This consultancy study aims at:(a) finding the ideal model (s) for developing SEs that target
different groups of people; and (b) developing ways to encourage effective and appropriate …

… the common categorization methods in machine learning … of dataset, we increased the
number of classified categories and … Then we could make use of segmentation process as batch. …

… This dataset is primarily used for vessel segmentation tasks. … uses deep learning to extract
features and then use … At present, traditional machine learning methods are still widely used. …

… Forecasting crop yields is critical food production. Crop yield … a machine learning approach
to yield modelling is presented… achieves 90% accuracy in a data set with an IR value of 9 is …

… on BraTsXXX database … present, a large number of deep learning algorithms have been
proposed with the great development of computer technologies in medical image segmentation, …

… a dataset was created and further processed through two kinds of machine learning methods:
feature-based machine learning … The color-based image segmentation method used in this …

… K-anonymization is then applied to the known segments in the dataset to … food will make
an extreme move in our lives. In this paper, an extensive description of CPS presented an …

… guidance, this paper would not have been in its present form. … Now we suppose that our time
sequence has only three segments, t1, … Additionally, we use a extra dataset to evaluate their …

… guidance, this paper would not have been in its present form. … Now we suppose that our time
sequence has only three segments, t1, … Additionally, we use a extra dataset to evaluate their …

… , machine learning techniques have emerged as promising tools in stroke medicine, enabling
efficient analysis of large-scale datasets and … AI-enabled automated segmentation of acute …