AIMC Topic: Algorithms

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SegMatch: semi-supervised surgical instrument segmentation.

Scientific reports Apr 23, 2025
Surgical instrument segmentation is recognised as a key enabler in providing advanced surgical assistance and improving computer-assisted interventions. In this work, we propose SegMatch, a semi-supervised learning method to reduce the need for expen...
Humans Algorithms Robotic Surgical Procedures Surgery, Computer-Assisted Supervised Machine Learning Image Processing, Computer-Assisted Surgical Instruments Laparoscopy
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Ambiguity-aware semi-supervised learning for leaf disease classification.

Scientific reports Apr 23, 2025
In deep learning, Semi-Supervised Learning is a highly effective technique to enhances neural network training by leveraging both labeled and unlabeled data. This process involves using a trained model to generate pseudo labels to the unlabeled sampl...
Deep Learning Plant Leaves Supervised Machine Learning Plant Diseases Neural Networks, Computer Algorithms Musa
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A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation.

Scientific reports Apr 23, 2025
Elucidating the tertiary structure of proteins is important for understanding their functions and interactions. While deep neural networks have advanced the prediction of a protein's native structure from its amino acid sequence, the focus on a singl...
Computational Biology Proteins Algorithms Protein Structure, Tertiary Neural Networks, Computer Models, Molecular Deep Learning Autoencoder
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A benchmarking framework and dataset for learning to defer in human-AI decision-making.

Scientific data Apr 23, 2025
Learning to Defer (L2D) algorithms improve human-AI collaboration by deferring decisions to human experts when they are likely to be more accurate than the AI model. These can be crucial in high-stakes tasks like fraud detection, where false negative...
Benchmarking Fraud Algorithms Decision Making Humans Artificial Intelligence
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Prediction of Patient Visits for Skin Diseases through Enhanced Evolutionary Computation and Ensemble Learning.

Journal of medical systems Apr 23, 2025
Skin diseases are an important global public health issue, causing significant health and psychological burdens. Predicting dermatology outpatient visits is essential for optimizing hospital resources and improving diagnosis and treatment methods. Ba...
China Skin Diseases Machine Learning Algorithms Acne Vulgaris Ensemble Learning Humans Neural Networks, Computer
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DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.

BMC biology Apr 23, 2025
BACKGROUND: Numerous studies have shown that circRNA can act as a miRNA sponge, competitively binding to miRNAs, thereby regulating gene expression and disease progression. Due to the high cost and time-consuming nature of traditional wet lab experim...
Humans Algorithms MicroRNAs RNA, Circular Deep Learning Computational Biology
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Bald eagle-optimized transformer networks with temporal-spatial mid-level features for pancreatic tumor classification.

Biomedical physics & engineering express Apr 23, 2025
The classification and diagnosis of pancreatic tumors present significant challenges due to their inherent complexity and variability. Traditional methods often struggle to capture the dynamic nature of these tumors, highlighting the need for advance...
Humans Pancreatic Neoplasms Neural Networks, Computer Algorithms Image Processing, Computer-Assisted
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Network traffic prediction based on transformer and temporal convolutional network.

PloS one Apr 23, 2025
This paper proposes a hybrid model combining Transformer and Temporal Convolutional Network (TCN). This model addresses the shortcomings of current approaches in capturing long-term and short-term dependencies in network traffic prediction tasks. The...
Algorithms Models, Theoretical Humans Neural Networks, Computer
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Research on memory failure prediction based on ensemble learning.

PloS one Apr 23, 2025
Timely prediction of memory failures is crucial for the stable operation of data centers. However, existing methods often rely on a single classifier, which can lead to inaccurate or unstable predictions. To address this, we propose a new ensemble mo...
Memory Ensemble Learning Humans Machine Learning Algorithms Models, Theoretical
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Digital image processing combined with machine learning: A novel approach for bee pollen classification.

Food research international (Ottawa, Ont.) Apr 22, 2025
The classification of bee pollen is crucial for ensuring product authenticity, quality control, and fraud prevention, particularly given the high commercial value of stingless bee pot-pollen. Although traditional pollen analysis methods are available...
Algorithms Animals Machine Learning Color Bees Pollen Image Processing, Computer-Assisted Discriminant Analysis
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