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Sathiyakumari, K.
- A Comparative Study of Machine Learning Algorithms Applied to Predictive Diabetes Data
Authors
1 Department of Applied Computer Technology, G. R. Govindarajulu School, Coimbatore, IN
2 Department of Applied Computer Technology, P.S.G.R. Krishnammal College for Women, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 1, No 8 (2009), Pagination: 405-412Abstract
Healthcare industry encompasses abundant data, which is increasing everyday. Conversely, tools for analyzing these records are incredibly less. Machine learning provides a lot of techniques for solving diagnostic problems in a variety of medical domains. Intelligent systems are able to learn from machine learning methods, when they are provided with a set of clinical cases as training set. This paper aims at a comparative study of widely used supervised classification algorithms-Naive Bayes, Multi Layer Perceptrons, Logistic Model Trees, and Nearest Neighbor with Generalized Exemplars applied to predictive diabetes dataset. The machine learning algorithms used in this study are chosen for their representability and diversity. They are evaluated on the basis of their accuracy, learning time and error rates.Keywords
Machine Learning, Diabetes Mellitus, Classification, Naive Bayes, Multi Layer Perceptrons, Logistic Model Trees, Nearest Neighbour with Generalized Exemplars, WEKA.
- A Brief Study of Image Processing and Techniques
Authors
1 PSGR Krishnammal College for Women, Coimbatore, IN
2 PSGR Krishnammal College for Women, Coimbatore, IN
Source
Digital Image Processing, Vol 9, No 1 (2017), Pagination: 11-13Abstract
The development of digital image processing is closely tied to the development of the digital computers. Because of its nature, digital image requires lot of storage space and their processing needs so much computational power that progress in the field of digital image processing had been highly dependent on the development of modern digital computer which came only in 1940s. This paper is a complete review of various image processing techniques and large number of related application in diverse disciplines, including medical, biometrics, moving object tracking, vehicle detection & monitoring, document analysis and retrieval, outdoor surveillance, remote sensing and Traffic queue detection algorithm for processing various real time image processing challenges. Techniques discussed segmentation, edge detection and corner detection also application areas and their future scope are explained. The intension of this paper is useful to researchers and practitioners interested in real time image processing.