p) 2010 - 16 Documents

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    SUPERVISED LEARNING APPROACH FOR PREDICTING THE QUALITY OF COTTON USING WEKA
    (Springer Link, 2010) Selvanayaki M; Vijaya M S; Jamuna K S; Karpagavalli S
    Cotton is the world’s most important natural fibre used in Textile manufacturing. Cotton fiber is processed into yarn and fabric. Yarn strength depends extremely on the quality of cotton. 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. Hence determining the quality of cotton accurately is an essential task to make better raw material choices in textile industry which in turn will support better buying and selling decisions. In this work, cotton quality prediction is modeled as classification task and implemented using supervised learning algorithms namely Multilayer Perceptron, Naive Bayes, J48 Decision tree, k-nearest neighbor 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 decision tree classifier produces high predictive accuracy compared to other models.
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    IMAGE COMPRESSION USING SINGLE LAYER LINEAR NEURAL NETWORKS
    (Elsevier Ltd, 2010) Arunapriya, B; Kavitha Devi, D
    Images and text form an integral part of website designing. Images have an engrossing appeal and that’s why they attract more and more visitors. But, due to expensive bandwidth and time-consuming downloads; it has become essential to compress images. There are various methods and techniques available to compress images. In this paper, an effective technique is introduced called Wavelet-Modified Single Layer Linear Forward Only Counter Propagation Network (MSLLFOCPN) technique to solve image compression. This technique inherits the properties of localizing the global spatial and frequency correlation from wavelets. Function approximation and prediction are obtained from neural networks. Consequently counter propagation network was considered for its superior performance and the research helps to propose a new neural network architecture named single layer linear counter propagation network (SLLC). Several benchmark images are used to test the proposed technique combined of wavelet and SLLC network. The experiment results when compared with existing and traditional neural networks shows that picture quality, compression ratio and approximation or prediction are highly enhanced.
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    SUPPORT VECTOR MACHINE BASED EPILEPSY PREDICTION USING TEXTURAL FEATURES OF MRI
    (Elsevier Ltd, 2010) Sujitha, V; Sivagami, P; Vijaya, M S
    Epilepsy is a disorder of the central nervous system, specifically the brain. It is a neurological malfunction affecting about 1% of the population and is the third most common neurological disorder following rheumatic heart disease and Alzheimer’s disease, but it imposes higher costs on society. Magnetic Resonance Imaging (MRI) is one of the most common diagnostic tests used for patients for epilepsy prediction. Shortage of radiologists and the large volume of MRI scan images that need to be analyzed may lead to labor intensive, expensive and inaccurate prediction. Hence there is a need to generate an efficient prediction model for making a correct diagnosis of epilepsy and accurate prediction of its type. This paper describes the modeling of epilepsy prediction using Support Vector Machines (SVM), a machine learning algorithm. The prediction model has been generated by training the support vector machine with descriptive features derived from MRI data of 350 patients and observed that the SVM based model with a Radial Basis Function (RBF) kernel produces 93.87% of prediction accuracy.
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    AN INTERACTIVE TOOL FOR YARN STRENGTH PREDICTION USING SUPPORT VECTOR REGRESSION
    (IEEE Xplore, 2010-05-06) Selvanayaki M; Vijaya M S; Jamuna K S; Karpagavalli S
    Cotton, popularly known as White Gold has been an important commercial crop of National significance due to the immense influence of its rural economy. Transfer of technology to identify the quality of fibre is gaining importance. The physical characteristics of cotton 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 determine the quality of cotton and in turn yarn strength. In this paper yarn strength prediction has been modeled using regression. Support Vector regression, the supervised machine learning technique has been employed for predicting the yarn strength. The trained model was evaluated based on mean squared error and correlation coefficient and was found that the prediction accuracy of SVR based model, the intelligence reasoning method is higher compared with the traditional statistical regression, the least square regression model.
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    CLASSIFICATION OF SEED COTTON YIELD BASED ON THE GROWTH STAGES OF COTTON CROP USING MACHINE LEARNING TECHNIQUES
    (IEEE Xplore, 2010-07-29) Jamuna, K S; Karpagavalli, S; Vijaya, M S; Revathi, P; Gokilavani, S; Madhiya, E
    Cotton, popularly known as "White Gold" has been an important commercial crop of national significance due to the immense influence of its rural economy. Cotton seed is an important and critical link in the chain of agricultural activities extending farmer industry linkage. Cotton yield is associated with high quality seed as the seed contains in itself the blue print for the agrarian prosperity in incipient form. Transfer of technology to identify the quality of seeds is gaining importance. Hence this work employs machine learning approach to classify the quality of seeds based on the different growth stages of the cotton crop. Machine learning techniques - Naïve Bayes Classifier, Decision Tree Classifier and Multilayer Perceptron were applied for training the model. Features are extracted from a set of 900 records of different categories to facilitate training and implementation. The performance of the model was evaluated using 10 -fold cross validation. The results obtained show that Decision Tree Classifier and Multilayer Perceptron provides the same accuracy in classifying the seed cotton yield. The time taken to build the model is higher in Multilayer Perceptron as compared to the Decision Tree Classifier.
