scholarly journals Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Lukas Falat ◽  
Dusan Marcek ◽  
Maria Durisova
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Decision Making ◽  
Soft Computing ◽  
Clustering Algorithm ◽  
Prediction Models ◽  
Moving Average ◽  
Series Data ◽  
Decision Making Process ◽  
Hybrid Neural Network

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined withK-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm

2017 ◽  
Vol 141 ◽  
pp. 19-26 ◽  
Author(s):  
Zeinab Arabasadi ◽  
Roohallah Alizadehsani ◽  
Mohamad Roshanzamir ◽  
Hossein Moosaei ◽  
Ali Asghar Yarifard
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Decision Making ◽  
Heart Disease ◽  
Disease Detection ◽  
Hybrid Neural Network ◽  
Computer Aided ◽  
Computer Aided Decision

scholarly journals Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

2017 ◽  
Vol 7 (1.1) ◽  
pp. 184
Author(s):  
Rincy Merlin Mathew ◽  
S. Purushothaman ◽  
P. Rajeswari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Band 3 ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Landsat 7 ◽  
Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

scholarly journals Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 668 ◽  
Author(s):  
S. Poornima ◽  
M. Pushpalatha
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Rainfall Prediction ◽  
Long Short Term Memory

Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall.

scholarly journals Would credit scoring work for Islamic finance? A neural network approach

2014 ◽  
Vol 7 (1) ◽  
pp. 112-125 ◽  
Author(s):  
Hussein A. Abdou ◽  
Shaair T. Alam ◽  
James Mulkeen
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Credit Scoring ◽  
Islamic Finance ◽  
Decision Making Process ◽  
Data Set ◽  
Content Type ◽  
Modeling Techniques ◽  
The Uk ◽  
Training Subset

Purpose – This paper aims to distinguish whether the decision-making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit, and highlight significant variables that are crucial in terms of accepting and rejecting applicants, which can further aid the decision-making process. Design/methodology/approach – A real data set of 487 applicants is used consisting of 336 accepted credit applications and 151 rejected credit applications made to an Islamic finance house in the UK. To build the proposed scoring models, the data set is divided into training and hold-out subsets. The training subset is used to build the scoring models, and the hold-out subset is used to test the predictive capabilities of the scoring models. Seventy per cent of the overall applicants will be used for the training subset, and 30 per cent will be used for the testing subset. Three statistical modeling techniques, namely, discriminant analysis, logistic regression (LR) and multilayer perceptron (MP) neural network, are used to build the proposed scoring models. Findings – The findings reveal that the LR model has the highest correct classification (CC) rate in the training subset, whereas MP outperforms other techniques and has the highest CC rate in the hold-out subset. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest misclassification cost above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision-making process. Originality/value – This contribution is the first to apply credit scoring modeling techniques in Islamic finance. Also in building a scoring model, the authors' application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected.

On Transfer Learning for Classifying COVID-19 in Chest X-Ray Images

2020 ◽  
Author(s):  
Elilson Santos ◽  
Lúcio Flavio De Jesus Silva ◽  
Omar Andres Carmona Cortes
Keyword(s):  
Neural Network ◽  
Infectious Disease ◽  
Decision Making ◽  
Severe Acute Respiratory Syndrome ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Decision Making Process ◽  
Network Architectures ◽  
X Ray ◽  
Chest X Ray

COVID-19 is an exceptionally infectious disease caused by severe acute respiratory syndrome. The illness has spread itself worldwide rapidly and can lead to death only in a few days. In this context, investigating fast ways of detection that help physicians in the decision-making process is essential to help in the task of saving lives. This work investigates fourteen convolutional neural network architectures using transfer learning. We used a database composed of 2,928 x-ray images divided into three classes: Normal, COVID-19, and Viral Pneumonia. Results showed that DenseNet169 presented the best results regarding classification reaching a mean accuracy of 94%, a precision of 97.6%, a recall of 95.6%, and an F1-score of 96,1%, approximately.

Safe Ship’s Control in a Fuzzy Environment Using a Genetic Algorithm

2011 ◽  
Vol 180 ◽  
pp. 70-75 ◽  
Author(s):  
Mostefa Mohamed Seghir
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Decision Making Process ◽  
Fuzzy Environment ◽  
Ship Control ◽  
Marine Accidents

Marine accidents occurring nowadays testify to the fact that systems enhancing the level of navigational safety are indispensable. This paper describes a method of safe ship control in the collision situation in a fuzzy environment based on a genetic algorithm. The optimal safe ship trajectory in a collision situation is presented as multistage decision-making process. The results have been discussed.

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