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Mattia Pellegrino, Gianfranco Lombardo, George Adosoglou, Stefano Cagnoni, Panos M. Pardalos and Agostino Poggi
With the recent advances in machine learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies? bankruptcy. However, time series have received little attention in the literature, w...
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Mingze Li, Bing Li, Zhigang Qi, Jiashuai Li and Jiawei Wu
Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emer...
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Ji-Woon Lee and Hyun-Soo Kang
The escalating use of security cameras has resulted in a surge in images requiring analysis, a task hindered by the inefficiency and error-prone nature of manual monitoring. In response, this study delves into the domain of anomaly detection in CCTV secu...
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Sijie Liu, Nan Zhou, Chenchen Song, Geng Chen and Yafeng Wu
This research introduces the Enhanced Scale-Aware efficient Transformer (ESAE-Transformer), a novel and advanced model dedicated to predicting Exhaust Gas Temperature (EGT). The ESAE-Transformer merges the Multi-Head ProbSparse Attention mechanism with t...
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Ting Guo, Nurmemet Yolwas and Wushour Slamu
Recently, the performance of end-to-end speech recognition has been further improved based on the proposed Conformer framework, which has also been widely used in the field of speech recognition. However, the Conformer model is mostly applied to very wid...
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Xiaobo Zhang, Huashun Li, Jingzhao Li and Xuehai Zhou
The rapid and accurate detection of orthopedic medical devices is pivotal in enhancing health care delivery, particularly by improving workflow efficiency. Despite advancements in medical imaging technology, current detection models often fail to meet th...
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Dibo Dong, Shangwei Wang, Qiaoying Guo, Xing Li, Weibin Zou and Zicheng You
Accurately predicting wind speed is crucial for the generation efficiency of offshore wind energy. This paper proposes an ultra-short-term wind speed prediction method using a graph neural network with a multi-head attention mechanism. The methodology ai...
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Rui Zhang, Chengrong Xue, Qingfu Qi, Liyuan Lin, Jing Zhang and Lun Zhang
The enrichment of social media expression makes multimodal sentiment analysis a research hotspot. However, modality heterogeneity brings great difficulties to effective cross-modal fusion, especially the modality alignment problem and the uncontrolled ve...
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Zhen Sun and Xinfu Li
Named entity recognition can deeply explore semantic features and enhance the ability of vector representation of text data. This paper proposes a named entity recognition method based on multi-head attention to aim at the problem of fuzzy lexical bounda...
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Xin Wang, Yi Li, Yaxi Xu, Xiaodong Liu, Tao Zheng and Bo Zheng
Data-driven Remaining Useful Life (RUL) prediction is one of the core technologies of Prognostics and Health Management (PHM). Committed to improving the accuracy of RUL prediction for aero-engines, this paper proposes a model that is entirely based on t...
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