Bivariate semi-Pareto distributions and processes

A bivariate semi-Pareto distribution is introduced and characterized using geometric minimization. Autoregressive minification models for bivariate random vectors with bivariate semi-Pareto and bivariate Pareto distributions are also discussed. Multivariate generalizations of the distributions and the processes are briefly indicated.

Influence of additives on the charactaristics of stone matrix asphalt

The increase in traffic growth and maintenance expenditures demands the urgent need for building better, long-lasting, and more efficient roads preventing or minimizing bituminous pavement distresses. Many of the principal distresses in pavements initiate or increase in severity due to the presence of water. In Kerala highways, where traditional dense graded mixtures are used for the surface courses, major distress is due to moisture induced damages. The Stone Matrix Asphalt (SMA) mixtures provide a durable surface course. Proven field performance of test track at Delhi recommends Stone Matrix Asphalt as a right choice to sustain severe climatic and heavy traffic conditions. But the concept of SMA in India is not so popularized and its application is very limited mainly due to the lack of proper specifications. This research is an attempt to study the influence of additives on the characteristics of SMA mixtures and to propose an ideal surface course for the pavements. The additives used for this investigation are coir, sisal, banana fibres (natural fibres), waste plastics (waste material) and polypropylene (polymer). A preliminary investigation is conducted to characterize the materials used in this study. Marshall test is conducted for optimizing the SMA mixtures (Control mixture-without additives and Stabilized mixtures with additives). Indirect tensile strength tests, compression strength tests, triaxial strength tests and drain down sensitivity tests are conducted to study the engineering properties of stabilized mixtures. The comparison of the performance of all stabilized mixtures with the control mixture and among themselves are carried out. A statistical analysis (SPSS package Ver.16) is performed to establish the findings of this study

Comparison of Statistical and Semantic Similarity Techniques for Paraphrase Identification

This paper compares statistical technique of paraphrase identification to semantic technique of paraphrase identification. The statistical techniques used for comparison are word set and word-order based methods where as the semantic technique used is the WordNet similarity matrix method described by Stevenson and Fernando in [3].

Bivariate distributions with weighted marginals and reliablity modelling

In this paper, a family of bivariate distributions whose marginals are weighted distributions in the original variables is studied. The relationship between the failure rates of the derived and original models are obtained. These relationships are used to provide some characterizations of specific bivariate models

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing