Intellectual Capital as a Factor Forming Economic Security of Enterprises in Society 5.0

The article shows that in Society 5. 0 intellectual capital is a key factor forming economic security of enterprises. The priority of structural components of intellectual capital as a factor of enterprise economic security formation is determined. Features of mining and beneficiation enterprises operation are considered while structuring intellectual capital. The resulting criteria for assessing efficiency of the intellectual capital use during economic security formation are established. Three key groups of competences of personnel necessary for economic security formation are identified. It is substantiated that organic connection of intellectual capital structural elements will enable forming economic security of the enterprise during the current period and providing economic security parameters in the long term. Based on the data obtained from mining and beneficiation enterprises, it is demonstrated that this will be possible due to growth of an innovative level of technological processes which will contribute to product quality enhancement that, in turn, will expand the client base.

Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification

Through the continued development of technology, applying deep learning to remote sensing scene classification tasks is quite mature. The keys to effective deep learning model training are model architecture, training strategies, and image quality. From previous studies of the author using explainable artificial intelligence (XAI), image cases that have been incorrectly classified can be improved when the model has adequate capacity to correct the classification after manual image quality correction; however, the manual image quality correction process takes a significant amount of time. Therefore, this research integrates technologies such as noise reduction, sharpening, partial color area equalization, and color channel adjustment to evaluate a set of automated strategies for enhancing image quality. These methods can enhance details, light and shadow, color, and other image features, which are beneficial for extracting image features from the deep learning model to further improve the classification efficiency. In this study, we demonstrate that the proposed image quality enhancement strategy and deep learning techniques can effectively improve the scene classification performance of remote sensing images and outperform previous state-of-the-art approaches.