Genetic algorithms for symbolic clustering

This paper introduces a novel methodology for clustering of symbolic objects by making use of Genetic Algorithms (GAs). GAs are a family of computational models inspired by evolution. These algorithms encode potential solutions to specific problems on simple chromosome-like data structures and apply recombination operators to these structures so as to preserve critical information. A new type of representation for chromosome structure is presented here along with a new method for mutation. The efficacy of the proposed method is examined by application to numeric data of known number of classes and also to assertion type of symbolic objects drawn from the domain of fat oil, microcomputers, microprocessors and botany. The validity of the clusters obtained is examined.     

Heat transfer in dusty fluid with suspended hybrid nanoparticles over a melting surface

The melting effect with the magnetic field performs a significant role in various manufacturing and industrial applications, such as welding, casting, magma-solidification, nuclear engineering, and so forth. The present study focuses on the impact of the melting effect and magnetic field with inhomogeneous heat origination and sink. The formulation of the mathematical model is done by considering fluid with hybrid nanoparticles and dust particles in two different phases. We have considered Fe₂SO₄ and Cu as nanoparticles dispersed in the base fluid water along with suspended dust particles. The set of partial differential equations is reduced by using apt similarity variables and boundary conditions to obtain ordinary differential equations. The numerical solution is approximated using MATLAB-bvp4c adopting the shooting technique. The impact of numerous pertinent physical parameters on the velocity and thermal profiles is plotted and deliberated. Furthermore, the rate of heat flow and friction factor is also tabulated and visualized through the graphs. Streamlines are also drawn to know the behavior of the fluid flow. The rise in values of M_E quickly increases the velocity of the fluid motion but declines the thermal gradient and thickness of its related boundary layer. Also, inclining values of Pr enhance the thermal profile due to the impact of melting.     

Magnetic resonance image denoising using nonlocal maximum likelihood paradigm in DCT‐framework

The data acquired by magnetic resonance (MR) imaging system are inherently degraded by noise that has its origin in the thermal Brownian motion of electrons. Denoising can enhance the quality (by improving the SNR) of the acquired MR image, which is important for both visual analysis and other post processing operations. Recent works on maximum likelihood (ML) based denoising shows that ML methods are very effective in denoising MR images and has an edge over the other state‐of‐the‐art methods for MRI denoising. Among the ML based approaches, the Nonlocal maximum likelihood (NLML) method is commonly used. In the conventional NLML method, the samples for the ML estimation of the unknown true pixel are chosen in a nonlocal fashion based on the intensity similarity of the pixel neighborhoods. Euclidean distance is generally used to measure this similarity. It has been recently shown that computing similarity measure is more robust in discrete cosine transform (DCT) subspace, compared with Euclidean image subspace. Motivated by this observation, we integrated DCT into NLML to produce an improved MRI filtration process. Other than improving the SNR, the time complexity of the conventional NLML can also be significantly reduced through the proposed approach. On synthetic MR brain image, an average improvement of 5% in PSNR and 86%reduction in execution time is achieved with a search window size of 91 × 91 after incorporating the improvements in the existing NLML method. On an experimental kiwi fruit image an improvement of 10% in PSNR is achieved. We did experiments on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 256–264, 2015     

Development of a method employing reversed‐phase thin‐layer chromatography for establishing milk fat purity with respect to adulteration with vegetable oils

A systematic study was conducted using reversed‐phase thin‐layer chromatography wherein unsaponifiable matter of samples namely, pure cow and buffalo milk fats, vegetable oils (groundnut oil, soya bean oil and sunflower oil) and milk fats adulterated with vegetable oils (≥1%) were run along with the reference standards using two new solvent systems to detect adulteration in milk fat. The results of the study revealed that adulteration at even a 1% level could easily be detected using this rapid, reliable and reproducible method based on the presence of β‐sitosterol as a marker and some additional spots ascribable to their occurrence in vegetable oils only.     

Agglomerative clustering using the concept of mutual nearest neighbourhood

A method for determining the mutual nearest neighbours (MNN) and mutual neighbourhood value (mnv) of a sample point, using the conventional nearest neighbours, is suggested. A nonparametric, hierarchical, agglomerative clustering algorithm is developed using the above concepts. The algorithm is simple, deterministic, noniterative, requires low storage and is able to discern spherical and nonspherical clusters. The method is applicable to a wide class of data of arbitrary shape, large size and high dimensionality. The algorithm can discern mutually homogenous clusters. Strong or weak patterns can be discerned by properly choosing the neighbourhood width.     

Remote sensing of contrasting tillage practices in the Texas Panhandle

Tillage information is crucial in environmental modelling as it has a direct impact on water holding capacity, evapotranspiration, carbon sequestration and water quality. In this study, a set of Landsat Thematic Mapper (TM)‐based linear logistic models were developed for mapping tillage practices and verified with an independent dataset. For data collection purposes, 35 and 41 commercial fields were randomly selected in Moore and Ochiltree counties, respectively, in the Texas Panhandle. Tillage survey was planned and conducted to coincide with Landsat 5 satellite overpasses during the 2005 planting season and two TM scenes were acquired. Using the Moore County dataset, seven logistic regression models were developed and these were evaluated with the data collected from Ochiltree County. The overall classification accuracy of the models varied from 86% to 91% with the Moore County dataset. These models were evaluated against independent Ochiltree County dataset and resulted in somewhat less accurate (classification accuracy of 67–85%) but still useful results. Analysis of these results indicates that logistic regression models that have indices derived from the combination of TM band 5 with bands 4 or 6 may provide consistent and acceptably accurate results when they are applied in the same geographic region.