AIMC Topic: Neural Networks, Computer

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Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data.

Journal of hazardous materials
Microplastics are ubiquitous and appear to be harmful, however, the full extent to which these inflict harm has not been fully elucidated. Analysing environmental sample data is challenging, as the complexity in real data makes both automated and man...

Radial SERS acquisition on coffee ring for Serum-based breast cancer diagnosis through Multilayer Perceptron.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The coffee-ring effect, involving spontaneous solute separation, has demonstrated promising potential in the context of patient serum analysis. In this study, an approach leveraging the coffee-ring-based analyte redistribution was developed for spect...

Enhancing Recommender Systems through Imputation and Social-Aware Graph Convolutional Neural Network.

Neural networks : the official journal of the International Neural Network Society
Recommendation systems are vital tools for helping users discover content that suits their interests. Collaborative filtering methods are one of the techniques employed for analyzing interactions between users and items, which are typically stored in...

Simignore: Exploring and enhancing multimodal large model complex reasoning via similarity computation.

Neural networks : the official journal of the International Neural Network Society
Recently, the field of multimodal large language models (MLLMs) has grown rapidly, with many Large Vision-Language Models (LVLMs) relying on sequential visual representations. In these models, images are broken down into numerous tokens before being ...

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...

Semantic prioritization in visual counterfactual explanations with weighted segmentation and auto-adaptive region selection.

Neural networks : the official journal of the International Neural Network Society
In the domain of non-generative visual counterfactual explanations (CE), traditional techniques frequently involve the substitution of sections within a query image with corresponding sections from distractor images. Such methods have historically ov...

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...