AIMC Topic: Neural Networks, Computer

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Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.

Medical & biological engineering & computing
This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence. The CNN...

Convergence analysis of sparse TSK fuzzy systems based on spectral Dai-Yuan conjugate gradient and application to high-dimensional feature selection.

Neural networks : the official journal of the International Neural Network Society
Dealing with high-dimensional problems has always been a key and challenging issue in the field of fuzzy systems. Traditional Takagi-Sugeno-Kang (TSK) fuzzy systems face the challenges of the curse of dimensionality and computational complexity when ...

Machine Learning Using Template-Based-Predicted Structure of Haemagglutinin Predicts Pathogenicity of Avian Influenza.

Journal of microbiology and biotechnology
Deep learning presents a promising approach to complex biological classifications, contingent upon the availability of well-curated datasets. This study addresses the challenge of analyzing three-dimensional protein structures by introducing a novel ...

Supporting data-enhanced hybrid ordinary differential equation model for phosphate dynamics in municipal wastewater treatment.

Bioresource technology
A parallel hybrid ordinary differential equation (ODE) integrating the Activated Sludge Model No. 2d (ASM2d) and an artificial neural network (ANN) was developed to simulate biological phosphorus removal (BPR) with high accuracy and interpretability....

EGCN++: A New Fusion Strategy for Ensemble Learning in Skeleton-Based Rehabilitation Exercise Assessment.

IEEE transactions on pattern analysis and machine intelligence
Skeleton-based exercise assessment focuses on evaluating the correctness or quality of an exercise performed by a subject. Skeleton data provide two groups of features (i.e., position and orientation), which existing methods have not fully harnessed....

A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning.

Sensors (Basel, Switzerland)
In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in ou...

An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements.

Sensors (Basel, Switzerland)
Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to det...

Development of an eye-tracking system based on a deep learning model to assess executive function in patients with mental illnesses.

Scientific reports
Patients with mental illnesses, particularly psychosis and obsessive‒compulsive disorder (OCD), frequently exhibit deficits in executive function and visuospatial memory. Traditional assessments, such as the Rey‒Osterrieth Complex Figure Test (RCFT),...

Signatures of Bayesian inference emerge from energy-efficient synapses.

eLife
Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mec...

Boundary-Aware Gradient Operator Network for Medical Image Segmentation.

IEEE journal of biomedical and health informatics
Medical image segmentation is a crucial task in computer-aided diagnosis. Although convolutional neural networks (CNNs) have made significant progress in the field of medical image segmentation, the convolution kernels of CNNs are optimized from rand...