National Conference
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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 AN OVERVIEW OF PATTERN RECOGNITION(Nehru College of Management, 2014-07-03) Selvanayaki M; Anushya Devi TSPattern recognition stems from the need for automated machine recognition of objects, signals or images, or they need for automated decision-making based on a given set of parameters. In machine learning, pattern recognition is the assignment of a label to a given input value. Pattern recognition is a more general problem that encompasses other types of output as well. It is relatively straight forward for humans to effortlessly identify the genders of these people, but now consider the problem of having a machine making the same decision. The real-world pattern recognition problems are considerably more difficult and such problems span a very wide spectrum of applications, including speech recognition (e.g., automated voice-activated customer service), speaker identification, handwritten character recognition (such as the one used by the postal system to automatically read the addresses on envelopes), topographical remote sensing, identification of a system malfunction based on sensor data or loan/credit card application decision based on an individual’s credit report data, among many others. The nature of this paper makes it impossible to provide more detailed discussion topics, or to provide specific algorithms for all techniques. Therefore, the paper will provide a fundamental background about the pattern recognition system.Item AN OVERVIEW OF WEB CONTENT MINING AND ITS TECHNIQUES(Nandha College of Technology, Erode, 2014-03-22) Selvanayaki M; Anushya Devi TSTraditional technique of searching the web was via contents. Web Content mining is the extended work performed by search engines. Web Content mining refers to the discovery of useful information from web content such as text, images videos etc. Two approaches used in web content mining are Agent based approach and database approach.