Browsing by Author "Kalpana, B"
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Item Crime Analysis And Prediction Using Machine Learning(2022-05) Pavithra, R; Kalpana, BItem Crime Rate Prediction(2021-05) Mahalakshmi, A; Kalpana, BItem Detecting Fake Accounts in Online Social Networks(2022-05) Reshma, R; Kalpana, BItem Detecting Phishing in Websites – a Comparison of Classification Techniques(2019-04) Bharathi, R; Kalpana, BItem Development of An Online Shopping Portal(2011-04) Divya, N; Kalpana, BItem Distributed Data Association for Multitarget Tracking-A Mathematical Perspective(2011) Kalpana, BTarget tracking is the technique of maintaining state estimates of one or more targets over a period of time. Multitarget tracking is concerned with the state estimation of an unknown number of moving targets. The objective of multitarget tracking is to enable the sensor subsystem to identify and track multiple targets given atmospheric disturbances and clutter environment, which obscure the target. In this paper, we present certain mathematical models with kalman consensus filter, data association concepts and maximum mutual information based sensor selection related to our work and propose track to track fusion which is a well-organized technique for multitarget tracking. We prove theoretically that mathematical models can improve the distributed data association technique with mutual information based sensor selection and it results in an optimal feasible solution for multitarget tracking.Item Dynamic Log Session Identification Using A Novel Incremental Learning Approach For Database Trace Logs(2015) Kalpana, BIdentification of session is very significant task in database for determining helpful patterns from database trace logs files In recent years, several number of work have been done to dynamic log session identification among them n-gram models is recently proposed, which produces higher log session identification results. But the major issue of the n-gram model is that it assumes the entire database query to be static, so dynamic query type is not applicable. In this paper, a novel online incremental learning for dynamic log data trace session identification schema based on the adaptation method to database trace logs is proposed. It is applied for dynamic log session identification with automatic selection of threshold based on the standard deviation schema, so it is named as DS-OILSD. The proposed novel DS-OILSD schema is varied from normal n-gram model since the proposed work consists of two major modifications. The first one is to solve the parameters adjustment problem of the IL in online and offline manner incrementally. The second one is used to dynamic management of query types and allocating initial probabilities to the n-grams models. The proposed DS-OILSD leaning method is based on modified MAP estimation schema for dynamic changing adaptation of the query types and is instinctively reasonable. It directly solves the problem of dynamic log session identification, in which three types of learning are performed such as labeled data, semi-labeled, unlabeled data for various categories of training data Finally experimental work is conducted to proposed and existing state of art schema for dynamic log session identification and experimentation results are evaluated based on the parameters like, F-measure, and precision for clinic database log files.Item Edge Adaptive Image Steganography Based on Lsb(2013-05) Karthikeyani, S; Kalpana, BItem Efficient Machine Learning Techniques for Intrusion Detection System in WSN(2015) Kalpana, BDue to the tremendous growth of Wireless Sensor Network (WSN) in various area especially military application, environment monitoring, health care application, home automation, etc., they are highly exposed to the security threats as well as online fraud. Intrusion Detection System (IDS) are the major research problem and an effective method to detect and analyze various kinds of attack in an internetwork system. Intrusion Detection System (IDS) is mainly designed for security purpose by alerting administrator automatically when someone or something is trying to compromise information system through malicious activities or through security policy violations. Therefore, developing IDS for WSN have attracted many recently and thus, there are many enhanced machine learning techniques that are integrated with IDS to identify unusual access or attacks that approach network system.Item Efficient Strategies for Mining Frequent Item sets and the Incremental Mining of Closed Frequent Itemsets Based on a Lattice Framework(2008-06-09) Kalpana, B; Nadarajan, RItem Email Spam Classification Using Machine Learning Algorithms(2022-05) Karthiga, T; Kalpana, BItem Employee Performance Appraisal Tracking System(2015-03) Kalaivani, R; Kalpana, BItem Enhanced Fuzzy Roughset based Feature Selection Strategy using Differential Evolution(2013) Kalpana, BDimensionality reduction technique aims to c J ^ ^ c e the performance o f the classification model by removing the irrelevant and redfummm data. Reducing the number o f attributes in the classification model helps to alleviate^Mp curse o f dimensionality, where it is the major crisis to data storage and also facilitates the mining techniques like classification, clustering, communi^lmb^, visualization and high-dimensional data storage. Once the dimensionality o f a n g ^ J ^ increases then it leads the data to sparsity. Sparsity problem is quite tricky for e^ytechnique that entails statistical importance. Thus, it is necessary to remove the^ ir iy ^ a n t and redundant features, augment the performance o f the classifier model, speed learning task, interpret the model and highlight the vital features with their ralmidm. In this paper, a modfied feature selection technique based on fuzzy Roughset t h e ^ ^ n d Differential Evolution is proposed. Here, the experimental results are carried outtSmhg binary and multiclass datasets taken from UCI Machine Learning R ep o sitory\Item Erp-Employee Training Management System(2014-03) Bhuvaneshwari, E; Kalpana, BItem Evaluating the Performance of an Admissible Kernel Function in Banach Space for Binary Data(2013) Kalpana, BClassification is a learning function that maps a given data item into one of several predefined classes. H is a data analysis technique that extracts models describing important data classes and predicts future values. Basically, classification techniques have better capability to handle a wider variety o f datasets than regression. It can also be described as a supervised learning algorithm in the machine learning process. Support Vector Machine (SVM) is an emerging classifier based on supervised machine learning approach. It is originally used to symbolize popular and modern classifiers that have a well-defined theoretical foundation to provide some enviable performances /IJ. In this paper, an admissible kernel function in Banach Space is proposed as an optima! kernel function for real time applications. The experimental results are carried out using benchmark binary datasets which are taken from UCI Machine Learning Repository and their performance are evaluated using various measures like support vector, support vector percentage, training time and accuracy.Item Facts Using Javagrids(2005-05) Remya M Warrier; Kalpana, BItem Food Consumption Life Style and Lipid Profile of Selected Adult Population(1994-05) Rupa, L; Kalpana, BItem Framework for Admissible Kernel Function in Support Vector Machines Using Lévy Distribution(2013-04) Sangeetha, R; Kalpana, BItem Gender Subaltern as a Theme in Select Plays of Mahaswetha Devi(2019-04) Kalpana, B; Grace PriyadarsiniItem High Performance Image Classifier(2004-04) Aruna, V R; Kalpana, B
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