To reduce the influences of outliers on support vector machine(SVM) classification problem,a new tangent loss function was constructed.Since the tangent loss function was not smooth in some interval,a smoothing functi...To reduce the influences of outliers on support vector machine(SVM) classification problem,a new tangent loss function was constructed.Since the tangent loss function was not smooth in some interval,a smoothing function was used to approximate it in this interval.According to this loss function,the corresponding tangent SVM(TSVM) was got.The experimental results show that TSVM is less sensitive to outliers than SVM.So the proposed new loss function and TSVM are both effective.展开更多
This paper provides a detailed mathematical model governing the operation of feedforward neural networks(FFNN)and derives the backpropagation formulation utilized in the training process.Network protection systems mus...This paper provides a detailed mathematical model governing the operation of feedforward neural networks(FFNN)and derives the backpropagation formulation utilized in the training process.Network protection systems must ensure secure access to the Internet,reliability of network services,consistency of applications,safeguarding of stored information,and data integrity while in transit across networks.The paper reports on the application of neural networks(NN)and deep learning(DL)analytics to the detection of network traffic anomalies,including network intrusions,and the timely prevention and mitigation of cyberattacks.Among the most prevalent cyber threats are R2L,U2L,probe,and distributed denial of service(DDoS),which disrupt normal network operations and interrupt vital services.Robust detection of the early stage of cyberattack phenomena and the consistent blockade of attack traffic including DDoS network packets comprise preventive measures that constitute effective means for cyber defense.The proposed system is an NN that utilizes a set of thirty-eight packet features for the real-time binary classification of network traffic.The NN system is trained with a dataset containing the packet attributes of a mix of normal and attack traffic.In this study,the KDD99 dataset,which was prepared by the MIT Lincoln Lab for the 1998 DARPA Intrusion Detection Evaluation Program,was used to train the NN and test its performance.It has been shown that an NN comprised of one or two hidden layers,with each layer containing a few neural nodes,can be trained to detect attack packets with concurrently high precision and recall.展开更多
In lung CT images, the edge of a tumor is frequently fuzzy because of the complex relationship between tumors and tissues, especially in cases that the tumor adheres to the chest and lung in the pathology area. This m...In lung CT images, the edge of a tumor is frequently fuzzy because of the complex relationship between tumors and tissues, especially in cases that the tumor adheres to the chest and lung in the pathology area. This makes the tumor segmentation more difficult. In order to segment tumors in lung CT images accurately, a method based on support vector machine(SVM) and improved level set model is proposed. Firstly, the image is divided into several block units; then the texture, gray and shape features of each block are extracted to construct eigenvector and then the SVM classifier is trained to detect suspicious lung lesion areas; finally, the suspicious edge is extracted as the initial contour after optimizing lesion areas, and the complete tumor segmentation can be obtained by level set model modified with morphological gradient. Experimental results show that this method can efficiently and fast segment the tumors from complex lung CT images with higher accuracy.展开更多
基金National Natural Science Foundations of China(Nos.61272015,11201123)the Scientific Research Foundation for the Doctor of Henan University of Science&Technology,China(No.09001476)School Foundation of Henan University of Science&Technology,China(No.2012QN011)
文摘To reduce the influences of outliers on support vector machine(SVM) classification problem,a new tangent loss function was constructed.Since the tangent loss function was not smooth in some interval,a smoothing function was used to approximate it in this interval.According to this loss function,the corresponding tangent SVM(TSVM) was got.The experimental results show that TSVM is less sensitive to outliers than SVM.So the proposed new loss function and TSVM are both effective.
基金Kaveh Heidary’s research project was partially funded by Quantum ResearchInternational Inc.through Contract QRI-SC-20-105.https://www.quantum-intl.com/.
文摘This paper provides a detailed mathematical model governing the operation of feedforward neural networks(FFNN)and derives the backpropagation formulation utilized in the training process.Network protection systems must ensure secure access to the Internet,reliability of network services,consistency of applications,safeguarding of stored information,and data integrity while in transit across networks.The paper reports on the application of neural networks(NN)and deep learning(DL)analytics to the detection of network traffic anomalies,including network intrusions,and the timely prevention and mitigation of cyberattacks.Among the most prevalent cyber threats are R2L,U2L,probe,and distributed denial of service(DDoS),which disrupt normal network operations and interrupt vital services.Robust detection of the early stage of cyberattack phenomena and the consistent blockade of attack traffic including DDoS network packets comprise preventive measures that constitute effective means for cyber defense.The proposed system is an NN that utilizes a set of thirty-eight packet features for the real-time binary classification of network traffic.The NN system is trained with a dataset containing the packet attributes of a mix of normal and attack traffic.In this study,the KDD99 dataset,which was prepared by the MIT Lincoln Lab for the 1998 DARPA Intrusion Detection Evaluation Program,was used to train the NN and test its performance.It has been shown that an NN comprised of one or two hidden layers,with each layer containing a few neural nodes,can be trained to detect attack packets with concurrently high precision and recall.
基金supported by the National Natural Science Foundation of China(No.61261029)Jinchuan Company Research Foundation(No.JCYY2013009)
文摘In lung CT images, the edge of a tumor is frequently fuzzy because of the complex relationship between tumors and tissues, especially in cases that the tumor adheres to the chest and lung in the pathology area. This makes the tumor segmentation more difficult. In order to segment tumors in lung CT images accurately, a method based on support vector machine(SVM) and improved level set model is proposed. Firstly, the image is divided into several block units; then the texture, gray and shape features of each block are extracted to construct eigenvector and then the SVM classifier is trained to detect suspicious lung lesion areas; finally, the suspicious edge is extracted as the initial contour after optimizing lesion areas, and the complete tumor segmentation can be obtained by level set model modified with morphological gradient. Experimental results show that this method can efficiently and fast segment the tumors from complex lung CT images with higher accuracy.
