Information system based on multi-value classification of fully connected neural network for construction management - Tetyana Honcharenko; Roman Akselrod; Andrii Shpakov; Oleksandr Khomenko (IAES International Journal of Artificial Intelligence (IJ-AI), 2023)
This study is devoted to solving the problem to determine the professional adaptive capabilities of construction management staff using artificial intelligence systems. It is proposed fully connected feed-forward neural network (FCF-FNN) architecture and performed empirical modeling to create a data set. Model of artificial intelligence system allows evaluating the processes in an FCF-FNN during the execution of multi-value classification of professional areas. A method has been developed for the training process of a machine learning model, which reflects the internal connections between the components of an artificial intelligence system that allow it to “learn” from training data. To train the neural network, a data set of 35 input parameters and 29 output parameters was used; the amount of data in the set is 936 data lines. Neural network training occurred in the proportion of 10% and 90%, respectively. Results of this study research can be used to further improve the knowledge and skills necessary for successful professional realization.