ABSTRACT
The global community is now coping with such a significant issue as the Covid-19 virus, public gatherings are experiencing certain restrictions in order to stop the virus from spreading further. The issue takes on a bigger significance during religious pilgrimages such as the Hajj and the Umrah, when tens of thousands, if not hundreds of thousands, of people gather in holy cities to participate in religious rituals. During such a time period, it is quite difficult to single out an infected person from among the big crowd that is there. The current screening approach only includes a single element of identity, which means that there is a possibility that the screening process may fail because there will not be enough identification. The use of thermal imaging provides a higher level of accuracy when compared to more conventional ways of testing for viral infections in the detection of these symptoms in crowded locations. The primary method that is utilised to determine whether or not a person is infected with the virus is an image processing algorithm that is built in MATLAB. The first step in the process of acquiring an image is to divide the video that is being captured into individual frames. Following this step, the frames that have been focussed are processed in a number of ways. The temperature of a person's body may be estimated by taking a thermal image and then using the RGB separation feature on it. In order to categorise and sort the data, the k-means approach was used as part of the segmentation operation. In addition to eliminating the skin frequency, it also gets rid of the background noise, which often has a higher frequency than the skin frequency. The Viola–Jones technique, which may be used to identify the person's breathing rate, can be used to locate the end of a person's nose, specifically the tip of the nose. The Cascaded Adaboost Classifier is an option that may be used to finish the classification process after the operation has been completed. The suggested method has an accuracy rate of 89.23 percent and a simulation period of around 60 seconds, which guarantees the safety of huge groups of people's public health. [ FROM AUTHOR]
ABSTRACT
Human emotion detection is necessary for social interaction and plays an important role in our daily lives. Artificial intelligence research is rising, focusing on automated emotion detection. The capability to identify the emotion, which is considered one of the traits of emotional intelligence, is a component of human intelligence. Although the study is limited dependent on facial expressions or voice is flourishing, it is identifying emotions via body movements, a less researched issue. To attain emotional intelligence, this study suggests a deep learning approach. Here initially the video can be converted into image frames after the converted image frames can be preprocessed using the Glitter bandpass butter worth filter and contrast stretch histogram equalization. Then from the enhanced image, the features can be clustered using the hybrid Gaussian BIRCH algorithm. Then the specialized features are retrieved from the body of human gestures using the AdaDelta bacteria foraging optimization algorithm, and the selected features are fed to a supervised Kernel Boosting LENET deep-learning algorithm. The experiment is conducted using Geneva multimodal emotion portrayals (GEMEPs) corpus data set. This data set includes, human body gestures portraying the archetypes of five emotions, such as anger, fear, joy, pride, and sad. In these emotion detection techniques, the suggested Kernel Boosting LENET classifier achieves 98.5% accuracy, 94% precision, 95% sensitivity, and F-Score 93% outperformed better than the other existing classifiers. As a result, emotional acknowledgment may help small and medium enterprises (SMEs) to improve their performance and entrepreneurial orientation. The correlation coefficient of 188 and the significance coefficient of 0.00 show that emotional intelligence and SMEs performance have a significant and positive association.