Informatics Publishing Limited

M. Nachamai ¹, Pallavi Banerjee ¹, Joy Paulose ¹

Affiliations
1 Department of Computer Science, Christ University, Bangalore-560029, IN

Abstract

One of the huge avenues of research in image processing is facial recognition. Automatic Facial Recognition (FR) has many applications. The paper deals with the various methods of facial recognition system. It includes the various methods of face recognition system along with feature extraction which makes the facial recognition system more versatile and robust. An attempt is made to make a complete survey of all FR systems regarding the methodology adopted along with the researches that took place, the accuracy rate of the methods and also the advantages and the disadvantages of the various methods. The paper discusses all the methods of face recognition system with or without feature extraction and expression recognition. Face recognition system has seen many new methods of development to cover as many different possibilities as possible, along with high rate of accuracy. An extensive and elaborate survey and a walk through of all those methodologies are covered along with the algorithms followed by the methods for facial recognition.

Keywords

Face Recognition System, Feature Extraction, Facial Expression Recognition.

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M. Nachamai ¹

Affiliations
1 Department of Computer Science, Christ University, Bengaluru - 560029, Karnataka, IN

Abstract

Background/Objectives: Attention deficit hyperactive disorder is one major neuropsychiatric disorder particularly found in children. This medical disorder is difficult to identify and quantify, even if done, it is very subjective as it is the discretion of the psychiatrists or parents. Methods/Statistical analysis: The most exigent task after identifying ADHD children is to find their exact deficiency of what is the category, is it a hyperactive disorder, an impulsive disorder or an attention deficit disorder. Each category insists a diverse form of treatment and training. With the MRI image data the Tr values are estimated and given for clustering, a k-means algorithm was deployed for clustering. Findings: With different distance measures k-means was able to cluster precisely the three categories from the data. The result obtained would be a very substantial data for the medical physicists and an inevitable philanthropic contribution for the children society combating against this disorder. Applications/Improvements: The method adopted is novel and concise approach to identify the type of ADHD prevalent children. The method can be further perfected and completely automated to identify the category of ADHD in children.

Keywords

ADHD, Clustering, K-Means Algorithm, MRI Spectroscopy, Unsupervised Learning

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Nalin Kumar ¹, M. Nachamai ¹

Affiliations
1 Department of Computer Science, Christ University, Bengaluru, IN

Abstract

Noise removal techniques have become an essential practice in medical imaging application for the study of anatomical structure and image processing of MRI medical images. To report these issues many de-noising algorithm has been developed like Weiner filter, Gaussian filter, median filter etc. In this research work is done with only three of the above filters which are already mentioned were successfully used in medical imaging. The most commonly affected noises in medical MRI image are Salt and Pepper, Speckle, Gaussian and Poisson noise. The medical images taken for comparison include MRI images, in gray scale and RGB. The performances of these algorithms are examined for various noise types which are salt-and-pepper, Poisson, speckle, blurred and Gaussian Noise. The evaluation of these algorithms is done by the measures of the image file size, histogram and clarity scale of the images. The median filter performs better for removing salt-and-pepper noise and Poisson Noise for images in gray scale, and Weiner filter performs better for removing Speckle and Gaussian Noise and Gaussian filter for the Blurred Noise as suggested in the experimental results.

Keywords

Weiner Filter, Median Filter, Gaussian Filter, Speckle Noise, Gaussian Noise, Salt and Pepper Noise, Blurred Noise, Poisson Noise.

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Remya Thomas ¹, M. Nachamai ¹

Affiliations
1 Department of Computer Science, Christ University, Bangalore, IN

Abstract

Antivirus as name implies prevent the devices such as computers, mobiles and pen-drive from viruses. All gadgets which interact with open network are prone to virus. Virus is a malicious software program which replicates by copying its code multiple times or by infecting any computer program (like modifying the existing program) which can affect its process. Virus perform harmful task on affected host computer such as possessing on hard disk, CPU time, accessing private information etc. This paper specifies the performance of (McAFee, Avast, Avira, Bitdefender, Norton) antivirus and its effectiveness on the computer. The performance is tested based on the time acquired by each antivirus to act on a computer. The parameters used to analyze the performance are quick scan, full scan and custom scan with respect to time. Through the analysis Bitdefender performance is better than other selected antivirus.

Keywords

Antivirus, Computer Scan, Virus, Performance Testing.

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