimization process. Lower accuracy metamodels are initially used for the larger trust regions, and higher accuracy metamodels are used for the smaller trust regions towards the end of optimization. Various metamodelling strategies are used in the AATR algorithm: optimal and inherited Latin hypercube sampling to generate experimental designs; quasi-Newton, kriging and polynomial regression metamodels to approximate the objective function; and the leave-k-out method for validation. The algorithm is tested with two-dimensional single-discipline problems. Results show that the AATR algorithm is a promising method when compared to a traditional trust region method. Polynomial regression in conjunction with a new hybrid inherited-optimal Latin hypercube sampling performed the best.     

Imbalanced Classification in Diabetics Using Ensembled Machine Learning

Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar. The risk of diabetics can be lowered if the diabetic is found at the early stage. In recent days, several machine learning models were developed to predict the diabetic presence at an early stage. In this paper, we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics. The proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of classification. Different measures such as Accuracy, Precision, Recall, and F1-Score are used to evaluate the model. The results we obtained using K-Nearest Neighbor (kNN), Naïve Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Trees (DT) were 89.32%, 91.44%, 95.78%, 89.3%, 81.76%, and 80.38% respectively. The SVM model is more efficient than other models which are 21.38% more than exiting machine learning-based works.     

Machine Learning and Classical Forecasting Methods Based Decision Support Systems for COVID-19

From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, Prophet, and Long-Short Term Memory (LSTM) forecasting models are applied to the novel COVID-19 dataset. According to the results, the Prophet model gives the lowest Root Mean Squared Error (RMSE) score compared to the other three models. Besides, according to this model, a projection for the future COVID-19 predictions of Turkey has been drawn and aimed to shape the current measures against the coronavirus.     

Fault detection and diagnosis method for cooling dehumidifier based on LS-SVM NARX model

Developing fault detection and diagnosis (FDD) for the cooling dehumidifier is very important for improving the equipment reliability and saving energy consumption. Due to the precise mathematic physical model for cooling dehumidifier FDD is difficult to build, a novel Nonlinear Autoregressive with Exogenous (NARX) method for the cooling dehumidifier FDD based on Least Squares Support Vector Machine (LS-SVM) is proposed. Firstly, the dehumidifier system is divided into two level models. Secondly, the parameters of the NARX model are identified by LS-SVM, and the parameters C and σ of the LS-SVM are optimized by adaptive genetic algorithm (AGA) in order to improve the model building precision. Lastly, two faults in condenser and compressor component are diagnosed by the built models. The experiment result indicates this proposed method is effective for cooling dehumidifier FDD and the model generalization ability is favorable.