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    ELECTROENCEPHALOGRAM WAVE SIGNAL ANALYSIS AND EPILEPTIC SEIZURE PREDICTION USING SUPERVISED CLASSIFICATION APPROACH
    (ACM Digital Library, 2010-09-16) Devi S. T, Pavithra; Vijaya, M S
    The transient and unexpected electrical disturbances of the brain results in an acute disease called Epileptic seizures. A significant way for identifying and analyzing epileptic seizure activity in human is by using electroencephalogram (EEG) signal. Manually reviewing and analyzing lengthy data of EEG recordings, for detection and classification of electro graphical patterns at present requires trained personnel and time consuming. Hence, there is a need for an efficient automated system based on pattern classification for analysis and classification of seizure-related EEG signals to assist the expert in the diagnosis. This paper presents the modeling of epileptic seizure prediction as binary classification problem and provides a suitable solution by implementing supervised classification algorithms, namely Decision table, Naive Baye's Tree, k-NN and support vector machine. The classification models are trained using the EEG data sets and the prediction accuracy of the classifier has been evaluated using 10-fold cross validation. It has been observed that the model produce about 86% of prediction accuracy in predicting the presence of epileptic seizure in human brain.
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    SPAM CLASSIFICATION USING SUPERVISED LEARNING TECHNIQUES
    (ACM Digital Library, 2010-09-16) Deepa Lakshmi, R; Radha, N
    Spam message is one of the major problems in today's Internet, which brings financial damage to companies and annoying individual users. Spam filtering is able to control the problem in a variety of ways. Many researches in spam filtering have been centered on the classifier-related issues. Currently, machine learning for spam classification is an important research issue at present. This paper explores and identifies the use of different machine learning algorithms for classifying spam messages from e-mail. Finally, a comparative analysis among the algorithms has also been presented with spam classification.
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    CUSTOMER PERCEPTION AND PREFERENCE TOWARDS BRANDED PRODUCTS (WITH SPECIAL REFERENCE TO TELEVISION SETS)
    (Indian Journal of Marketing, 2010-02) Lilly, J
    People begin to develop preferences at a very early age. Some babies like apple juice, others water. Some kids play softball, others read. Some people thrive in the city and some need the quiet of the country. Some drink Coke while others prefer Pepsi. Our preferences are part of what makes us who we are. And the brands we seek out reflect our preferences. The competition among brands is fierce. In every product category, consumers have more choices, more information and higher expectations than ever before. Jockeying for position in a consumer's preference set requires an aggressive strategy and constant vigilance. The marketer's principal objective is typically to build a relationship with buyers, rather than merely to make a single sale. Ideally, the essence of that relationship consists of a strong bond between the buyer and the brand. The choice of an individual strategy or combination depends mainly on the nature of the branded product or service. The success of the strategy depends heavily on the marketer's understanding of the preference building and bonding process.
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    STUDY ON THE INHIBITION OF MILD STEEL CORROSION BY BENZOISOXAZOLE AND BENZOPYRAZOLE DERIVATIVES IN H2SO4 MEDIUM
    (Portugaliae Electrochimica Acta, 2010) Parameswari, K; Rekha, S; Subramanian, Chitra; Kayalvizhy, E
    Four heterocyclic compounds, namely 4- phenyl-5-acetyl/carbethoxy-3-methyl-6-hydroxyl-6-methyl-4,5,6,7-tetrahydro-2,1-benzoisoxazole and benzopyrazole (BIS1, BP1and BIS2, BP2), were synthesized and their influence on the inhibition of corrosion of mild steel in 1 M H2SO4 was investigated by means of weight loss, potentiodynamic polarization, electrochemical impedance (EIS) and scanning electron microscopy (SEM). The values of activation energy and free energy of adsorption of these compounds were also calculated. Adsorption obeys Langmuir adsorption isotherm. The IE of the compounds was found to vary with concentration and temperature. Synergistic effect was also investigated for the four compounds at 0.05 mM concentration by weight loss method in 1 M H2SO4 medium in presence of KI, KBr and KCl. Results obtained revealed that all the four compounds performed excellently as a corrosion inhibitor for mild steel in 1 M H2S04 and their efficiency attains more than 90% at 0.6 mM at 298 K. Polarisation studies showed them to be mixed type inhibitors.
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    EFFECT OF AZLACTONES ON CORROSION INHIBITION OF MILD STEEL IN ACID MEDIUM
    (Journal of Applied Sciences Research, 2010) Parameswari, K; Chitra, S; Nusrath Unnisa, C; Selvaraj, A
    The inhibition efficiency of azlactones on corrosion behaviour of mild steel in 1M H2SO4 has been evaluated using weight loss, gasometry and atomic absorption spectroscopy techniques which shows increase in inhibition efficiency with increase in concentration.Effect of temperature strongly ensures stronger physisorption of the compounds, which obeys Langmuir adsorption isotherm. The inhibition efficiency has been synergistically enhanced by the addition of halide ions. The kinetic corrosion parameters analysed in terms of impedance data shows a satisfactory agreement with those obtained by potentiodynamic polarisation method.