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In response, we propose a hybrid runoff prediction model that combines Dung Beetle Optimization (DBO)'s optimization capabilities, Temporal Convolutional Networks (TCN)’s proficiency in extracting local temporal features, and the Transformer’s ability to capture long-term dependencies.

New hydrogeological insights from the region: To address these limitations, we introduce RadarDiT, a Vision Transformer-based diffusion model specifically engineered for radar extrapolation in the TGRA.

To address these limitations, this paper proposes RL-TBTNet, a novel teaching optimization recommendation framework that integrates a bidirectional Transformer, BERT, and reinforcement learning (DQN).

Our architecture employs a Heterogeneous Graph Transformer (HGT) to learn the unique patterns emerging from these different relationship types. It then synergistically fuses these relational insights with context-aware representations of user-generated content.

Abstract Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing.

This paper introduces E2ETrADS, an end-to-end transformer-based autonomous driving framework designed to operate robustly under such conditions. A comprehensive dataset was generated using the CARLA simulator, encompassing both nominal and adverse weather scenarios.

The article presents an algorithm for processing data by the acoustic method of insulation power transformers. The article presents data processing of diagnosing by the acoustic method of power transformers.

To reduce the feedback overhead, an increasing number of deep learning (DL)-based networks have emerged, aimed at compressing and subsequently recovering CSI. Various novel structures are introduced, among which Transformer architecture has enabled a new level of precision in CSI feedback.

However, it is challenging to differentiate between classes due to the subtle visual differences. This study introduces a novel two-stage deep learning architecture that integrates self-supervised representation learning with supervised classification for kidney CT image analysis using a publicly available kidney CT image dataset.MethodsIn the first stage,…

This paper suggests a highly interpretable, asymmetric classification framework that combines a lightweight Vision Transformer (ViT) with multifractal detrended fluctuation analysis (MFDFA) in order to overcome this obstacle.

Traditional methods rely on different machine learning algorithms tailored to specific types of line and polygon elements, limiting their general applicability. This study introduces a novel approach called “Pre-Trained Shape Feature Representations from Transformers (PSRT)”, which utilizes transformer encoders designed with three self-supervised pre-traini…

Therefore, inspired by the successful application of Transformer architecture in natural language processing (NLP) tasks, a novel Transformer method called LeViT was introduced for automatic asphalt pavement image classification.

Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design.

In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model.