53 天前 - … In this paper, we develop a deep learning model of predicting students’ satisfactions
… early failures. The model can also predict dropout rates and identify the main baseline risk …

138 天前 - … lies clinical risk prediction, empowering predictive … neural network architecture,
allowing for proficiently managing graph data throughout time. At its core, the complex network …

165 天前 - … framework, which employs three homogeneous convolutional neural networks (CNNs)
as base learners … Based on the predictions of each round, we can calculate the precision …

223 天前 - … a deep neural network model for identifying predictive variables and evaluating
student … the proposed model in predicting and evaluating student academic performance. The …

234 天前 - SciSec is an annual international forum for researchers and industry experts to present
and discuss the latest research, trends, breakthroughs, and challenges in the domain of …

239 天前 - … paradigm shift powered by deep machine-learning … the utilization of Convolutional
Neural Network (CNN) models, … of Deep Learning algorithms in stock market predictions from …

264 天前 - … ; graph embeddings and link predictions; predictive analysis and machine learning;
… and threat detection; streaming data; miscellaneous; explainability and scalability in AI. …

289 天前 - … may lead to incorrect inference or biased predictions [35]. In Chapter 2, we propose
a novel, multi-task deep neural network for semicompeting risks based on the illness-death …