AIMC Topic: Algorithms

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Fully automated condyle segmentation using 3D convolutional neural networks.

Scientific reports
The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy. 234 cone-beam computed tomogra...

Generative adversarial network-based deep learning approach in classification of retinal conditions with optical coherence tomography images.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: To determine whether a deep learning approach using generative adversarial networks (GANs) is beneficial for the classification of retinal conditions with Optical coherence tomography (OCT) images.

TwinEDA: a sustainable deep-learning approach for limb-position estimation in preterm infants' depth images.

Medical & biological engineering & computing
Early diagnosis of neurodevelopmental impairments in preterm infants is currently based on the visual analysis of newborns' motion patterns by trained operators. To help automatize this time-consuming and qualitative procedure, we propose a sustainab...

Applying Ternion Stream DCNN for Real-Time Vehicle Re-Identification and Tracking across Multiple Non-Overlapping Cameras.

Sensors (Basel, Switzerland)
The increase in security threats and a huge demand for smart transportation applications for vehicle identification and tracking with multiple non-overlapping cameras have gained a lot of attention. Moreover, extracting meaningful and semantic vehicl...

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting.

Sensors (Basel, Switzerland)
Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting ...

Spiking Neural Networks for Structural Health Monitoring.

Sensors (Basel, Switzerland)
This paper presents the first implementation of a spiking neural network (SNN) for the extraction of cepstral coefficients in structural health monitoring (SHM) applications and demonstrates the possibilities of neuromorphic computing in this field. ...

Text Analysis of Radiology Reports with Signs of Intracranial Hemorrhage on Brain CT Scans Using the Decision Tree Algorithm.

Sovremennye tekhnologii v meditsine
UNLABELLED: is to create, train, and test the algorithm for the analysis of brain CT text reports using a decision tree model to solve the task of simple binary classification of presence/absence of intracranial hemorrhage (ICH) signs.

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.

Frontiers in endocrinology
BACKGROUND: Opportunely screening for diabetes is crucial to reduce its related morbidity, mortality, and socioeconomic burden. Machine learning (ML) has excellent capability to maximize predictive accuracy. We aim to develop ML-augmented models for ...

Mortality prediction in ICU Using a Stacked Ensemble Model.

Computational and mathematical methods in medicine
Artificial intelligence (AI) technology has huge scope in developing models to predict the survival rate of critically ill patients in the intensive care unit (ICU). The availability of electronic clinical data has led to the widespread use of variou...

Enterprise Human Resource Management Model by Artificial Intelligence Digital Technology.

Computational intelligence and neuroscience
Artificial intelligence (AI) is a potentially transformative force that is likely to change the role of management and organizational practices. AI is revolutionizing corporate decision-making and changing management structures. The visible effects o...