Data Mining - Concepts and Techniques.

Statistical Analysis with Missing Data

Fluoride removal from water by adsorption—A review

Computational and Mathematical Methods in Medicine

Texture features for image classification and retrieval.

This paper deals with the recognition of handwritten Malayalam character using wavelet energy feature (WEF) and extreme learning machine (ELM). The wavelet energy (WE) is a new and robust parameter, and is derived using wavelet transform. It can reduce the influences of different types of noise at different levels. WEF can reflect the WE distribution of characters in several directions at different scales. To a non oscillating pattern, the amplitudes of wavelet coefficients increase when the scale of wavelet decomposition increase. WE of different decomposition levels have different powers to discriminate the character images. These features constitute patterns of handwritten characters for classification. The traditional learning algorithms of the different classifiers are far slower than required. So we have used an extremely fast leaning algorithm called ELM for single hidden layer feed forward networks (SLFN), which randomly chooses the input weights and analytically determines the output weights of SLFN. This algorithm learns much faster than traditional popular learning algorithms for feed forward neural networks. This feature vector, classifier combination gave good recognition accuracy at level 6 of the wavelet decomposition.

Handwritten character recognition using wavelet energy and extreme learning machine

Antennas and Wave Propagation

The batch adsorptive fluoride removal from water by Zirconium ion impregnated coconut shell carbon (ZICSC) was investigated. ZICSC was found to have fluoride adsorption capacity, 25 to 30 times that of plain activated carbon. The effect of various parameters such as pH, agitation time, and adsorbent dosage on fluoride removal were studied. The fluoride adsorption by ZICSC was above 90% for the entire pH range of 2–9 and the adsorption rate was extremely rapid, with 91% of the adsorption being achieved within 10 min of ZICSC contact for an initial fluoride concentration of 10 mg/L. The experimental data have been analyzed by Langmuir, Freundlich, Redlich‐Peterson, and Temkin sorption isotherm models and the adsorption data for fluoride onto ZICSC were better correlated to the Langmuir isotherm. The batch adsorption kinetics have been tested by first order, pseudo‐first order, and pseudo‐second order kinetic models with the subsequent determination of the rate constants of adsorption. The compariso...

Equilibrium and Kinetic Studies for Fluoride Adsorption from Water on Zirconium Impregnated Coconut Shell Carbon

A new fuzzy logic and histogram based algorithm for enhancing low contrast color images has been proposed here. The method is computationally fast compared to conventional and other advanced enhancement techniques. It is based on two important parameters M and K , where M is the average intensity value of the image, calculated from the histogram and K is the contrast intensification parameter. The given RGB image is converted into HSV color space to preserve the chromatic information contained in the original image. To enhance the image, only the V component is stretched under the control of the parameters M and K . The proposed method has been compared with conventional contrast enhancement techniques as well as with advanced algorithms. All the above techniques were based on the principle of transforming the skewed histogram of the original image into a uniform histogram. The performance of the different contrast enhancement algorithms are evaluated based on the visual quality, Tenengrad, CII and the computational time. The inter comparison of different techniques was carried out on different low contrast color images. Based on the performance analysis, we advocate that our proposed Fuzzy Logic method is well suited for contrast enhancement of low contrast color images.

A fast and efficient color image enhancement method based on fuzzy-logic and histogram

This paper proposes a new efficient fuzzy-based decision algorithm (FBDA) for the restoration of images that are corrupted with high density of impulse noises. FBDA is a fuzzy-based switching median filter in which the filtering is applied only to corrupted pixels in the image while the uncorrupted pixels are left unchanged. The proposed algorithm computes the difference measure for each pixel based on the central pixel (corrupted pixel) in a selected window and then calculates the membership value for each pixel based on the highest difference. The algorithm then eliminates those pixels from the window with very high and very low membership values, which might represent the impulse noises. Median filter is then applied to the remaining pixels in the window to get the restored value for the current pixel position. The proposed algorithm produces excellent results compared to conventional method such as standard median filter (SMF) as well as some advanced techniques such as adaptive median filters (AMF), efficient decision-based algorithm (EDBA), improved efficient decision-based algorithm (IDBA) and boundary discriminative noise detection (BDND) switching median filter. The efficiency of the proposed algorithm is evaluated using different standard images. From experimental analysis, it has been found that FBDA produces better results in terms of both quantitative measures such as PSNR, SSIM, IEF and qualitative measures such as Image Quality Index (IQI).

G. Raju

Papers

Handwritten character recognition using wavelet energy and extreme learning machine

Antennas and Wave Propagation

Equilibrium and Kinetic Studies for Fluoride Adsorption from Water on Zirconium Impregnated Coconut Shell Carbon

A fast and efficient color image enhancement method based on fuzzy-logic and histogram

A new fuzzy-based decision algorithm for high-density impulse noise removal