Background: Depression is recognized as a serious public health concern in developing countries. It is the most common psychiatric disorder among the elder person. There is a relationship between coping and depression. Individuals in poorer mental health and under greater stress tended to employ less adaptive coping strategies and that these coping efforts affected the level of mental health. Objective: To find out the prevalence of depression with coping strategies among medically ill elderly patients. Materials and Methods: A cross sectional study was conducted among elderly patients above 60 years of age. A total of 100 medically ill elderly patients attending the Psychiatric Outpatient department [OPD] were evaluated by Geriatric Depression Scale and Coping inventory for stressful situations Results: The prevalence of depression was 74% among medically ill elderly patients. Out of that 70.27% patients had mild depression and 29.72% had severe depression. Depressed patients used more of emotion oriented coping and less of task oriented and avoidance based coping mechanisms as compared to non depressed patients who used more of task oriented and avoidance based coping than emotion oriented coping mechanisms. Severity of depression positively correlated with emotion oriented coping mechanisms and it was negatively correlated with task and avoidance oriented coping mechanisms. Conclusion: Prevalence of depression was 74% among the medically ill elderly patients. Patients with depression more often used emotion based coping, less often used task and avoidance coping mechanisms

Introduction: This study was carried to examine the body image satisfaction and its relationship with self-esteem, body mass index (BMI), and influence of media on body image. Another objective was to observe any existing relationship between gender and body image dissatisfaction. Materials and methods: Exploration of relationship of body image satisfaction with BMI, media influence, self-esteem, and other variables like socioeconomic demographic data, overall satisfaction in life (academic/professional), and current health status was carried out via a cross-sectional study using 5-item-based Likert scale in 303 participants. Results: Males showed less concern about body image. Significant relationship of body mass was seen with BMI (p < 0.001), eating attitude (p < 0.001), influence of media (p < 0.001), and self-esteem (p < 0.001). Overweight students had a significantly higher prevalence of dissatisfaction (p < 0.001) than students with low weight who reported a higher body image satisfaction. Conclusion: To conclude, this study proves that there exists a significant relationship between eating attitude, media influence, and self-esteem with body image. Adequate anticipatory measures are required for improvement in individuality, selfacknowledgment, and individual contrasts while keeping up ideal weight and dynamic lifestyle.

In this paper, we approach with four different CNN-based models i.e., VGG-19, VGG-16, InceptionV3 and MobileNetV3 with an improved version of the previous models for violence detection and recognition from videos. The proposed models use the pre-trained models as the base model for feature extraction and for classification after freezing the rest of the layer, the head model is prepared with averagepooling2D of (5, 5), and after flattening only one dense layer having 512 nodes with ‘ReLU’ activation function, dropout layer of 0.5 and last output layer with only 2 classes and ‘softmax’ activation function. This head model of fully connected layers was used in the proposed models. These models are trained and evaluated on the Hockey fight dataset and Real life violence situations detection datasets. The experimental results are far better in terms of accuracy and other performance metrics and the models have reduced parameters and less computational time than previous models.