AIMC Topic: Neural Networks, Computer

Clear Filters Showing 7741 to 7750 of 31376 articles

Classification of EMG signals with CNN features and voting ensemble classifier.

Computer methods in biomechanics and biomedical engineering
Electromyography (EMG) signals are primarily used to control prosthetic hands. Classifying hand gestures efficiently with EMG signals presents numerous challenges. In addition to overcoming these challenges, a successful combination of feature extrac...

Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
PURPOSE: To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledg...

Detection and quantitative analysis of patient-ventilator interactions in ventilated infants by deep learning networks.

Pediatric research
BACKGROUND: The study of patient-ventilator interactions (PVI) in mechanically ventilated neonates is limited by the lack of unified PVI definitions and tools to perform large scale analyses.

Artificial intelligence-based forecasting model for incinerator in sulfur recovery units to predict SO emissions.

Environmental research
Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In th...

Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning.

Journal of Korean medical science
BACKGROUND: The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial fibrillation (AF) identification enhances screening efficiency. ...

Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.

Scientific reports
DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer ...

Deep learning-based tooth segmentation methods in medical imaging: A review.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analys...

MAEF-Net: MLP Attention for Feature Enhancement in U-Net based Medical Image Segmentation Networks.

IEEE journal of biomedical and health informatics
Medical image segmentation plays an important role in diagnosis. Since the introduction of U-Net, numerous advancements have been implemented to enhance its performance and expand its applicability. The advent of Transformers in computer vision has l...

PIRNet: Personality-Enhanced Iterative Refinement Network for Emotion Recognition in Conversation.

IEEE transactions on neural networks and learning systems
Emotion recognition in conversation (ERC) is important for enhancing user experience in human-computer interaction. Unlike vanilla emotion recognition in individual utterances, ERC aims to classify constituent utterances in a dialog into correspondin...

Self-Supervised Learning for Electroencephalography.

IEEE transactions on neural networks and learning systems
Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning ...