AIMC Topic: Neural Networks, Computer

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Deep Learning for Lumbar Disc Herniation Diagnosis and Treatment Decision-Making Using Magnetic Resonance Imagings: A Retrospective Study.

World neurosurgery
BACKGROUND: Lumbar disc herniation (LDH) is a common cause of back and leg pain. Diagnosis relies on clinical history, physical exam, and imaging, with magnetic resonance imaging (MRI) being an important reference standard. While artificial intellige...

ADMM-TransNet: ADMM-Based Sparse-View CT Reconstruction Method Combining Convolution and Transformer Network.

Tomography (Ann Arbor, Mich.)
BACKGROUND: X-ray computed tomography (CT) imaging technology provides high-precision anatomical visualization of patients and has become a standard modality in clinical diagnostics. A widely adopted strategy to mitigate radiation exposure is sparse-...

Back Propagation Artificial Neural Network Enhanced Accuracy of Multi-Mode Sensors.

Biosensors
The detection of small molecules is critical in many fields, but traditional electrochemical detection methods often exhibit limited accuracy. The construction of multi-mode sensors is a common strategy to improve detection accuracy. However, most ex...

A Novel Framework for Quantum-Enhanced Federated Learning with Edge Computing for Advanced Pain Assessment Using ECG Signals via Continuous Wavelet Transform Images.

Sensors (Basel, Switzerland)
Our research introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level assessment through ECG signal analysis. The primary focus lies in developing a robust, privacy-preservin...

Combining an improved political optimizer with convolutional neural networks for accurate anterior cruciate ligament tear detection in sports injuries.

Scientific reports
A new technique has been developed to identify ACL tears in sports injuries. This method utilizes a Convolutional Neural Network (CNN) in combination with a modified Political Optimizer (IPO) algorithm, resulting in a major breakthrough in detecting ...

An intelligent network framework for driver distraction monitoring based on RES-SE-CNN.

Scientific reports
As the quantity of motor vehicles and drivers experiences a continuous upsurge, the road driving environment has grown progressively more complex. This complexity has led to a concomitant increase in the probability of traffic accidents. Ample resear...

Modelling of pome fruit pollen performance using machine learning.

Scientific reports
Agriculture, particularly fruit production, is considered a crucial industry with a significant economic impact in many countries. Extreme fluctuations in air temperature can negatively affect the flowering periods of fruit species. Therefore, it is ...

Machine learning-based optimal design of fibrillar adhesives.

Journal of the Royal Society, Interface
Fibrillar adhesion, observed in animals like beetles, spiders and geckos, relies on nanoscopic or microscopic fibrils to enhance surface adhesion via 'contact splitting'. This concept has inspired engineering applications across robotics, transportat...

Adaptive control and state error prediction of flexible manipulators using radial basis function neural network and dynamic surface control method.

PloS one
This paper introduces a novel control strategy for managing the uncertainties in flexible joint manipulators, incorporating a Radial Basis Function Neural Network (RBFNN) with Adaptive Dynamic Surface Control (ADSC). This strategy innovatively utiliz...