scholarly journals A Hybrid Model for Bankruptcy prediction Using Genetic Algorithm,Fuzzy C-Means and Mars

2011 ◽  
Vol 2 (1) ◽  
pp. 12-24 ◽  
Author(s):  
A Martin ◽  
V Gayathri ◽  
G Saranya ◽  
P Gayathri ◽  
Prasanna Venkatesan
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Model ◽  
Bankruptcy Prediction ◽  
Fuzzy C Means

scholarly journals Research and Application of a New Hybrid Forecasting Model Based on Genetic Algorithm Optimization: A Case Study of Shandong Wind Farm in China

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Ping Jiang ◽  
Xiaofei Li ◽  
Yao Dong
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Hybrid Model ◽  
Hybrid System ◽  
Wind Farm ◽  
Evaluation Criteria ◽  
Renewable Resource ◽  
Original Data ◽  
Determine Parameter ◽  
Forecasting Method

With the increasing depletion of fossil fuel and serious destruction of environment, wind power, as a kind of clean and renewable resource, is more and more connected to the power system and plays a crucial role in power dispatch of hybrid system. Thus, it is necessary to forecast wind speed accurately for the operation of wind farm in hybrid system. In this paper, we propose a hybrid model called EEMD-GA-FAC/SAC to forecast wind speed. First, the Ensemble empirical mode decomposition (EEMD) can be applied to eliminate the noise of the original data. After data preprocessing, first-order adaptive coefficient forecasting method (FAC) or second-order adaptive coefficient forecasting method (SAC) can be employed to do forecast. It is significant to select optimal parameters for an effective model. Thus, genetic algorithm (GA) is used to determine parameter of the hybrid model. In order to verify the validity of the proposed model, every ten-minute wind speed data from three observation sites in Shandong Peninsula of China and several error evaluation criteria can be collected. Through comparing with traditional BP, ARIMA, FAC, and SAC model, the experimental results show that the proposed hybrid model EEMD-GA-FAC/SAC has the best forecasting performance.

scholarly journals Hybrid model using logit and nonparametric methods for predicting micro-entity failure

2016 ◽  
Vol 13 (3) ◽  
pp. 35-46 ◽  
Author(s):  
A. Blanco-Oliver ◽  
A. Irimia-Dieguez ◽  
M.D. Oliver-Alfonso ◽  
M.J. Vázquez-Cueto
Keyword(s):  
Logistic Regression ◽  
Hybrid Model ◽  
Rough Set ◽  
Multilayer Perceptron ◽  
Predictive Power ◽  
Bankruptcy Prediction ◽  
Parametric Methods ◽  
Macroeconomic Variables ◽  
Accuracy Performance ◽  
Non Parametric

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

Fuzzy C-Means, ANFIS and Genetic Algorithm for Segmenting Astrocytoma –A Type of Brain Tumor

2014 ◽  
Vol 3 (1) ◽  
pp. 16 ◽  
Author(s):  
Minakshi Sharma ◽  
Saourabh Mukherjee
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Fuzzy Inference ◽  
Research Work ◽  
Diagnostic Technique ◽  
Clustering Methods ◽  
Inference System ◽  
Fuzzy C Means ◽  
Soft Computing Techniques

<p>Imaging plays an important role in medical field like medical diagnosis, treatment planning and patient follow up. Image segmentation is the backbone process to accomplish these tasks by dividing an image in to meaningful parts which share similar properties.  Medical Resonance Imaging (MRI) is primary diagnostic technique to do image segmentation. There are several techniques proposed for image segmentation of different parts of body like Region growing, Thresholding, Clustering methods and Soft computing techniques  (Fuzzy Logic, Neural Network, Genetic Algorithm).The proposed research work uses Grey level Co-occurrence Matrix (GLCM) for texture feature extraction, ANFIS(Adaptive Network Fuzzy inference System) plus  Genetic Algorithm for feature selection and FCM(Fuzzy C-Means) for segmentation of  Astrocytoma (Brain Tumor) with all four Grades. The comparative study between FCM, FCM plus K-mean, Genetic Algorithm, ANFIS and proposed technique shows improved Accuracy, Sensitivity and Specificity.</p>

Prediction of air-pollutant concentrations using hybrid model of regression and genetic algorithm

2020 ◽  
Vol 38 (5) ◽  
pp. 5909-5919
Author(s):  
Sanjiban Sekhar Roy ◽  
Nicolae Paraschiv ◽  
Mihaela Popa ◽  
Ramona Lile ◽  
Ishan Naktode
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Model ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Index fund optimization using a hybrid model: genetic algorithm and mixed-integer nonlinear programming

2019 ◽  
Vol 64 (3) ◽  
pp. 298-309 ◽  
Author(s):  
Juan Díaz ◽  
María Cortés ◽  
Juan Hernández ◽  
Óscar Clavijo ◽  
Carlos Ardila ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Programming ◽  
Hybrid Model ◽  
Mixed Integer Nonlinear Programming ◽  
Mixed Integer ◽  
Integer Nonlinear Programming
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