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- The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report91
This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes, and gauge the state-of-the-art in efficient single-image super-resolution.
- Review of sEMG for Exoskeleton Robots: Motion Intention Recognition Techniques and Applications43
A model with stronger generalization ability is obtained after training, thereby improving the accuracy of human movement intention recognition based on sEMG technology, which provides important support for the realization of human–machine fusion-embodied intelligence of exoskeleton robots.
- AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results43
This paper introduces the real image Super-Resolution challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020, and gauges the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
- Unsupervised change detection in SAR images based on frequency difference and a modified fuzzy c-means clustering29
This article proposes an unsupervised change detection approach based on frequency difference in wavelet domain and a modified fuzzy c-means (FCM) clustering algorithm and incorporates weighted local information into the FCM to reduce the influence of speckle noise.
- DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions24
A novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes respectively to the vulnerable population of 14 types of adverse reactions is presented.
- The advantages of artificial intelligence-assisted total hip arthroplasty: A randomized controlled trial followed by 12 months17
Artificial intelligence-assisted total hip arthroplasty demonstrated superior accuracy in prosthesis prediction, shorter operation time, higher average Harris score at postoperative follow-up, and better hip function recovery at 3 months compared to traditional two-dimensional X-ray planning.
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- Semi-supervised learning methods for large scale healthcare data analysis15
This article addresses the problem of making use of unlabeled or un-scored data, together with only a few supervised data, to improve the performance of analysis model for healthcare decision making and proposes to apply the learned kernel in two algorithms, i.e., support vector machine SVM and kernel regularised least squares KRLS.
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- Histopathological Image Recognition with Domain Knowledge Based Deep Features10
A deep model for histopathological image feature representation in a supervised-learning manner is proposed based on some pre-trained convolutional neural networks that is superior to current state-of-the-art models.
- High‐Resolution Histopathological Image Classification Model Based on Fused Heterogeneous Networks with Self‐Supervised Feature Representation9
A novel model for the task of classification of WSIs is proposed, which effectively expresses the feature and preserves location information of each patch and integrates heterogeneous features generated by different networks which yields robust classification results.
- An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD‐10 Labels9
The proposed ensemble model can effectively model the implied knowledge and experience in historic clinical data records and the computational cost of training a set of base learners is relatively low.
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- Data Dependant Learners Ensemble Pruning6
This work proposes to ensemble with base learners trained by both labeled and unlabeled data, by adopting data dependant kernel mapping, which has been proved successful in semi-supervised learning, to get more generalized base learners and proof that ensemble of learnerstrained by bothlabeled data is of better generalization ability in the meaning of graph Laplacian.
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- Machine Learning Based Teaching Quality Evaluation4
This paper performs teaching quality evaluation within machine learning framework, focusing on learning and modeling electronic information associated with quality of teaching, to get a stable model described the substantial principles of teaching quality.
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- Research of machine learning methods for student management performance evaluation2
Evaluation on a student management data set collected from a polytechnic school shows the effectiveness of the proposed algorithm framework based on Artificial Neural Network aiming at constructing an intelligent integrated model through data mining technology.
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 2026-10-14. 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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