Browsing by Author "Elizabeth Shanthi, I"
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Item Adaptive Route Optimization in Hierarchical Mobile IPV6 Networks(2010-04) Sathya Priya, P; Elizabeth Shanthi, IItem Analysing Performance Monitoring of Different Classification Techniques for Diebetic Data(2016) Elizabeth Shanthi, IData mining is the current research area to solve various problems and classification is one o f the main problem in the field o f data mining. It allows users to analyze data from different dimensions or angles, categorize it, and summarize the relationships identified. Classification is used to classify each item in a set o f data into one o f predefined set o f classes or groups. In this paper we present algorithmic discussion ofJ48, Random tree, J48 Graft, LAD and REP. Here the performance is compared for computing time, correctly classified instances, kappa statistics, RMSE, MAE, RRSE, RAE and to find the error rate measurement for different classifiers using weka tool. We have taken the classification o f data for diabetic patients data set is developed by collecting data from hospital repository which has 1865 instances with different attributes. The dataset instances consist o f two categories o f blood tests and urine tests. Weka tool classifies the data and is evaluated using 10 fold cross validation and the results are then measured. It discovered that J48 performs better in most o f the cases.Item Applying Clustering Techniques for Efficient Text Mining in Twitter Data(2015) Elizabeth Shanthi, IBCnowledge is the ultimate output of decisions on a dataset. The revolution of the Internet has made the global distance closer with the touch on the hand held electronic devices. Usage of social media sites have increased in the past decades. One of the most popular social media micro blog is Twitter. Twitter has millions of users in the world.^In this paper the analysis of Twitter data is performed through the text contained in hash tags. After Preprocessing clustering algorithms are applied on text data. The different clusters igjj compared through various parameters. alization techniques are used to portray the results from which inferences like time series and topic flow can be easily made. The observed results show that the hierarchical clustering algorithm performs better than other algorithms.Item Biometric Authentication Using Iris(2009-04) Saranya, R; Elizabeth Shanthi, IItem Bitcoin Price Prediction Using Deep Learning and Sentiment Analysis(2021-05) Vibitha, M; Elizabeth Shanthi, IItem Cluster Based Boosting Techniques for Accuracy Prediction(2016) Elizabeth Shanthi, IIn recent years encroachment of social media in human life is inexplicable. The greatest and most significant issue in latest technology is the fast retrieval of information from large databases. To overcome this, different techniques have been developed. Improving the retrieval process and its accuracy is a vital factor in the predictive analyses of Machine learning. Boosting is one of the techniques used for enhancing the accuracy of prediction in supervised learning. In this process, lot of noise data may arise which adds another problem in handling training data set. Extracting accurate data from the training dataset is an essential factor in applying clustering techniques. This paper surveys the various boosting/clustering techniques and compares them with a motive to suggest a better environment which is free from noise/errors so as to have maximum accuracy on the output data.Item A Comparative Analysis of Classification Algorithms for Text Classification Using Fuzzy Self-Constructing Feature(2013-05) Saranya, J; Elizabeth Shanthi, IItem Content-Based Image Retrieval Using Enhanced Local Tetra Patterns(2013-05) Kavitha, K; Elizabeth Shanthi, IItem Contrivance Planning Management System(2015-03) Vaishnavi, D; Elizabeth Shanthi, IItem Detection Of Phishing Websites Using Ensemble Machine Learning Algorithms(2022-05) Sharmila Devi, M; Elizabeth Shanthi, IItem Distributed Download Manager(2004-04) Vineetha P S; Elizabeth Shanthi, IItem Electronic Cheque Processing System(2005-05) Remya Subramanyam; Elizabeth Shanthi, IItem An Enhanced Approach for Compress Transaction Databases(2012) Elizabeth Shanthi, IAssociative rule mining is defined as the task that deals with the extraction of hidden knowledge and-frequent patterns from very large databases. Traditional associative mining processes are iterative, time consuming and storage expensive. To solve these processes, a way of representation that reduces this size and at the same time maintains all the important and relevant data needed to extract the desired knowledge from transaction databases is needed. This paper proposes a method that merges the transactions in the transaction database and uses FP-Growth algorithm for mining associative knowledge is presented. The experimental results in terms of compression ratio, both in terms of storage required and number of transactions, prove that the proposed algorithm is an improved version to the existing systems.Item Flying Bird Species Recognition Using Selective Machine Learning Techniques(2019-07) Annalakshmi, K; Elizabeth Shanthi, IItem Going Green with loT for Smart World - An Overview(2016) Elizabeth Shanthi, ISmart world is planned as an epoch in which objects (e.g., watches, mobile phones, computers, cars, buses and trains) can ii^ ed ia te ly and intelligently serve people in a coefficient maimer. Internet of things links up everything in the smart world. Internet of things allows objects to be sensed and controlled remotely across exiting network infrastructure, creating opportunities for more-direct integration between the physical world and computer -based systems, and resulting in improved efficiency, accuracy and economic benefits. Each thing is exclusively different through its embedded computing system but is able to interoperate within the surviving internet infrastructure. Today’s earth encloses a smart reminder that the internet of things can be made green- and green technology can be maximized with smart use of loT. loT implement the collection of data at finer levels of details, and deeper analysis of that data, business and individuals can drive bigger results from smaller changes to their immediate environment. Internet of things is that things can correspond to each other without human with each other and, helps to save energy with user. This permits peoples and things to be connected Anytime, Anyplace, with anything and anyone, ideally using any path/network and any service. Green loT forecast to familiarize changes in our daily life and would help realizing the vision of “Green ambient intelligence”.Item Implementation of Modbus Protocol(2004-04) Annamole Paul; Elizabeth Shanthi, IItem Incidnet Management System(2014-03) Dhivya, S; Elizabeth Shanthi, IItem Information Retrieval on Object Oriented Data Base Systems(2008-07-07) Elizabeth Shanthi, I; Nadarajan, RItem An Integrated Framework for Students’ Academic Performance Prediction Based on Affective and Cognitive State using Mixed Numeric and Categorical Data(2023-08) Amala Jayanthi, M; Elizabeth Shanthi, IItem Mailserver For TNPL(2005-05) Gayathri.k; Elizabeth Shanthi, I