Road intersection detection and classification using hierarchical SVM classifier

In this paper, a hierarchical multi-classification approach using support vector machines (SVM) has been proposed for road intersection detection and classification. Our method has two main steps. The first involves the road detection. For this purpose, an edge-based approach has been developed using the bird’s eye view image which is mapped from the perspective view of the road scene. Then, the concept of vertical spoke has been introduced for road boundary form extraction. The second step deals with the problem of road intersection detection and classification. It consists on building a hierarchical SVM classifier of the extracted road forms using the unbalanced decision tree architecture. Many measures are incorporated for good evaluation of the proposed solution. The obtained results are compared to those of Choi et al. (2007).     

A feature reduced intrusion detection system using ANN classifier

Rapid increase in internet and network technologies has led to considerable increase in number of attacks and intrusions. Detection and prevention of these attacks has become an important part of security. Intrusion detection system is one of the important ways to achieve high security in computer networks and used to thwart different attacks. Intrusion detection systems have curse of dimensionality which tends to increase time complexity and decrease resource utilization. As a result, it is desirable that important features of data must be analyzed by intrusion detection system to reduce dimensionality. This work proposes an intelligent system which first performs feature ranking on the basis of information gain and correlation. Feature reduction is then done by combining ranks obtained from both information gain and correlation using a novel approach to identify useful and useless features. These reduced features are then fed to a feed forward neural network for training and testing on KDD99 dataset. Pre-processing of KDD-99 dataset has been done to normalize number of instances of each class before training. The system then behaves intelligently to classify test data into attack and non-attack classes. The aim of the feature reduced system is to achieve same degree of performance as a normal system. The system is tested on five different test datasets and both individual and average results of all datasets are reported. Comparison of proposed method with and without feature reduction is done in terms of various performance metrics. Comparisons with recent and relevant approaches are also tabled. Results obtained for proposed method are really encouraging.     

A parallel incremental extreme SVM classifier

The classification algorithm extreme SVM (ESVM) proposed recently has been proved to provide very good generalization performance in relatively short time, however, it is inappropriate to deal with large-scale data set due to the highly intensive computation. Thus we propose to implement an efficient parallel ESVM (PESVM) based on the current and powerful parallel programming framework MapReduce. Furthermore, we investigate that for some new coming training data, it is brutal for ESVM to always retrain a new model on all training data (including old and new coming data). Along this line, we develop an incremental learning algorithm for ESVM (IESVM), which can meet the requirement of online learning to update the existing model. Following that we also provide the parallel version of IESVM (PIESVM), which can solve both the large-scale problem and the online problem at the same time. The experimental results show that the proposed parallel algorithms not only can tackle large-scale data set, but also scale well in terms of the evaluation metrics of speedup, sizeup and scaleup. It is also worth to mention that PESVM, IESVM and PIESVM are much more efficient than ESVM, while the same solutions as ESVM are exactly obtained.     

Intrusion detection using a linguistic hedged fuzzy-XCS classifier system

Intrusion detection systems (IDS) are a fundamental defence component in the architecture of the current telecommunication systems. Misuse detection is one of the different approaches to create IDS. It is based on the automatic generation of detection rules from labelled examples. Such examples are either attacks or normal situations. From this perspective the problem can be viewed as a supervised classification one. In this sense, this paper proposes the use of XCS as a classification technique to aid in the tasks of misuse detection in IDS systems. The final proposed XCS variant includes the use of hedged linguistic fuzzy classifiers to allow for interpretability. The use of this linguistic fuzzy approach provides with both the possibility of testing human designed detectors and a posteriori human fine tuning of the models obtained. To evaluate the performance not only several classic classification problems as Wine or Breast Cancer datasets are considered, but also a problem based on real data, the KDD-99. This latter problem, the KDD-99, is a classic in the literature of intrusion systems. It shows that with simple configurations the proposed variant obtains competitive results compared with other techniques shown in the recent literature. It also generates human interpretable knowledge, something very appreciated by security experts. In fact, this effort is integrated into a global detection architecture, where the security administrator is guiding part of the intrusion detection (and prevention) process.     

