Healthcare Risk Prediction on Data Streams Employing Cross Ensemble Deep Learning

在本项目中，在交叉分层连接的两个GPU的体系结构中，通过构建基于组装的CNN的深度神经网络(DNN)，使用了集成深度学习技术。此外，我们还有一个图形处理器系统，作为训练实验的后备，使用大规模的数据进行对比，我可以在多变量数据上进行零射击学习，取得很好的研究结果。此外，我还培训了关于动态数据和图像数据的深度学习架构。我们还实现了基于多任务学习的注意特征融合网络用于场景文本图像的超分辨率，取得了良好的效果。虽然我们使用我们报道的算法取得了很好的结果(如文献列表所示)，但对于一些医疗数据分析的准确性还有待提高。2022年由于大流行，由于出行和走动紧张，材料变得比平时昂贵。尽管如此，取得的结果是好的，从期刊上征集的出版物来看就是明显的。然而，我们预计2023年的最终结果会取得更多进展。

In this project, have used ensemble deep learning techniques by constructing Deep Neural Networks (DNNs) based on assembled CNN in architecture of two GPUs in cross layered connection. In addition, we have one GPU system, running as backup for training experiments using large scale data for comparison purpose.I could achieve good research results using zero shot learning on multi-variate data. Also, I have trained the deep-learning architecture on dynamic data, and image data. The result was promising and therefore we publish international journal articles.We also, implemented Multi-task Learning-based Attentional Feature Fusion Network for Scene Text Image Super-resolution, with good results.Although we achieved good results using our reported algorithms (as shown in the list of publications), however, the accuracy for some health care data analytics needs to be enhanced.The year2022 was hard due to the pandemic as traveling and moving around was tight, also materials become expensive than usual. Nevertheless, the results achieved is good, as evident from the publication enlisted in journals. However, we expect more progressing final outcome in 2023.