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The rapid development of automobile industry in China promotes the stable growth of the automotive aftermarket. For optimizing supply chain operations and reducing costs, it is critical for a company to forecast the demands for auto spare parts in the future. This paper proposes an improved Regression-Bayesian-BBNN (RBBPNN) based model to realize the demands forecasting. Compared with a classic ARMA...
A novel Neural Network Based Fuzzy Inference System for financial default forecast will be introduced. A wide range of financial forecasts is known. This method is focusing on the economical default forecast, but the method can be used generally for other financial forecasts as well, for example for calculating the Value at Risk. This hybrid method is combined by two classical methods: the artificial...
DDoS attack is a major Internet security problem-DoS is that lots of clients simultaneously send service requests to certain server on the internet such that this server is too busy to provide normal services for others. Attackers using legitimate packets and often changing package information, so that traditional detection methods based on feature descriptions is difficult to detect it. This paper...
In order to improve the Vehicle Weigh in Motion System with precision, the paper introduces Nerve Net Algorithm to error analysis. The article sets up a neural network model by determining the neural network input and output variables. Then, the function is defined by net training on MATLAB software. Finally through the experimental verification of Nerve Net Algorithm improving Weigh in Motion System...
Vietnamese is the national and official language in Vietnam. In the Vietnamese writing system, most of vowels have diacritical signs. With this special feature, secret information can be embedded into Vietnamese documents by slightly shifting up/down and left/right these signs. The embedded documents are nearly the same as the original ones, and so the reader could not find any differences by eyes...
This paper presents a new approach for breast cancer diagnosis using a combination of an Adaptive Network based Fuzzy Inference System (ANFIS) and the Information Gain method. In this approach, the ANFIS is to build an input-output mapping using both human knowledge and machine learning ability and the information gain method is to reduce the number of input features to ANFIS. An experimental result...
This paper provides a method of discriminate analysis based on artificial neural network (ANN). 2-Class and multi-class discriminant analysis are separately discuss using Back Propagation network. The results of our study indicate that discriminate analysis based on ANN could classify the observation more accurately than the traditional methods.
The survival and development of human being are seriously threatened by the decrease of nature forest in all over the world. As a result, it has been widely focused on the intensive farming of plantation and scientific utilization of wood resource. The characteristics of regression analysis method, time series method and neural network method commonly used in wood quality forecast were analyzed. The...
The paper deals with the design and development of classifiers and, in particular, with the problem of selecting the most relevant input variables to be used as inputs for classification purpose in practical applications. In many real problems the selection of input variables is a very important task: often real datasets used for developing a classifier contain a high number of inputs but no a priori...
This paper presents a new approach for breast cancer detection based on Hierarchical Fuzzy Neural Network (HFNN). Generally in formal fuzzy neural networks (FNN), increasing the number of inputs, causes exponential growth in the number of parameters of the FNN system. This phenomenon named as "curse of dimensionality". An approach to deal with this problem is to use the hierarchical fuzzy...
In this study, an investigation over digestive diseases has been done in which the sound acts as a detector medium. Pursue to the processing, the extracted signal in wavelet domain is registered. Genetic Algorithm (G.A) with binary chromosomes is used for feature selection to reduce the dimensions of feature space. Classification of digestive diseases was carried out by fuzzy neural network and fuzzy...
The county level of basic public services analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of basic public services data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of basic public services. The method...
In this paper, a new kind of three-stage neural network was developed to identify the sorts of the biological surface. The visible spectrum (from 380nm to 780nm) of the micro areas with some specks on the surface of the apples was measured with the self-made fiber sensor spectrometer. To sort the apples, A kind of BP-ANN with single hidden layer was devised to identify the characteristics on the biological...
This paper deals with the advanced and developed methodology know for cancer multi classification using an Extreme Learning Machine (ELM) for microarray gene expression cancer diagnosis, this used for directing multicategory classification problems in the cancer diagnosis area. ELM avoids problems like local minima; improper learning rate and over fitting commonly faced by iterative learning methods...
The data related to our life experiences is called lifelog, which can easily be collected with mobile electronic devices in recent years. Although lifelog research has been conducted for a long time, practical applications such as a memory assistant system have not been fully developed yet. This is mainly due to the lack of methods to structurize the lifelog data efficiently. In our research, we developed...
Accurate short term load forecasting (STLF) is a prerequisite for proper generation scheduling and reliable operation of power utilities. Conventional methods of STLF, suffer from the disadvantages such as lack of ability to accurately model the weather parameters affecting the load, lack of robustness for representing weekends and public holidays and of being computation intensive. Application of...
Several adaptation approaches, such as policy-based and reinforcement learning, have been devised to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable for distributed real-time and embedded (DRE) systems, however, which have stringent accuracy, timeliness, and development complexity requirements. Supervised...
Accurate land use/cover (LUC) classification data derived from remotely sensed data are very important for land use planning and environment sustainable development. Traditionally, statistical classifiers are often used to generate these data, but these classifiers rely on assumptions that may limit their utilities for many datasets. Conversely, artificial neural network (ANN) and decision tree (DT)...
Supplier performance evaluation is a key issue of supply chain and is complicated since a variety of attributes must be considered. In this article, an integrated DEA-NN model is proposed. By taking advantages from both data envelopment analysis (DEA) and neural networks (NN), an application of the integrated DEA-NN method is given. The results indicate that the method is effective and applicable.
Membrane proteins play an important role in many biological processes and are attractive drug targets. In this study, membrane proteins are classified using two feature extraction and several classification strategies. The first feature extraction strategy is pseudo amino acid (PseAA) composition; utilizing hydrophobicity and hydrophilicity for reflecting the sequence order effects, while the second...
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