This communication article provides a call for unmanned aerial vehicle (UAV) users in archaeology to make imagery data more publicly available while developing a new application to facilitate the use of a common deep learning algorithm (mask region-based convolutional neural network; Mask R-CNN) for instance segmentation. The intent is to provide specialists with a GUI-based tool that can apply annotation used for training for neural network models, enable training and development of segmentation models, and allow classification of imagery data to facilitate auto-discovery of features. The tool is generic and can be used for a variety of settings, although the tool was tested using datasets from the United Arab Emirates (UAE), Oman, Iran, Iraq, and Jordan. Current outputs suggest that trained data are able to help identify ruined structures, that is, structures such as burials, exposed building ruins, and other surface features that are in some degraded state. Additionally, qanat(s), or ancient underground channels having surface access holes, and mounded sites, which have distinctive hill-shaped features, are also identified. Other classes are also possible, and the tool helps users make their own training-based approach and feature identification classes. To improve accuracy, we strongly urge greater publication of UAV imagery data by projects using open journal publications and public repositories. This is something done in other fields with UAV data and is now needed in heritage and archaeology. Our tool is provided as part of the outputs given.

This paper mainly studies the important role of the percentage prediction of the number of users and the number of word-filling attempts of Wordle game in the future development strategy. The purpose of this article is to research and develop two mathematical models of Wordle game, one of which is the prediction model of user volume and the other is the percentage prediction model of the number of word-filling attempts. The fitting effect is very good, and the result is R2=0.984. By quantifying specific letters at different positions in a word, it is found that the proportion of difficult modes has a direct impact on specific positions and specific letters. Secondly, for the second model, the proportion of each corresponding variable is obtained through comprehensive evaluation and analysis by using the mean expectation model for the percentage of the predicted number of attempts. The average variance formula is used by calculating the average of the relevant percentage of each characteristic value under the corresponding number of attempts. The relevant percentage distribution results of "EERIE" (1, 2, 3, 4, 5, 6, X) are (1.20%, 16.01%, 28.31%, 27.83%, 18.16%, 5.81%). Through the analysis of variance, the P value is less than 0.05, and the prediction result of the model has high confidence.