Yatesegeberg5021

e. the effect of eyeglasses, effect of eye occlusion and pose variations. The proposed approach shows remarkable improvement in performance over pre-existing approaches.Climate change and the need for sustainable development have become part of our daily lives. In this context, it is crucial to involve the educational community to the discussion, both students and teachers; by increasing awareness about these issues and the ways school communities can contribute to energy savings, we can kick-start a change towards more sustainable practices in our societies. The Green Awareness in Action (GAIA) H2020 research project implemented an IoT-based approach in several European schools for sustainability awareness and energy efficiency, while at the same time aiming for increasing students' digital skills. By using gamification, competitions and IoT-based educational activities, GAIA engaged directly with teachers and students in order to realize energy-saving activities in their environment. We report here on the use of gamification and competition among schools in this context, and how they helped together with IoT-based lab activities to engage students and educators to participate in the project more actively. We provide details on the implementation of GAIA's intervention in specific school settings to showcase our approach. Our findings, backed up by evaluation data and answers to a survey by 30 educators in Greece and Italy, confirm that the inclusion of competition and gamification aspects can significantly increase students' engagement, especially when having groups/schools competing with each other. Moreover, IoT-based educational activities can supplement existing educational activities in interesting ways, with students evaluating positively the experience and educators reporting increased overall student engagement in their class during the intervention period, and, on average, better class performance compared to previous periods.This research study focused on the dynamic response and mechanical performance of fiber-reinforced concrete columns using hybrid numerical algorithms. Whereas test data has non-linearity, an artificial intelligence (AI) algorithm has been incorporated with different metaheuristic algorithms. About 317 datasets have been applied from the real test results to detect the promising factor of strength subjected to the seismic loads. Adaptive neuro-fuzzy inference system (ANFIS) was carried out as an AI beside the combination of particle swarm optimization (PSO) and genetic algorithm (GA). Extreme Machine Learning (ELM) was also performed in order to approve the obtained results. According to the findings, it is demonstrated that ANFIS-PSO predicts the lateral load with promising evaluation indexes [R2 (test) = 0.86, R2 (train) = 0.90]. Mechanical performance prediction was also carried out in this study, and the results showed that ELM predicts the compressive strength with promising evaluation indexes [R2 (test) = 0.66, R2 (train) = 0.86]. Finally, both ANFIS-GA and ANFIS-PSO techniques illustrated a reliable performance for prediction, which encourage scholars to replace costly and time-consuming experimental tests with predicting utilities.The contagious disease transmission pattern outbreak caused a massive human casualty and became a pandemic, as confirmed by the World Health Organization (WHO). The present research aims to understand the infectious disease transmission pattern outbreak due to molecular epidemiology. Hence, infected patients over time can spread infectious disease. The virus may develop further mutations, and that there might be a more toxic virulent strain, which leads to several environmental risk factors. Therefore, it is essential to monitor and characterize patient profiles, variants, symptoms, geographic locations, and treatment responses to analyze and evaluate infectious disease patterns among humans. This research proposes the Evolutionary tree analysis (ETA) for the molecular evolutionary genetic analysis to reduce medical risk factors. Furthermore, The Maximum likelihood tree method (MLTM) has been used to analyze the selective pressure, which is examined to identify a mutation that may influence the infectious disease transmission pattern's clinical progress. This study also utilizes ETA with Markov Chain Bayesian Statistics (MCBS) approach to reconstruct transmission trees with sequence information. The experimental shows that the proposed ETA-MCBS method achieves a 97.55% accuracy, prediction of 99.56%, and 98.55% performance compared to other existing methods.The novel coronavirus disease (COVID-19) spread quickly worldwide, changing the everyday lives of billions of individuals. The preliminary diagnosis of COVID-19 empowers health experts and government professionals to break the chain of change and level the epidemic curve. The regular sort of COVID-19 detection test, be that as it may, requires specific hardware and generally has low sensitivity. find more Chest X-ray images to be used to diagnosis the COVID-19. In this work, a dataset of X-ray images with COVID-19, bacterial pneumonia, and normal was used to diagnose the COVID-19 automatically. This work to assess the execution of best in class Convolutional Neural Network (CNN) models proposed over ongoing years for clinical image classification. In particular, the modified pre-trained CNN-ResNet50 based Extreme Learning Machine classifier (ELM) has proposed for different diagnosis abnormalities such as COVID-19, Pneumonia, and normal. The proposed CNN method has trained and tested with the publicly available COVID-19, pneumonia, and normal datasets. The presented pre-trained ResNet CNN model provides accuracy, sensitivity, specificity, recall, precision, and F1 score values of 94.07, 98.15, 91.48, 85.21, 98.15, and 91.22, respectively, which is the best classification performance than other states of the art methods. This study introduced a computationally productive and exceptionally exact model for multi-class grouping of three diverse contamination types from alongside Normal people. This CNN model can help in the automatic diagnosis of COVID-19 cases and help decrease the burden on medicinal services frameworks.