Data Mining Practical Machine Learning Tools and Techniques

Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

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Social Media and Fake News in the 2016 Election

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

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Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Brain is an important part of human body. Many complex human body functions are controlled by brain. Brain imaging is a widely applicable method for diagnosing many brain abnormalities like brain tumor, stroke, paralysis etc. Magnetic Resonance Imaging (MRI) is a recently emerged method for brain imaging. Two major limitations of brain MR Images are low contrast image and presence of skull region in the image. So before detailed analysis of an image, as a preprocessing stage these two problems must be resolved. In this study we propose a method for MR Image contrast enhancement and skull stripping based on the Morphological image processing technique. The proposed method works on T1, T2 and FLAIR axial images. Experimental results show that the proposed method efficiently works for enhancing and skull removal of brain MR Images.

Morphology Based Enhancement and Skull Stripping of MRI Brain Images

Brain is the master and commanding organ of human body. Human brain is affected by many dangerous diseases. Brain tumor or neoplasm is the abnormal growth of tissues in the brain and surrounding regions. MRI is one of the method used for brain tumor diagnosis. Many algorithms are proposed for the automatic extraction of brain tumor tissues from MR brain images. Fuzzy c-Means (FCM) clustering and watershed algorithm are the two commonly used methods for brain tumor extraction. In this paper we implemented the improved version of fuzzy c-Means clustering and watershed algorithm. In fuzzy c-Means clustering we proposed an effective method for the initial centroid selection based on histogram calculation and in watershed algorithm we proposed an atlas based marker detection method for avoiding the over-segmentation problem. Before applying the segmentation algorithms as a pre-processing stage we performed three operations-noise removal, skull stripping and contrast enhancement. We achieved an accuracy of 88.91 and 81.56 of Dice and Tanimoto coefficients for the improved FCM clustering and an accuracy of 93.13 and 88.64 of Dice and Tanimoto coefficients for the improved watershed algorithm.

Brain tumor segmentation from MR brain images using improved fuzzy c-means clustering and watershed algorithm

Human brain is the most complex and mysterious part of human body Many complex functions are controlled by brain Brain imaging is a widely applicable method for diagnosing many brain abnormalities such as brain tumor, stroke, paralysis etc Magnetic Resonance Imaging (MRI) is one of the methods used for brain imaging It is used for analysing internal structures in detail Brain tumor is an abnormal mass of tissue in which cells grow and multiply uncontrollably, seemingly unchecked by the mechanisms that control normal cells The aim of this paper is to extract tumor region from the brain MRI image using watershed algorithm based on different feature combinations such as colour, edge, orientation and texture The results are compared with the ground truth images Here we used marker based watershed algorithm for extracting tumored region and Dice and Tanimoto coefficients are used for comparison of the results The method proposed here is found to be producing a promising result

Brain tumor extraction from MRI brain images using marker based watershed algorithm

This paper present a novel feature extraction method for offline recognition of segmented handwritten characters based on the fuzzy-zoning and normalized vector distance measures. Experiments are conducted on forty four basic Malayalam handwritten characters. In the recognition experiments are conducted using class modular neural network with the proposed features and this method is found to be promising.

Handwritten Character Recognition using Perceptual Fuzzy-Zoning and Class Modular Neural Networks

We present a novel feature extraction method for offline recognition of segmented Malayalam handwritten characters from their gray-scale images without the usual step of binarization. We investigate a new approach to model handwritten characters using state-space map (SSM) and state-space point distribution (SSPD) parameters. In the recognition stage we used class modular neural network with the proposed SSPD features and this method is found to be promising.

V L Lajish

Papers

Morphology Based Enhancement and Skull Stripping of MRI Brain Images

Brain tumor segmentation from MR brain images using improved fuzzy c-means clustering and watershed algorithm

Brain tumor extraction from MRI brain images using marker based watershed algorithm

Handwritten Character Recognition using Perceptual Fuzzy-Zoning and Class Modular Neural Networks

Handwritten Character Recognition Using Gray-scale Based State-Space Parameters and Class Modular NN