Department of Computer Science (UG)

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Author: search.filters.author.Antony Selvadoss Thanamani

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    GREEN COMPUTING – CURRENT TO FUTURE TRENDS
    (PSGR Krishnammal College for Women, Coimbstore, 2013-01-10) Arunpriya C; Antony Selvadoss Thanamani
    Green Computing starts from design to manufacturing, use to disposing –off computer resources in an efficient and effective manner. In recent, year attention in the research area of Green Computing' has moved energy saving methods from home computers to enterprise systems. The IT Community has a significant impact on the World wide carbon foot print saving energy or reduction of carbon footprints is the main aspect of Green computing. The research in Green Computing is more than just saving energy and reducing carbon foot prints. In current trends of green computing the impact is on the reduced energy utilization and increased performance of computing. The major issue that is to be considered in today's IT scenario is the shifting of infra structure. This shift is a great challenge for IT industry. Therefore researchers are focusing on cooling system, power and data center space. Green computing challenge is not only for equipment users but also for IT equipment vendors. This study provides a brief account on current trends in Green Computing; E challenges in the field of Green Computing and the future trends of Green Computing.
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    AN EFFICIENT LEAF RECOGNITION ALGORITHM FOR PLANT CLASSIFICATION USING SUPPORT VECTOR MACHINE
    (Periyar University, Salem., 2012-03-21) Arunpriya C; Balasaravanan T; Antony Selvadoss Thanamani
    Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. This paper uses an efficient machine learning approach for the classification purpose. This proposed approach consists of three phases such as preprocessing, feature extraction and classification. The preprocessing phase involves a typical image processing steps such as transforming to gray scale and boundary enhancement. The feature extraction phase derives the common DMF from five fundamental features. The main contribution of this approach is the Support Vector Machine (SVM) classification for efficient leaf recognition. 12 leaf features which are extracted and orthogonalized into 5 principal variables are given as input vector to the SVM. Classifier tested with flavia dataset and a real dataset and compared with k-NN approach, the proposed approach produces very high accuracy and takes very less execution time.
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    AN EFFICIENT CANCER CLASSIFICATION USING EXPRESSIONS OF VERY FEW GENES USING SUPPORT VECTOR MACHINE
    (Sun College of Engineering and Technology, Nagercoil, 2011-03-24) Arunpriya C; Balasaravanan T; Antony Selvadoss Thanamani
    Gene expression profiling by microarray technique has been effectively utilized for classification and diagnostic guessing of cancer nodules. Several machine learning and data mining techniques are presently applied for identifying cancer using gene expression data. Though, these techniques have not been proposed to deal with the particular needs of gene microarray examination. Initially, microarray data is featured by a high-dimensional feature space repeatedly surpassing the sample space dimensionality by a factor of 100 or higher. Additionally, microarray data contains a high degree of noise. The majority of the existing techniques do not sufficiently deal with the drawbacks like dimensionality and noise. Gene ranking method is later introduced to overcome those problems. Some of the widely used Gene ranking techniques are T-Score, ANOVA, etc. But those techniques will sometimes wrongly predict the rank when large database is used. To overcome these issues, this paper proposes a technique called Enrichment Score for ranking purpose. The classifier used in the proposed technique is Support Vector Machine (SVM). The experiment is performed on lymphoma data set and the result shows the better accuracy of classification when compared to the conventional method.
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    FACIAL ANIMATION TECHNIQUE
    (PSGR Krishnammal College for Women, Coimbatore, 2011-10-01) Arunpriya C; Antony Selvadoss Thanamani
    An unsolved problem in computer graphics is the construction and animation of realistic human facial models. Traditionally, facial models have been built painstakingly by manual digitization and animated by ad hoc parametrically controlled facial mesh deformations or kinematics approximation of muscle actions. Fortunately, animators are now able to digitize facial geometries through the use of scanning range sensors and animate them through the dynamic simulation of facial tissues and muscles. However, these techniques require considerable user input to construct facial models of individuals suitable for animation polygonal modeling specifies exactly each 3d point, which connected to each other as polygons. This is an exacting way to get topology. Patches indirectly defines a smooth curve surface from a set of control points. A small amount of control points can define a complex surface. One type of spline is called NURBS, which stands for Non Uniform Rational B-Splines. This type of batch allows each control point to have its own weight that can affect the "pinch'" of the curve at the point. So they are considered the most versatile of batches. They work very well for organic smooth objects so hence they are well suited for facial modeling.
