AIMC Topic: Neural Networks, Computer

Clear Filters Showing 4401 to 4410 of 31376 articles

Barrier-critic-disturbance approximate optimal control of nonzero-sum differential games for modular robot manipulators.

Neural networks : the official journal of the International Neural Network Society
In this paper, for addressing the safe control problem of modular robot manipulators (MRMs) system with uncertain disturbances, an approximate optimal control scheme of nonzero-sum (NZS) differential games is proposed based on the control barrier fun...

Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

IEEE transactions on pattern analysis and machine intelligence
Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels o...

Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.

Sensors (Basel, Switzerland)
The integration of artificial intelligence (AI) into medical disciplines is rapidly transforming healthcare delivery, with audiology being no exception. By synthesizing the existing literature, this review seeks to inform clinicians, researchers, and...

Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.

BMC bioinformatics
BACKGROUND: Antibodies play a crucial role in disease treatment, leveraging their ability to selectively interact with the specific antigen. However, screening antibody gene sequences for target antigens via biological experiments is extremely time-c...

Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer.

Scientific reports
It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residua...

Enhanced convolutional neural network architecture optimized by improved chameleon swarm algorithm for melanoma detection using dermatological images.

Scientific reports
Early detection and treatment of skin cancer are important for patient recovery and survival. Dermoscopy images can help clinicians for timely identification of cancer, but manual diagnosis is time-consuming, costly, and prone to human error. To cond...

Multi-label text classification via secondary use of large clinical real-world data sets.

Scientific reports
Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural language processing offer a promising avenue for developing applications that assist clinicians, thereby alleviating their administrative burdens. This ...

Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model.

Scientific reports
Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle branch blocks and irregular heart rhythms. Manual analysis of ECGs is...

Optimizing pulmonary chest x-ray classification with stacked feature ensemble and swin transformer integration.

Biomedical physics & engineering express
This research presents an integrated framework designed to automate the classification of pulmonary chest x-ray images. Leveraging convolutional neural networks (CNNs) with a focus on transformer architectures, the aim is to improve both the accuracy...

Establishing an AI model and application for automated capsule endoscopy recognition based on convolutional neural networks (with video).

BMC gastroenterology
BACKGROUND: Although capsule endoscopy (CE) is a crucial tool for diagnosing small bowel diseases, the need to process a vast number of images imposes a significant workload on physicians, leading to a high risk of missed diagnoses. This study aims t...