Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment

Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real world, and therefore using deep neural networks to evaluate cognitive health is becoming a hot research topic. Deep learning and human activity recognition are two domains that have received attention for the past few years. The former is for its relevance in application fields like health monitoring or ambient assisted living, and the latter is due to their excellent performance and recent achievements in various fields of application, namely, speech and image recognition. This research develops a novel Symbiotic Organism Search with a Deep Convolutional Neural Network-based Human Activity Recognition (SOSDCNN-HAR) model for Cognitive Health Assessment. The goal of the SOSDCNN-HAR model is to recognize human activities in an end-to-end way. For the noise elimination process, the presented SOSDCNN-HAR model involves the Wiener filtering (WF) technique. In addition, the presented SOSDCNN-HAR model follows a RetinaNet-based feature extractor for automated extraction of features. Moreover, the SOS procedure is exploited as a hyperparameter optimizing tool to enhance recognition efficiency. Furthermore, a gated recurrent unit (GRU) prototype can be employed as a categorizer to allot proper class labels. The performance validation of the SOSDCNN-HAR prototype is examined using a set of benchmark datasets. A far-reaching experimental examination reported the betterment of the SOSDCNN-HAR prototype over current approaches with enhanced precision of 86.51% and 89.50% on Penn Action and NW-UCLA datasets, respectively

Prevalence of nocturnal enuresis among children of Aseer region in Saudi Arabia

Introduction: Nocturnal enuresis (NE) in children is a very common problem managed in pediatric urology. In this study, we present the prevalence of NE in children in Aseer region in Saudi Arabia. Methodology: This study was conducted as a descriptive cross-sectional survey to estimate the prevalence of NE among 555 Saudi children aged 5–15 years in Aseer region in Saudi Arabia. Data collection was done through a questionnaire, which included questions on sociodemographic data, personal knowledge, enuresis-related characteristics, risk factors, and management modalities. Results: This study identified a prevalence of enuresis of 24% of the study population, most of whom were boys. The majority of the parents had a high educational level. Clinical characteristics of the study population showed: 9% have a family history of NE, 2.2% have a history of neurological disorder, 10.0% have a history of urinary tract infections, 66.8% have associated daytime urgency, 67% have urine-holding behavior, and 19.5% have associated daytime enuresis of the study population. Conclusion: Our study found that 24% of children in the Aseer region in Saudi Arabia have NE. Our study finding helps us to understand the prevalence of NE in Aseer region in Saudi Arabia, and this can be applied to other regions in the kingdom. Furthermore, this finding helps us to understand the need to raise awareness in the community about NE and the need to educate the nonpediatric urologist health-care provider about the best management practice for NE