Automated classification of medical images with high accuracy is crucial when dealing with human life. In this paper, Discrete Wavelet Transform (DWT) based classification of Magnetic Resonance Images (MRI) of the brain is presented. The given input images are de-noised using a median filter in the preprocessing stage. Then, the de-noised images are given as inputs to the wavelet transform. The wavelet transform is used for feature extraction purpose. Most transformation techniques produce coefficient values with their dimension same as the original image. Further processing of the coefficient values must be applied to extract the image feature vectors. Predefined families of wavelets such as Daubechies (db8), Symlets (sym8) and Biorthogonal (bio3.7) are used. From that energy information's are extracted and provided as input to the recognition or classification stage. Finally, the brain images are classified by Support Vector Machine (SVM) classifier whether it is normal or abnormal. Results show that db8 filter provides higher accuracy than other wavelets.
Precise rainfall forecasting is a common challenge across the globe in meteorological predictions. As rainfall forecasting involves rather complex dynamic parameters, an increasing demand for novel approaches to improve the forecasting accuracy has heightened. Recently, Rough Set Theory (RST) has attracted a wide variety of scientific applications and is extensively adopted in decision support systems. Although there are several weather prediction techniques in the existing literature, identifying significant input for modelling effective rainfall prediction is not addressed in the present mechanisms. Therefore, this investigation has examined the feasibility of using rough set based feature selection and data mining methods, namely Naïve Bayes (NB), Bayesian Logistic Regression (BLR), Multi-Layer Perceptron (MLP), J48, Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM), to forecast rainfall. Feature selection or reduction process is a process of identifying a significant feature subset, in which the generated subset must characterize the information system as a complete feature set. This paper introduces a novel rough set based Maximum Frequency Weighted (MFW) feature reduction technique for finding an effective feature subset for modelling an efficient rainfall forecast system. The experimental analysis and the results indicate substantial improvements of prediction models when trained using the selected feature subset. CART and J48 classifiers have achieved an improved accuracy of 83.42% and 89.72%, respectively. From the experimental study, relative humidity2 (a4) and solar radiation (a6) have been identified as the effective parameters for modelling rainfall prediction.
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