F-KCW-Department Publications
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Item CARDIO VASCULAR DISEASE PREDICITON ANALYSIS(Dr. NGP Arts and Science College, 2020-02) Selvanayaki MIn this paper, the user could predict the diseases of cardiovascular. It is the process in which the different types of retinal images are downloaded from the databases. The retina can be photographed relatively straight forwardly with a fundus camera and now with direct digital imaging there is much interest in computer analysis of retinal images for identifying and quantifying the effects of diseases. A retinal image provides a snapshot of what is happening inside the human body. In particular, the state of the retinal vessels has been shown to reflect the cardiovascular condition of the body. In this paper, the implementation of automate segmentation approach is carried out based on active contour method to provide regional information. It is developed in the web mode to access dynamically by using HTML as front-end tool, server side as Python script and client side as JavaScript. The retinal based disease prediction includes Retinal image acquisition, Pre-processing, Vessel Segmentation, Vessel classification, Disease diagnosis.Item PREDICTION OF COTTON QUALITY USING WEKA TOOL(PSGR Krishnammal College for Women, Coimbatore, 2014-02) Selvanayaki M; Anushya Devi TSCotton is a soft, staple fiber that grows in a form known as a boll around the seeds of the cotton plant, a shrub native to tropical and subtropical regions around the world, including the Americas, India and Africa. The fiber most often is spun into yarn or thread and used to make a soft, breathable textile, which is the most widely, used natural-fiber cloth in clothing today. Its widespread use is largely due to the ease with which its fibers are spun into yarns. Cotton's strength, absorbency, and capacity to be washed and dyed also make it adaptable to a considerable variety of textile products. Cotton It’s fashionable, natural and versatile. The physical characteristics such as fiber length, length distribution, trash value, color grade, strength, shape, tenacity, density, moisture absorption, dimensional stability, resistance, thermal reaction, count, etc., contributes to the quality of cotton. In this work, cotton quality prediction is modeled as classification task and implemented using supervised learning algorithms namely REP tree, Classificationviaclustering, Classificationviaregression and MulticlassClassifier in WEKA environment on the cotton quality assessment dataset. The classification models have been trained using the data collected from a spinning mill. The prediction accuracy of the classifiers is evaluated using 10-fold cross validation and the results are compared. It is observed that the model based on REP tree classifier produces high predictive accuracy compared to other models.Item PREDICTION OF COTTON QUALITY USING WEKA TOOL(PSGR Krishnammal College for Women, Coimbatore, 2014-02-21) Selvanayaki M; Anushya Devi TSCotton is a soft, staple fiber that grows in a form known as a boll around the seeds of the cotton plant, a shrub native to tropical and subtropical regions around the world, including the Americas, India and Africa. The fiber most often is spun into yarn or thread and used to make a soft, breathable textile, which is the most widely, used natural-fiber cloth in clothing today. Its widespread use is largely due to the ease with which its fibers are spun into yarns. Cotton's strength, absorbency, and capacity to be washed and dyed also make it adaptable to a considerable variety of textile products. Cotton It’s fashionable, natural and versatile. The physical characteristics such as fiber length, length distribution, trash value, color grade, strength, shape, tenacity, density, moisture absorption, dimensional stability, resistance, thermal reaction, count, etc., contributes to the quality of cotton. In this work, cotton quality prediction is modeled as classification task and implemented using supervised learning algorithms namely REP tree, Classification via clustering, Classification via regression and Multiclass Classifier in WEKA environment on the cotton quality assessment dataset. The classification models have been trained using the data collected from a spinning mill. The prediction accuracy of the classifiers is evaluated using 10-fold cross validation and the results are compared. It is observed that the model based on REP tree classifier produces high predictive accuracy compared to other models.Item STUDY ON MACHINE LEARNING TECHNIQUES USING SVM(Pioneer College of Arts and Science, Coimbatore, 2017-01-06) Selvanayaki M; Anushya Devi T SMachine Learning is the study of computer algorithms that improve automatically through experience. Applications range from data mining programs that discover general rules in large data sets, to information filtering systems that automatically learn user’s interests. An important task of machine learning is classification, also referred as pattern recognition; where one attempts to build algorithms capable of automatically constructing methods for distinguish between different exemplars. This paper deals about different machine learning techniques for the prediction process.