Cabin crew aero medicine and first aid training in China

作者： Xu   Wenwen     Mohamad   Nasri   Nurfaradilla     Azhar   Jamaludin   Khairul     Jin   Kai   来源： Cogent Education 年份： 2023 文献类型 ： 期刊 关键词： blended   Crew   strategies   cabin   instructional   training   learning   First   behavior   changing   aid  

描述： due to COVID-19. This study aims to explore the current status of aero medicine and first aid

A Comparative Sentiment Analysis of Airline Customer Reviews Using Bidirectional Encoder Representations from Transformers (BERT) and Its Variants

作者： Zehong   Li     Chuyang   Yang     Chenyu   Huang   来源： Mathematics 年份： 2023 文献类型 ： 期刊 关键词： sentiment   natural   Airline   Analysis   62P25   Service   processing   language   learning   Machine   customer  

描述： The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual analysis of customer reviews and feedback from social media, such as Twitter and Facebook. However, this form of analytical endeavor is labor-intensive and time-consuming. Extensive studies have investigated NLP algorithms for sentiment analysis based on textual customer feedback, thereby underscoring the necessity for an in-depth investigation of transformer architecture-based NLP models. In this study, we conducted an exploration of the large language model BERT and three of its derivatives using an airline sentiment tweet dataset for downstream tasks. We further honed this fine-tuning by adjusting the hyperparameters, thus improving the model’s consistency and precision of outcomes. With RoBERTa distinctly emerging as the most precise and overall effective model in both the binary (96.97%) and tri-class (86.89%) sentiment classification tasks and persisting in outperforming others in the balanced dataset for tri-class sentiment classification, our results validate the BERT models’ application in analyzing airline industry customer sentiment. In addition, this study identifies the scope for improvement in future studies, such as investigating more systematic and balanced datasets, applying other large language models, and using novel fine-tuning approaches. Our study serves as a pivotal benchmark for future exploration in customer sentiment analysis, with implications that extend from the airline industry to broader transportation sectors, where customer feedback plays a crucial role.

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

作者： Shiou   Li     Xianyun   Fei     Peilong   Chen     Zhen   Wang     Yajun   Gao     Kai   Cheng     Huilong   Wang     Yuanzhi   Zhang   来源： Remote Sensing 年份： 2023 文献类型 ： 期刊 关键词： based   image   sensing   deep   images   vegetation   learning   type   classification   aerial   remote   mountain   high   Analysis   spatial   object  

描述： The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

Cabin crew aero medicine and first aid training in China

作者： Xu   Wenwen     Mohamad   Nasri   Nurfaradilla     Azhar   Jamaludin   Khairul     Jin   Kai   来源： Cogent Education 年份： 2023 文献类型 ： 期刊 关键词： blended   Crew   strategies   cabin   instructional   training   learning   First   behavior   changing   aid  

描述： due to COVID-19. This study aims to explore the current status of aero medicine and first aid

Crew recovery optimization with deep learning and column generation for sustainable airline operation management

作者： Ahmet   Herekoğlu     Özgür   Kabak   来源： Annals of Operations Research 年份： 2023 文献类型 ： 期刊 关键词： Artificial   generation   Crew   disruptions   Airline   learning   recovery   Management   Machine   intelligence   problem   optimization   Column   Sustainability   AutoML   Sustainable   Business  

描述： a pivotal role in addressing crew disruptions. Leveraging historical data, learning-based approaches have t

Hypergraph convolution mix DDPG for multi-aerial base station deployment

作者： He   Haoran     Zhou   Fanqin     Zhao   Yikun     Li   Wenjing     Feng   Lei   来源： Journal of Cloud Computing 年份： 2023 文献类型 ： 期刊 关键词： Hypergraph   Agent   deep   efficiency   (AeBS)   learning   decomposition   aerial   (HGCN)   multi   (MADRL)   optimization   station   Value   convolution   reinforcement   base   energy  

描述： deep reinforcement learning (MADRL). We describe the multi-AeBS deployment challenge as a decentralized

Crew recovery optimization with deep learning and column generation for sustainable airline operation management

作者： Ahmet   Herekoğlu     Özgür   Kabak   来源： Annals of Operations Research 年份： 2023 文献类型 ： 期刊 关键词： Artificial   generation   Crew   disruptions   Airline   learning   recovery   Management   Machine   intelligence   problem   optimization   Column   Sustainability   AutoML   Sustainable   Business  

描述： a pivotal role in addressing crew disruptions. Leveraging historical data, learning-based approaches have t

Hypergraph convolution mix DDPG for multi-aerial base station deployment

作者： He   Haoran     Zhou   Fanqin     Zhao   Yikun     Li   Wenjing     Feng   Lei   来源： Journal of Cloud Computing 年份： 2023 文献类型 ： 期刊 关键词： Hypergraph   Agent   deep   efficiency   (AeBS)   learning   decomposition   aerial   (HGCN)   multi   (MADRL)   optimization   station   Value   convolution   reinforcement   base   energy  

描述： deep reinforcement learning (MADRL). We describe the multi-AeBS deployment challenge as a decentralized