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Since the first case of COVID‐19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID‐19 and COVID‐19 findings from computed tomography images using pre‐trained models using two different datasets. COVID‐19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy‐paving pattern, ground‐glass opacity, ground‐glass opacity and consolidation, ground‐glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre‐trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient‐weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID‐19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID‐19 in the lungs was an innovative and successful approach.

Construction sector, which plays an economically active role, has been one of the areas with the highest risk in terms of occupational health and safety. For this reason, it is necessary to minimize the accidents that may occur in the construction industry and their negative effects in order to prevent economic losses that may result in temporary or permanent incapacity or labor losses due to death. In recent years in Turkey, decreases in the frequency of occupational accidents have been observed as a result of improvements in Occupational Health and Safety regulations. However, both overall accident frequencies and the number of accidents that result in death are still high in the country. Therefore, the measures taken with legal regulations in the sector are not sufficient alone; in this situation workers' attitudes towards occupational health and safety should also be evaluated. In this study it was aimed to determine the perceptions of employees in the construction projects in Istanbul, Van and Malatya cities toward occupational health and safety, and to present some suggestions based on research findings. In this context, results are expected to shed light on workers' health and safety for companies in the construction industry.