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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.
- Developing a Continuous Severity Scale for Macular Telangiectasia Type 2 Using Deep Learning and Implications for Disease Grading13
A novel continuous severity scaling system for Macular Telangiectasia Type II (MacTel) by combining a DL classification model with Uniform Manifold Approximation and Projection (UMAP) and UMAP, which generated a continuous MacTel severity scale, without requiring continuous training labels.
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- Functional identification of ZDS gene in apple (Malus halliana) and demonstration of it's role in improving saline–alkali stress tolerance12
Overexpression of MhZDS enhanced the tolerance of A. thaliana, tobacco and apple calli under saline-alkali stress and caused a variety of physiological and biochemical changes: compared with wild-type, transgenic plants grew better under saline stress and MhZDS-OE lines showed higher chlorophyll content, POD, SOD, CAT activities and proline content, lower electrical conductivity and MDA content.
- IDH mutation assessment of glioma using texture features of multimodal MR images12
This study presented a radiomic strategy for noninvasively discriminating IDH mutation of patients with glioma that effectively incorporated kinds of texture features from multimodal MRI, and SVM-based classification strategy.
- Retrieval Augmented Generation for Dynamic Graph Modeling11
This work proposes a novel framework, Retrieval-Augmented Generation for Dy namic Graph modeling (RAG4DyG), which enhances dynamic graph predictions by incorporating contextually and temporally relevant examples from broader graph structures.
- Few-Shot Learning on Graphs: From Meta-Learning to LLM-empowered Pre-Training and Beyond11
This tutorial offers an in-depth exploration of recent advancements in few-shot learning for graphs, providing a comparative analysis of state-of-the-art methods and identifying future research directions.
- Semi-supervised New Slot Discovery with Incremental Clustering11
This work proposes a Semi-supervised Incremental Clustering method, to discover new slots with the aid of existing linguistic annotation models and limited known slot data, and demonstrates that the proposed method significantly outperforms state-of-the-art models.
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- Accelerometer-based single-handed video browsing on mobile devices10
Evaluation on a group study indicates that this new design provides a natural user experience for single-handed video navigation, and therefore an effective complement to existing video browsing mechanisms on mobile devices.
- Fine-grained semantic textual similarity measurement via a feature separation network9
A feature separation network (FSN) model is proposed, which can further separate and extract attribute features and description features and then measure the semantic similarity according to the separated features.
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- Aberrant temporal–spatial complexity of intrinsic fluctuations in major depression9
This study investigated temporal expressions of spatial modes within the default mode network, salience network and cognitive control network using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets to find enhanced CAP-to-CAP transitions of the SN in patients with MDD.
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- [Association of genetic polymorphism of XRCC1 with susceptibility to acute childhood leukemia].9
Polymorphism of XRCC1 gene may contribute to the higher risk of childhood leukemia and no interaction was found between X-ray exposure and XR CC1 polymorphisms using Logistic regression analysis.
- Active Discovering New Slots for Task-Oriented Conversation8
A general new slot discovery task is formulated in an information extraction fashion and incorporate it into an active learning framework to realize human-in-the-loop learning and proposes a bi-criteria selection scheme which incorporates two major strategies, namely, uncertainty-based and diversity-based sampling to efficiently identify terms of interest.
- HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs6
The HeTGB is introduced, a novel benchmark comprising five real-world heterophilic graph datasets from diverse domains, with nodes enriched by extensive textual descriptions that enables systematic evaluation of GNNs, pre-trained language models (PLMs) and co-training methods on the node classification task.
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- Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent Discovery4
A Pseudo-Label enhanced Prototypical Contrastive Learning (PLPCL) model for uniformed intent discovery is proposed and a prototype learning method integrating the supervised and pseudo signals from IND and OOD samples is designed.
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- Construction of a multidimensional quality evaluation system for Scutellariae Radix: Integrated research on chemical components, biological potency, near-infrared spectroscopy technology, and intelligent algorithms2
A shift in quality control of SR is accomplished, moving from single-component quantitative analysis to comprehensive evaluation combining active ingredient detection and bioactivity assessment, while also advancing from precise measurement to rapid predictive methods.
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- Multi-contrast MRI registration of carotid arteries based on cross-sectional images and lumen boundaries2
A coarse-to-fine registration strategy based on cross-sectional images and wall boundaries based on deterministic annealing technique has improved the accuracy of registration effectively for further component analysis of carotid plaques.
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- Prefix Tuning for Few-shot Malware Classification with Supervised Contrastive Cross-Entropy Learning1
- Actively Discovering New Slots for Task-oriented Conversation1
This work formulate a general new slot discovery task in an information extraction fashion and incorporate it into an active learning framework to realize human-in-the-loop learning and proposes a bi-criteria selection scheme which incorporates two major strategies, namely, uncertainty-based sampling and diversity- based sampling to efficiently identify terms of interest.
- Redefining and subtyping of major depression based on brain functional connectivity signatures with high generalization: an ensemble hybrid framework1
A novel ensemble hybrid deep-learning framework based on graph neural network for redefining and subtyping of MDD with the brain’s “fingerprinting”, i.e. nuanced functional connectivity profiles is proposed, which paves the way towards precision medicine of mental disorders.
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- ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation–
Mechanism analysis shows that ReCast mitigates the persistent all-zero / single-hit regime, restores learnability when natural positives are scarce, and converts otherwise wasted rollout budget into more stable policy updates, suggesting that, for generative recommendation, the decisive RL problem is how to assign rewards, but how to construct learnable optimization events from sparse, structured supervision.
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- Study on the Surgical Approaches for Patients with Involved Double Columns( wall ) Acetabular Fractures–
The surgical approaches for patients with involved double columns( wall) acetabular fracture should be selected according to the types of fracture and complications, scientific selection of surgical approach has great importance in improving the surgical effect.
- Application of optimal FCM algorithm to information processing of sea-surface target surveillance radar–
Experimental results show that the optimal fuzzy C-mean(FCM) clustering algorithm can achieve the better clustering effects than the traditional one, in the application of sea-surface surveillance radar to target information processing.
- Investigation and analysis of current status of clinical transfusion passage of high-risk drugs in primary hospitals–
It is important and urgent to educate nurses in primary hospitals on the concept, knowledge and skills of safe venous transfusion and the current status of transfusion passage establishment of clinical high-risk drugs in county level hospitals.
- Community Health Services and the Quality Requirements for Nurses–
This paper describes the background of community health service and hygiene services, development, the content of community care services and the necessary quality requirements of community nurses, in order to provide better hygiene services to community residen.
- The technic of nursing intervention during blue light treatment in neonatal jaundice patients–
The blue light treatment in neonatal jaundice patients, mental nursing to parents and comfortable nursing and fondle to touch nursing measures to the baby patient are efficient to prevent or decrease excessive cry noisy arouse of vomit, sultry syndrome, overventilation,skin scratching, and hyperbilirubinemia of newborn etc.
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- Research Advance on Risk Factors of Childhood Leukemia–
This review summarized the overall etiological researches of childhood leukemia in and out of China during the recent years.
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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