Healthcare Risk Prediction on Data Streams Employing Cross Ensemble Deep Learning

在这个项目中，我们使用了集成深度学习技术，通过构建基于组装CNN的深度神经网络（DNN），在跨层连接的两个GPU架构中。此外，我们还有一个GPU系统，作为备份运行，用于使用大规模数据进行比较的训练实验。我可以在多变量数据上使用零射击学习来获得良好的研究结果。此外，我还在动态数据和图像数据上训练了深度学习架构。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超分辨率，取得了良好的效果（如出版物清单所示），但是，部分医疗数据分析的准确性有待提高。2022年由于疫情的影响，出行和移动都很紧张，材料也比平时贵。然而，从期刊上的出版物可以看出，取得的成果是好的。然而，我们预期二零二三年的最终结果会更有进展。

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.