Classification of News Articles using Supervised Machine Learning Approach

Today the big challenge for NEWS organization to well organize the news and well categorize the news in automatically no need the data entry people to enter and select the category and then based on the category and its sub-category they will be manually selected and enter the details and then after this the analysis will later on used for different aspects. The news is almost every second used in different sources of media in soft and hard. We use the both sources of the Pakistan News in dual languages English and Urdu both and process them and prepare them for machine learning and based on the Machine learning trained data we build a very effective and efficient model that can predict the title category of the news and category of description of the news. We use different machine learning algorithms and different features extraction finally we build the model using the machine learning algorithm with 89% accuracy with logistic regression.

Rician Noise Removal in Mr Images

This research work addresses a major denoising problem in Magnetic Resonance (MR) Images. Magnetic Resonance Imaging (MRI) is a powerful and e ective di- agnostic tool in basic research, clinical investigation, and disease diagnosis since it provides both chemical and physiological information about the tissue. MR Images are a ected by Rician noise during acquisition phase which decreases the image quality, image analysis and becomes di cult to diagnose it accurately. This thesis is an attempt to suppress low and high categories of Rician noise from MR data in such a manner to enhance the diagnostically relevant image content. Supervised and unsupervisedltering techniques are applied to suppress the Rician noise hence improving its quality for diagnostic process. A new supervisedltering model, based on genetic programming (GP), is proposed that evolves an optimal composite mor- phological supervisedlter (FOCMSF ) by combining the gray-scale mathematical morphological operators. (FOCMSF ) is evolved through evaluating thetness of sev- eral individuals over certain number of generations. The proposed method does not need any prior information about the noise variance. In the domain of unsupervisedltering, three techniques are proposed. These are collaborative techniques based on statistical and fuzzy logic. Fuzzy similarity based non local meanslter (FSNLM) is designed to non-locally search out similar and non-similar regions of a noisy pixel. Fuzzy weights are assigned to these regions on the base of similarity. Then the noisy pixel is replaced with the fuzzy weighted average of these regions. Another hybridlter is proposed that combines FSNLM and local order statisticallters to suppress Rician noise. This hybridlter uses the strengths of non-local and locallters and adaptively calculates the fuzzy weighted estimation of the noisy pixels. Another non local fuzzy weighted Enhanced LMMSE (Linear Minimum Mean Square Estimator) is designed. The aim of this approach is to handle adaptively the low and high levels of variation of Rician noise and to estimate a closed-form of Rician distributed signal. It estimates the noise free pixel value based on similarity of the non-local neighborhood pixels around a window of certain prede ned radius. Similarity is computed using fuzzy logic approach which is served as fuzzy weights in enhanced LMMSE module for accurate estimation of noise free pixel value. The proposed schemes handle the problem with better accuracy than several well knownltering schemes NLM, LMMSE, Wavelet based techniques etc. and therefore can be considered as original contribution of this research work. The pro- posed schemes handle the problem of Rician noise at low and high noise variances on smooth as well as detailed regions where existing methods fail due to multifar- ious nature of this noise. The improved performance of the developedlters are investigated using the standard MRI dataset and its performance is compared with previously proposed state-of-the art methods. Detailed experimentation has been performed using simulated and real datasets based on well known quantitative mea- sures. Comparative analysis demonstrates the superiority of the proposed schemes over the existing techniques.