AIMC Topic: Algorithms

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Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity.

Computer methods in biomechanics and biomedical engineering
Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem....

Interactive machine learning for soybean seed and seedling quality classification.

Scientific reports
New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of this study was to propose an approach based on inte...

Identifying diagnosis evidence of cardiogenic stroke from Chinese echocardiograph reports.

BMC medical informatics and decision making
BACKGROUND: Cardiogenic stroke has increasing morbidity in China and brought economic burden to patient families. In cardiogenic stroke diagnosis, echocardiograph examination is one of the most important examinations. Sonographers will investigate pa...

Investigation of singular ordinary differential equations by a neuroevolutionary approach.

PloS one
In this research, we have investigated doubly singular ordinary differential equations and a real application problem of studying the temperature profile in a porous fin model. We have suggested a novel soft computing strategy for the training of unk...

qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks.

Magnetic resonance in medicine
PURPOSE: To develop a set of artificial neural networks, collectively termed qMTNet, to accelerate data acquisition and fitting for quantitative magnetization transfer (qMT) imaging.

Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines.

Neural networks : the official journal of the International Neural Network Society
In this work, a novel data-driven fault diagnostic framework is developed by using hybrid multi-mode machine learning strategies to monitor system health status. The coexistence of multi-mode and concurrent faults and their adverse coupling effects p...

Deep Learning-Based Imbalanced Data Classification for Drug Discovery.

Journal of chemical information and modeling
Drug discovery studies have become increasingly expensive and time-consuming processes. In the early phase of drug discovery studies, an extensive search has been performed to find drug-like compounds, which then can be optimized over time to become ...

AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
BACKGROUND:  MR imaging is an essential component in managing patients with Multiple sclerosis (MS). This holds true for the initial diagnosis as well as for assessing the clinical course of MS. In recent years, a growing number of computer tools wer...

Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment.

International journal of environmental research and public health
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled u...

Molecular docking and machine learning analysis of Abemaciclib in colon cancer.

BMC molecular and cell biology
BACKGROUND: The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design ...