Predicting Airline Choices: A Decision Support Perspective and Alternative Approaches

The ability to predict the choices of prospective passengers allows airlines to alleviate the need for overbooking flights and subsequently bumping passengers, potentially leading to improved customer satisfaction. Past studies have typically focused on identifying the important factors that influence choice behaviors and applied discrete choice framework models to model passengers’ airline choices. Typical discrete choice models rely on two major assumptions: the existence of a utility function that represents the preferences over a choice set and the linearity of the utility function with respect to attributes of alternatives and decision makers. These assumptions allow the discrete choice models to be easily interpreted, as each unit change of an input attribute can be directly translated into change in utility that eventually affects the optimal choice. However, these restrictive assumptions might impede the ability of typical discrete choice models to deliver operational accurate prediction and forecasts. In this paper, we focus on developing operational models that are intended for supporting the actual prediction decisions of airlines. We propose two alternative approaches, pairwise preference learning using classification techniques and ranking function learning using evolutionary computation. We have empirically compared these approaches against the standard discrete choice framework models and report some promising results in this paper

Region-Aware Portrait Retouching with Sparse Interactive Guidance

Portrait retouching aims to improve the aesthetic quality of input portrait photos and especially requires human-region priority. \pink{The deep learning-based methods largely elevate the retouching efficiency and provide promising retouched results. However, existing portrait retouching methods focus on automatic retouching, which treats all human-regions equally and ignores users' preferences for specific individuals,} thus suffering from limited flexibility in interactive scenarios. In this work, we emphasize the importance of users' intents and explore the interactive portrait retouching task. Specifically, we propose a region-aware retouching framework with two branches: an automatic branch and an interactive branch. \pink{The automatic branch involves an encoding-decoding process, which searches region candidates and performs automatic region-aware retouching without user guidance. The interactive branch encodes sparse user guidance into a priority condition vector and modulates latent features with a region selection module to further emphasize the user-specified regions. Experimental results show that our interactive branch effectively captures users' intents and generalizes well to unseen scenes with sparse user guidance, while our automatic branch also outperforms the state-of-the-art retouching methods due to improved region-awareness.

Empirical Regression Model Using Ndvi, Meteorological Factors For Estimation Of Wheat Yield In Yunnan, China

Crop yield estimation is of great importance to food security. NDVI, as an effective crop monitoring tool, is extensively used in crop yield estimation. However there are few studies conducted in the regions where mixed crops are grown. In this study, a statistical approach for crop area identification is proposed and applied to wheat in Jianshui County in the Nanpan River Basin, Yunnan Province of China. Based on the correlation analysis between MODIS NDVI data and crop yield, the planting areas are identified, as well as the best periods for a reliable estimation. Regression models are presented to predict the crop yield with the retrieved NDVI from the corresponding crop planting-areas. Besides, the crop yield is also strongly influenced by meteorological factors, such as precipitation, temperature and potential evapotranspiration data. Therefore, new regression model by adding those factors is presented and compared with the former one. This study has proposed a simple and convenient method on crop yield estimation using meteorological factors and NDVI data in small regions where crop type is unknown exactly