AIMC Topic: Neural Networks, Computer

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A deep learning approach for automatic recognition of abnormalities in the cytoplasm of neutrophils.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: This study aims to develop and evaluate NeuNN, a system based on convolutional neural networks (CNN) and generative adversarial networks (GAN) for the automatic identification of normal neutrophils and those containing seve...

Adaptive neural fault-tolerant prescribed performance control of a rehabilitation exoskeleton for lower limb passive training.

ISA transactions
This article studies the passive tracking problem of a wearable exoskeleton for lower limb rehabilitation therapy in the face of unmodeled dynamics, interactive friction, disturbance, prescribed performance constraints, and actuator faults. Adaptive ...

New vision of HookEfficientNet deep neural network: Intelligent histopathological recognition system of non-small cell lung cancer.

Computers in biology and medicine
BACKGROUND: Efficient and precise diagnosis of non-small cell lung cancer (NSCLC) is quite critical for subsequent targeted therapy and immunotherapy. Since the advent of whole slide images (WSIs), the transition from traditional histopathology to di...

Designing an artificial intelligence system for dental occlusion classification using intraoral photographs: A comparative analysis between artificial intelligence-based and clinical diagnoses.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
INTRODUCTION: This study aimed to design an artificial intelligence (AI) system for dental occlusion classification using intraoral photographs. Moreover, the performance of this system was compared with that of an expert clinician.

Feasibility study of ResNet-50 in the distinction of intraoral neural tumors using histopathological images.

Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
BACKGROUND: Neural tumors are difficult to distinguish based solely on cellularity and often require immunohistochemical staining to aid in identifying the cell lineage. This article investigates the potential of a Convolutional Neural Network for th...

An efficient hybrid deep learning architecture for predicting short antimicrobial peptides.

Proteomics
Short-length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing various typ...

Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition.

Sensors (Basel, Switzerland)
Gesture recognition using electromyography (EMG) signals has prevailed recently in the field of human-computer interactions for controlling intelligent prosthetics. Currently, machine learning and deep learning are the two most commonly employed meth...

Enhancing IoT Healthcare with Federated Learning and Variational Autoencoder.

Sensors (Basel, Switzerland)
The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing data from local hospitals. However, privacy concerns can impede data sharing among third parties. Federated learning offers a solution by enabling the trai...

ECG autoencoder based on low-rank attention.

Scientific reports
The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogram (ECG), being one of the pivotal diagnostic tools for cardiovascular diseases, is increasingly gai...

HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.

Communications biology
Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, w...