Browsing by Author "Arunpriya C"

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ASSESSING FOOD VOLUME AND NUTRITIOUS VALUES FROM FOOD IMAGES USING DECISION TREE APPROACH
(International Research Journal of Engineering and Technology, 2019-12) Gopiga T; Arunpriya C
Obesity and being overweight have become growing concerns due to their association with many diseases, such as type II diabetes, several types of cancer and heart disease. Thus, obesity treatments have been the focus of a large number of recent studies. Because of these studies, researchers have found that the treatment of obesity and being overweight requires constant monitoring of the patient’s diet. One of the important steps in the success of healthy diet is measuring food intake each day. One of the challenges in obesity management studies is measuring daily food consumption for obese patients. Countless recent studies have suggested that using technology like smart phones may enhance the under-reporting issue in dietary intake consumption. In this thesis, we propose a Food Recognition System (FRS) for calories and nutrient values assumption. The user employs the built-in camera of the smart phone to take a picture of any food before and after eating. The system then processes and classifies the photographs to discover the kind of food, portion size and then uses the knowledge to estimate the quantity of calories within the food using decision tree. An essential step in the system as it is used to estimate and calculate the food volume and amount of calories in the image
BREAST CANCER DETECTION USING BPN CLASSIFIER AND GREY LEVEL CO-OCCURRENCE MATRIX
(International Journal for Science and Advance Research In Technology, 2019-12) Gayathri J; Arunpriya C
This paper describes a computer-aided detection and diagnosis system for breast cancer, the most common form of cancer among women, using mammography. The system relies on the Multiple-Instance Learning (MIL) paradigm, which has been proven useful for medical decision support in previous works. In the proposed framework, the initial step is Partitioning; breasts are first partitioned adaptively into regions. The Grey level cooccurrence Matrix (GLCM) Features are extracted from wavelet sub bands. Then, features derived from the appearance of textural features as well as detection of lesions (masses and micro calcifications) are extracted from each region and combined in order to classify it into examinations of mammography as “normal” or “abnormal”. Whenever an abnormal examination record is detected, the regions that induced the automated diagnosis can be highlighted. There arise two strategies to define this anomaly detector. In a first scenario, manual segmentations of lesions are used to train an NN that assigns an anomaly index to each region; local anomaly indices are then combined into a global anomaly index.
CLOUD COMPUTING HYBRID SECURITY FROM SINGLE TO MULTI CLOUD SERVERS
(Iconic Research and Engineering Journals, 2019-12) Deepika K; Deepika M; Arunpriya C
Nowadays, storing and accessing data in multi-cloud infrastructure is a common solution adopted by large organizations. In this paper it presents two components mainly Administration Management and User Management. It contains the list of branches available for the bank in different countries and tree view which shows the country names under each country created. End User has manifested by administrator with the ability to identify and control the state of users logged into the account. The saving/current account holders can check person’s own account balance; list of transactions done by the user, account personal information can be edited efficiently by giving request to the admin. The account holder can view that information only with the unique user id and password provided by the bank. After those process completed successfully a message will be displayed to the user about the transaction. If the account holder provides the wrong user ID or Password it will provide an error. If the intruder deletes the database, the database will be backed up by checking the nearest server, traffic and available storage of the multi-server. The encrypted key will be received immediately by the admin through mail to restore the deleted database. Data security for such a cloud service encompasses several aspects including secure channels, access controls, and encryption. And, when it considers the security of data in a cloud, it also must consider the security triad such as: confidentiality, integrity, and availability. In the cloud storage model, data is stored on multiple virtualized servers.
A COMPARATIVE ANALYSIS OF WEB BASED MULTIPLE SEQUENCE ALIGNMENT TOOLS USING CERTAIN METABOLICALLY IMPORTANT PROTEIN CODING GENE SEQUENCES
(Dr.NGP Arts and Science College, Coimbatore, 2018-09-02) Boobashini S; Arunpriya C; Balasaravanan T
Multiple sequence alignment is an alignment of three or more biological sequences, generally protein, DNA, or RNA. The input set of query sequences are assumed to have an evolutionary relationship i.e., they are descended from a common ancestor. The resulting MSA, sequence homology can be inferred and phylogenetic analysis can be conducted to assess the sequences' shared evolutionary origins. In this paper, six different mammalian species gene sequence were compared with human gene sequences. Metabolically important genes such as Amylase, ATPase, Cytochrome-B, Haemoglobin, and Insulin where chosen for comparison. The DNA sequences of FASTA format was retrieved from NCBI databank and used as input sequences for Multiple sequence analysis using ClustalW, MUSCLE, and T-Coffee. Multiple sequence alignment score and phylogenetic trees where obtained from all the three tools and discussed with the snapshots and findings.
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.
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
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.
