Forgery investigation and detection has been a relevant topic of interest for human beings since ages. Important messages written and transported by kings in old ages were sealed with signatures and stamps to achieve this purpose. But with the advent of digital technology, forgery detection has become even more important since tools for forgery have become vast as well. In this paper a technique based on pixel clustering has been introduced for detection of modification, alteration or forgery done with a different ink color pen. Hyperspectral images are used for ink mismatch detection in a handwritten note. We propose ink classification based on pixel intensities values present in all the bands of hyperspectral images of the handwritten note. Our proposed technique is quite simple yet effective in detecting ink mismatch with relatively high accuracy.

Forgery investigation and detection has been a relevant topic of interest for human beings since ages. Important messages written and transported by kings in old ages were sealed with signatures and stamps to achieve this purpose. But with the advent of digital technology, forgery detection has become even more important since tools for forgery have become vast as well. In this paper a technique based on pixel clustering has been introduced for detection of modification, alteration or forgery done with a different ink color pen. Hyperspectral images are used for ink mismatch detection in a handwritten note. We propose ink classification based on pixel intensities values present in all the bands of hyperspectral images of the handwritten note. Our proposed technique is quite simple yet effective in detecting ink mismatch with relatively high accuracy.

Medical students are at risk of developing stress due to sleep deprivation. Students become agitated and increase their day time sleeping time. This stress greatly affects the academic performance and later on their clinical skills. Objectives: To analyze association between perceived stresses and sleep disturbance in medical students. Study Design: Cross-sectional study. Methodology: Present study was conducted in Gujranwala after ethical approval. Sample size was 218. Convenient sampling technique was used. Sample was taken from intermediate and Undergraduate Medical students (MBBS, DPT, Psychology and allied health sciences). Medical students who were using sedative or anti-depressants, facing family stress and have chronic illness were not included. PSS (perceived stress scale) is used for evaluation of stress in students and PSQI (Pittsburg sleep quality index) is used for assessment of sleep disturbance in students. Statistical analysis: Data was analyzed by SPSS software, version 19. Chi square was applied with P ≤0.05 was considered significant. Results: Most of the students had moderate stress associated with poor sleep. 25 students had mild stress, 126 had moderate stress and 23 had severe stress due to poor sleep. P value of stress and sleep quality is 0.054 which is considered statistically significant. Conclusion: We concluded that there was significant association between stress and sleep quality in the target medical student population of Gujranwala. Keywords: Perceived Stress Scale (PSS), Stress, Pittsburg Sleep Quality Index (PSQI) and Sleep Disturbance.