No abstract

Context:Dental trauma has become an important attribute of dental public health. The primary requisite before actively dealing with such problems is to describe the extent, distribution, and associated variables with the specific condition.Aims:The aim of the present study was to assess the prevalence and distribution of traumatic dental injuries (TDI) to anterior teeth among 3 to 13 years old Chidambaram school children.Settings and Design:A cross-sectional study was conducted. Data was collected through a survey form and clinical examination.Materials and Methods:A total of 3200 school children in the age group of 3-13 years were selected from 10 schools of Chidambaram, Tamilnadu. Information concerning sex, age, cause of trauma, number of injured teeth, type of the teeth, lip competence, terminal plane relationship and the molar relationship were recorded.Statistical Analysis Used:The statistical software EPIINFO (Version 6.0) was used for statistical analysis. In the present study, P≤0.05 was considered as the level of significance.Results:The trauma prevalence in the present study was 10.13%. Children with class I type 2 and mesial step molar relationship exhibited more number of dental injuries. Enamel fracture was the most common injury recorded. Only 3.37% of the children had undergone treatment.Conclusion:The high level of dental trauma and low percentage of children with trauma seeking treatment stresses the need for increased awareness in Chidambaram population.

Purpose: Dental trauma has become an important aspect of dental public health. The primary requisite before actively dealing with such problems is to describe the extent, distribution, and variables associated with the specific condition. The purpose of this study was to assess the prevalence and role of socioeconomic status and anatomic risk factors in traumatic dental injuries (TDI) to permanent anterior teeth in 10 to 16 year old Sainik (Army) school, children in India. Methods:A cross-sectional study was conducted. Data was collected through a survey form and clinical examination. The permanent anterior teeth of four hundred and forty six male school children were examined for TDI. The socio-economic status, lip coverage and overjet were recorded. Statistical significance for the association between occurrence of TDI and the various risk factors was carried out. Results:The prevalence of TDI to permanent anterior teeth was 23.8%. A large number of injuries occurred during participation in sports. Inadequate lip coverage and a large maxillary overjet were identified as important predictors for dental trauma. Conclusion:A high prevalence of dental trauma was observed in the study population suggestive of low awareness regarding the cause, effects and prevention of the condition.

No abstract

-One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed for homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of real and benchmark data sets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase and combining phase. A wide range of comparative experiments are conducted for real and benchmark data sets of intrusion detection. The accuracy of base classifiers is compared with homogeneous and heterogeneous models for data mining problem. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and also heterogeneous models exhibit better results than homogeneous models for real and benchmark data sets of intrusion detection.

No abstract

-One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed for homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of real and benchmark data sets of recognizing totally unconstrained handwritten numerals. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase and combining phase. A wide range of comparative experiments are conducted for real and benchmark data sets of recognizing totally unconstrained handwritten numerals. The accuracy of base classifiers is compared with homogeneous and heterogeneous models for data mining problem. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and also heterogeneous models exhibit better results than homogeneous models for real and benchmark data sets of recognizing totally unconstrained handwritten numerals.

In the modern Digital Era, Data Mining is the powerful area for analyzing the large data sets to get unexpected relationships (models). The analysis of statistical data on sequential data points measured at regular time interval over a period of time is time series analysis. Time series analysis is used in predicting future occurrence of a time based event. One of the main areas where time series analysis is implied is in stock market prediction. The two important classification ways are Support Vector Machine (SVM) and Naïve Bayes. SVM is a method used for the foreseeing of financial time based data sets. It uses a function called risk which contains a mistake and a term. The basic principle which is used to obtain this may be called as minimization of structural risk. Naïve Bayes model assigns class labels for problem instances which can be denoted as vectors of feature measurements. For a given group variable, it takes into consideration that the numerical record of a specified feature is unique of others. Purpose of the present investigation is to develop an ensemble model namely AdaSVM and AdaNaive to analyse the stock data by comparing SVM and Naïve Bayes methods. The performance evaluation measures such as accuracy and classification error were computed individually for the stock market data set. The experimental result shows AdaSVM and AdaNaive is acceptable than SVM and Naïve Bayes.