Development of a noise prediction model based on advanced fuzzy approaches in typical industrial workrooms

Noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. One of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, advanced fuzzy approaches were employed to develop relatively accurate models for predicting noise in noisy industrial workrooms. The data were collected from 60 industrial embroidery workrooms in the Khorasan Province, East of Iran. The main acoustic and embroidery process features that influence the noise were used to develop prediction models using MATLAB software. Multiple regression technique was also employed and its results were compared with those of fuzzy approaches. Prediction errors of all prediction models based on fuzzy approaches were within the acceptable level [lower than one dB]. However, Neuro-fuzzy model [RMSE=0.53dB and R[2]=0.88] could slightly improve the accuracy of noise prediction compared with generate fuzzy model. Moreover, fuzzy approaches provided more accurate predictions than did regression technique. The developed models based on fuzzy approaches as useful prediction tools give professionals the opportunity to have an optimum decision about the effectiveness of acoustic treatment scenarios in embroidery workrooms

gene selection method for survival prediction in diffuse large B-cell lymphomas patients using 1D discrete wavelet transform

An important aspect of microarray studies includes the prediction of patient survival based on their gene expression profile. To deal with the high dimensionality of this data, use of a dimension reduction procedure along with the survival prediction model is necessary. This study aimed to present a new method based on wavelet transform for survival relevant gene selection. The data included 2042 gene expression measurements from 40 patients with Diffuse Large B-Cell Lymphomas [DLBCL]. The pre-processing gene expression data is decomposed using third level of the 1D discrete wavelet transform. The detail coefficients at levels 1 and 2 are filtered out and expression data reconstructed using the approximation and detailed coefficients at the third level. All the genes are then scored based on the t score. Then genes with the highest scores are selected. By using forward selection method in Cox regression model, significant genes were identified. The results showed wavelet-based gene selection method presents acceptable survival prediction. Using this method, six significant genes were selected. It was indicated the expression of GENE3359X andGENE3968X decreased the survival time, whereas the expression of GENE967X, GENE3980X, GENE3405X andGENE1813X increased the survival time. Wavelet-based gene selection method is a potentially useful tool for the gene selection from microarray data in the context of survival analysis