Self-efficacy, Life Satisfaction and Quality of Life in Local Tourists of Pakistan

The present study was conducted with the aim to investigate the relationship between Selfefficacy, Life satisfaction, and Quality of life in local tourists of Pakistan. Data was collected from local tourists, a total of 151 men and women. A correlational research design was used along with snowball sampling to collect data from participants by using of Self-efficacy scale (SES), Satisfaction with Life Scale (SWLS), and Quality of life scale (QOL). The results revealed that there was a significant positive relationship among self-efficacy, life satisfaction, and quality of life in local tourists of Pakistan. Further, the results showed that self-efficacy and satisfaction with life are significantly correlated with factors of quality of life. Self-efficacy, satisfaction with life, and quality of life are found more in tourists who travel more frequently in a year. This study has implications for developing the tourist industry and encouraging local travel for Pakistani‘s as an intervention to improve the health and wellbeing of people.

A Framework for De-Noising Digital Images Through Machine Learning

There exist many image processing methodologies while most of them do not provide accuracy when images are corrupted with Random Value Impulse Noise (RVIN). Therefore removal of RVIN from the degraded images is believed as preprocessing step in nearly all of the image processing applications. Identification and removal of RVIN from the digital gray scale images is an operational vicinity of research as the detection of RVIN is hard on various noise ratios. In this research a framework has been proposed which leads to the de-noising of RVIN from the digital gray scale images. The detection of noisy pixels provides significant basis for the de-noising process. This proposed framework consists of two main stages. First stage introduces four statistics based features to illustrate the characteristics of the noisy and noise free pixels in the digital image and the Support Vector Machine classifier used to classify the noisy and noise free pixels. In the second stage, modified version of the LPG-PCA algorithm is used for the de-noising of the noisy pixels while preserving the local image structure. The accuracy of this proposed methodology has been compared with the peak-signal-to-noise ratio (PSNR), a well-known performance measure. Simulation results show that the proposed filters provide better accuracy and performance of de-nosing the random value impulse noise in most of the situations.