AIMC Topic: Machine Learning

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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...

Interpretable clinical prediction via attention-based neural network.

BMC medical informatics and decision making
BACKGROUND: The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by the medical organizations in th...

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...

Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage.

Acta neurochirurgica
BACKGROUND: Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Mac...

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 ...

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...