Atiq Islam

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The phenomenal growth of Image/Video on the web and the increasing sparseness of meta information to go along with forces us to look for signals from the Image/Video content for Search / Information Retrieval and Browsing based corpus exploration. One of the prominent type of information that users look for while searching/browsing through such corpora is(More)
A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a(More)
In this work, we analyze both acoustic and discourse information for Dialog Act (DA) classification of HCRC MapTask dataset. We extract several different acoustic features and exploit these features in a Hidden Markov Model (HMM) to classify acoustic information. For discourse feature extraction, we propose a novel parts-of-speech (POS) tagging technique(More)
In this work, we characterize genes using an oligonucleotide affymetrix gene expression dataset and propose a novel gene selection method based on samples from the posterior distributions of class-specific gene expression measures. We construct a hierarchical Bayesian framework for a random effect ANOVA model that allows us to obtain the posterior(More)
—Based on microarray gene expression datasets, many statistical methods have been proposed to locate the significant differentially expressed genes (marker genes) among different sample groups. Although robust models for identifying marker genes more accurately is an area of intense research, effective tools for the evaluation of results is often ignored in(More)
Content is one of the most essential parts of products on e-commerce websites such as eBay. It not only drives user-engagement but also traffic from various search engine websites based on the relevance. Generating the content for the products, however comes with a wide set of challenges, due to the complexity of commerce at scale, and requires new(More)
To detect the tumor in the brain is very important task but the major problem occurred is that its very time consuming. We provide an approach towards the automation of this process in this paper. We take magnetic resonance images of the brain as a input and attempt to calculated the position and the size of the tumor. Each pixel in each slice will be(More)
In the last few years, many image processing techniques have been presented in order to perform different brain tumor detection tasks. These cover Content-Based Retrieval Technique, Component Labelling Algorithm, Fuzzy C-Mean Algorithm. There is fast growth of image processing available in last few years; image segmentation have work together to get(More)
Background Automatic detection of tumors is a challenging task due to the heterogeneous phenotypic and genotypic behaviors of cells within tumor types [1-3]. In recent years, a number of research endeavors have been reported in lit-eratures that exploit microarray gene expression data to predict tissue/tumor types with high confidence [3-14]. However, in(More)
INTRODUCTION Automated diagnosis and prognosis of tumors of the central nervous system (CNS) offer overwhelming challenges because of heterogeneous phenotype and genotype behavior of tumor cells (Yang et al. 2003, Pomeroy et al. 2002). Unambiguous characterization of these tumors is essential for accurate prognosis and therapy. Although the present imaging(More)