Several efforts have been made to develop effective and robust vision-based solutions for human aerial action recognition. Generally, the existing methods rely on the extraction of either spatial features (patch-based methods) or skeletal key points (pose-based methods) that are fed to a classifier. The pose-based methods are generally regarded to be more robust to background changes and computationally efficient. Moreover, at the classification stage, the use of deep networks has generated significant interest within the community; however, the need remains to develop accurate and computationally effective deep learning-based solutions. To this end, this paper proposes a lightweight Transformer network-based method for human action recognition in aerial videos using the skeletal keypoints extracted with YOLOv8. The effectiveness of the proposed method is shown on a well-known public dataset containing …

The objectives of this study were to compare dengue virus (DENV) cases, deaths, case-fatality ratio [CFR], and meteorological parameters between the first and the recent decades of this century (2000–2010 vs. 2011–2022) and to describe the trends, seasonality, and impact of change of temperature and rainfall patterns on transmission dynamics of dengue in Bangladesh. For the period 2000–2022, dengue cases and death data from Bangladesh’s Ministry of Health and Family Welfare’s website, and meteorological data from the Bangladesh Meteorological Department were analyzed. A Poisson regression model was performed to identify the impact of meteorological parameters on the monthly dengue cases. A forecast of dengue cases was performed using an autoregressive integrated moving average model. Over the past 23 yr, a total of 244,246 dengue cases were reported including 849 deaths (CFR …

Several efforts have been made to develop effective and robust vision-based solutions for human action recognition in aerial videos. Generally, the existing methods rely on the extraction of either spatial features (patch-based methods) or skeletal key points (pose-based methods) that are fed to a classifier. Unlike the patch-based methods, the pose-based methods are generally regarded to be more robust to background changes and computationally efficient. Moreover, at the classification stage, the use of deep networks has generated significant interest within the community; however, the need remains to develop accurate and computationally effective deep learning-based solutions. To this end, this paper proposes a lightweight Transformer network-based method for human action recognition in aerial videos using the skeletal keypoints extracted using YOLOv8. The effectiveness of the proposed method is …