The World Health Organization (WHO) has given people various protective warnings for Monkeypox. If monkeypox spreads rapidly, it becomes a serious public health problem. In this case, it creates a serious congestion in hospitals. Therefore, auxiliary systems can be needed in hospitals. In this study, explainable artificial intelligence (xAI) assisted convolutional neural networks (CNNs) based a decision support system was proposed. The data set was used for this task consists of 572 images in two classes, such as Monkeypox and Normal. 12 different CNN models were used for Monkeypox and Normal skin classification. MobileNet V2 model achieved best performance with the accuracy of 98.25%, sensitivity of 96.55%, specificity of 100.00% and F1-Score of 98.25%. This model was supported by explainable AI methods. As a result, an artificial intelligence (AI) assisted auxiliary diagnosis system has been proposed for Monkeypox skin lesion.

It is essential that the control and management of the work of labors in construction project management is effective. In this study, it is aimed to building artificial intelligence models to recognition on activities in a construction work to effectively utilization project management and control. In accordance with this purpose, 3-axis accelerometer, gyroscope, and magnetometer data were obtained from the labors through the sensor to predict the activities determined for a construction work. These raw data were made compliance for the model by going through a series of preprocessing applications. These data are trained and modeled with basic machine learning algorithms logistic regression, SVC, DT and KNN algorithms. According to the results of the analysis, the best prediction was obtained with the SVC algorithm with an accuracy of 90%. In other algorithms, respectively, 87% accuracy was contrived in the KNN algorithm, and approximately 80% accuracy in the logistic regression and DT algorithms. According to these values, it has been observed that the activities performed in a construction work can be estimated at a high rate. In this way, at the construction sites, it can be automatically determined which work the laborer do at a certain accuracy rate.

PurposeThe study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.Design/methodology/approachCategorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.FindingsMeta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R2, were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R2 are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R2 are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.Research limitations/implicationsThe study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.Originality/valueThe study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

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Today's construction industry changes and develops with the developing technology. One of the most important technological changes is the Building Information Modeling (BIM) system. Adoption of the BIM system is essential in CM services, which include consulting services in addition to engineering services. In this study, it is aimed to examine the benefits and challenges of using the BIM system in CM services and the necessity of using this system by determining the duties of the Construction Manager. In this context, a survey was conducted and analyzed for professional CM companies regarding the implementation of BIM system in CM services in construction projects in Turkey, a developing country. According to the results of the analysis, although the lack of trained staff emerged as the most important challenge, the increase in interdisciplinary coordination and the reduction of design errors with conflict analysis were determined as the most important benefits. In addition, the tasks of the Construction Manager in the use of the BIM system are listed in order of importance. This study is one of the pioneering studies on the implementation of the BIM system in CM services. In the light of the result obtained, legal regulations, standards, contracts, and execution plans can be composed for the use of the BIM system in CM services. Therefore, it is hoped that this study will contribute to the building production stakeholders in the integration of the BIM system with CM services.