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    FUZZY INFERENCE SYSTEM ALGORITHM OF PLANT CLASSIFICATION FOR TEA LEAF RECOGNITION
    (Indian Journal of Science and Technology, 2015) Arunpriya C; Antony Selvadoss Thanamani
    Background/Objectives: Biologists found that the morphological, physiological, bio-chemical and molecular methods of plant identification are found to be laborious and require great amount of technical knowledge. This research paper concentrates on the identification of varieties of tea using leaf images. It aims to identify the species in an easy and an accurate manner. Methods/Statistical analysis: The phases involved in this work are image pre processing, feature extraction and classification. Three classification algorithms such as Fuzzy Inference system, Radial basis function network and K-nearest neighbour were used and optimized to achieve a better accuracy and execution time. Results/Findings: The classification algorithm K-nearest neighbor, Radial basis function neural network and Fuzzy Inference System are trained with 40 samples and tested using 20 samples. Conclusions: Fuzz
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    A NEW FRAMEWORK FOR TEA PLANT RECOGNITION USING EXTREME LEARNING MACHINE WITH VERY FEW FEATURES
    (International Journal of Applied Engineering Research, 2015) Arunpriya C; Antony Selvadoss Thanamani
    Due to more and more tea varieties in the current tea market, rapid and accurate identification of tea varieties is crucial for tea quality control. Tea quality mainly depends on the variety of leaf, growing environment, manufacturing conditions, size of ground tea leaves and infusion preparation. In the past few years, tea cultivar has been assessed by morphological assessment coupled with pattern recognition. This paper uses an efficient machine learning approach called Extreme Learning Machine (ELM) for the classification purpose. The proposed approach consists of four phases which are as preprocessing, feature extraction, feature clustering and classification. Additionally, this work proposes an iterative algorithm for feature clustering and applies it to leaf recognition. Feature clustering is a powerful tool to reduce the dimensionality of the selected feature. For improving the accuracy and performance of tea leaf recognition, ELM is implemented. The classifier is tested with 20 leaves from each variety and compared with k-NN and RBF approach. The proposed ELM classification produces effective results.
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    AN EFFECTIVE TEA LEAF RECOGNITION ALGORITHM FOR PLANT CLASSIFICATION USING IMPROVED ANFIS ALGORITHM
    (European Journal of Scientific Research, 2014) Arunpriya C; Antony Selvadoss Thanamani
    A leaf is an organ of a vascular plant, as identified in botanical terms, and in particular in plant morphology. Naturally a leaf is a thin, flattened organ bear above the ground and it is mainly used for photosynthesis. Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. Most of the leaves cannot be recognized easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons). In this paper we have attempted to identify tea plant cultivars using classification techniques. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction and classification to process the loaded image. The tea leaf images can be identified accurately in the preprocessing phase by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT) in order to remove the noisy features and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) are derived to improve the classification accuracy. Improved Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for efficient classification. The ANFIS is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method classifies the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in identifying the leaf type.
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    AN EFFECTIVE TEA LEAF RECOGNITION ALGORITHM FOR PLANT CLASSIFICATION USING RADIAL BASIS FUNCTION MACHINE
    (International Journal of Modern Engineering Research, 2014-03) Arunpriya C; Antony Selvadoss Thanamani
    A leaf is an organ of a vascular plant, as identified in botanical terms, and in particular in plant morphology. Naturally a leaf is a thin, flattened organ bear above ground and it is mainly used for photosynthesis. Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. Most of the leaves cannot be recognized easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons).In this paper, we mainly focused on tea leaves to identify the leaf type for improving tea leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction and classification to process the loaded image. The tea leaf images can be identified accurately in the preprocessing phase by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT) in order to remove the noisy features and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) are derived to improve the classification accuracy. Radial Basis Function (RBF) is used for efficient classification. The RBF is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method classifies the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in retrieving the leaf type. Keywords
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    A NOVEL LEAF RECOGNITION TECHNIQUE FOR PLANT CLASSIFICATION
    (International Journal of Computer Engineering and Applications, 2014-02) Arunpriya C; Antony Selvadoss Thanamani
    Plants are the distinctive living things which incorporate many good things in it. At present due to environment degradation, many rare plant species on the earth are still unknown and are at the boundary of extinction. This must be avoided and they have to be preserved. This paper mainly focused on extraction of features for accurate classification of its types. Each plant leaves are different in shape, texture etc. By extracting the unique features in it they can be easily classified. Morphological and geometrical features from leaves are extracted here. Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for efficient classification. The ANFIS is trained by 50 different leaves to classify them into 5 types and its efficiency is calculated by accuracy and execution time factor.
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    A NEW LEAF ANALYSIS AND CLUSTERING FOR TEA SPECIES IDENTIFICATION
    (International Journal of Computer Engineering and Technology (IJCET), 2014-02) Arunpriya C; Antony Selvadoss Thanamani
    Leaf is an important organ of the plant. It is widely used for many purposes such as in medical field, chemical and other research purposes. Now it becomes active area for analysis of plants as most of the plant species are at the risk of extinction. Most of the leaves cannot be analyzed easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons).In this paper, we mainly focused on tea leaves to identify the leaf type for improving tea leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction, selection and finally clustering of leaves. The tea leaf images are first preprocessed to remove the noise and enhanced by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) and Geometrical features are extracted and from that main features are selected. They are given to the clustering process which is done by using Fuzzy C-Means algorithm, it clearly cluster different type of tea leaves. The Fuzzy C-Means is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method clustered the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in clustering the leaf type.