AIMC Topic: Neural Networks, Computer

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Task-oriented EEG denoising generative adversarial network for enhancing SSVEP-BCI performance.

Journal of neural engineering
The quality of electroencephalogram (EEG) signals directly impacts the performance of brain-computer interface (BCI) tasks. Many methods have been proposed to eliminate noise from EEG signals, but most of these methods focus solely on signal denoisin...

Performance of artificial neural network compared to multi-linear regression in prediction of countermovement jump height.

Journal of bodywork and movement therapies
Previous research has used primarily linear regression models to predict jump height and establish contributors of performance. The purpose of this study was to compare the performance of artificial neural network (ANN) and multi-linear regression (M...

Nordic environmental resilience: balancing air quality and energy efficiency by applying artificial neural network.

Frontiers in public health
Maintaining public health and environmental safety in the Nordic nations calls for a strict plan to define exact benchmarks on air quality and energy efficiency. This study investigates the complicated interaction of decentralized energy production (...

A multimodal deep learning-based algorithm for specific fetal heart rate events detection.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: This study aims to develop a multimodal deep learning-based algorithm for detecting specific fetal heart rate (FHR) events, to enhance automatic monitoring and intelligent assessment of fetal well-being.

Artificial intelligence versus conventional methods for RGP lens fitting in keratoconus.

Contact lens & anterior eye : the journal of the British Contact Lens Association
BACKGROUND: To compare the efficiency of three artificial intelligence (AI) frameworks (Standard Machine Learning (ML), Multi-Layer Perceptron (MLP) and Convolution Neural Networks (CNN)) with a reference method (Mean radius of curvature (K)) to pred...

A unified noise and watermark removal from information bottleneck-based modeling.

Neural networks : the official journal of the International Neural Network Society
Both image denoising and watermark removal aim to restore a clean image from an observed noisy or watermarked one. The past research consists of the non-learning type with limited effectiveness or the learning types with limited interpretability. To ...

FedART: A neural model integrating federated learning and adaptive resonance theory.

Neural networks : the official journal of the International Neural Network Society
Federated Learning (FL) has emerged as a promising paradigm for collaborative model training across distributed clients while preserving data privacy. However, prevailing FL approaches aggregate the clients' local models into a global model through m...

HirMTL: Hierarchical Multi-Task Learning for dense scene understanding.

Neural networks : the official journal of the International Neural Network Society
In the realm of artificial intelligence, simultaneous multi-task learning is crucial, particularly for dense scene understanding. To address this, we introduce HirMTL, a novel hierarchical multi-task learning framework designed to enhance dense scene...

Global practical finite-time synchronization of disturbed inertial neural networks by delayed impulsive control.

Neural networks : the official journal of the International Neural Network Society
This paper delves into the practical finite-time synchronization (FTS) problem for inertial neural networks (INNs) with external disturbances. Firstly, based on Lyapunov theory, the local practical FTS of INNs with bounded external disturbances can b...

Predicting lane change maneuver and associated collision risks based on multi-task learning.

Accident; analysis and prevention
The lane-changing (LC) maneuver of vehicles significantly impacts highway traffic safety. Therefore, proactively predicting LC maneuver and associated collision risk is of paramount importance. However, most of the previous LC risk prediction researc...