Fuzzy decision ontology for melanoma diagnosis using KNN classifier

Multimedia Tools and Applications - Melanoma is the most dangerous type of skin cancer when discovered in an advanced stage. Early detection of melanoma improves survival. Several Computer -Aided...     

A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier

Advances in computer vision have led to the development of promising solutions for challenging problems in agriculture. Fruit grading and sorting are complex problems which require a great deal of human expertise. In this paper, we propose a non-destructive system for sorting and grading tomatoes, which is confounding even for expert human sorters. The proposed system performs classifications of tomatoes in three stages with digital images of samples captured in an experimental setup deployed using microcontroller. In the first stage, a binary classification is performed to discriminate tomatoes from other species using a species vector constructed from these images. In the second stage, the tomatoes are classified into ripe and unripe categories based on the color attribute. Then, the defects in the fruits are identified using Gabor wavelet transform to segment the infected regions of these images. The third stage identifies three types of defects namely the black spots, cankers and Melanose, based on a defect vector constructed from additional color and geometric features. Due to the complexity involved in solving such a non-linear problem, the proposed system is implemented as a cascade of two support vector machine classifiers. The performance of this system is assessed with the accuracy, specificity, sensitivity and precision metrics. The experimental results and comparative analyses with similar methods testify the efficacy of the proposed system over existing systems on the sorting and grading of tomatoes.The results obtained from each of the three classification stages i.e. Tomato/Non-Tomato, Good/Defective and the type of defect in the case of defective are communicated to the microcontroller to enable the respective motor, so that the given fruit is classified and collected in the respective bin.     

基于支持向量机的无线传感器网络的入侵检测系统

无线传感器网络入侵检测方法是目前的研究热点。在介绍支持向量机(SVM)原理、无线传感器网络的入侵类型以及不同类型所具有的数据特性的基础上,提出了一种基于SVM的无线传感器网络的入侵检测系统,该系统把网络拓扑分为簇成员、簇头和Sink三层结构,每层均能根据SVM的训练结果进行入侵检测的判断。实验结果表明:该检测系统具有较高的检测率和较低的能量消耗。     

液晶字符识别的CNN和SVM组合分类器

针对仪表液晶显示字符识别问题,提出一种结合了卷积神经网络(CNN)和支持向量机(SVM)的字符识别方法.分别采用具有并联结构的CNN模型和基于梯度方向直方图(HOG)特征的SVM方法构建基本分类器,当2个分类器的结果存在冲突时,利用CNN的softmax输出最大值判决最终结果,当其大于设定阈值时采用CNN分类器的结果,...     

A case-based classifier for hypertension detection

The exploration of three-dimensional (3D) anthropometry scanning data along with other existing subject medical profiles using data mining techniques becomes an important research issue for medical decision support. This research attempts to construct a classification approach based on the hybrid use of case-based reasoning (CBR) and genetic algorithms (GAs) for hypertension detection using anthropometric body surface scanning data. The obtained result reveals the relationship between a subject’s 3D scanning data and hypertension disease. The GA is adopted to determine the appropriate feature weights for CBR. The proposed approaches were experimented and compared with a regular CBR and other widely used approaches including neural nets and decision trees. The experiment showed that applying GA to determine the suitable weights in CBR is a feasible approach to improving the effectiveness of case matching of hypertension disease. It also demonstrated that different weighted CBR approach presents better classification accuracy over the results obtained from other approaches.     

A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection

In this paper, we propose a cascade classifier combining AdaBoost and support vector machine, and applied this to pedestrian detection. The pedestrian detection involved using a window of fixed size to extract the candidate region from left to right and top to bottom of the image, and performing feature extractions on the candidate region. Finally, our proposed cascade classifier completed the classification of the candidate region. The cascade-AdaBoost classifier has been successfully used in pedestrian detection. We have improved the initial setting method for the weights of the training samples in the AdaBoost classifier, so that the selected weak classifier would be able to focus on a higher detection rate other than accuracy. The proposed cascade classifier can automatically select the AdaBoost classifier or SVM to construct a cascade classifier according to the training samples, so as to effectively improve classification performance and reduce training time. In order to verify our proposed method, we have used our extracted database of pedestrian training samples, PETs database, INRIA database and MIT database. This completed the pedestrian detection experiment whose result was compared to those of the cascade-AdaBoost classifier and support vector machine. The result of the experiment showed that in a simple environment involving campus experimental image and PETs database, both our cascade classifier and other classifiers can attain good results, while in a complicated environment involving INRA and MIT database experiments, our cascade classifier had better results than those of other classifiers.     

