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- Gated Residual Recurrent Graph Neural Networks for Traffic Prediction261
A novel end-to-end multiple Res- RGNNs framework, referred to as “MRes-RGNN”, for traffic prediction is proposed, which outperforms state-of-the-art methods significantly.
- Multiple convolutional neural networks for multivariate time series prediction238
Tests on two large real-world datasets, show that the proposed model, called Multiple CNNs, has a strong advantage over other time series prediction methods.
- Citywide Traffic Flow Prediction Based on Multiple Gated Spatio-temporal Convolutional Neural Networks235
An end-to-end framework, multiple gated spatio-temporal CNNs (MGSTC), for citywide traffic flow prediction and demonstrates that MGSTC outperforms other state-of-the-art baselines.
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- Tumor‐derived exosomal lncRNA GAS5 as a biomarker for early‐stage non‐small‐cell lung cancer diagnosis176
Exo‐GAS5 may function as an ideal noninvasive serum‐based marker for identifying patients with early NSCLC and could be used to distinguish patients with Stage INSCLC with an AUC of 0.822.
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- A hierarchical deep convolutional neural network and gated recurrent unit framework for structural damage detection122
This work proposes a novel Hierarchical CNN and Gated recurrent unit (GRU) framework to model both spatial and temporal relations, termed as HCG, for structural damage detection, where CNN is utilized to model the spatial relations and the short-term temporal dependencies among sensors, while the output features of CNN are fed into the GRU to learn the long- term temporal dependencies jointly.
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- Hydrothermal fabrication of porous hollow hydroxyapatite microspheres for a drug delivery system111
The PHHMs displayed a high drug loading capacity and pH-responsive sustained-controlled drug release behavior when the authors used doxorubicin hydrochloride (DOX) as a loading drug and the controlled drug release system showed a high ability to kill cancer cells and less damage to normal cells.
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- WiFi-Based Indoor Robot Positioning Using Deep Fuzzy Forests89
A lightweight indoor robot positioning system which operates on cost-effective WiFi-based received signal strength (RSS) and could be readily pluggable into any existing WiFi network infrastructures is designed and a novel deep fuzzy forest is proposed to inherit the merits of decision trees and deep neural networks within an end-to-end trainable architecture.
- TitAnt78
The TitAnt is introduced, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech companies in the world, able to predict online real-time transaction fraud in mere milliseconds.
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- DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning72
Empirical results demonstrate that the DS-AL, despite being an exemplar-free CIL technique, delivers performance comparable with or better than that of replay-based methods across various datasets, including CIFAR-100, ImageNet-100 and ImageNet-Full.
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- Multiple local 3D CNNs for region-based prediction in smart cities69
A novel region-based information extraction mechanism and an end-to-end multiple spatial-temporal dependency learning structure are designed for local regions and the superior performance of the proposed method over the exisiting state-of-the-art methods is demonstrated.
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- Three-dimensional graphene biointerface with extremely high sensitivity to single cancer cell monitoring63
Simultaneous CCD recording and electrical signal extraction from the entrapped single cell on the graphene biointerface enabled to investigate multidimensional cell-electrode interactions and predict cancerous stage and pathology.
- miR-151a-3p-rich small extracellular vesicles derived from gastric cancer accelerate liver metastasis via initiating a hepatic stemness-enhancing niche62
A previously unidentified regulatory axis initiated by sEV-miR-151a-3p that establishes a hepatic stemness-permissive niche to support GC-LM is revealed.
- Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge61
A novel defense method called Eraser is proposed, which mainly includes three goals: unlearning harmful knowledge, retaining general knowledge, and maintaining safety alignment, which can significantly reduce the jailbreaking success rate for various attacks without compromising the general capabilities of the model.
- Modeling Temporal Patterns with Dilated Convolutions for Time-Series Forecasting60
The proposed HyDCNN is a novel hybrid framework based on fully Dilated CNN for time-series forecasting tasks, in which the proposed position-aware dilated CNNs are utilized to capture the sequential non-linear dynamics and an autoregressive model is leveraged to captured the sequential linear dependencies.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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