Indoor Smoking Detection Based on YOLO Framework with Infrared Image

This study recommends combining the efficacy of YOLO with the greater visibility provided by infrared imaging to create a better indoor smoking detection system. The YOLO system divides photos into a grid and anticipates bounding boxes and class probabilities at the same time, making it an obvious choice for its real-time item detection capabilities. The approach improves its robustness by identifying heat signals associated with smoking sessions and overcoming limitations posed by low-light or blocked circumstances. The addition of infrared images significantly improved the system's performance in low-light conditions. A dual spectrum thermal camera is used in the entire indoor smoking detection system to obtain a large collection of infrared images representing various interior locations with documented smoking episodes. During the training phase, data augmentation processes such as random rotations, flips, and brightness and contrast fluctuations were used to improve the system's performance. The CIoU loss function improved the system's localization accuracy significantly, reducing false positives and improving overall detection performance. The combination of YOLO and infrared photography, in conjunction with data augmentation and the CIoU loss function, not only improves indoor smoking detection but also demonstrates the benefits of merging several technologies in the development of more effective and adaptive systems

Prevalence of comorbidities and its associated factors among type-2 diabetes patients: a hospital-based study in Jashore District, Bangladesh

Objective This study aimed to estimate the prevalence of comorbidity and its associated factors among Bangladeshi type-2 diabetes (T2D) patients.Design A hospital-based cross-sectional study.Setting This study was conducted in two specialised diabetic centres residing in the Jashore District of Bangladesh. A systematic random sampling procedure was applied to identify the T2D patients through a face-to-face interview.Participants A total of 1036 patients with T2D were included in this study. A structured questionnaire was administered to collect data on demographic, lifestyle, medical and healthcare access-related data through face-to-face and medical record reviews.Outcome measures and analyses The main outcome variable for this study was comorbidities. The prevalence of comorbidity was measured using descriptive statistics. A logistic regression model was performed to explore the factors associated with comorbidity among Bangladeshi T2D patients.Results The overall prevalence of comorbidity was 41.4% and the most prevalent conditions were hypertension (50.4%), retinopathy (49.6%), obesity (28.7%) and oral problem (26.2). In the regression model, the odds of comorbidities increased with gender (male: OR: 1.27, 95% CI 1.12 to 1.87), age (50–64 years: OR: 2.14, 95% CI 1.32 to 2.93; and above 65 years: OR: 2.96, 95% CI 1.83 to 4.16), occupation (unemployment: OR: 3.32, 95% CI 1.92 to 6.02 and non-manual worker: OR: 2.31, 95% CI 1.91 to 5.82), duration of diabetes (above 15 years: OR: 3.28, 95% CI 1.44 to 5.37), body mass index (obese: OR: 2.62, 95% CI 1.24 to 4.26) of patients. We also found that individuals with recommended moderate to vigorous physical activity levels (OR: 0.41, 95% CI 0.25 to 0.74) had the lowest odds of having comorbidity. Meanwhile, respondents with limited self-care practice, unaffordable medicine and financial problems had 1.82 times, 1.94 times and 1.86 times higher odds of developing comorbidities.Conclusion The findings could be useful in designing and implementing effective intervention strategies and programmes for people with T2D to reduce the burden of comorbidity