Conference Papers

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

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Author: search.filters.author.Agarwal, A.

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    BCON: Back pressure based congestion avoidance model for Named Data Networks
    (Institute of Electrical and Electronics Engineers Inc., 2017) Agarwal, A.; Tahiliani, M.P.
    Queue management in Named Data Networks (NDN) has gained a lot of attention recently. Unlike the traditional IP architecture, the transport mechanism in NDN is intricate and comprises of in-network caching of data packets at routers. Hence, the most effective avoidance of congestion can occur at the routers itself. In this paper, we make two contributions: first, we propose a back pressure based congestion avoidance model for NDN which leverages the benefits of Active Queue Management (AQM) mechanisms. Using this model, we apply the existing AQM mechanisms like Random Early Detection (RED), Adaptive RED (ARED), Controlled Delay (CoDel) and Proportional Integral controller Enhanced (PIE) in NDN. Second, we study the effectiveness of our proposed model by performing simulations using ndnSIM. Our simulation results indicate that the proposed model successfully balances the tradeoff between link utilization and Data drop rate. © 2016 IEEE.
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    Preprocessing Techniques of Solar Irradiation Data
    (Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.; Agarwal, A.
    Solar energy being abundant, non-exhaustive, environmentally friendly attracts the people attention towards the alternate renewable energy. High-quality time series data is essential for producing an accurate estimate of solar power generation. In most cases, the plethora of information hidden in time series data cannot be accessed. Common issues with time series include outliers, noise, missing data, and a lack of order in the timestamps itself that impair forecasting accuracy. So, preprocessing of the input data is a mandate in order to achieve a precise and dependable forecast. This study proposes various pre-processing techniques to improve the performance of the forecasting accuracy. The different ways to handle the missing values and outliers detection by sliding window method and box plots are presented in this study. The solar irradiation data collected from solar center Alice Springs, Australia used for validation of the preprocessing results. The efficacy of the proposed method in detecting the missing values and outliers is effective from the obtained results. © 2023 IEEE.
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    Comparative analysis of Software Reliability using Grey Wolf Optimisation and Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Kelkar, S.; Vishvasrao, S.P.; Agarwal, A.; Rajput, C.; Mohan, B.R.; Das, M.
    Software reliability is a crucial aspect of software quality. In this paper, we aim to explore the application of Gray Wolf Optimization (GWO) for feature selection and classification on various software dataset, such as KC1, JM1, and PC5. We compare the performance of Machine Learning models (Random Forest, Decision Tree, Support Vector Machine, XGBoost and Neural Networks) with and without GWO-based feature selection. Our results demonstrate the effectiveness of GWO in enhancing the accuracy of software reliability analysis. Or Math in Paper Title or Abstract. © 2024 IEEE.