Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Kamath S․, S.Subject: search.filters.subject.Ensemble learning

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    Designing Scalable Intrusion Detection Systems with Stacking Based Ensemble Learning
    (Springer Science and Business Media Deutschland GmbH, 2022) Sujan Reddy, A.S.; Akashdeep, S.; Kamath S․, S.; Rudra, B.
    Network Intrusion Detection Systems monitor the network traffic and reports any malicious activity. In this paper, a combination of feature engineering techniques and Ensemble Learning is proposed to build an effective Intrusion Detection System. The zero importance feature selection method is used to extract 23 features. Random forests, Feed Forward Neural Networks and Auto encoders are used as the base models and the predictions from these base models are combined using Extreme Gradient Boosting (XGB). To ensure that the proposed ensemble model is scalable as well, parallel programming is used for parallel computation of class probabilities from each model of the ensemble. The NSL-KDD dataset is used to train our models. To test our models, we use KDD+test dataset. Experimental results show that the proposed ensemble model outperforms several state-of-the-art works. The proposed parallel programming approach decreases the average prediction time of the model ensuring that the model is scalable. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Ensemble Learning Approach for Short-term Energy Consumption Prediction
    (Association for Computing Machinery, 2022) Sujan Reddy, A.; Akashdeep; Harshvardhan; Kamath S․, S.
    Predicting electricity consumption accurately is crucial for garnering insights and potential trends into energy consumption for effective resource management. Due to the linearity/non-linearity in usage patterns, electricity consumption prediction is challenging and cannot be adequately solved by using a single model. In this paper, we propose ensemble learning based approaches for short-term electricity consumption on an open dataset. The ensemble model is built on the combined predictions of supervised machine learning and deep learning base models. Experimental validation showed that the proposed ensemble model is more accurate and decreases the training time of the second layer of the ensemble by a factor close to ten, compared to the state-of-the-art. We observed a reduction of approximately 34% in the Root mean squared error for the same size of historical window. © 2022 Owner/Author.