AIMC Topic: Neural Networks, Computer

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Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.

Methods (San Diego, Calif.)
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations betwe...

Deep learning in surgical process modeling: A systematic review of workflow recognition.

Journal of biomedical informatics
OBJECTIVE: The application of artificial intelligence (AI) in health care has led to a surge of interest in surgical process modeling (SPM). The objective of this study is to investigate the role of deep learning in recognizing surgical workflows and...

A Deep Learning Approach for Mental Fatigue State Assessment.

Sensors (Basel, Switzerland)
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neur...

(ω,c)-Asymptotically periodic oscillation of cellular neural networks on time scales with leakage delays and mixed time-varying delays.

Neural networks : the official journal of the International Neural Network Society
In this paper, we introduce the concept of (ω,c)-asymptotic periodicity within the context of translation-invariant time scales. This concept generalizes various types of function, including asymptotically periodic, asymptotically antiperiodic, asymp...

Supporting vision-language model few-shot inference with confounder-pruned knowledge prompt.

Neural networks : the official journal of the International Neural Network Society
Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. Recent works adopt fixed or learnable prompts, i.e., classification weights are synthesized from natural language description...

ACformer: A unified transformer for arbitrary-frame image exposure correction.

Neural networks : the official journal of the International Neural Network Society
Both the single-image exposure correction (SEC) methods and multi-image exposure fusion (MEF) methods aim to obtain a well-exposed image, but from different number of input image(s). Despite their promising performance on the specific SEC or MEF task...

DCTCNet: Sequency discrete cosine transform convolution network for visual recognition.

Neural networks : the official journal of the International Neural Network Society
The discrete cosine transform (DCT) has been widely used in computer vision tasks due to its ability of high compression ratio and high-quality visual presentation. However, conventional DCT is usually affected by the size of transform region and res...

A Novel session-based recommendation system using capsule graph neural network.

Neural networks : the official journal of the International Neural Network Society
Session-based recommendation systems (SBRS) are essential for enhancing the customer experience, improving sales and loyalty, and providing the possibility to discover products in dynamic and real-world scenarios without needing user history. Despite...

PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease.

Journal of neuroscience methods
BACKGROUND: Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages of the disease, when patients might have already missed the best treatment period. Therefore, more ef...

Deep learning-based defect detection in film-coated tablets using a convolutional neural network.

International journal of pharmaceutics
Film-coating is a critical step in pharmaceutical manufacturing. Traditional visual inspections for film-coated tablet defect assessment are subjective, inefficient, and labor-intensive. We propose a novel approach utilizing machine learning and imag...