一种改进的多项式核支持向量机文本分类器 *

多项式核函数由于具有良好的泛化性能而受到重视,并被研究用于文本分类问题。针对多项式核学习 能力较差的缺点,将学习能力较强的条件正定核与多项式核构成一个混合核函数作为改进的多项式核。实验表 明,改进的多项式核SVM文本分类器的分类效果要好于多项式核SVM文本分类器。     

采用加性核SVM的二尖瓣瓣根识别

目的 超声心动图中图像噪声严重、分辨率低以及成像范围有限等缺点,导致二尖瓣（MA）瓣根的识别非常困难,采用加性核函数的支持向量机（SVM）分类器识别超声心动图中的二尖瓣瓣根位置。方法 心脏二尖瓣瓣根位置对于心室的分割、心脏建模以及多模态配准很重要。本文提出将加性核支撑向量机分类算法并结合一个局部的上下文特征用于二尖瓣瓣根的识别。主要创新点有：1）利用图像中的上下文特征提取二尖瓣瓣根部特征;2）应用最小加性核的SVM分类器快速识别二尖瓣瓣根的候选点;3）对于候选点应用加权模板,计算候选点的加权密度;4）在加权密度场中,采用二分查找算法,自适应确定一个阈值,剔除二尖瓣瓣根的错分点,确定二尖瓣瓣根的位置。结果 本文算法在10个儿科病人的超声四腔心动图上测试,和手动选出的二尖瓣瓣根点相比,平均误差控制在1.52±2.25个像素。结论 采用加性核函数的SVM分类器能够快速、准确地识别二尖瓣瓣根点。     

A robust video text detection approach using SVM

A new method for detecting text in video images is proposed in this article. Variations in background complexity, font size and color, make detecting text regions in video images a difficult task. A pyramidal scheme is utilized to solve these problems. First, two downsized images are generated by bilinear interpolation from the original image. Then, the gradient difference of each pixel is calculated for three differently sized images, including the original one. Next, three K-means clustering procedures are applied to separate all the pixels of the three gradient difference images into two clusters: text and non-text, separately. The K-means clustering results are then combined to form the text regions. Thereafter, projection profile analysis is applied to the Sobel edge map of each text region to determine the boundaries of candidate text regions. Finally, we identify text candidates through two verification phases. In the first verification phase, we verify the geometrical properties and texture of each text candidate. In the second verification phase, statistical characteristics of the text candidate are computed using a discrete wavelet transform, and then the principal component analysis is further used to reduce the number of dimensions of these features. Next, the optimal decision function of the support vector machine, obtained by sequential minimal optimization, is applied to determine whether the text candidates contain texts or not.     

Identification of EMG signals using discriminant analysis and SVM classifier

The electromyography (EMG) signal is a bioelectrical signal variation, generated in muscles during voluntary or involuntary muscle activities. The muscle activities such as contraction or relaxation are always controlled by the nervous system. The EMG signal is a complicated biomedical signal due to anatomical/physiological properties of the muscles and its noisy environment. In this paper, a classification technique is proposed to classify signals required for a prosperous arm prosthesis control by using surface EMG signals. This work uses recorded EMG signals generated by biceps and triceps muscles for four different movements. Each signal has one single pattern and it is essential to separate and classify these patterns properly. Discriminant analysis and support vector machine (SVM) classifier have been used to classify four different arm movement signals. Prior to classification, proper feature vectors are derived from the signal. The feature vectors are generated by using mean absolute value (MAV). These feature vectors are provided as inputs to the identification/classification system. Discriminant analysis using five different approaches, classification accuracy rates achieved from very good (98%) to poor (96%) by using 10-fold cross validation. SVM classifier gives a very good average accuracy rate (99%) for four movements with the classification error rate 1%. Correct classification rates of the applied techniques are very high which can be used to classify EMG signals for prosperous arm prosthesis control studies.     

