Machine Learning

The dataset included 640 patients' vital records, which ranged in age from 18 to 60.

In this paper, a novel time-constrained global and local nonlinear analytic stationary subspace analysis (Tc-GLNASSA) is proposed to enhance blast furnace ironmaking process (BFIP) monitoring. Although existing analytic stationary subspace analysis method has been available for deriving process consistent relationships. However, the presence of complex nonlinear, periodic nonstationary and time-varying smelting conditions renders the satisfactory estimation of stationary projections unattainable.

Accurate detection and segmentation of apple trees are crucial in high throughput phenotyping, further guiding apple trees yield or quality management. A LiDAR and a camera were attached to the UAV to acquire RGB information and coordinate information of a whole orchard. The information was integrated by simultaneous localization and mapping network to form a dataset of RGB-colored point clouds. The dataset can be used for methods related to apple detection and segmentation based on point clouds.

Data preprocessing is a fundamental stage in deep learning modeling and serves as the cornerstone of reliable data analytics. These deep learning models require significant amounts of training data to be effective, with small datasets often resulting in overfitting and poor performance on large datasets. One solution to this problem is parallelization in data modeling, which allows the model to fit the training data more effectively, leading to higher accuracy on large data sets and higher performance overall.