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Assoc. Prof 

Krishnannair, Syamala

Faculty: Faculty of Science, Agriculture and Engineering
Department: Mathematical Sciences
Research Interest(s): Signal processing, Image processing, Multivariate statistical process monitoring, Time series analysis, Machine learning.
Active Research Project(s): Thuthuka (NRF) Project.
Biography: Dr. Krishnannair is a lecturer in the Department of Mathematical Sciences in the Faculty of Education. He teaches mathematics content and method modules to undergraduate students. He also supervises MEd and PhD students.
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20221
Author: Bhardwaj, Vinod Kumar ×
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    PublicationJournal Article
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    Corrected holographic dark energy with power-law entropy and Hubble horizon cut-off in FRW universe
    2022
     | Elsevier
    In the present work, we investigate the power-law entropy corrected holographic dark energy (PLECHDE) model with Hubble horizon cutoff. We use 46 observational Hubble data points in the redshift range 0 ≤ 𝑧 ≤ 2.36 to determine the present Hubble constant 𝐻0 and the model parameter 𝑛. It represents a phase transition of the universe from deceleration to acceleration and has the transition point at 𝑧𝑡 = 0.71165. We investigate the observational constraints on the model and calculate some relevant cosmological parameters. We examine the model’s validity by drawing state-finder parameters that yield the result compatible with the modern observational data. The model’s physical and geometrical characteristics are also explored, and they are shown to match well with current observations of observational Hubble data (OHD) and the latest joint light curves(JLA) datasets.
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