International Journals

Permanent URI for this collectionhttps://dspace.psgrkcw.com/handle/123456789/157

Browse

Author: search.filters.author.V, Pream SudhaEnd date: 2019

Search Results

Now showing 1 - 5 of 5
  • No Thumbnail Available
    Item
    FEATURE SELECTION TECHNIQUES FOR THE CLASSIFICATION OF LEAF DISEASES IN TURMERIC
    (International Journal of Computer Trends and Technology (IJCTT), 2017) V, Pream Sudha
    Crop maintenance is one of the crucial factors that determine the quantity and quality of the agricultural products. Protecting crops from plant diseases is an important aspect that increases the profit of the farmer. This study aims at developing a computational model that will facilitate crop production by accurately identifying diseases that affect productivity of turmeric plants. The turmeric leaf is highly exposed to diseases like rhizome rot, leaf spot, and leaf blotch. This system uses technologies such as feature selection and machine learning techniques for the identification and classification of diseases in turmeric leaf. Principal component analysis, Information gain and Relief-f attribute evaluator methods were investigated in combination with machine learning algorithms like Support Vector Machine, Decision Tree and Naïve Bayes. The performance of the models were evaluated using 10 fold cross validation and the results were reported. Comparatively, the model using SVM applied to features selected using Information gain performed well with an accuracy of 93.75.
  • No Thumbnail Available
    Item
    A SURVEY ON DEEP LEARNING TECHNIQUES APPLICATIONS AND CHALLENGES
    (International Journal of Advance Research In Science And Engineering, 2015) V, Pream Sudha; R, Kowsalya
    Deep learning is an emerging research area in machine learning and pattern recognition field. Deep learning refers to machine learning techniques that use supervised or unsupervised strategies to automatically learn hierarchical representations in deep architectures for classification. The objective is to discover more abstract features in the higher levels of the representation, by using neural networks which easily separates the various explanatory factors in the data. In the recent years it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. This paper presents a brief overview of deep learning, techniques, current research efforts and the challenges involved in it
  • No Thumbnail Available
    Item
    A TREE BASED MODEL FOR HIGH PERFORMANCE CONCRETE MIX DESIGN
    (International Journal of Engineering Science and Technology, 2010) C, Deepa; K, Sathiya Kumari; V, Pream Sudha
    Concrete is the sustainable construction material, which is most widely used in the world as it provides superior fire resistance, gains strength over time and gives an extremely long service life. Its annual consumption is estimated between 21 and 31 billion tones. The paper is aimed at guiding the selection of available materials and proportioning them as to produce the most economical concrete suitable for the desired purpose. According to the National Council for Cement and Building Materials (NCBM), New Delhi, the compressive strength of concrete is governed generally, by the water-cement ratio. The mineral admixtures like fly ash, ground granulated blast furnace, silica fume and fine aggregates also influence it. The main purpose of this paper is to find the accuracy for the compressive strength of high performance concrete by using classification algorithms like Multilayer Perceptron, Rnd tree models and C-RT regression. The result from this study suggests that tree based models perform remarkably well for designing the concrete mix.
  • No Thumbnail Available
    Item
    PREDICTION OF THE COMPRESSIVE STRENGTH OF HIGH PERFORMANCE CONCRETE MIX USING TREE BASED MODELING
    (International Journal of Computer Applications, 2010) C, Deepa; V, Pream Sudha; K, SathiyaKumari
    Concrete is the safest and sustainable construction material which is most widely used in the world as it provides superior fire resistance, gains strength over time and gives an extremely long service life. Its annual consumption is estimated between 21 and 31 billion tones. Designing a concrete mix involves the process of selecting suitable ingredients of concrete and determining their relative amounts with the objective of producing a concrete of the required, strength, durability, and workability as economically as possible. According to the National Council for Cement and Building Materials (NCBM), New Delhi, the compressive strength of concrete is governed generally, by the water-cement ratio. The mineral admixtures like fly ash, ground granulated blast furnace, silica fume and fine aggregates also influence it. The main purpose of this paper is to predict the compressive strength of the high performance concrete by using classification algorithms like Multilayer Perceptron, M5P Tree models and Linear Regression. The result from this study suggests that tree based models perform remarkably well in predicting the compressive strength of the concrete mix.
  • No Thumbnail Available
    Item
    A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS APPLIED TO PREDICTIVE DIABETES DATA
    (CiiT International Journal of Data Mining and Knowledge Engineering, 2009-11-25) K, Sathiyakumari; V, Pream Sudha
    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 – Naïve 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.