AIMC Topic: Neural Networks, Computer

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The Impact of Feature Extraction on Classification Accuracy Examined by Employing a Signal Transformer to Classify Hand Gestures Using Surface Electromyography Signals.

Sensors (Basel, Switzerland) Feb 16, 2024
Interest in developing techniques for acquiring and decoding biological signals is on the rise in the research community. This interest spans various applications, with a particular focus on prosthetic control and rehabilitation, where achieving prec...
Signal Processing, Computer-Assisted Algorithms Neural Networks, Computer Gestures Electromyography
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Effects of various cross-linked collagen scaffolds on wound healing in rats model by deep-learning CNN.

Computer methods in biomechanics and biomedical engineering Feb 15, 2024
Scar tissue is connective tissue formed on the wound during the wound-healing process. The most significant distinction between scar tissue and normal tissue is the appearance of covalent cross-linking and the amount of collagen fibers in the tissue....
Rats Rats, Sprague-Dawley Animals Deep Learning Collagen Tissue Scaffolds Cross-Linking Reagents Wound Healing Disease Models, Animal Neural Networks, Computer Female
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Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network.

Physical and engineering sciences in medicine Feb 15, 2024
Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identific...
Female Heart Failure Aged Proportional Hazards Models Humans Middle Aged Arteries Male Neural Networks, Computer Risk Assessment Case-Control Studies Deep Learning ROC Curve Pulse Wave Analysis
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Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.

Physical and engineering sciences in medicine Feb 15, 2024
In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) pa...
Signal Processing, Computer-Assisted Selective Serotonin Reuptake Inhibitors Depressive Disorder, Major Deep Learning Treatment Outcome Electroencephalography Humans Male Neural Networks, Computer Middle Aged Female Adult
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Noncompact uniform universal approximation.

Neural networks : the official journal of the International Neural Network Society Feb 15, 2024
The universal approximation theorem is generalised to uniform convergence on the (noncompact) input space R. All continuous functions that vanish at infinity can be uniformly approximated by neural networks with one hidden layer, for all activation f...
Quantum Theory Neural Networks, Computer Algorithms
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Fading memory as inductive bias in residual recurrent networks.

Neural networks : the official journal of the International Neural Network Society Feb 15, 2024
Residual connections have been proposed as an architecture-based inductive bias to mitigate the problem of exploding and vanishing gradients and increased task performance in both feed-forward and recurrent networks (RNNs) when trained with the backp...
Algorithms Language Neural Networks, Computer
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Convolutional neuronal network for identifying single-cell-platelet-platelet-aggregates in human whole blood using imaging flow cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology Feb 15, 2024
Imaging flow cytometry is an attractive method to investigate individual cells by optical properties. However, imaging flow cytometry applications with clinical relevance are scarce so far. Platelet aggregation naturally occurs during blood coagulati...
Humans Neural Networks, Computer Blood Platelets Platelet Aggregation Inhibitors Platelet Aggregation Single-Cell Analysis Flow Cytometry
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Which model is more efficient in carbon emission prediction research? A comparative study of deep learning models, machine learning models, and econometric models.

Environmental science and pollution research international Feb 15, 2024
Accurately predicting future carbon emissions is of great significance for the government to scientifically promote carbon emission reduction policies. Among the current technologies for forecasting carbon emissions, the most prominent ones are econo...
China Carbon Forecasting Models, Econometric Deep Learning Neural Networks, Computer Machine Learning
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Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments.

PloS one Feb 15, 2024
In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogra...
Biometry Electrocardiography Neural Networks, Computer Biometric Identification Deep Learning
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Building an ab initio solvated DNA model using Euclidean neural networks.

PloS one Feb 15, 2024
Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecule...
Neural Networks, Computer DNA Solvents
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