Traffic sign recognition application using speeded-up robust features (SURF) and support vector machine (SVM) based on android

This study aims to develop an intelligent algorithm by integrating robust independent component analysis (RobustICA) and support vector machines (SVMs). According to different characteristics of source signals including real or complex, super-Gaussian or sub-Gaussian and pollution of signal noise, a new method for fault detection that uses RobustICA based on kurtosis is put forward in this paper. The basic idea of the approach is to use RobustICA optimized by iterative technique to separate independent components which drive a process after wavelet de-noising of the original data. On this basis, statistics are established for fault detection and the kernel density estimation is used in calculating the confidence limit of statistics. After that, support vector machines (SVMs) is utilized to classify the faults. The simulation results of signal experiment and TE model clearly show the effectiveness and advantages of the proposed method in comparison to FastICA method.

In this paper, a proposed method to authentication system based on a hand writing recognition without segmentation to sub letters based on feature extraction Speed Up Robust Features (SURF) and Support Vector Machine (SVM) to enhance the accuracy. After feature extraction, the result can be clustered by using the K-means algorithm to standardize the number of features. These two operations together (feature extraction and feature clustering) called Bag of Word (BOW). It converts the arbitrary number of image features to uniform length features vector. The proposed method experimented using two databases; the standard (IFN/ENIT) database and the proposed database consist of 30 different writers with 70 margins images. The proposed system achieves (96.67%) recognition rate for IFN/IENT database.

BackgroundTo develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function.MethodsA retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models.ResultsThe models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935–0.940), 0.919 (95%CI 0.916–0.922), and 0.959 (95%CI 0.956–0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800–0.807), 0.852 (95%CI 0.846–0.857), and 0.863 (95%CI 0.857–0.887) in the validation cohorts, respectively.ConclusionWe developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.

In this paper, we introduce a set of new kernel functions derived from the generalized Legendre polynomials to obtain more robust and higher support vector machine (SVM) classification accuracy. The generalized Legendre kernel functions are suggested to provide a value of how two given vectors are like each other by changing the inner product of these two vectors into a greater dimensional space. The proposed kernel functions satisfy the Mercer’s condition and orthogonality properties for reaching the optimal result with low number support vector (SV). For that, the new set of Legendre kernel functions could be utilized in classification applications as effective substitutes to those generally used like Gaussian, Polynomial and Wavelet kernel functions. The suggested kernel functions are calculated in compared to the current kernels such as Gaussian, Polynomial, Wavelets and Chebyshev kernels by application to various non-separable data sets with some attributes. It is seen that the suggested kernel functions could give competitive classification outcomes in comparison with other kernel functions. Thus, on the basis test outcomes, we show that the suggested kernel functions are more robust about the kernel parameter change and reach the minimal SV number for classification generally.

Support vector machines (SVMs) are versatile learning models which are used for both classification and regression. Several authors have reported successful applications of SVM in a wide range of fields. With the continuous growth and development in machine learning using SVM, it was observed that SVM also has some limitations. This paper focuses on limitation regarding its boundary, i.e., sensitivity to noise or outliers in the dataset. Researchers have proposed many variants and extensions of SVM to make it robust. This paper gives an overview of the developments in the field of robust statistics in support vector machines and its variants. This paper includes an up to date survey of the research development in the field of robustness in SVM and its extensions. It also includes a discussion part which not only discusses the pros and cons of the proposed approaches but also highlights some important future directions in it. This paper would be helpful for researchers working in the field of robust statistics as well as supervised machine learning. This study would also encourage the researchers to work further in the development of SVM and even its variants to improve them.

