AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

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DOPNet: Achieving Accurate and Efficient Point Cloud Registration Based on Deep Learning and Multi-Level Features.

Sensors (Basel, Switzerland)
Point cloud registration aims to find a rigid spatial transformation to align two given point clouds; it is widely deployed in many areas of computer vision, such as target detection, 3D localization, and so on. In order to achieve the desired result...

A Hard Voting Policy-Driven Deep Learning Architectural Ensemble Strategy for Industrial Products Defect Recognition and Classification.

Sensors (Basel, Switzerland)
Manual or traditional industrial product inspection and defect-recognition models have some limitations, including process complexity, time-consuming, error-prone, and expensiveness. These issues negatively impact the quality control processes. There...

Fault Early Warning Model for High-Speed Railway Train Based on Feature Contribution and Causal Inference.

Sensors (Basel, Switzerland)
The demands for model accuracy and computing efficiency in fault warning scenarios are increasing as high-speed railway train technology continues to advance. The black box model is difficult to interpret, making it impossible for this technology to ...

MLWAN: Multi-Scale Learning Wavelet Attention Module Network for Image Super Resolution.

Sensors (Basel, Switzerland)
Image super resolution (SR) is an important image processing technique in computer vision to improve the resolution of images and videos. In recent years, deep convolutional neural network (CNN) has made significant progress in the field of image SR;...

Forecasting medical state transition using machine learning methods.

Scientific reports
Early circulatory failure detection is an effective way to reduce medical fatigue and improve state pre-warning ability. Instead of using 0-1 original state, a transformed state is proposed in this research, which reflects how the state is transforme...

Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals.

Sensors (Basel, Switzerland)
Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (B...

A multiattribute group decision-making method based on a new aggregation operator and the means and variances of interval-valued intuitionistic fuzzy values.

Scientific reports
The development of information measures associated with interval-valued intuitionistic fuzzy values (IVIFVs) has been an important research area over the past few decades. In the literature, the existing decision -making method using IVIFVs has some ...

A Novel CNN-LSTM Hybrid Model for Prediction of Electro-Mechanical Impedance Signal Based Bond Strength Monitoring.

Sensors (Basel, Switzerland)
The recent application of deep learning for structural health monitoring systems for damage detection has potential for improvised structure performance and maintenance for long term durability, and reliable strength. Advancements in electro-mechanic...

Research on the Fault Diagnosis Method of an Internal Gear Pump Based on a Convolutional Auto-Encoder and PSO-LSSVM.

Sensors (Basel, Switzerland)
The raw signals produced by internal gear pumps are susceptible to noises brought on by mechanical vibrations and the surrounding environment, and the sample count collected during the various operating periods is not distributed evenly. Accurately d...

Frame Structure Fault Diagnosis Based on a High-Precision Convolution Neural Network.

Sensors (Basel, Switzerland)
Structural health monitoring and fault diagnosis are important scientific issues in mechanical engineering, civil engineering, and other disciplines. The basic premise of structural health work is to be able to accurately diagnose the fault in the st...