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With financial globalization, the rapid development of financial derivatives and the complexity of banks management, operational risk measurement and management in commercial bank management is becoming increasingly important. How to effectively predict, control and prevent operational risk in commercial banks have become an important issue. Using BP neural network model to predict the risk has its...
This work studies the use of Particle Swarm Optimization (PSO) as a classification technique. Beyond assessing classification accuracy, it investigates the following questions: does PSO present limitations for high dimensional application domains? Is it less efficient for multi class problems? To answer the questions, an experimental set up was realized that uses three high dimensional data sets....
This paper presents a hybrid intelligent method to design Morphological-Rank-Linear (MRL) perceptrons to solve the Software Development Cost Estimation (SDCE) problem. The proposed method uses a modified genetic algorithm (MGA) to determine the best particular features to improve the MRL perceptron performance, as well as its initial parameters. Furthermore, for each individual of MGA, a gradient...
Face to the actual demand of current society on applicational personnel training, according to the characteristics of the non-computer major students in private colleges and the personnel training objectives, we analyze in depth the problem in the teaching of current private colleges non-computer professional database course. Combined with years of teaching reform and a detailed study of the syllabus,...
In this paper a novel view-invariant movement recognition method is presented. A multi-camera setup is used to capture the movement from different observation angles. Identification of the position of each camera with respect to the subject's body is achieved by a procedure based on morphological operations and the proportions of the human body. Binary body masks from frames of all cameras, consistently...
This paper proposes a two-step prototype-face-based scheme of hallucinating the high-resolution detail of a low-resolution input face image. The proposed scheme is mainly composed of two steps: the global estimation step and the local facial-parts refinement step. In the global estimation step, the initial high-resolution face image is hallucinated via a linear combination of the global prototype...
Conventional approaches to image annotation tackle the problem based on the low-level visual information. Considering the importance of the information on the constrained interaction among the objects in a real world scene, contextual information has been utilized to recognize scene and object categories. In this paper, we propose a Bayesian approach to region-based image annotation, which integrates...
In multi-instance multi-label learning (i.e. MIML), each example is not only represented by multiple instances but also associated with multiple labels. Most existing algorithms solve MIML problem via the intuitive way of identifying its equivalence in degenerated version of MIML. However, this identification process may lose useful information encoded in training examples and therefore be harmful...
An application of Parallel Radial Basis Function (PRBF) network model on prediction of chaotic time series is presented in this paper. The PRBF net consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of PRBF is a weighted...
We introduce a new plan repair method for problems cast as Mixed Integer Programs. In order to tackle the inherent complexity of these NP-hard problems, our approach relies on the use of Supervised Learning method for the offline construction of a predictor which takes the problem's parameters as input and infers values for the discrete optimization variables. This way, the online resolution time...
This paper presents an adaptive prediction-regret driven negotiation strategy for bilateral bargaining without modeling opponents, which combines the prediction idea in heuristic method and the regret principle in psychology. Experimental results show that agents that employ this strategy outperform agents that use other strategies previously proposed in the literature.
In this work, we proposed a novel authentication system based on facial features. The proposed method is based on PCA and LDA for feature extraction, these extracted features are combined using wavelet fusion. In this work we use neural networks to classify extracted features of faces. The proposed method consists of six steps: i) Extraction of images from the database, ii) Preprocessing, iii) Feature...
In order to construct the term corpus of physics teaching materials for elementary education, the characters of physics terms were studied, the prediction templates for the unknown terms was built, all kinds of rules for identifying terms was extracted, and the labeling errors of maximum matching algorithm was analyzed, at last, an auto-labeling system was developed. Firstly, this algorithm scans...
In linear space, the classical perceptron algorithm is simple and practical. But when concerning the nonlinear space it is severely confined mainly on its signal layer structure. This paper analyzes the geometry characteristic of solve region in the pattern set, and presents a new algorithm based on the solve region. The new algorithm could find the better solve vector in the solve region on condition...
A pulmonary nodule is the most common sign of lung cancer. The proposed system efficiently predicts lung tumor from Computed Tomography (CT) images through image processing techniques coupled with neural network classification as either benign or malignant. The lung CT image is denoised using non-linear total variation algorithm to remove random noise prevalent in CT images. Optimal thresholding is...
A method for recognizing the emotion states of subjects based on 30 features extracted from their Galvanic Skin Response (GSR) signals was proposed. GSR signals were acquired by means of experiments attended by those subjects. Next the data was normalized with the calm signal of the same subject after being de-noised. Then the normalized data were extracted features before the step of feature selection...
Anomaly detection in computer networks is an actively researched topic in the field of intrusion detection. The Internet Analysis System (IAS) is a software framework which provides passive probes and centralized backend services to collect purely statistical network data in distributed computer networks. This paper presents an empirical evaluation of the IAS data format for detecting anomalies, caused...
The Covering algorithm is proposed by Professor ZhangLing and ZhangBo in the 20th century, which simulates the structure of human learning, building a Constructive Neural Network Learning Model. Covering algorithm has been widely used to solve massive data classification problem, because its performance. The covering classification algorithm has fast learning, high recognition rate, massive data processing...
Meta-search engine uses multiple independent search engines to search information together. In order to search fast and accurately for the professional requirements, this paper developed a meta-search engine oriented the educational professional field. It fully analyzed the web page information, used CC4 neural network algorithm to judge the relevant web pages and optimal rank., and then combined...
The paper achieved the application of rough set-SVM model in the prediction of supply chain performance evaluation. Firstly, the paper rejected redundant factors and extracted key factors by use of rough set theory; then, safe class of supply chain performance evaluation was gained based on the key factors which have achieved with the method of SVM (support vector machines). In the end, result of...
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