Nowadays, the Structural Building Health Damage Monitoring System (SBHDMS) is a crucial technology for predicting the civil building structures' health. SBHDMS contains abnormal changes in the buildings in terms of damage levels. Natural Disasters like Earthquakes, Floods, and cyclones affect the unusual changes in the buildings. If the building undergoes any natural disaster, the sensors capture the vibration data or change the buildings' structure. Due to the vibration data, these unusual changes can be analyzed. Here sensors or Machine Learning based Building Damage Prediction (MLBDP) are used for capturing and collecting the vibration data. This paper proposes a Novel Rough Set based Artificial Neural Network with Support Vector Machine (RAS) metaheuristic method. RAS method is used to predict the damaged building's vibration data levels captured by the sensors. For the feature reduction subset, we use one of the essential pre-processing method called the Rough set theory (RST) strategy. RAS has two contributions. The first one is the Support Vector Machine (SVM) classification method used for identifying the structures of the buildings. The artificial Neural Network (ANN) method used to predict the buildings' damage levels is the second contribution. The proposed method (RAS) is accurately predicting the conditions of the construction building structure and predicting the damage levels, without human intervention. Comparing the results states that the proposed method accuracy is better than SVM's classification methods, ANN. The prediction analysis depicts that the RAS method can effectively detect the damage levels.

PurposeTwin support vector machine (TSVM) is an effective machine learning technique. However, the TSVM model does not consider the influence of different data samples on the optimal hyperplane, which results in its sensitivity to noise. To solve this problem, this study proposes a twin support vector machine model based on fuzzy systems (FSTSVM).Design/methodology/approachThis study designs an effective fuzzy membership assignment strategy based on fuzzy systems. It describes the relationship between the three inputs and the fuzzy membership of the sample by defining fuzzy inference rules and then exports the fuzzy membership of the sample. Combining this strategy with TSVM, the FSTSVM is proposed. Moreover, to speed up the model training, this study employs a coordinate descent strategy with shrinking by active set. To evaluate the performance of FSTSVM, this study conducts experiments designed on artificial data sets and UCI data sets.FindingsThe experimental results affirm the effectiveness of FSTSVM in addressing binary classification problems with noise, demonstrating its superior robustness and generalization performance compared to existing learning models. This can be attributed to the proposed fuzzy membership assignment strategy based on fuzzy systems, which effectively mitigates the adverse effects of noise.Originality/valueThis study designs a fuzzy membership assignment strategy based on fuzzy systems that effectively reduces the negative impact caused by noise and then proposes the noise-robust FSTSVM model. Moreover, the model employs a coordinate descent strategy with shrinking by active set to accelerate the training speed of the model.

Proximal support vector machines (PSVMs) are support vector machines (SVMs) version which involve equality instead of inequality constraints assigning data points to one of two classes, and have a significant speedup in comparison with traditional SVM-type classifiers. However, PSVMs are very sensitive to outliers or noises because of overfitting problem. In this paper, a robust proximal support vector machines (RPSVMs) is proposed to deal with the problem of overfitting in PSVMs for two-class classification. In experiments, two methods are used to give membership values to training data points according to their relative importance in the training set. Results show that the proposed RPSVMs actually reduce the effect of outliers and yield higher classification rate than PSVMs do

Because the solution of Least squares projection twin support vector machine (LSPTSVM) is obtained by solving two smaller-size systems of linear equations, LSPTSVM has closed form solution and it is faster than classical least squares support vector machine (LSSVM). However, LSPTSVM has two disadvantages, it is sensitive to outliers and its solution lacks sparsity. Therefore, the LSPTSVM is difficult to training large-scale datasets. To solve this problem, we propose the robust LSPTSVM (R-LSPTSVM) by applying truncated least squares loss function which is a bounded function. R-LSPTSVM can be represented as difference of convex programming, then the primal solution of R-LSPTSVM can be obtained by using Concave-Convex Procedure. The sparse solution of R-LSPTSVM is iteratively obtained by using the pivoting Cholesky factorization. Furthermore, sparse R-LSPTSVM algorithm (SR-LSPTSVM) is proposed. Experimental results show that the SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fast.

