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Red blood cells (RBCs) become sickle‐shaped and stiff under hypoxia as a consequence of hemoglobin (Hb) polymerization in sickle cell anemia. Distinguishing between sickle cell disease and trait is crucial during the diagnosis of sickle cell disease. While genetic analysis or high‐performance liquid chromatography (HPLC) can accurately differentiate between these two genotypes, these tests are unsuitable for field use. Here, we report a novel microscopy‐based diagnostic test called ShapeDx™ to distinguish between disease and trait blood in less than 1 h. This is achieved by mixing an unknown blood sample with low and high concentrations of a chemical oxygen scavenger and thereby subjecting the blood to slow and fast hypoxia, respectively. The different rates of Hb polymerization resulting from slow and fast hypoxia lead to two distinct RBC shape distributions in the same blood sample, which allows us to identify it as healthy, trait, or disease. The controlled hypoxic environment necessary for differential Hb polymerization is generated using an imaging microchamber, which also reduces the sickling time of trait blood from several hours to just 30 min. In a single‐blinded proof‐of‐concept study conducted on a small cohort of clinical samples, the results of the ShapeDx™ test were 100% concordant with HPLC results. Additionally, our field studies have demonstrated that ShapeDx™ is the first reported microscopy test capable of distinguishing between sickle cell disease and trait samples in resource‐limited settings with the same accuracy as a gold standard test. [ABSTRACT FROM AUTHOR]

Airborne diseases cause detriment in the human life. Many were found in history such as TB, SARS, MERS and recently COVID 19. These diseases hit the dead rate and crushes health and wealth of the world population. Mostly, airborne diseases will spread rapidly in crowdy places. Especially, in case of COVID-19, Wearing mask and monitoring body temperature by individual is good solution to prevent rapid spread of the disease. So, keeping safety measures in monitoring face mask and body temperature is crucial in crowdy places such as Airports, railway stations, Bus Stations, malls, temples, etc. is obligatory. With a focus on emphasizing the health of the people we proposed integrated system that monitors body temperature and face mask of each individual and open/close pathway gate to allow after knee verification. Proposed Prototype uses Raspberry pi to monitor Face mask using CNN and Arduino to monitor body temperature and enable motor drivers to open or close the pathway Gate. Efficiency and loss of Proposed CNN model was trained and tested with multiple epoches. [ABSTRACT FROM AUTHOR]

Aim:This study article's major goal is to classify facial expressions more accurately by utilizing Convolutional Neural Networks (CNNs) in contrast to AlexNet. Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle. The Convolutional neural network (CNN) is the algorithm most common method of visual analysis with best accuracy and Alexney is also an algorithm used here to compare the accuracy of novel image classification. Results: The Convolution neural network (CNN) produces 82% accuracy in predicting facial expressions on the dataset, whereas AlexNet produces 76% accuracy. Convolutional neural network (CNN) is better than AlexNet. The study groups differ from one another in a statistically significant way (p <0.05). Conclusion: Convolutional Neural Network provides better outcomes in accuracy rate when compared to AlexNet for predicting facial expressions. [ABSTRACT FROM AUTHOR]

Aim: The aim of this research article is classification of facial expression recognition Convolutional Neural Network (CNN) compared with ResNet yielded better accuracy. Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle. The convolutional neural network (CNN) is the most widely used algorithm for image analysis with best accuracy and ResNet is also an algorithm used here to compare the accuracy of Novel image classification. Results: The Convolution neural network (CNN) produces 82.14% accuracy in predicting facial expressions on the dataset, whereas ResNet produces 78.01% accuracy. Convolutional neural network (CNN) is better than ResNet. With (p<0.05), there is a statistically significant difference between the research groups. Conclusion: Convolutional Neural Network provides better outcomes in accuracy rate when compared to ResNet for predicting facial expressions. [ABSTRACT FROM AUTHOR]