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  1. Article: Enhancing Data Security of Cloud Based LMS.

    Chatterjee, Paramita / Bose, Rajesh / Banerjee, Subhasish / Roy, Sandip

    Wireless personal communications

    2023  Volume 130, Issue 2, Page(s) 1123–1139

    Abstract: Around the world, the educational system is evolving. The new trend can be found in traditional classroom systems as well as digitalization systems. Cloud-based Learning Management Systems (LMS) will accelerate the educational industry forward in the ... ...

    Abstract Around the world, the educational system is evolving. The new trend can be found in traditional classroom systems as well as digitalization systems. Cloud-based Learning Management Systems (LMS) will accelerate the educational industry forward in the next years because they can provide end-user with a versatile, convenient, secure, and cost-effective learning process. The cloud-based LMS approach is the most effective and proper learning model in the worldwide educational sector, particularly if the organization is in a state of depression owing to a global pandemic. It can be utilized over the internet with several users on the same platform. As a result, the initial requirement is important to enable to the LMS model. Despite its many advantages, LMS confronts challenges such as confidentiality, user acceptance, and traffic. In a pandemic like Covid 19, the entire planet depends on a safe LMS platform to establish student and instructor trust. Therefore, with this work, the attempt has been made to explain one LMS model that may provide its users with optimal security, a user-friendly environment, and quick access. This paper discusses the use of the cloud attack, and also cryptographic and steganographic security models and techniques to address these issues. There's also information on what kinds of security vulnerabilities or operations on cloud data are feasible, and also how to deal with them using various algorithms.
    Language English
    Publishing date 2023-03-18
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1479327-1
    ISSN 1572-834X ; 0929-6212
    ISSN (online) 1572-834X
    ISSN 0929-6212
    DOI 10.1007/s11277-023-10323-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Mathematical modelling for decision making of lockdown during COVID-19.

    Ghosh, Ahona / Roy, Sandip / Mondal, Haraprasad / Biswas, Suparna / Bose, Rajesh

    Applied intelligence (Dordrecht, Netherlands)

    2021  Volume 52, Issue 1, Page(s) 699–715

    Abstract: Due to the recent worldwide outbreak of COVID-19, there has been an enormous change in our lifestyle and it has a severe impact in different fields like finance, education, business, travel, tourism, economy, etc., in all the affected countries. In this ... ...

    Abstract Due to the recent worldwide outbreak of COVID-19, there has been an enormous change in our lifestyle and it has a severe impact in different fields like finance, education, business, travel, tourism, economy, etc., in all the affected countries. In this scenario, people must be careful and cautious about the symptoms and should act accordingly. Accurate predictions of different factors, like the end date of the pandemic, duration of lockdown and spreading trend can guide us through the pandemic and precautions can be taken accordingly. Multiple attempts have been made to model the virus transmission, but none of them has investigated it at a global level. The novelty of the proposed work lies here. In this paper, first, authors have analysed spreading of the said disease using data collected from various platforms and then, have presented a predictive mathematical model for fifteen countries from first, second and third world for probable future projections of this pandemic. The prediction can be used by planning commission, healthcare organizations and the government agencies as well for creating suitable arrangements against this pandemic.
    Language English
    Publishing date 2021-05-10
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1479519-X
    ISSN 1573-7497 ; 0924-669X
    ISSN (online) 1573-7497
    ISSN 0924-669X
    DOI 10.1007/s10489-021-02463-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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