Bejoy Varghese
@hamad.qa
Nurse/Midwife Educator
Hamad Medical Corporation
Mr. Bejoy Varghese is an esteemed Nurse/Midwife educator specializing in Neuroscience and medical departments at Hamad General Hospital, the largest healthcare institution in the Hamad Medical Corporation. With a strong research inclination, he actively contributes to policy and protocol development for practical implementation. Mr. Varghese's commitment to excellence is exemplified by leading his team to multiple accolades in national poster competitions. His dedication to advancing healthcare practices and remarkable achievements have earned him respect as a prominent figure in Qatar's nursing community. Additionally, he serves as a research reviewer, further showcasing his expertise.
RESEARCH, TEACHING, or OTHER INTERESTS
Nursing, Education, Leadership and Management, Medicine
Scopus Publications
Scopus Publications
Bejoy Varghese, Chithra Maria Joseph, Adnan Anwar Ahmad Al- Akkam, Rida Moh’d Odeh A. M. AL-Balawi, Esmat Swallmeh, and Kalpana Singh
Springer Science and Business Media LLC
Abstract Background The use of 12-h shifts for nursing staff has become common in many healthcare settings, including tertiary hospitals, due to its potential benefits such as reduced handover time and increased continuity of care. However, there is limited research on the experiences of nurses working 12-h shifts, particularly in the context of Qatar, where the healthcare system and nursing workforce may have unique characteristics and challenges. This study aimed to explore the experiences of nurses working 12-h shifts in a tertiary hospital in Qatar, including their perceptions of physical health, fatigue, stress, job satisfaction, service quality, and patient safety. Methods A mixed method study design was applied consisting of a survey and semi-structured interviews. Data was collected from 350 nurses through an online survey and from 11 nurses through semi-structured interviews. Data was analyzed using Shapiro–Wilk test and the difference between demographic variables and scores were examined using Whitney U test and Kruskal- Wallis test. Thematic analysis was used for qualitative interviews. Results The results from quantitative study revealed nurses perception in working 12-h shift has negative impact in their wellbeing, satisfaction as well as patient care outcomes. Thematic analysis revealed real stress and burnout and experienced an enormous amount of pressure going for work. Conclusions Our study provides an understanding of the nurse’s experience working 12-h shift in a tertiary level hospital in Qatar. A mixed method approach informed us that, nurses are not satisfied with the 12-h shift and interviews revealed high level of stress and burnout among nurses resulting in job dissatisfaction and negative health concerns. Nurses also reported that it is challenging to stay productive and focused throughout their new shift pattern.
Bejoy Varghese and S. Krishnakumar
Inderscience Publishers
Bejoy Varghese and S. Krishnakumar
Springer Singapore
Bejoy Varghese and S. Krishnakumar
The Science and Information Organization
This paper presents an algorithm based on Fractal theory by using Iterated Function Systems (IFS). An efficient and fast coding mechanism is proposed by exploiting the self similarity nature in the Brain MRI images. The proposed algorithm utilizes Deep Reinforcement Learning (DRL) technique to learn the transformations required to recreate the original image.We avail of the Adaptive Iterated Function System (AIFS) as the encoding scheme. The proposed algorithm is trained and customised to compress the Medical images, especially Magnetic Resonance Imaging (MRI). The algorithm is tested and evaluated by using the original MR head scan test images. It learns from an existing biomedical dataset viz The Internet Brain Segmentation Repository (IBSR) to predict the new local affine transformations. The empirical analysis shows that the proposed algorithm is at least 4 times faster than the competitive methods and the decoding quality is far distinct with a reduction in the bit rate. Keywords—Fractal compression; deep reinforcement learning; MRI image compression; deep learning; adaptive fractal coding
Bejoy Varghese and Krishnakumar S.
IEEE
Fractal coding is a lossy compression scheme and had an extra edge in the early 90s because of its ability to consider the self-similarities in an image. But the high computational cost involved in its encoding process made it impractical for commercial applications. This is due to the hard task of calculating various affine transformations in the comparison of range and domain pools. But the advantages in using FIC algorithms are less complex decoding process, high PSNR, better SSIM, dimensionless scheme and high compression ratio. Recent improvements in the computing techniques put back the FIC on track by reducing the computational cost involved in the encoding process. This paper suggests the importance and use of parallel computation techniques such as General Purpose Graphical Processing Unit (GPGPU) and Compute Unified Device Architecture (CUDA) in Fractal based image compression algorithms. Analysis of the empirical results shows that the parallel computational approaches could significantly reduce the encoding time as the calculation of fractal affine transformations (AT) can be largely parallelized.
Bejoy Varghese and S. Krishnakumar
Inderscience Publishers
The concept of digital image compression is of considerable interest in the area of transmission and storage of images. The recent research in this area explores the combination of different coding techniques to achieve a better compression ratio without compromising the image quality. Fractal-based coding techniques got the attention of the research community from the very earlier days of data compression. However, those methods are computationally intensive at that time because of the exhaustive search involved to select a transformation sequence. In this paper, we propose a system that replaces the current domain range comparison in the fractal compression with a reinforcement learning technique that reduces the compression time and increases the PSNR. The system will learn from the output of the exhaustive algorithm in the initial state and discard the combinatorial search after trained on a data set. The recommended method shows a good improvement in the compression ratio, PSNR and compression time.
Publications
Nurse’s experience working 12-hour shift in a tertiary level hospital in Qatar: a mixed method study
Varghese, B., Joseph, C.M., Al- Akkam, A.A.A. et al. Nurse’s experience working 12-hour shift in a tertiary level hospital in Qatar: a mixed method study. BMC Nurs 22, 213 (2023).