AIMC Topic: Algorithms

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Identity Model Transformation for boosting performance and efficiency in object detection network.

Neural networks : the official journal of the International Neural Network Society
Modifying the structure of an existing network is a common method to further improve the performance of the network. However, modifying some layers in network often results in pre-trained weight mismatch, and fine-tune process is time-consuming and r...

Improving the performance of echo state networks through state feedback.

Neural networks : the official journal of the International Neural Network Society
Reservoir computing, using nonlinear dynamical systems, offers a cost-effective alternative to neural networks for complex tasks involving processing of sequential data, time series modeling, and system identification. Echo state networks (ESNs), a t...

Information-controlled graph convolutional network for multi-view semi-supervised classification.

Neural networks : the official journal of the International Neural Network Society
Graph convolutional networks have achieved remarkable success in the field of multi-view learning. Unfortunately, most graph convolutional network-based multi-view learning methods fail to capture long-range dependencies due to the over-smoothing pro...

TIMAR: Transition-informed representation for sample-efficient multi-agent reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multiple agents requires massive interactions to produce training samples, significantly increasing both the training cost and difficulty. Therefore, enhanci...

Interpretable deep learning for acoustic leak detection in water distribution systems.

Water research
Leak detection is crucial for ensuring the safety of water systems and conserving water resources. However, current research on machine learning methods for leak detection focuses excessively on model development while neglecting model interpretabili...

WALINET: A water and lipid identification convolutional neural network for nuisance signal removal in MR spectroscopic imaging.

Magnetic resonance in medicine
PURPOSE: Proton magnetic resonance spectroscopic imaging ( -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain -MRSI are spectral overlap of metabolite peaks with large lipid signal fro...

MEFA-Net: A mask enhanced feature aggregation network for polyp segmentation.

Computers in biology and medicine
Accurate polyp segmentation is crucial for early diagnosis and treatment of colorectal cancer. This is a challenging task for three main reasons: (i) the problem of model overfitting and weak generalization due to the multi-center distribution of dat...

An explainable machine learning system for efficient use of waste glasses in durable concrete to maximise carbon credits towards net zero emissions.

Waste management (New York, N.Y.)
Recycling waste glass (WG) can be time-consuming, costly, and impractical. However, its incorporation into concrete significantly reduces environmental impact and carbon emissions. This paper introduces machine learning (ML) to civil engineering to o...

Unveiling pathology-related predictive uncertainty of glomerular lesion recognition using prototype learning.

Journal of biomedical informatics
OBJECTIVE: Recognizing glomerular lesions is essential in diagnosing chronic kidney disease. However, deep learning faces challenges due to the lesion heterogeneity, superposition, progression, and tissue incompleteness, leading to uncertainty in mod...

Optimizing Corn Tar Spot Measurement: A Deep Learning Approach Using Red-Green-Blue Imaging and the Stromata Contour Detection Algorithm for Leaf-Level Disease Severity Analysis.

Plant disease
Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is the primary method for quantifying tar spot early in the season because these structures are definitive signs of the disease and essential for effective disease monitoring...