AIMC Topic: Machine Learning

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Accelerating hydrodynamic simulations of urban drainage systems with physics-guided machine learning.

Water research
We propose and demonstrate a new approach for fast and accurate surrogate modelling of urban drainage system hydraulics based on physics-guided machine learning. The surrogates are trained against a limited set of simulation results from a hydrodynam...

Dynamic Sepsis Prediction for Intensive Care Unit Patients Using XGBoost-Based Model With Novel Time-Dependent Features.

IEEE journal of biomedical and health informatics
Sepsis is a systemic inflammatory response caused by pathogens such as bacteria. Because its pathogenesis is not clear, the clinical manifestations of patients vary greatly, and the alarming incidence and mortality pose a great threat to patients and...

Artificial Intelligence for Colonoscopy: Past, Present, and Future.

IEEE journal of biomedical and health informatics
During the past decades, many automated image analysis methods have been developed for colonoscopy. Real-time implementation of the most promising methods during colonoscopy has been tested in clinical trials, including several recent multi-center st...

Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning-A Survey in Myoelectric Control.

IEEE journal of biomedical and health informatics
To develop multi-functionalhuman-machine interfaces that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) techniques have been widely implemented to decode human movement intentions from...

Research on the Audit Prediction Model of "Special Bonds + PPP" Project based on Machine Learning.

Computational intelligence and neuroscience
This paper aims at the whole-process tracking audit problem of "special bonds + PPP" mode (hereinafter referred to as "special bonds + PPP") in public infrastructure construction projects and establishes an audit evaluation prediction model based on ...

A reservoir bubble point pressure prediction model using the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique with trend analysis.

PloS one
The bubble point pressure (Pb) could be obtained from pressure-volume-temperature (PVT) measurements; nonetheless, these measurements have drawbacks such as time, cost, and difficulties associated with conducting experiments at high-pressure-high-tem...

Machine learning for risk stratification in kidney disease.

Current opinion in nephrology and hypertension
PURPOSE OF REVIEW: Risk stratification for chronic kidney is becoming increasingly important as a clinical tool for both treatment and prevention measures. The goal of this review is to identify how machine learning tools contribute and facilitate ri...

Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach.

Medical & biological engineering & computing
Numerous studies have been conducted to elucidate the relation of tumor proximity to cancer prognosis and treatment efficacy in colorectal cancer. However, the molecular pathways and prognoses of left- and right-sided colorectal cancers are different...

RootPainter: deep learning segmentation of biological images with corrective annotation.

The New phytologist
Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interf...