Department of Computer Science (UG)

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Author: search.filters.author.Arunpriya CStart date: 2009

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Now showing 1 - 10 of 28
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    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.
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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 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
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    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.
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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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    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
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    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.
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    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.