Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Chandrasekaran, K.Subject: search.filters.subject.XGBoost

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    A Stacked Model Approach for Machine Learning-Based Traffic Prediction
    (Springer Science and Business Media Deutschland GmbH, 2024) Divakarla, U.; Chandrasekaran, K.
    The application of technology for sensing, analysis, control, and communication within ground transportation is referred to as an intelligent transportation system. This system aims to enhance safety, mobility, and efficiency. Intelligent Transportation Systems (ITSs) are in the process of development and implementation, leading to improved accuracy in predicting traffic flow. The efficacy of traveler information systems, public transportation, and advanced traffic control is said to depend on these systems. In order to effectively manage and lessen traffic congestion, practical execution is essential, as evidenced by the expanding use of data in transportation management. By employing machine learning (ML), it is possible to construct predictive models that incorporate diverse data from numerous sources. Predicting traffic movement, reducing congestion, and identifying optimal routes that consume the least time or energy all require traffic prediction, which involves forecasting traffic volume and density. Traffic estimation and prediction systems have the potential to reduce travel times and enhance traffic conditions by enabling more efficient utilization of available capacity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Malware Classification Using XGBoost and Genetic Algorithm for Hyperparameter Tuning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Divakarla, U.; Chandrasekaran, K.; Harish, S.V.; Kanal, P.G.; Shalini, C.
    All human activities are being moved into the virtual world due to technological advancements. Since so much of our data is stored on computers and networks, the frequency of cyberattacks has sharply increased. Understanding the many types of malware, their danger level, defense strategies, and potential methods of infecting computers and other devices requires the ability to identify and classify them. In this research, we propose a malware categorization model. Our proposed model is based on XGBoost and uses a Genetic Algorithm for hyperparameter tuning. The system achieved high accuracy with the help of two different malware datasets used for testing and training: Malevis and Malimg. © 2024 IEEE.