Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A parallel dynamic programming approach for data analysis(Institute of Electrical and Electronics Engineers Inc., 2016) Deepak, A.; Shravya, K.S.; Chandrasekaran, K.In spite of presence of many classical and modified data analysis techniques, data analysis in the field of software engineering still remains a challenge because of the presence of large number of both continuous and discreet explanatory variables judging the outcome of one and more than one dependant variables. Requirement for an efficient multivariate data analysis technique which fulfils the constraints associated with software data led to the design of OSR (optimized set reduction) which uses a greedy algorithm for data analysis using both the principles of machine learning and conventional statistics. With the incoming of big data and other increasing dimensions of data set, we, through this paper, try to propose a new algorithm, based on the similar lines of optimised set reduction, using its strength to extract subsets. As the current trend of programming demands an algorithm to execute in parallel, we also propose a modification to our algorithm for it to run in a multicore platform with good efficiency. © 2015 IEEE.Item NEAT Algorithm for Testsuite generation in Automated Software Testing(Institute of Electrical and Electronics Engineers Inc., 2018) Praveen Raj, H.L.P.; Chandrasekaran, K.Software testing is one of the most essential and an indispensable part of Software production life cycle. Software testing helps in validating if the product meets with the requirements or not, and also testing helps to validate the performance of the product. Unfortunately, this process takes up about 50% of the production time and budget, due to its laboriosity. Hence, in order to reduce the time it takes, Automated Software Testing becomes essential. Here we propose a novel idea of using Machine Learning for automatically generating the test suites. In this paper we present an approach that uses NEAT (Neuroevolution of Augmenting Topologies) Algorithm to automatically generate new test suites or for improving the coverage of already produced test suite. Our approach automatically generates test suites for white box testing. White box testing refers to testing of the internal structure and the working of the Software Under Test. © 2018 IEEE.Item Recursive Harmony Search Based Classifier Ensemble Reduction(Institute of Electrical and Electronics Engineers Inc., 2018) Kailas, P.; Chandrasekaran, K.In recent times classifier ensembles have become a mainstay in data mining and machine learning. The combination of several classifiers generally results in better performance and accuracy as compared to a single classifier. There are many different methods and techniques for constructing ensembles. Most of the time however, when these ensemble classifiers are constructed, the data used in the construction of ensemble classifiers becomes redundant. This redundant data results in a loss of accuracy and an increase in memory and system overhead. Therefore by removing this redundant data we can reduce the memory and system overhead as well as obtain an increase in accuracy. The redundant data can be eliminated by using a technique called feature selection. Feature selection is used to select the most relevant features while performing any task. There are many different feature selection algorithms such as memetic algorithms, sub-modular feature selection, etc. The feature selection technique can be used to choose the relevant data and eliminate the redundant data. The way to eliminate redundant data in ensemble classifiers is to perform classifier ensemble reduction. This paper discusses using feature selection and in particular employing recursive harmony search to perform classifier ensemble reduction via feature selection. The final ensemble classifier will be a reduced set of the original ensemble classifier, while maintaining diversity and accuracy of the original one. © 2018 IEEE.Item Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe(Institute of Electrical and Electronics Engineers Inc., 2020) Jain, M.; Singh, S.; Chandrasekaran, K.; Rathnamma, M.V.; Ramana, V.In the present-day scenario, several clothing recommender systems have been developed for the online e-commerce industry. However, when it comes to recommending clothes that a person already possesses, i.e, from their personal wardrobe, there are very few systems that have been proposed to perform the task. In this paper, we tackle the latter issue, and perform experimental analysis of the various Machine Learning techniques that can be used for carrying out the task. Since the recommendations must be made from a user's personal wardrobe, the recommender system doesn't follow a traditional approach. This is explained in detail in the following sections. Further, the paper contains a complete description of the results obtained from the experiments conducted, and the best approach is specified, with appropriate justification for the same. © 2020 IEEE.Item IOT Devices Using Supervised Machine Learning Models for Anomaly Based Intrusion Detection(Institute of Electrical and Electronics Engineers Inc., 2023) Divakarla, U.; Chandrasekaran, K.Identifying dangers and irregularities in any infrastructure is a growing problem in the Internet of Things (IoT) industry. IoT infrastructure is utilised more frequently across a wide spectrum of organisations, which increases the risks and attack methods. Attacks and anomalies that could lead an IoT system to malfunction include denial of service attacks, data type probing, malicious control, malicious operation, scans, surveillance, and improper configuration. This article studies the ability of several machine learning models to predict attacks and abnormalities on IoT devices. The f1 score, area under the receiver operating characteristic curve, accuracy, precision, recall, and precision are among the metrics used to assess performance. ANNs, decision trees, and random forests all shown performance with a 99.4% accuracy rate in the system's tests. © 2023 IEEE.