Measuring the impact of tourism on society is important for efficient tourism planning and budgeting. Considerable amount of financial resources has been allocated for tourism promotional efforts. Therefore, it is important to understand how well promotional efforts have stimulated tourism social impacts. This study aims to predict the UNWTO promotional video views and its resulting impact on society. A CNN model with VGG16 was used to classify videos and images into clusters and RGB models. Quantitative analysis was conducted to predict video views and annual GDP as a societal impact. Our model performance for predicting official video views reveals strong relationship between image characteristics and promotional video views. Most notably, the strong correlation between image/video data and countries’ GDP growth rate highlighted by the model’s performance illuminates the potential of image data as a predictive tool in understanding the economic impacts of tourism particularly in rural areas.

The technology transfer is crucial in strategic planning for the manufacturing industry. The transfer of patents, with a focus on the importance of licensing and selling, has been emphasized as a foundational element of innovation within the manufacturing sector. This paper introduces a machine learning approach incorporating detailed financial data from manufacturing firms and patent information from the USPTO, to predicts patent transfers as key indicators of patent evaluation. This is achieved by using BERT to convert textual data into numerical vectors, combined with financial metrics, to project patent values as reflected in the number of assignments, claim numbers, and citations. Our findings highlight the significant role of real time analytics in understanding the intricacies of patent transfer activities. This research not only offers an insight into the intrinsic value of patents but also reveals the efficacy of BERT analysis in navigating the intricate nexus of patent information and manufacturing data, enhancing the understanding of patent transfer and its relation to commercialization.

As carbon emission reduction is being emphasized globally, the importance of efficient building operations to reduce energy use is emerging as an important factor. To optimize building operations (i.e., achieving better facility conditions with less energy use), this research proposes a methodology that leverages 1) the DBSCAN algorithm to not only cluster groups but identify anomalous behaviors and 2) the DBA algorithm to have a representative case of each cluster group. The methodology is demonstrated against real-world operational data (e.g., time-series temperature) of Building A operated by the Mastern Investment Group located in Seoul, South Korea. As a result, the anomalous cases are identified by the methodology, leading to interviews with the facility managers. After the cause of anomalies is defined and reasoned through discussion with the managers, a couple of strategies are suggested to the managers with the aim of managing the building in an efficient manner.