In the realm of machine learning, classification models are important for identifying patterns and grouping data. Support Vector Machine (SVM) and Robust SVM are two types of models that are often used. SVM works by finding an optimal hyperplane to separate data classes, while Robust SVM is designed to deal with uncertainty and noise in the data, making it more resistant to outliers. However, SVM has limitations in dealing with class imbalance and outliers in the dataset. Class imbalance makes the model tend to predict the majority class, and outliers can interfere with model formation. This research compares the performance of SVM and Robust SVM on normal, unbalanced and outlier datasets. The software uses Python and Scikit-learn for implementation and comparison of the two models. Key features include automatic data preprocessing, model training, and evaluation with metrics such as accuracy, precision, recall, and F1 score. The results show that Robust SVM is superior in accuracy on normal datasets and is very effective in dealing with class imbalance, achieving a maximum accuracy of 100%. On datasets with outliers, Robust SVM maintains stable accuracy, demonstrating its robustness to outliers. This research contributes to correspondence management by providing more reliable classification models, improving data processing accuracy, and supporting more informed decision making in software development

Pattern recognition techniques have been widely used in security-sensitive applications to distinguish malicious samples from legitimate ones. However, there usually exist some intelligent attackers who intend to have malicious samples to be mis-classified as legitimate at test time, i.e. evasion attack. Current researches show that traditional Support Vector Machines (SVMs) are vulnerable to evasion attacks as they do not consider the existence of an attack. In this paper, we propose to increase the robustness of SVMs against evasion attacks by adding random generated malicious samples to the training set. The experimental result on spam filtering shows that the proposed method can increase the true positive rate of SVMs under evasion attacks without significantly affecting the false positive rate.

Unveiling the power of bene (Pistacia atlantica) hull scum: Boosting oxidative stability with methanolic extract and ferrous ions

Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. This paper represents a new approach for TSR system where detection of traffic sign is carried out using fuzzy rules based color segmentation method and recognition is accomplished using Speeded Up Robust Features(SURF) descriptor, trained by artificial neural network (ANN) classifier. In the detection step, the region of interest (sign area) is segmented using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space, post processed to filter unwanted region. Finally the recognition of the traffic sign is implemented using ANN classifier upon the training of SURF features descriptor. The proposed system simulated on offline road scene images captured under different illumination conditions. The detection algorithm shows a high robustness and the recognition rate is quite satisfactory. The performance of the ANN model is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curves. Also, performances of some classifier such as Support Vector Machine (SVM), Decision Trees, Ensembles Learners (Adaboost) and K-Nearest Neighbor (KNN) classifier are assessed with ANN approach. The simulation results illustrate that recognition using ANN model is higher than classifiers stated above.

EMU(Electric Multiple Units) maintenance platform is one of the key equipments of the motor Train base and EMU application, provides convenient for EMU daily maintenance and inspection, maintenance and repair. In the process design of the equipment, hydraulic crossover and other methods are adopted to meet the needs of the maintenance personnel on different types of EMUs. Therefore, the tipping boards are set up on the actual platform and protection network to ensure the safety of the workers' reaching the top. The EMUs usually use a four line storehouse, and the number of tipping plates is hundreds of them. Most of them are reversing / receiving by cylinder driving, and a large number of flip plates are distributed in the workshop. Besides, the state of the retractable boards and the faults have important influence on the operation. Therefore, it is very important to monitor the state of the flip plate. At present, it is more dependent on the monitoring and controlling system of the mechanical stroke switch in China. Due to the state of the hardware and the stability of the system, the method has the risk of omission and false alarm in some cases. Meanwhile, the switch signal can not directly display the real-time state of the tipping board. Therefore, the present invention provides a high-definition network camera image processing board state detection system based on double plate state real-time monitoring and detection, enhance the safety of EMU maintenance platform. The real-time image acquisition, calculating the specified texture information area gray co-occurrence matrix, generating feature vector descriptor, using support vector machine (SVM) classification method for feature vector classification, training classifier, and use the classifier to recognize the flap, and locate the flap position. And SVM is a novel method of machine learning evolving from statistics. SVM presents many own advantages in solving machine learning problems such as small samples, nonlinearity and high dimension. In this paper, the image texture SVM classification method construct feature vectors through the extraction of image gray level co-occurrence matrix texture information.The metioned texture imformation include gray-level co-occurrence matrix energy, contrast and entropy. And the gray level co-occurrence matrix reflects the image direction, adjacent interval and the change in value of integrated information.The results show that the image texture SVM classification method can effectively combine the method of template matching for the identification of condition detection of overhaul platform.

Transductive polyhedral conic classifiers for machine learning applications