AN EFFICIENT HIERARCHICAL CLUSTERING ALGORITHM FOR PROTEIN SEQUENCING
(Government College of Technology, Coimbatore, 2009-02-22) Arunpriya C; Meera S; Balasaravanan T
Clustering is the division of data into groups of similar objects. The main objective of this unsupervised leaming technique is to find a meaningful partition by using a distance or similarity function. This paper discusses about the incremental clustering algorithm-Leaders and Sub leaders- an extension of leader algorithm, suitable for protein sequences of bioinformatics is proposed for effective clustering and prototype selection for pattern classification .It is a simple and efficient technique to generate a hierarchical structure for finding the sub clusters within each cluster. The experimental results of the proposed algorithm are compared with that of the Nearest Neighbour Classifier (NNC) methods. It is found to be computationally efficient when compared to NNC. Classification accuracy obtained using the representatives generated by Leader - Sub leader method is found to be better than that of using the Leaders method and NNC method. Even if more number of prototypes is generated classification time is less when compared to NNC methods
AN EFFICIENT LEAF RECOGNITION ALGORITHM FOR PLANT CLASSIFICATION USING KERNELIZED SUPPORT VECTOR MACHINE
(International Journal of Computer Science and Management Research (IJCSMR), 2013-02) Arunpriya C; Antony Selvadoss Thanamani
Plant recognition 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 values of the optimized σ are then used as a gauge for variable selection. In this study Kernelized (K-SVM) model is applied to several benchmark data sets in order to estimate the effectiveness of the second-order sigma tuning procedure for an RBF kernel.12 leaf features which are extracted and orthogonalized into 5 principal variables are given as input vector to the K-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.
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.
ENHANCED SENTENCE-LEVEL TEXT CLUSTERING USING SEMANTIC SENTENCE SIMILARITY FROM DIFFERENT ASPECTS
(International Journal of Computer Science and Information Technologies, 2014) Saranya J; Arunpriya C
Sentence clustering plays a significant role in many text processing activities. For instance, several authors have discussed that integrate sentence clustering into extractive multi document summarization useful to address issues of content overlap, leading to better coverage. Existing work proposed fuzzy clustering algorithm which is used for relational input data. This existing algorithm uses a graph representation of the data, and performs based on Expectation-Maximization framework. Proposed system improves the result of the clustering by introducing the novel sentence similarity technique. In our proposed system we are propose a new way to determine sentence similarities from different aspects. Probably based on information people can obtain from a sentence, which is objects the sentence describes, properties of these objects and behaviors of these objects. Four aspects, Objects-Specified Similarity, Objects-Property Similarity, Objects-Behavior Similarity and Overall Similarity are calculated to estimate the sentence similarities. First, for each sentence, all nouns in noun phrases are chosen as the objects specified in the sentence, all adjectives and adverbs in noun phrases as the objects properties and all verb phrases as the objects behaviors. Then, the four similarities are calculated based on a semantic vector method. We also conducted an experimental study with that could help us to efficiently clustering the sentence level text. Our study shows that this algorithm generates better quality clusters than traditional algorithms; in other words, it is benefits to increase the accuracy of the clustering result.
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.
FORECASTING VEGETABLE PRICE USING TIME SERIES DATA
(International Journal of Advanced Research, 2016-05) Subhasree M; Arunpriya C
Predicting the vegetable price is essential in agriculture sector for effective decision making. This forecasting task is quite difficult. Neural network is self-adapt and has excellent learning capability and used to solve variety of tasks that are intricate. This model is used to predict the next day price of vegetable using the previous price of time series data. The three machine learning algorithms are incorporated in this work namely Radial basis function, back propagation neural network and genetic based neural network are compared. The models are assessed and it is concluded from the derived accuracy that the performance of genetic based neural network is better than back propagation neural network and radial basis function and improves the accuracy percentage of vegetable price prediction.
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
FUZZY SHRINK THRESHOLDING BASED TEA LEAF IMAGE ENHANCEMENT USING WAVELET TRANSFORM
(International Journal of Computer Applications (IJCA), 2013-02) Arunpriya C; Antony Selvadoss Thanamani
In this paper a wavelet shrinkage algorithm based on fuzzy logic is proposed to improve the tea leaf image. The Tea Leaf images are normally changes to unclear images by the presence of noise, low or high dissimilarity both in the edge area and also in the image area. The Fuzzy shrink is used to enhance the image. In exacting, intra-scale dependency within wavelet coefficients is modeled using a fuzzy characteristic. This characteristic space distinguishes between significant coefficients, which depends on image discontinuity and noisy coefficients. This fuzzy characteristic is used for enhancing wavelet coefficients' information in the shrinkage step in this paper. Then a fuzzy membership function known as the spline-based curve is used to shrinks the wavelet coefficients based on the fuzzy characteristic. Here by using the interrelation between different channels as a fuzzy characteristic for improving the denoising performance compared to denoising each channel, separately. Examine the image denoising algorithm in the dual-tree discrete wavelet transform, which is the latest shiftable and customized version of discrete wavelet transform. Extensive comparisons with the high-tech image denoising algorithm indicate that the image denoising algorithm has a better performance in noise suppression and edge preservation as compared with the other methods. The spline based curve of a fuzzy membership function is more efficient one
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.
MONEY DEMONETIZATION TOWARDS MOBILE DIGITIZATION IN INDIA
(PSGR Krishnammal College for Women, Coimbstore, 2017-02-22) Arunpriya C; Kowsalya S
The demonetisation of all Rs. 500 and Rs.1,000 would curtail the shadow economy and crack down on the use of illicit and counterfeit cash to fund illegal activity and terrorism. The scarcity of cash due to demonetisation led to chaos, and most people holding old banknotes faced difficulties exchanging them due to endless lines outside banks and ATM across India. At this point, India moved to modernize the way things are paid for. New bank accounts are being opened at a heightened rate, e-payment services are seeing rapid go cash-on-delivery in e-commerce has crashed, and digitally-focused sectors like the online grocery business have started booming. In such a scenario, mobile as a platform has a unique set of capabilities that can overcome the challenges posed by the Indian payments landscape. Mobiles offer a low-cost means to create financial access and payments.
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.
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.
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.