一种基于FPGA的IPv6网络入侵检测系统

IPv6技术将是下一代互联网的核心技术,对IPv6网络入侵检测系统的研究与下一代网络的安全问题息息相关。在分析了现有网络安全系统的基本原理和IPv6网络主要特点之后,提出了一种IPv6网络入侵检测系统的框架,并着重分析了采用现场可编程门阵列（FPGA）来实现模式匹配的方法。     

Improvement of new automatic differential fuzzy clustering using SVM classifier for microarray analysis

In recent year, the problem of clustering in microarray data has been gaining significant attention. However most of the clustering methods attempt to find the group of genes where the number of cluster is known a priori. This fact motivated us to develop a new real-coded improved differential evolution based automatic fuzzy clustering algorithm which automatically evolves the number of clusters as well as the proper partitioning of a gene expression data set. To improve the result further, the clustering method is integrated with a support vector machine, a well-known technique for supervised learning. A fraction of the gene expression data points selected from different clusters based on their proximity to the respective centers, is used for training the SVM. The clustering assignments of the remaining gene expression data points are thereafter determined using the trained classifier. The performance of the proposed clustering technique has been demonstrated on five gene expression data sets by comparing it with the differential evolution based automatic fuzzy clustering, variable length genetic algorithm based fuzzy clustering and well known Fuzzy C-Means algorithm. Statistical significance test has been carried out to establish the statistical superiority of the proposed clustering approach. Biological significance test has also been carried out using a web based gene annotation tool to show that the proposed method is able to produce biologically relevant clusters of genes. The processed data sets and the matlab version of the software are available at http://bio.icm.edu.pl/～darman/IDEAFC-SVM/.     

A random-forests-based classifier using class association rules and its application to an intrusion detection system

With the rapid developments of network technology, devices connected to the network in a variety of fields have increased, and then, network security has become more important. Rule-based classification for intrusion detection is useful, because it is not only easily understood by humans, but also accurate for the classification of new patterns. Genetic network programming (GNP) is one of the rule-mining techniques as well as the evolutionary-optimization techniques. It can extract rules efficiently even from an enormous database, but still needs more accuracy and stability for practical use. This paper describes a classification system with random forests, employing weighted majority vote in the classification to enhance its performance. For the performance evaluation, NSL-KDD (Network Security Laboratory-Knowledge Discovery and Data Mining) data set is used and the proposed method is compared with the conventional methods, including other machine-learning techniques (Random forests, SVM, J4.8) in terms of the accuracy and false positive rate.     

Design of a real time automatic speech recognition system using Modified One Against All SVM classifier

In this paper, Texas Instruments TMS320C6713 DSP based real-time speech recognition system using Modified One Against All Support Vector Machine (SVM) classifier is proposed. The major contributions of this paper are: the study and evaluation of the performance of the classifier using three feature extraction techniques and proposal for minimizing the computation time for the classifier. From this study, it is found that the recognition accuracies of 93.33%, 98.67% and 96.67% are achieved for the classifier using Mel Frequency Cepstral Coefficients (MFCC) features, zerocrossing (ZC) and zerocrossing with peak amplitude (ZCPA) features respectively. To reduce the computation time required for the systems, two techniques – one using optimum threshold technique for the SVM classifier and another using linear assembly are proposed. The ZC based system requires the least computation time and the above techniques reduce the execution time by a factor of 6.56 and 5.95 respectively. For the purpose of comparison, the speech recognition system is also implemented using Altera Cyclone II FPGA with Nios II soft processor and custom instructions. Of the two approaches, the DSP approach requires 87.40% less number of clock cycles. Custom design of the recognition system on the FPGA without using the soft-core processor would have resulted in less computational complexity. The proposed classifier is also found to reduce the number of support vectors by a factor of 1.12–3.73 when applied to speaker identification and isolated letter recognition problems. The techniques proposed here can be adapted for various other SVM based pattern recognition systems.