AN EFFICIENT ALGORITHM FOR SEQUENCE GENERATION IN DATA MINING

ABSTRAC

Facial Feature Extraction Based On FPD and GLCM Algorithms

ABSTRACT: Image mining is defined as the discovery of image patterns in a given collection of images. It is an effort that fundamentally draws upon knowledge in computer vision, image processing, data mining, machine learning, database, and artificial intelligence. Facial recognition helps to analyze and compare the patterns from the facial images. Facial feature extraction is an automatic recognition of human faces by detecting its features i.e. eyes, eyebrows and lips. In this research work, features are extracted from the human facial images by using the existing Face Part Detection (FPD) algorithm and the newly proposed Gray Level Co-occurrence Matrix (GLCM) algorithm. FPD uses bounding box method and GLCM uses affine moment invariants method. Performance factors applied here are feature extraction accuracy and execution time. The implementation of this work is performed in MATLAB 7.0. Based on the experimental results, it is observed that the proposed GLCM algorithm extracted the features more accurately with minimum execution time than FPD algorithm

INTRUSION DETECTION SYSTEM -A STUDY

ABSTRACT Intrusion Detection System (IDS) is meant to be a software application which monitors the network or system activities and finds if any malicious operations occur. Tremendous growth and usage of internet raises concerns about how to protect and communicate the digital information in a safe manner. Nowadays, hackers use different types of attacks for getting the valuable information. Many intrusion detection techniques, methods and algorithms help to detect these attacks. This main objective of this paper is to provide a complete study about the definition of intrusion detection, history, life cycle, types of intrusion detection methods, types of attacks, different tools and techniques, research needs, challenges and applications

Comparative Analysis of Dimensionality Reduction Techniques

ABSTRACT: Datasets are most important for performing all the type of data mining tasks. Every dataset has many numbers of attributes and instances. Dimensionality reduction (DR) is one of the preprocessing steps which is used to reduce the dimensions (attributes or features) without losing the data. There are two divisions of reduction they are feature extraction and feature reduction. Feature extraction is the process of decomposition of attributes of the original data (i.e.) merging the attributes of the data Feature selection is the process of selecting the subset of attributes by eliminating features with little or no predictive information. Feature extraction techniques are more adequate than the feature selection. Reduction is done to the larger dataset to decrease the curse of dimensionality. The main objective of this paper is to provide a systematic comparative analysis on feature reduction algorithms such as PCA, LDA and FA to medical dataset (Thyroid, Oesophagal).The performance factor considered are number of attributes reduced and time is observed

Prevalence of dental fluorosis among primary school children in rural areas of Chidambaram taluk, Cuddalore district, Tamil Nadu, India

<b>Background:</b> Fluorosis is one of the common but major emerging areas of research in the tropics. It is considered endemic in 17 states of India. However, the Cuddalore district of Tamil Nadu is categorised as a fluorosis non-endemic area. But clinical cases of dental fluorosis were reported in the field practice area of Department of Community Medicine, Rajah Muthiah Medical College, Annamalai University, Chidambaram. Since dental fluorosis has been described as a biomarker of exposure to fluoride, we assessed the prevalence and severity of dental fluorosis among primary school children in the service area. <b> Materials and</b> <b> Methods:</b> Children studying in six primary schools of six villages in the field practice area of Rural Health Centre of Faculty of Medicine, Annamalai University, Chidambaram, were surveyed. Every child was clinically examined at the school by calibrated examiners with Dean&#x2032;s fluorosis index recommended by WHO (1997). Chi-square test, Chi-square trend test and Spearman&#x2032;s rank correlation coefficient test were used for statistical analysis. <b> Results:</b> Five hundred and twenty-five 5- to 12-year-old school children (255 boys and 270 girls) were surveyed. The overall dental fluorosis prevalence was found to be 31.4&#x0025; in our study sample. Dental fluorosis increased with age <i> P</i> &lt; 0.001, whereas gender difference was not statistically significant. Aesthetically objectionable dental fluorosis was found in 2.1&#x0025; of the sample. Villages Senjicherry, Keezhaperambai and Kanagarapattu revealed a community fluorosis index (CFI) score of 0.43, 0.54 and 0.54 with 5.6&#x0025;, 4.8&#x0025; and 1.4&#x0025; of objectionable dental fluorosis, respectively. Correlation between water fluoride content and CFI values in four villages was noted to be positively significant. <b> Conclusion:</b> Three out of six villages studied were in &#x2032;borderline&#x2032; public health significance (CFI score 0.4-0.6). A well-designed epidemiological investigation can be undertaken to evaluate the risk factors associated with the condition in the study region