Founded in 2015, Yunhai Zhichuang has a registered capital of 10 million RMB and is located in Wuxi City. The company focuses on software and information technology services. According to data from Tianyancha,

In the quest for sustainable and efficient water treatment solutions, a new study titled "Enhanced Machine Learning Prediction of Biochar Adsorption for Dyes: Parameter Optimization and Experimental Validation" is making significant strides.

Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in weather and climate, but a new study by MIT researchers shows that bigger models are not always better.

The integration of machine learning into the GEOS-Chem model enhances aerosol simulations, providing critical insights for climate and air quality assessments.

Background There is a lack of atrial fibrillation (AF) prediction models tailored for individuals without prior cardiovascular diseases (CVDs) to facilitate early intervention. This study aimed to develop and validate an AF prediction model using machine-learning methods based on routine biomarkers in middle-aged individuals without overt CVD.

Gynecological cancers, including breast, ovarian, and cervical malignancies, account for a significant global health burden among women. The review outlines how a spectrum of machine learning (ML) models, ranging from traditional algorithms to deep learning architectures, are driving significant advances in prediction and management.

Researchers at Penn State are using machine learning and existing electrocardiogram (ECG) data to help doctors make more accurate predictions. A team of artificial intelligence engineers, in ...

The increased rate of data collection relating to athlete load has led to interest in machine learning (ML) approaches for sports data analysis, including injury risk prediction. Prior reviews have examined the application of ML for both performance analysis and injury risk prediction, although no charting of study characteristics were conducted.

Kirsten Hilger and colleagues used machine learning models to predict multiple kinds of intelligence from brain connections of 806 healthy adults while resting and while completing tasks.