AIMC Topic: Neural Networks, Computer

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Incremental model-based reinforcement learning with model constraint.

Neural networks : the official journal of the International Neural Network Society
In model-based reinforcement learning (RL) approaches, the estimated model of a real environment is learned with limited data and then utilized for policy optimization. As a result, the policy optimization process in model-based RL is influenced by b...

Towards a latent space cartography of subjective experience in mental health.

Psychiatry and clinical neurosciences
AIMS: The way that individuals subjectively experience the world greatly influences their own mental well-being. However, it remains a considerable challenge to precisely characterize the breadth and depth of such experiences. One persistent problem ...

EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Deep learning models have gained widespread adoption in healthcare for accurate diagnosis through the analysis of brain signals. Neurodegenerative disorders like Alzheimer's Disease (AD) and Frontotemporal Dementia (FD) are ...

Spatial heterogeneity effect of built environment on traffic safety using geographically weighted atrous convolutions neural network.

Accident; analysis and prevention
The built environment exerts a significant influence on the frequency and severity of traffic accidents. Spatially uniform assumptions on the impacts of built environment factors commonly employed in existing research may lead to inconsistent and con...

ResGloTBNet: An interpretable deep residual network with global long-range dependency for tuberculosis screening of sputum smear microscopy images.

Medical engineering & physics
Tuberculosis is a high-mortality infectious disease. Manual sputum smear microscopy is a common and effective method for screening tuberculosis. However, it is time-consuming, labor-intensive, and has low sensitivity. In this study, we propose ResGlo...

HybProm: An attention-assisted hybrid CNN-BiLSTM model for the interpretable prediction of DNA promoter.

Methods (San Diego, Calif.)
Promoter prediction is essential for analyzing gene structures, understanding regulatory networks, transcription mechanisms, and precisely controlling gene expression. Recently, computational and deep learning methods for promoter prediction have gai...

Co-pyrolysis kinetics and enhanced synergy for furfural residues and polyethylene using artificial neural network and fast heating.

Waste management (New York, N.Y.)
The efficient co-utilization of biomass and waste plastics is a key method to address the widely concerned environmental problem and replace traditional energy. Co-pyrolysis behaviors and synergistic effects of furfural residues (FR) and polyethylene...

A numerical treatment through Bayesian regularization neural network for the chickenpox disease model.

Computers in biology and medicine
OBJECTIVES: The current research investigations designates the numerical solutions of the chickenpox disease model by applying a proficient optimization framework based on the artificial neural network. The mathematical form of the chickenpox disease...

Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images.

Scientific reports
Cancer is a global health concern because of a significant mortality rate and a wide range of affected organs. Early detection and accurate classification of cancer types are crucial for effective treatment. Imaging tests on different image modalitie...

Machine learning models for segmentation and classification of cyanobacterial cells.

Photosynthesis research
Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional inten...