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Dialogue systems frequently misinterpret user input containing vague, hedged, or perception-based expressions such as “somewhat warm” or “not bright”. Traditional sentence similarity measures ...

Farmer credit default identification in the context of inclusive finance is significantly hindered by sample scarcity, heterogeneous data sources, and the underutilization of “soft information” embedded in ...

This paper introduces CONV_GBERT, a hybrid deep learning framework for robust sentiment classification. The model integrates the bidirectional contextual understanding of BERT, the generative capabilities of GPT, and the...

The Zulfikar sword is a highly standardized sacred weapon symbol in Shia visual culture, forming a stable iconographic lineage across the Middle East and the Irano-Ottoman world. However, it is notably absent from Chines...

Spark detection in distribution line videos remains challenging due to weak visual saliency, multi-scale variation, and complex environmental interference. To address these issues, this paper presents an enhanced Swin Tr...

Understanding how semantic information flow reorganizes market topology remains an open challenge in financial network analysis. Prior work typically emphasizes price correlations or sentiment polarity, but rarely quanti...

Time series analysis is essential for finance, transportation, energy, healthcare, and climate research. While time-frequency domain representation underpins effective time series modeling, its holistic utilization and i...

Unmanned aerial vehicle (UAV) aerial photography technology is crucial in various fields, including daily life, production, and security. However, it faces challenges in small object detection, such as extremely small ob...

For autonomous vehicles to effectively navigate urban environments where pedestrians and personal vehicles are in close proximity, it is crucial to accurately track and predict their positions and velocities to enable ef...

Reservoirs are critical infrastructure for global water, food, and energy security, yet their storage dynamics remain poorly characterized across most of the world. Accurate and timely knowledge of reservoir storage is e...

Efficient and intelligent operation of raw-material yards is essential for reducing energy consumption in iron and steel production. However, fully automated reclaiming of complex and irregular stockpiles remains challen...

The inherent non-stationarity, weak periodicity, and abrupt transient mutations of wind turbine gearbox vibration signals present substantial challenges to traditional diagnostic frameworks. To improve diagnostic accurac...

Heart disease is rapidly increasing. For early and accurate prediction, advanced machine learning (ML) models are needed. However, the sensitive nature of clinical data, along with strict regulatory constraints, creates ...

To address issues such as missed detections and false positives in safety equipment wear detection tasks caused by complex backgrounds, severe occlusions, and small target sizes, as well as frequent ID switching and low ...

Timely prediction of Intensive Care Unit (ICU) admission at hospital arrival is critical for clinical decision-making, resource allocation, and patient management, particularly during large-scale health crises such as th...

This study focuses on the explainability of AI operations for diagnosing degradations in cloud applications. Log data can provide essential contextual information on misbehaving applications in monitoring, evaluated agai...

The low visual contrast between dermatological lesions and normal skin, coupled with the masking effect of surface artefacts, presents two major obstacles to precise automated skin lesion segmentation. In order to addres...

Unsupervised anomaly detection identifying both structural and logical anomalies is essential for modern industrial manufacturing. Although multi-score frameworks have been proposed, existing methods tend to reuse identi...