Browsing by Author "Alishba"

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    Cheating detection system
    (UMT, Lahore, 2025) Ahmed Noor; Alishba; Wajeeha Arif; M Talha
    Exam cheating is a prevalent issue that undermines fairness in academia. Conventional approaches, where teachers or supervisors monitor students, are not guaranteed to be effective, particularly in large exam rooms. The purpose of this project is to create an AI- based system that assists in automatically detecting cheating. The system monitors students' posture motion while undertaking exams to detect abnormality. Deep learning models are employed by the system to detect four primary positions: normal, left, right, and back. By processing webcam images in real-time, the system is able to identify abnormal movements and notify examiners for investigation. For greater accuracy, we designed our own student body movement dataset rather than using online available data. We gathered, annotated, and processed the dataset very carefully to make it simulate real exam situations. A CNN-based system and image processing algorithms were employed for training to ensure robust movement detection. The system was tested and validated to monitor the system's accuracy and performance. The system is so designed that it can be operated with real-time exam monitoring software. It offers examiners an easy-to-use dashboard through which they can observe warnings if any student is caught attempting suspicious activities. This saves human effort, reduces errors, and provides a fair exam environment for all the students. In the future, we intend to enhance the system further by incorporating more data, employing eye tracking technology, and experimenting with other deep learning models to make it more accurate. We also intend to integrate it with sophisticated exam monitoring software so that greater institutions may utilize it. This AI-powered solution assists with ensuring fairness in exams, avoiding cheating, and making an exam environment more secure
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    Distress tolerance, caregiver burden, and psychological vulnerability among caregivers of the clinical population
    (UMT, Lahore, 2025) Alishba; Amna Arshad; Hafsa Tariq
    Caregivers of the clinical population commonly experience psychological strain, emotional exhaustion, increased burden of caregiving demands and high levels of stress due to the intensive caregiving responsibilities. Therefore, the current study aimed to identify the relationship between distress tolerance, caregiver burden and psychological vulnerability among caregivers of the clinical population. It was hypothesized that caregivers who encounter difficulty in distress tolerance are more likely to experience increased caregiver burden, which in turn increases their psychological vulnerability. A correlational research design was employed and data was collected from 150 caregivers through purposive sampling. Standardized tools included Distress Tolerance Scale (DTS), Zarit Caregiver Burden Assessment (ZCBA) and Depression Anxiety Stress Scale (DASS-21). The results showed significant positive correlation between difficulty in distress tolerance, caregiver burden and psychological vulnerability (p>.001) among caregivers. Regression analysis indicate that distress tolerance and caregiver burden significantly predict psychological vulnerability among caregivers. Independent sample t test revealed significant differences among study variables on the basis of gender and institute. One way ANOVA analysis showed significant differences in the study variables based on diagnosis of patient, education and employment status of caregivers. These results have implications for mental health professionals to design culturally sensitive caregiver focused programs aimed to promote their emotional regulation and reduce risks of mental health problems.