AIMC Topic: Neural Networks, Computer

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Dynamic events-based adaptive NN output feedback control of interconnected nonlinear systems under general output constraint.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the adaptive NN output feedback tracking control problem for a class of interconnected nonlinear systems. Unlike the existing control algorithms, we propose a dynamic event-triggered output constraint control algorithm.First, ...

A multi-domain constraint learning system inspired by adaptive cognitive graphs for emotion recognition.

Neural networks : the official journal of the International Neural Network Society
Neuroscience shows that the brain stimulated by external information can induce functional responses to emotions, which can be measured and analyzed by electroencephalogram (EEG). Most existing works focus on extracting specific spatial topological i...

Compact CNN module balancing between feature diversity and redundancy.

Neural networks : the official journal of the International Neural Network Society
Feature diversity and redundancy play a crucial role in enhancing a model's performance, although their effect on network design remains underexplored. Herein, we introduce BDRConv, a compact convolutional neural network (CNN) module that establishes...

Event-triggered control for input-constrained nonzero-sum games through particle swarm optimized neural networks.

Neural networks : the official journal of the International Neural Network Society
To accommodate the increasing system scale, improve the system operation success rate and save the computational and communication resources, it is urgent to obtain the Nash equilibrium solution for systems with increasing controllers in an effective...

Comparison of Ground Reaction Forces and Net Joint Moment Predictions: Skeletal Model Versus Artificial Neural Network-Based Approach.

Journal of applied biomechanics
Artificial neural networks (ANNs) are becoming a regular tool to support biomechanical methods, while physics-based models are widespread to understand the mechanics of body in motion. Thus, this study aimed to demonstrate the accuracy of recurrent A...

Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks.

Medical image analysis
Multimodal neuroimaging data modeling has become a widely used approach but confronts considerable challenges due to their heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitate...

AI-based human whole-body posture-prediction in continuous load reaching/leaving activities.

Journal of biomechanics
Determining worker's body posture during load handling activities is the first step toward assessing and managing occupational risk of musculoskeletal injuries. Traditional approaches for the measurement of body posture are impractical in real work s...

Deep learning based abiotic crop stress assessment for precision agriculture: A comprehensive review.

Journal of environmental management
Abiotic stresses are a leading cause of crop loss and a severe peril to global food security. Precise and prompt identification of abiotic stresses in crops is crucial for effective mitigation strategies. In recent years, Deep learning (DL) technique...

Obstacle Avoidance Technique for Mobile Robots at Autonomous Human-Robot Collaborative Warehouse Environments.

Sensors (Basel, Switzerland)
This paper presents an obstacle avoidance technique for a mobile robot in human-robot collaborative (HRC) tasks. The proposed solution uses fuzzy logic rules and a convolutional neural network (CNN) in an integrated approach to detect objects during ...

Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma.

BMC cancer
OBJECTIVE: The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise i...