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Spinocerebellar ataxia type 12 (SCA12) is a neurogenetic disease, marked with prominent action tremors in the upper limbs. Neuroimaging techniques like magnetic resonance imaging (MRI) are used by doctors to find the affected areas of SCA12 disease. In literature, most of the research work have used 2D-feature extraction methods, which do not consider pixel information from adjacent slices of the MRI volume, which may be relevant to distinguish healthy from the patient suffering from a particular disease. To overcome the problem of 2D-feature extraction method, we investigate six well-recognized 3D-feature extraction techniques based on varied principles individually and in combination from whole brain gray matter volume. To obtain the optimal set of relevant features, we investigated eight well-known feature selection methods. The support vector machine (SVM) was used as the classifier. Experimental results demonstrate the superior performance of 3D-feature extraction methods in comparison to 2D-feature extraction methods. The features obtained from the combination of feature extraction methods (COFEMS) combined with SVM with Recursive Feature Elimination method achieved maximum classification accuracy of 90% and F1-score of 89.25%. The subset of features so obtained is found statistically relevant and non-redundant. Ranking analysis on both feature extraction and feature selection methods is also carried out.

ABSTRACTIn this paper we analyse the effect of an anti-worm patching into a network inspired from human epidemiology. To achieve this we propose a new extended model of Susceptible–Infected–Recovered model. In which we introduced a new compartment, named as ‘Patched’. This is a compartment where nodes/computers are not prone to worm attacks owing to an anti-worm patch being installed in them. We call our model Susceptible–Infected–Recovered–Patched model. We perform an exhaustive equilibrium analysis both analytically and numerically for the ‘Worm’ and ‘Worm-free’ scenarios. The model assumes that the anti-worm patch spreads like the worm does in the network. The equilibrium analysis illustrates how the population dynamics responds to different parameters involved in the model. We have also shown the effect of user vigilance in the model and observed that a more vigilant population of computer users is helpful to remove worm quickly from a network.