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- Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics360
Both the simulation and experimental results show that the optimized KELM based fault diagnosis model can achieve high accuracy, reliability, and good generalization performance.
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- Radiomics assessment of bladder cancer grade using texture features from diffusion‐weighted imaging163
Textural features from diffusion‐weighted images (DWI) and apparent diffusion coefficient (ADC) maps that can distinguish low‐grade bladder cancer from high‐grade, and a radiomics‐based strategy for cancer grading using texture features are proposed.
- Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI85
Texture features optimally selected together with sample augmentation could improve the performance on differentiating bladder carcinomas from wall tissues, suggesting a potential way for tumor noninvasive staging of bladder cancer preoperatively.
- Long- and Short-term Preference Learning for Next POI Recommendation83
A long- and short-term preference learning model (LSPL) considering the sequential and context information of users and outperforms the state-of-art approaches for next POI recommendation.
- Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps69
3D radiomic signatures derived from T2WI and its high-order derivative maps could reflect muscular invasiveness of bladder cancer, and the proposed strategy can be used to facilitate the preoperative prediction of muscularinvasiveness in patients with bladder cancer.
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- On the Feasibility of Simple Transformer for Dynamic Graph Modeling64
This work re-conceptualize dynamic graphs as a sequence modeling challenge and introduces a novel temporal alignment technique that not only captures the inherent temporal evolution patterns within dynamic graphs but also streamlines the modeling process of their evolution.
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- Bidirectional Conditional Generative Adversarial Networks42
The Bidirectional cGAN (BiCoGAN), which effectively disentangles z and c in the generation process and provides an encoder that learns inverse mappings from $x$ to both $z and $c$, trained jointly with the generator and the discriminator.
- Multisample-Based Contrastive Loss for Top-K Recommendation40
A multisample-based contrastive loss (MSCL) function that solves the two problems by balancing the importance of positive and negative samples and data augmentation by using multiple positive items simultaneously with the CL loss function.
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- Experimental Comparisons of Multi-class Classifiers30
The experiment shows that gradient boosted trees, nonlinear support vector machine, K nearest neighbor reach high performance under the circumstance of binary classification and minor data capacity; under the condition of high dimension, multi-class and big data, however, gradient boosted Trees, linear support Vector machine, multi -class logistic classifier get good results.
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- In Situ Recovery of Serotonin Synthesis by a Tryptophan Hydroxylase-Like Nanozyme for the Treatment of Depression23
In vivo Fe3O4@CS treatment results in the recovery of 5-hydroxytryptophan and serotonin levels and improvement of neuronal signal transduction ability in a depression mouse model, thus ameliorating depressive-like behaviors.
- A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning23
This survey systematically categorizes existing studies into three major families: meta-learning approaches, pre-training approaches, and hybrid approaches, with a finer-grained classification in each family to aid readers in their method selection process.
- Introducing Machine Learning Models to Response Surface Methodologies20
Several commonly used regression models in the ML including the improved linear models including the least absolute shrinkage and selection operator model and the generalized linear model, the decision trees family, the model of the neural nets, the multi-layer perceptrons and the support vector machine will be introduced.
- MTHFR Gene Polymorphism Is Associated With DNA Hypomethylation and Genetic Damage Among Benzene-Exposed Workers in Southeast China19
The results imply that the TT genotype in rs1801133 is associated with global DNA hypomethylation, which may influence the induction of MN following exposure to benzene.
- SOL: A library for scalable online learning algorithms19
SOL is an open-source library for scalable online learning with high-dimensional data that provides a family of regular and sparse online learning algorithms for large-scale classification tasks with high efficiency, scalability, portability, and extensibility.
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- Family in the Wild (FIW): A Large-scale Kinship Recognition Database.15
A large-scale dataset for visual kin-based problems, the Family in the Wild (FIW) dataset is introduced, with only a small team and an efficient labelling tool designed to optimize the process of marking complex hierarchical relationships, attributes, and local label information in family photos.
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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