K. Senthamarai Kannan

Manonmaniam Sundaranar University

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V. Deneshkumar

Manonmaniam Sundaranar University

M. Mohamed Sathik

Manonmaniam Sundaranar University

Rizwan Suliankatchi Abdulkader

National Institute of Epidemiology

K. Manoj

Manonmaniam Sundaranar University

A. Jawahar Farook

Manonmaniam Sundaranar University

Balasubramaniam Ramakrishnan

Apollo Hospitals

Y. Jacob Vetha Raj

Christian Medical College

S Anitha

Manonmaniam Sundaranar University

A. Jabarali

D. Nagarajan

St. Joseph’s College of Engineering

Cooperative Institutions

Manonmaniam Sundaranar University

Southern Connecticut State University

Lebanese University

Bircham International University

Notre Dame University – Louaize

Aix-Marseille Université

Anna University, Chennai

St. Joseph’s College of Engineering

Vellore Institute of Technology University

Indian Council of Medical Research

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Analysis of Liver Cancer DNA Sequence Data using Data Mining

International Journal of Computer Applications (2013)

N. SenthilVelMurugan V. Vallinayagam V. Vallinayagam K. Senthamarai Kannan T. Viveka

Data mining is flourishing during recent years and it is establishing itself as a major discipline in Computer Science and Statistics with industrial relevance.Data mining is finding interesting structure in databases [2].DNA is an extraordinary chip data with thousands of attributes which represents the gene expression values [4].The DNA data set are stored in huge biological databases for several purposes [1].Cancer occurs when lumps of cells usually group together to form tumors.A growing tumor becomes a lump of cancer cells that can destroy the normal cells around the tumor and damage the body's health tissues.In the last two decades the researchers have drawn much attention about liver cancer.Liver cancer is a disease in which malignant cells form in the tissues of the liver.It is relatively rare form of cancer but has a high mortality rate.The aim of this paper is to analyze the liver cancer DNA sequence data using the generalization of Kimura Models and Markov Chain.The reasonable results verify the validity of our method.

Liver Cancer

Sequence (biology)

10.5120/9909-4502

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Citations (8)

A Roadmap for Designing an Automated Visual Inspection System

International Journal of Computer Applications (2010)

D. Kumar K. Senthamarai Kannan

The new machine vision that is emerging is already generating millions of dollars per year in thousands of successful applications.Machine vision is becoming established as a successful tool for industrial automation where 100 % inspection of manufactured parts during production is becoming a reality.Two-thirds of applications of machine vision are found in quality control in which objects are categorized using its dimensions in metric measurements that are extracted from its 2D-pixel image.Building a machine vision system requires careful selection of appropriate sensor, lens, extraction and integration of information from the available cues, sensed data and evaluation of system performance and robustness.In this paper, it is proposed to provide a roadmap with an overview of Imaging system, Radiometric and Geometric Modeling to design a machine vision system with the illustration of design of automated visual inspection system to image new bottles on a conveyor line at a distance of 100 cm from the camera in order to ensure 30 mm diameter of the bottle neck.

10.5120/407-603

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Analysis of human fertility using gamma and Weibull distribution

Journal of Interdisciplinary Mathematics (2007)

K. Senthamarai Kannan V. Nagarajan

Abstract Human fertility is being influenced by many factors. In India during the 1940s and 1950s a relatively low level fertility rate of 6–8 children per woman of unbroken marriage was implicated by the social and cultural factors. The observed differentials in fertility between different places is determined by different socio economical developmental indicators. In this paper the relationship between the fertility and socio economical factors such as age of mothers, education and income of women, education and income of husband are studied by using Gamma and weibull distributions.

Total fertility rate

10.1080/09720502.2007.10700519

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Citations (1)

Bayesian queueing model with multiple working vacations under Gumbel distribution

Applied Mathematical Sciences (2015)

A. Jabarali K. Senthamarai Kannan

Gumbel distribution

10.12988/ams.2015.53217

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Hidden Markov Model of Evaluation of Break-Even Point of HIV patients: A Simulation Study

International Journal of Medical Sciences and Nursing Research (2021)

Mahalakshmi Rajendran K. Senthamarai Kannan Balasubramaniam Ramakrishnan

Background:The HIV virus carries projection of significant global population with specific estimations of the mathematical results of evolutionary methods which was presented in Tree Hidden Markov model (HMM). Materials and Methods:Hidden Markov models used to model the progression of the disease among HIV infected people.The author predicts a Baum Welch Algorithm method through HMM that can assess an unknown state of transition. Results:The Tree HMM model predicts the break down point starts once patient is infected with the HIV virus as it affects the immune system.The immune system drops more quickly in the initial inter arrival time when compared with the later time interval.The HIV virus length in the n th state within regrouping is uncertain to occur in each state of the given model.A simulation study was done to assess the goodness of fit for the model. Conclusion:The HIV virus length in the n th state within regrouping is uncertain to occur in each state of the given model.The inter arrival censoring between each state is essential in each infected HIV patients.The outcome of this works states that health care expert can use this model for effective patient cares.

10.55349/ijmsnr.2021.121922

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A Constructive Feature Grouping Approach for Analyzing the Feature Dominance to Predict Cardiovascular Disease

Cognitive science and technology (2023)

K. Senthamarai Kannan A. Lakshmi Bhargav A. Anil Kumar Reddy Ravi Kumar Chandu

Interpretability

Imputation (statistics)

Boosting

Feature (linguistics)

Predictive modelling

10.1007/978-981-19-8086-2_62

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COMPUTATION OF IMAGE SIMILARITY WITH TIME SERIES

DOAJ (DOAJ: Directory of Open Access Journals) (2011)

V. Balamurugan K. Senthamarai Kannan S. Selvakumar

Similarity (geometry)

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Hyperparameter Optimization for Gradient-Boosted Tree-Based Machine Learning Models and Their Effect on Model Performance

Springer proceedings in mathematics & statistics (2024)

Rizwan Suliankatchi Abdulkader V. Deneshkumar K. Senthamarai Kannan

Hyperparameter

Tree (set theory)

10.1007/978-3-031-51167-7_43

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Inter Channel Correlation based Demosaicking Algorithm for Enhanced Bayer Color Filter Array

Research Journal of Applied Sciences Engineering and Technology (2014)

Kathrin Peter S. Arumugam K. Senthamarai Kannan

Demosaicking is a process of obtaining a full color image by interpolating the missing colors of an image captured from a digital still and video cameras that use a single-sensor array. In this study a new Color Filter Array (CFA) is proposed. Which is based on the actual weight of the Human Visual System. It is developed based on the sensitivity level of the human eye to red as 29.9%, green as 58.7% and blue as 11.4%. This study also provides an effective iterative demosaicing algorithm applying a weighted-edge interpolation to handle green pixels followed by a series of color difference interpolation to update red, blue and green pixels. Before applying demosaicking algorithm Gaussian filter is applied to remove noise of the sensor applied image and also enhance the image quality. Experimental results show that the proposed method performs much better than other latest demosaicing techniques in terms of image quality and PSNR value.

Demosaicing

Color filter array

Bayer filter

Interpolation

10.19026/rjaset.7.641

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Comparison Of Fuzzy Time Series And ARIMA Model

International journal of scientific and technology research (2019)

K. Senthamarai Kannan M. SulaigaBeevi S. Syed Ali Fathima

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Citations (3)