DEPRESSION AND PSYCHOLOGICAL WELL-BEING AMONG PEOPLE HAVING COVID-19 UNDERGONE QUARANTINE

Aims Of Study: Coronavirus is a potentially deadly disease that mostly affects human lung tissue. Multiple cases of unexplained respiratory tract infections were reported to the World Health Organization China in December 2019. The focus of this research was to assess association between depression and PWB in patients who had been isolated due to covid-19. Methodology: A sample of 250 people with covid-19 post quarantine, with age ranges from 25 to 45 were selected by using purposive sampling methods. Depression, anxiety, stress scale and psychological well-being scales were administered. Regression analysis and t-test were employed for statistical analysis. Results: Obtained results evidenced that depression is significantly predict psychological health in the covid-19 population [B= -.015, -.019, F (7.915) sig= .000]. Gender difference is also observed in variable of depression also [(4.78) =.030, p.000]. Limitations and future implications: Future research may base on longitudinal analyses focusing on familial and social factors that may influence the psychological well-being of people living in quarantine. Originality: I certify that the intellectual substance of this article is the result of my own effort and that all assistance and sources used in the preparation of this article have been acknowledged. Conclusion: It was concluded that Depression affects people with COVID-19 and PWB after quarantine. future research may focus more on the influence of the interaction between quarantine and adults’ mental health to fully comprehend the link. 

Variational Models in Image Segmentation Using Fuzzy Set Theory

Computer vision is an important field in which techniques are developed to study and understand the properties and structure of a 3D scene present in a digital image (2D scene) and output is just some descriptive information. While an intermediate step to computer vision is field of image processing in which input and output are both images and has five main components, i.e image segmentation, detection, tracking, registration and shape analysis. Image segmentation is main concern of this research work in context of the theory of fuzzy sets [97]. The key to fuzzy sets is fuzzy membership (FMB) u obeys the constraint 0 6 u 6 1. Level set method (LSM) [66] which naturally handles splitting and merging of the evolving curve C, is a well established technique for performing image segmentation. In this thesis, to solve a partial differential equation numerically, a pseudo level set expression [38] relied on u alike LSM is used and the curve C is considered as pseudo zero level set of u. Fuzzy set is generalization to crisp set and is more generalized approach towards image domain classification based on idea of partial membership of belonging described by a FMB function. Therefore, in many real situations in images, issues like poor contrast, limited spatial resolution, overlapping intensities, noise and inhomogeneities produces fuzziness in the object boundaries and hence fuzzy set theoretic approach is an ultimate option to utilize. Utilizing such approach, we have developed five models for segmentation of variety of images. First model (see Chapter 4) is developed for segmenting images having multiobjects with variable intensities and background having maximum, minimum, average or cluttered intensities. For such achievements generalized averages are merged in kernel metric and FMB is utilized as region descriptor. Second model (see Chapter 5) developed uses approximate image in kernel metric obtained by multi scale filtering technique to ensure segmentation of images consisting of less or severe inhomogeneity. To tackle more complicated task of segmenting images having noise, texture and inhomogeneity at the same time our third model (see Chapter 6) is developed utilizing idea of measure of relative variability i.e coefficient of variation (COV), which is further extended to multiphase image segmentation model (see Chapter 7). Extraction of particular features in medical images is another very challenging task, to solve we have developed our selective segmentation model (SSM) (see Chapter 8) by utilizing idea of multi scale difference image with COV. Overall in this thesis, our main focuss is on the development of variational models in fuzzy sets framework. Evaluation of the proposed approach on a number of datasets and benchmark validate its superiority over other existing models in terms of accuracy and efficiency.