Browsing by Author "Pavan, R."

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Bayesian optimization and gradient boosting to detect phishing websites
(Institute of Electrical and Electronics Engineers Inc., 2021) Pavan, R.; Nara, M.; Gopinath, S.; Patil, N.
We propose an Extreme Gradient Boosting framework for classification and regression problems emerging in machine learning for small-sized data sources sampled from a discrete distribution, i.e. data containing discrete or quantized attributes. The model parameters are iteratively refined from a prior belief for specific use cases using Bayesian optimization. We focus the application area of this framework on detecting fraudulent websites. With properly stated reasoning, we empirically test our methodology on a publicly available and bench-marked UCI Phishing dataset to demonstrate the superior performance of this approach as compared to existing methods in the literature. Â© 2021 IEEE.
Optimal Resource Allocation for Public Safety Device to Device Communication Using PSO
(Springer Science and Business Media Deutschland GmbH, 2023) Dhruvik, N.; Pavan, R.; Neeraj; Manjappa, M.
The Device to Device (D2D) communication allows two different devices in close proximity to communicate directly among themselves without relaying through the base stations (eNodeB or eNB). The D2D communication offloads the traffic from eNB and thus, has many advantages, including higher throughput and less end-to-end delay. Though the PSC was basically invented for Public Safety Communication (PSC) and to help the first responders, its distinct advantages have attracted other commercial applications as well. The eNB treats all the D2D applications equally during resource allocation and does a uniform resource allocation where one application is engaged in commercial activities. At the same time, the other saves one’s life. Thus, in this work authors proposed a novel optimized resource allocation algorithm for D2D applications which prioritizes PSC over commercial applications. In order to achieve the objective, Particle Swarm Optimization (PSO) technique was employed in the proposed work. Furthermore, a new weighted average fitness function was designed for PSO to suit the requirements. The proposed algorithm was simulated in NS-3, and the results were taken for different iterations. It was observed that the PSO algorithm for the designed fitness function achieved the local and global optimum values in a considerable amount of time. It was apparent from the results that PSC D2D pairs produced convincing results when compared to D2D pairs with commercial applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Performance evaluation of dimensionality reduction techniques on high dimensional data
(2019) Vikram, M.; Pavan, R.; Dineshbhai, N.D.; Mohan, B.
With a large amount of data being generated each day, the task on analyzing and making inferences from data is becoming an increasingly challenging task. One of the major challenges is the curse of dimensionality which is dealt with by using several popular dimensionality reduction techniques such as ICA, PCA, NMF etc. In this work, we make a systematic performance evaluation of the efficiency and effectiveness of various dimensionality reduction techniques. We present a rigorous evaluation of various techniques benchmarked on real-world datasets. This work is intended to assist data science practitioners to select the most suitable dimensionality reduction technique based on the trade-off between the corresponding effectiveness and efficiency. �2019 IEEE.
Performance evaluation of dimensionality reduction techniques on high dimensional data
(Institute of Electrical and Electronics Engineers Inc., 2019) Vikram, M.; Pavan, R.; Dineshbhai, N.D.; Mohan, B.R.
With a large amount of data being generated each day, the task on analyzing and making inferences from data is becoming an increasingly challenging task. One of the major challenges is the curse of dimensionality which is dealt with by using several popular dimensionality reduction techniques such as ICA, PCA, NMF etc. In this work, we make a systematic performance evaluation of the efficiency and effectiveness of various dimensionality reduction techniques. We present a rigorous evaluation of various techniques benchmarked on real-world datasets. This work is intended to assist data science practitioners to select the most suitable dimensionality reduction technique based on the trade-off between the corresponding effectiveness and efficiency. Â©2019 IEEE.
Stack generalized deep ensemble learning for retinal layer segmentation in Optical Coherence Tomography images
(Elsevier Sp. z o.o., 2020) Anoop, B.N.; Pavan, R.; Girish, G.N.; Kothari, A.R.; Rajan, J.
Segmentation of retinal layers is a vital and important step in computerized processing and the study of retinal Optical Coherence Tomography (OCT) images. However, automatic segmentation of retinal layers is challenging due to the presence of noise, widely varying reflectivity of image components, variations in morphology and alignment of layers in the presence of retinal diseases. In this paper, we propose a Fully Convolutional Network (FCN) termed as DelNet based on a deep ensemble learning approach to selectively segment retinal layers from OCT scans. The proposed model is tested on a publicly available DUKE DME dataset. Comparative analysis with other state-of-the-art methods on a benchmark dataset shows that the performance of DelNet is superior to other methods. © 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences