Informatics Publishing Limited

R. Rathipriya ¹, K. Thangavel ¹

Affiliations
1 Department of Computer Science, Periyar University, Salem, Tamil Nadu, IN

Abstract

In this paper, the Simulated Annealing (SA) based biclustering approach is proposed in which SA is used as an optimization tool for biclustering of web usage data to identify the optimal user profile from the given web usage data. Extracted biclusters are consists of correlated users whose usage behaviors are similar across the subset of web pages of a web site where as these users are uncorrelated for remaining pages of a web site. These results are very useful in web personalization so that it communicates better with its users and for making customized prediction. Also useful for providing customized web service too. Experiment was conducted on the real web usage dataset called CTI dataset. Results show that proposed SA based biclustering approach can extract highly correlated user groups from the preprocessed web usage data.

Keywords

Biclustering, Clickstream Data, Simulated Annealing (SA), Web Personalization, Web User Profile, Web Recommendations, Web Usage Mining.

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P. Shanthi ¹, Z. Johnkennedy ¹, K. Parvathi ¹, D. Malathi ¹, K. Thangavel ¹, G. S. V. Raghavan ²

Affiliations
1 Tamil Nadu Agricultural University, Coimbatore-641 043, IN
2 McGill University, CA

Abstract

The challenges directed towards the food industry will be to fulfil the needs of the changing world markets and to meet new consumer product needs. Nowadays many pasta products flood the market. All the pasta and Asian noodles are generally wheat based products. The functional properties of wheat protein fractions are well defined for bread making and pasta cooking quality.

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K. Thangavel ¹, R. K. Murali Baskaran ¹, P. Parthiban ¹

Affiliations
1 Department of Agricultural Entomology, Agricultural College and Research Institute, (T.N.A.U.), Madurai (T.N.), IN

Abstract

Laboratory studies were carried out to assess the contact toxicity of emamectin benzoate 5 WG to workers of Indian bee, Apis cerana indica and Italian bee, Apis mellifera. Emamectin benzoate 5 WG to Indian bees showed that there was no mortality in emamectin benzoate 5 WG @ 100 and 125 g/ha, while emamectin benzoate 5 WG @ 150 g/ha caused 10.00 per cent at 6 (Hours after Treatment) HAT, and it increased to 6.67, 16.67 and 23.33 per cent, respectively after 24 HAT. However, standard insecticides lufenuron 5 EC @ 600 ml/ha, chlorantroniliprole 18.5 SC @ 150 ml/ha and spinosad 45 SC @ 125 ml/ha caused increased mortality of 30.00, 50.00 and 70.00 per cent, respectively. The mortality increased as the time of exposure increased from 6 to 24 HAT. Similar trend was also observed in Italian bees. Hence, emamectin benzoate 5 WG considered could be highly safe to both the species of honey bees than standard insecticides.

Keywords

Emamectin Benzoate, Honey Bees, Safety, Toxicity.

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A. Jenneth ¹, K. Thangavel ²

Affiliations
1 Computer Science Department, Sri Krishna Arts and Science College, Sugunapuram, Kuniamuthur, Coimbatore - 641008, Tamil Nadu, IN
2 Department of Computer Science, Periyar University, Periyar Palkalai Nagar, Salem - 636011, Tamil Nadu, IN

Abstract

Grouping of high dimensional information is an imperative exploration subject in the information mining, in light of the fact that the genuine datasets frequently have high dimensional components. The objective of the clustering is to group the features which should be similar to each other. Many text mining approaches are optimized to mine the sparse data which incurs high computation cost. In this paper, we process a novel technique named as affine subspace clustering which incorporates the Hubness property to handle the local feature relevance value and Curse of dimensionality. The Hubness property reduces the discrimination problem in the cluster formation and used as clustering method with effects relevant to cluster structures. Rather than endeavoring to keep away from the scourge of dimensionality by watching a lower dimensional component subspace, we use substantial dimensionality by exploiting downward closure property and outlier detection in the k nearest neighbor list. Additionally we combine Feature weighting method to minimize the average inside cluster scattering and augment the average between cluster scatterings along all the element spaces. The experimental results prove that proposed system yields the good performance in numerous settings, especially within the sight of huge amounts of commotion. The proposed techniques are optimized for the most part to detect the cluster center accuracy and extended properly to handle clusters of random sizes. Average inside cluster scattering is minimized and average between-cluster scattering is expanded along all the element spaces.

Keywords

Clustering, Curse of Dimensionality, Dynamic Centroid

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I. Laurence Aroquiaraj ¹, K. Thangavel ¹

Affiliations
1 Department of Computer Science, Periyar University, Tamil Nadu, IN

Abstract

Breast Cancer involves the uncontrolled growth of abnormal cells that have mutated from normal tissues. A radiologist looks for certain signs and characteristics indicative of cancer when evaluating a mammogram. The main task is to obtain the locations of suspicious regions to assist radiologists in diagnosis. Image segmentation has been approached from a wide variety of perspectives: region-based approach, morphological operation, multi-scale analysis, fuzzy approaches and stochastic approaches have been used for mammogram image segmentation but with some limitations. In spite of the several methods available in the literature, image segmentation still a challenging problem in most of image processing applications. The challenge comes from the fuzziness of image objects and the overlapping of the different regions. In this paper we propose fast auto adaptive image segmentation algorithm for finding the optimal thresholds for segmenting gray scale images. The proposed method is based on fuzzy index which decreases the similarity between pixels increases. The system uses initial estimation of the parameters. The fuzzy subsets derived from the image histogram using weighted fuzzy entropywill shows the similar cost measure as in pixels of the same subset. Experimental results demonstrate the effectiveness of the proposed approach.

Keywords

X-ray Mammography, Fuzzy Entropy, Ostu Multi-Level Method, Segmentation.

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