Browsing by Author "Roy, S.K."

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    Comparative analysis of the impact of epidemiological modeling on COVID-19
    (De Gruyter, 2023) Bhattacharjee, S.; Das, K.; Zaman, S.; Sadhu, A.; Roy, S.K.; Naha, A.; Khan, F.S.; Sarkar, B.
    This chapter provides a comprehensive review on different existing epidemiological models proposed for analyzing the impact of COVID-19. Since December 2019, COVID-19 emerged as an alarming threat to mankind. To mitigate the impact of pandemic, several preventive measures have been practiced by nations. But due to mutation of the virus, the pandemic prevails. This review provides a vivid description of the contributions of different existing epidemiological models on COVID-19. A comparative analysis of SIR, ESIR, SEPIR, SEIR, SEIJR, SEIAR, SEIR-P, SIRD, SEIRD, R-SEIRD, SEIRDH, SEIQARDT, SIDARTHE, θ-SEIHRD, and SIRDV models have been highlighted. Effects of important parameters like infection rate and recovery rate on different epidemiological models have been addressed. Model parameters, assumptions about the model, techniques used, and contributions and drawbacks of the respective models have also been discussed. Apart from epidemical models, this chapter aims to focus on precise illustration on multiple strains of SARS-CoV-2. Comprehensive analysis on the impact of vaccination on multiple strains has also been reported. © 2023 Walter de Gruyter GmbH, Berlin/Boston.
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    Quantum-inspired hybrid algorithm for image classification and segmentation: Q-Means++ max-cut method
    (John Wiley and Sons Inc, 2024) Roy, S.K.; Rudra, B.
    Finding brain tumors is a crucial step in medical diagnosis that can have a big impact on how patients turn out. Conventional detection techniques can be laborious and demand a lot of computing power. Brain tumor detection could be made more effective and precise, thanks to the quickly developing field of quantum computing. In this article, we propose a quantum machine learning (QML)-based method for brain tumor extraction and detection based on quantum computing. To implement our strategy, we use a Hybrid Quantum-Classical Convolutional Neural Network (HQC-CNN) that has been trained using a collection of brain MRI images. Additionally, we employ Batchwise Q-Means++ Clustering for segmenting the images and a Max-cut approach with Adiabatic Quantum Computation (AQC) to extract the tumor region from the segmented MRI image. Our results highlight the strength of Quanvolutional Layer in Neural Network and reduced time complexity exponentially or quadratically in clustering and max-cut algorithms respectively and see the potential of quantum computing for improving the accuracy and speed of medical diagnosis and have implications for the future of healthcare technology. © 2024 Wiley Periodicals LLC.