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This paper presents a decision support system for classification of hotel guests in the terms of additional spending. The research is conducted on three stars medium-sized hotel. Guests are classified on arrival, during check-in, in one of the two groups: low spending group or high spending group. A low spending group consists of visitors that are anticipated to spend less than 25 Euros per day for...
Forecasting the returns of stock markets is gaining importance nowadays in finance. For this aim, in the last decade, Artificial Neural Networks (ANN) have been widely used to forecast stock market movements. In Baltic countries, artificial neural networks are not commonly used in predicting financial failures. This study aims using artificial neural networks to predict OMX Baltic Benchmark GI (OMXBBGI)...
This work presents the development and evaluation of a new scheme based on Artificial Neural Network (ANN) for fault detection and fault location in distribution systems with distributed generator. Two different ANNs are used, where the first one is able to detect which part of the distribution system the fault occurred and the second one is able to precisely locate the fault along the faulty line...
At present, the researches on credit risk analysis mainly focus on commercial bank loan or consumer credit risk, and there is little research about the credit risk of rural credit cooperatives. The purpose of this paper is to evaluate credit risk for the rural credit cooperatives using artificial neural network model. We establish credit risk assessment index system for rural credit cooperatives....
Support vector machines (SVMs) are promising methods for the prediction of the financial time-series because they use a risk function, consisting of an empirical error and a regularized term, which is derived from the structural risk minimization principle. This study applies SVM for predicting the stock price index. In addition, this study examines the feasibility of the applying SVM in financial...
This article introduces a novel hardware friendly multi-layer Winner-Take-All (WTA) architecture using neurons with nonlinear dendrites and binary synapses. The network is trained by an unsupervised spike based learning rule that modifies the network connections. Inspired by the multi-layer models of human visual cortex, the proposed architecture contains multiple layers of neurons. We show that if...
The forecast of Singapore condominium prices is important for potential buyers to make informed decisions. This paper applies two algorithms to predict Singapore housing market and to compares the predictive performance of artificial neural network (ANN) model, i.e., the multilayer perceptron, with autoregressive integrated moving average (ARIMA) model. The more superior model is used to predict the...
Tax audit has vital influence on improving professional quality of tax team, impartial law enforcement and construction of a clean government. View of the complexity of performance evaluation of tax audit, this paper established the performance evaluation model of tax audit to select the various influential factors via gray-relation analysis. Based on artificial neural network, it built the performance...
Prediction of stock market indices is an interesting and challenging research problem in financial data mining area because movement of stock indices are nonlinear and they are dependent upon different constitutional and extraneous aspects. In this paper we come up with the practice of different techniques of Artificial Neural Network (ANN) in stock market prediction. Here we have selected Multilayer...
Artificial Neural Networks (ANNs) play a vital role in the medical field in solving various health problems like estimating the risk of cardiovascular diseases. The article concerns the process of developing ANNs for estimating the risk of arterial hypertension. ANNs proposed in this article use anthropometrical predictors, easy to control for everybody at home without special equipment. In the article...
Recently, an efficient learning algorithm called extreme learning machine (ELM) has been proposed for training of single hidden layer feed forward neural networks (SLFNs). ELM has shown good generalization performances for many real applications with an extremely fast learning speed. This study proposes a computational efficient functional link artificial neural network (CEFLANN) trained with ELM...
This work aims to describe the implementation of a renewable energy based Microgrid test site facility at the University of Genova (Italy) and to depict the advanced functionalities there implemented within the national supported project Smartgen. The developed advanced Distribution Management System (DMS) is based on a Supervisory Control And Data Acquisition (SCADA) system for the remote monitoring...
Development of intelligent decision-making systems for complex problems, such as land covers classification of hyperspectral remote sensing (HSRS) images, requires efficient interpretation of available information through conceptual rather than numerical level. Granular neural network (GNN) in combination with the granular representation of information using linguistic terms is one such system. GNN...
In the last decade we have witnessed a huge increase of interest in data stream learning algorithms. A stream is an ordered sequence of data records. It is characterized by properties such as the potentially infinite and rapid flow of instances. However, a property that is common to various application domains and is frequently disregarded is the very high fluctuating data rates. In domains with fluctuating...
This paper investigates the use of support vector machine (SVM) to forecast hourly solar irradiance for a tropical country. The hourly irradiance data was obtained from Sepang Malaysia, recorded throughout 2011. The data is converted into corresponding clearness index values to facilitate model convergence. The forecast is tested against the standard multilayer perceptron (MLP) technique and persistence...
A novel concept, uniqueness logic represented via decimal numbers (UL-D), is proposed and defined in this paper. Aiming at achieving the UL-D, we construct a neural network (i.e., NN) based on weights-and-structure-determination algorithm (i.e., the resultant WASD-NN). Differing from the back-propagation neural network (BP-NN) adjusting weights by lengthy iterative process and being unable to acquire...
Modern technologies such as DNA microarray have been developed to study the transcriptome of cancer cells. It has been used in many studies for tumor classification and of identification of marker genes associated with cancer. However, this technique often suffers the ‘curse of dimensionality’. A general approach to overcome this setback is to perform feature selection technique prior to classification...
This paper focuses on the problem of risk assessment method for large scale sports events which is an important problem in modern sports management. The index system for large scale sports events risk assessment is proposed in advance, which is made up of three categories: 1) risk before match, 2) risk in match and 3) risk after match. Particularly, eighteen influencing factors are design which can...
This paper proposes the development of a Neural Network (NN) model for the prediction of the F2 layer critical frequency (foF2) at the magnetic equator over Chumphon (10.72°N, 99.37°E, dip angle 3.3°N), Thailand and then compared with the IRI model and the experimental ones. The feed forward network with backpropagation algorithm has been developed for predicting the foF2 values. The NN is trained...
The goal of ensemble construction with several classifiers is to achieve better generalization ability over individual classifiers. An ensemble method produces diverse classifiers and combines their decisions for ensemble's decision. A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. This study investigates a decision...
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