V. Shanthi ¹, N. Gajendran ²

Affiliations
1 University of Madras, 1Dept. of Economics, Chellammal Women’s College, Guindy, Chennai-600 005, IN
2 University of Madras, C.A.S. in Botany, University of Madras, Chennai-25, IN

Abstract

No Abstract

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E. Manigandan ¹, V. Shanthi ², Magesh Kasthuri ¹

Affiliations
1 SCSVMV University, Enathur, Kanchipuram-631561, Tamil Nadu, IN
2 Department of MCA, St. Joseph’s College of Engineering, Chennai – 600119, Tamil Nadu, IN

Abstract

Objectives: Clustering algorithm is broadly used as spectral algorithm in social media, where a reference of contact is used and mined further for various combinations of suggested friends and lookups. This paper Identifies key lead generation techniques to be used in customer relationship management for sales marketing to decide data gathering. Also to define key merits and demerits of these techniques and to prepare a Matrix of comparison of these techniques to justify the data source and data set. M-Cluster algorithm is used for lead qualification i.e. training set preparation and data evaluation. Methods: Todefine a training set based on various attributes/fields in the data given for classification. This training set is used to run the data process and to produce expected result. This is assessed and accepted for definite data set processing or additional run for interim training data set preparation. Findings: This study is taken to customize clustering algorithm for data mining process in a customer relationship management field as the space of data is more and variant. Also proving the usability of customized clustering algorithm in data mining and the efficiency in processing mechanism compared to other methods used in current situation of data mining in customer relationship management is the part of this study. The customized algorithm developed as part of this study considers the two major areas of data mining using clusters. Applications: The results produced tremendous trends that the clustered algorithm suits to any data mining process when scaling and data classification are diversified and less in control.

Keywords

Classification, Clustering Algorithm, Data Mining, K-Means, Lead Generation, M-Clustering Algorithm.

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L. Rajesh ¹, V. Shanthi ², V. Lakshmi Narasimhan ³

Affiliations
1 Department of CSA, SCSVMV University, Enathur, Tamil Nadu, IN
2 Department of MCA, St. Joseph’s College of Engineering, Chennai, IN
3 Department of Computer Science & Engineering, RGMCET, Andhra Pradesh, IN

Abstract

Objectives: To develop a suitable model to study the behavior of web crawled dataset and perform simulation on the modeled data for better understanding of the system Methods/Statistical Analysis: M/M/1 model is a variation of Single Birth Single Death (SBSD) model which is applied to study the behavior of web crawled dataset for the Classification Problem. KanchiCrawler, a stylized focused web crawler is implemented to collect the data for this application. The size of the corpora (Population) is 500k. Control corpus (sample) can be drawn from the corpora based on enforcing certain pre-determined conditions. Findings: A 20-state model starting with an initial test corpus of 25k and then by gradually increasing with an increment of 25k up to 500k is developed. This is achieved through the computation of Forward State Transition Probability and Reverse State Transition Probability for the respective states. This model provides fairly good results by testing the algorithmic efficiency of a KanchiCrawler and to model the web crawled dataset for the classification problem. Applications: M/M/1 models are tractable and often used to model various operations of nature. In most situations where large numbers are involved, M/M/1 model are statistically stable and reflective of reality.

Keywords

Dataset Modeling, KanchiCrawler, M/M/1 Model, State Transition Probability.

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R. Sonia ¹, V. Shanthi ²

Affiliations
1 Sathyabama University, Chennai - 600119, Tamil Nadu, IN
2 St Joseph’s College of Engineering, Chennai - 600119, Tamil Nadu, IN

Abstract

Objectives: Nuchal Translucency (NT) thickness measurement is a Down syndrome marker for chromosomal abnormalities which is detected by non-invasive test during first trimester. Methods: In this paper, a computerized method to measure NT thickness is proposed. It consists of region of interest extraction, NT segmentation using morphological operation with Otsu thresholding. The NT thickness is measured from the segmented area. The performance is analyzed on 80 ultrasound fetal images. Findings: Proposed approach for NT measurement is implemented in MATLAB software. Experimental results show that NT thickness for normal foetus is 1.99±.62 mm and abnormal foetus is 4.10±.90 mm during first trimester of pregnancy from 11 to 13+6 weeks of gestation. The proposed algorithm efficiently computes NT measurement and produces consistent result during first trimester in singleton pregnancies. Applications/Improvements: Proposed semi-automated technique helps the medical sonographer for accurate NT thickness measurement to detect Down syndrome marker during first trimester ultrasound scan.

Keywords

Down Syndrome, Morphological Operation, Nuchal Translucency, Trimester, Thresholding.

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