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In Parkinson's disease, there exists a prodromal or a premotor phase characterized by symptoms like olfactory loss and sleep disorders, which may last for years or even decades before the onset of motor clinical symptoms. Diagnostic tools based on machine learning using these features can be very useful as they have the potential in early diagnosis of the disease. In the paper, we use olfactory loss...
In this paper we propose a robust and fully automated lumbar herniation diagnosis system based on clinical MRI data which will not only aid a radiologist to make a decision with increased confidence, but will also reduce the time needed to analyze each case. Our method is based on three steps : 1) We automatically label the five lumbar intervertebral discs in a sagittal MRI slice using a probabilistic...
In this paper, we present a methodology for automatic geomorphic mapping of planetary surfaces that incorporates machine-learning techniques. Our application transforms remotely sensed topographic data gathered by orbiting satellites into semantically meaningful maps of landforms; such maps are valuable research tools for planetary science. As topographic data become increasingly available, the ability...
The land use or land cover map depicts the physical coverage of the Earth's terrestrial surface according to its use (viz. vegetation, habitation, water body, bare soil, artificial structures etc.). Land use map generation from remotely sensed images is one of the challenging task of remote sensing technology. In this article, motivated from group forming behaviour of real ants, we have proposed two...
Social insects like ants, bees deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone, that causes clumping behavior in a species and brings individuals into a closer proximity, is called aggregation pheromone. This article presents a new algorithm (called, APC) for pattern classification based on the property of aggregation pheromone found...
Combination of structure and content features is necessary for effective retrieval and classification of XML documents. Composite kernels provide a way for fusion of content and structure information. In this paper, we demonstrate that a linear combination of simple and low cost kernels such as cosine similarity on terms and selective paths provide a good classification performance. We also propose...
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