Catálogo de publicaciones - libros

Hierarchical RBF networks consist of multiple RBF networks assembled in different level or cascade architecture. In this paper, an evolved hierarchical RBF network was employed to detect the breast cancel. For evolving a hierarchical RBF network model, Extended Compact Genetic Programming (ECGP), a tree-structure based evolutionary algorithm and the Differential Evolution (DE) are used to find an optimal detection model. The performance of proposed method was then compared with Flexible Neural Tree (FNT), Neural Network (NN), and RBF Neural Network (RBF-NN) by using the same breast cancer data set. Simulation results show that the obtained hierarchical RBF network model has a fewer number of variables with reduced number of input features and with the high detection accuracy.

Ovarian cancer is a major cause of deaths worldwide. As a result, women are not diagnosed until the cancer has advanced to later stages. Accurate prognosis is required to determine the suitable therapeutic decision. Since abnormalities of hemostasis and increased risk of thrombosis are observed in cancer patient, assay involving hemostatic parameters can be potential prognosis tool. Thus a biological brain-inspired (CLFNN) is proposed, to complement the hemostasis in ovarian cancer prognosis. Experimental results that demonstrate the confluence of hemostasis and CLFNN offers a promising prognosis tool. Apart from superior performance, CLFNN provides interpretable rules to facilitate validation and justification of the system. Besides, CLFNN can be used as a concept validation tool for ovarian cancer prognosis.

Support vector machines (SVMs), originally designed for binary classification, have been applied for multi-class classification, where an effective fusion scheme is required for combining outputs from them and producing a final result. In this work, we propose a novel method in which the SVMs are generated with the one-vs-rest (OVR) scheme and dynamically organized by the naïve Bayes classifiers (NBs). This method might break the ties that frequently occur when working with multi-class classification systems with OVR SVMs. More specifically, we use the Pearson correlation measure to select informative genes and reduce the dimensionality of gene expression profiles when constructing the NBs. The proposed method has been validated on GCM cancer dataset consisting of 14 types of tumors with 16,063 gene expression levels and produced higher accuracy than other methods.

Breast cancer is one of the leading causes of mortality among women, and the early diagnosis is of significant clinical importance. In this paper, we describe several linear fusion strategies, in particular the Majority Vote, Simple Average, Weighted Average, and Perceptron Average, which are used to combine a group of component multilayer perceptrons with optimal architecture for the classification of breast lesions. In our experiments, we utilize the criteria of mean squared error, absolute classification error, relative error ratio, and Receiver Operating Characteristic (ROC) curve to concretely evaluate and compare the performances of the four fusion strategies. The experimental results demonstrate that the Weighted Average and Perceptron Average strategies can achieve better diagnostic performance compared to the Majority Vote and Simple Average methods.

Traditional Chinese Medicine (TCM) is one of the most important complementary and alternative medicines. Due to the subjectivity and fuzziness of diagnosis in TCM, quantitative model or methods are needed to facilitate the popularization of TCM. In this article, a novel quantitative method for syndrome differentiation based on BNs is proposed. First the symptoms are selected by a novel mutual information based symptom selection algorithm (MISS) and then the mapping relationships between the selected symptoms and key elements are constructed. Finally, the corresponding syndromes are output by combining the key elements. The results show that the diagnostic model obtains relative reliable predictions of syndrome, and its average predictive accuracy rate reach 91.68%, which testifies that the method we proposed is feasible and effective and can be expected to be useful in the modernization of TCM.

In intrusion detection systems, sequences of system calls executed by running programs can be used as evidence to detect anomalies. Markov chain is often adopted as the model in the detection systems, in which high-order Markov chain model is well suited for the detection, but as the order of the chain increases, the number of parameters of the model increases exponentially and rapidly becomes too large to be estimated efficiently. In this paper, one-class support vector machines (SVMs) using high-order Markov kernel are adopted as the anomaly detectors. This approach solves the problem of high dimension parameter space. Experiments show that this system can produce good detection performance with low computational overhead.

We developed earlier version of realtime intrusion detection system using emperical kernel map combining least squares SVM(LS-SVM). I consists of two parts. One part is feature extraction by empirical kernel map and the other one is classification by LS-SVM. The main problem of earlier system is that it is not operated realtime because LS-SVM is executed in batch way. In this paper we propose an improved real time intrusion detection system incorporating earlier developed system with incremental LS-SVM. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature feature extraction, classification performance and reducing detection time compared to earlier version of realtime ntrusion detection system.

To make network intrusion detection systems can be used in Gigabit Ethernet, a distributed neural network learning algorithm (DNNL) is put forward to keep up with the increasing network throughput. The main idea of DNNL is splitting the overall traffic into subsets and several sensors learn them in parallel way. The advantage of this method is that the large data set can be split randomly thus reduce the complicacy of the splitting algorithm. The experiments are performed on the KDD’99 Data Set which is a standard intrusion detection benchmark. Comparisons with other approaches on the same benchmark show that DNNL can perform detection with high detection rate.

The data mining techniques used for extracting patterns that represent abnormal network behavior for intrusion detection is an important research area in network security. This paper introduces the concept of gravitation and gravitation field into data classification by utilizing analogical inference, and studied the method to calculate data gravitation. Based on the theoretical model of data gravitation and data gravitation field, the paper presented a new classification model called Data Gravitation based Classifier (DGC). The proposed approach was applied to an Intrusion Detection System (IDS) with 41 inputs (features). Experimental results show that the proposed method was efficient in data classification and suitable for abnormal detection using netowrk processor-based platforms.

In the past a few years, many watermarking approaches have been proposed for solving the copyright protection problems, most of the watermarking schemes employ gray-level images to embed the watermarks, whereas the application to color images is scarce and usually works on the luminous or individual color channel. In this paper, a novel intensity adaptive color image watermarking algorithm based on genetic algorithm (CIWGA) is presented. The adaptive embedding scheme in color image’s three component sub-images’ wavelet coefficients, which belong to texture-active regions, not only improves image quality, but also furthest enhances security and robustness of the watermarked image. Then a novel watermark recovering method is proposed based on neural networks, which enhance the performance of watermark system successfully. The experimental results show that our method is more flexible than traditional methods and successfully fulfills the compromise between robustness and image quality.