An expert is able to tell the system developer in many image-related tasks what a prototypical image should look like. Usually he will choose several prototypes for one class, but he cannot provide a good and large enough sample set for the class to train a classifier. Therefore, we mapped his technical procedure into a technical system based on proper theoretical methods that assist him in acq...

An Image Segmentation Algorithm is an algorithm that delineates (an) object(s) of interest in an image. The output of the image segmentation algorithm is referred to as a segmentation. Developing image segmentation algorithms is a manual, iterative process involving repetitive verification and validation tasks. This process is time-consuming and depends on the availability of medical experts, w...

MOTIVATION Genome-wide association studies are commonly used to identify possible associations between genetic variations and diseases. These studies mainly focus on identifying individual single nucleotide polymorphisms (SNPs) potentially linked with one disease of interest. In this work, we introduce a novel methodology that identifies similarities between diseases using information from a la...

An expert is able to tell the system developer in many image-related tasks how a prototypical image should look like. Usually he will choose several prototypes for one class but he cannot provide a good and large enough sample set for the class to train a classifier. Therefore, we mapped his technical procedure into a technical system based on proper theoretical methods that assist him in acqui...

We have proposed a decision tree classifier named MMC (multi-valued and multi-labeled classifier) before. MMC is known as its capability of classifying a large multi-valued and multi-labeled data. Aiming to improve the accuracy of MMC, this paper has developed another classifier named MMDT (multi-valued and multi-labeled decision tree). MMDT differs from MMC mainly in attribute selection. MMC a...

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, ...

This paper formally defines similarities as tolerance relations, which are reflexive and symmetric binary relations. An abstract set with a similarity is called a tolerance space. The training data set in a learning task is a given database of independent identically distributed random pairs (Xi, Yi), where each Xi is a record and Yi is its label: Yi ∈ {0, 1}. The goal of the learning is to des...

Classification of hyperspectral data using a classifier ensemble that is based on support vector machines (SVMs) are addressed. First, the hyperspectral data set is decomposed into a few data sources according to the similarity of the spectral bands. Then, each source is processed separately by performing classification based on SVM. Finally, all outputs are used as input for final decision fus...

‘Steganalysis’ is one of the challenging and attractive interests for the researchers with the development of information hiding techniques. It is the procedure to detect the hidden information from the stego created by known steganographic algorithm. paper, a novel feature based image steganalysis technique is proposed. Various statistical moments have been used along with some similarity metr...

A hybrid neural network and tree classification system for handwritten numeral recognition is proposed. The recognition system consists of coarse and fine classification based on a variety of stable and reliable global features and local features. For the coarse classifier: a four-layer feed forward neural networks with back propagation learning algorithm is employed to distinguish six subsets ...