scholarly journals QoS Prediction Model of Cloud Services Based on Deep Learning

2022 ◽  
Vol 9 (3) ◽  
pp. 564-566
Author(s):  
WenJun Huang ◽  
PeiYun Zhang ◽  
YuTong Chen ◽  
MengChu Zhou ◽  
Yusuf Al-Turki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Cloud Services ◽  
Qos Prediction

A Deep Learning Risk Prediction Model for Overall Survival in Patients with Gastric Cancer: A Multicenter Study

2020 ◽  
Author(s):  
Liwen Zhang ◽  
Di Dong ◽  
Wenjuan Zhang ◽  
Xiaohan Hao ◽  
Mengjie Fang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Deep Learning ◽  
Prediction Model ◽  
Risk Prediction ◽  
Multicenter Study ◽  
Risk Prediction Model

EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

Energy ◽  
2020 ◽  
pp. 119692
Author(s):  
Xiaosheng Peng ◽  
Hongyu Wang ◽  
Jianxun Lang ◽  
Wenze Li ◽  
Qiyou Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Power Prediction ◽  
Numerical Weather ◽  
Model Based ◽  
Wind Power Prediction

scholarly journals O12-1 A novel gene-prediction model, virtual sequencing with deep learning to predict gene alterations in colorectal cancer

2021 ◽  
Vol 32 ◽  
pp. S290
Author(s):  
Daisuke Kotani ◽  
Satoshi Fujii ◽  
Tomoyuki Yamada ◽  
Mizuto Suzuki ◽  
Takayuki Yoshino
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Prediction Model ◽  
Gene Prediction ◽  
Novel Gene ◽  
Gene Alterations

scholarly journals Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6460
Author(s):  
Dae-Yeon Kim ◽  
Dong-Sik Choi ◽  
Jaeyun Kim ◽  
Sung Wan Chun ◽  
Hyo-Wook Gil ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Glucose Level ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Percentage Error ◽  
Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

A Study on the Prediction Model for the Development of Vacant Homes Using Deep Learning

2019 ◽  
Vol 54 (3) ◽  
pp. 1468-1474
Author(s):  
Sae Kitajima ◽  
Nobuhiro Roppongi ◽  
Hidetora Tomioka ◽  
Akinori Morimoto
Keyword(s):  
Deep Learning ◽  
Prediction Model
Sign in / Sign up

Export Citation Format

Share Document