Evaluation of a Failure Prediction Model for Large Scale Cloud Applications

2020 ◽  
pp. 321-327
Author(s):  
Mohammad S. Jassas ◽  
Qusay H. Mahmoud
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Failure Prediction ◽  
Cloud Applications

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

On Workload-Aware DRAM Failure Prediction in Large-Scale Data Centers

2021 ◽  
Author(s):  
Xingyi Wang ◽  
Yu Li ◽  
Yiquan Chen ◽  
Shiwen Wang ◽  
Yin Du ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Failure Prediction ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Failure Prediction in Commercial Banks with a Hybrid Prediction Model

2014 ◽  
Vol 1 (2) ◽  
pp. 209-220
Author(s):  
Yinhua Li ◽  
Yong Shi ◽  
Anqiang Huang ◽  
Haizhen Yang
Keyword(s):  
Prediction Model ◽  
Commercial Banks ◽  
Failure Prediction ◽  
Hybrid Prediction

DRAM Failure Prediction in Large-Scale Data Centers

2021 ◽  
Author(s):  
Fengyuan Yu ◽  
Hongzuo Xu ◽  
Songlei Jian ◽  
Chenlin Huang ◽  
Yijie Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Centers ◽  
Failure Prediction ◽  
Large Scale Data ◽  
Scale Data

Wind Turbine Failure Prediction Model using SCADA-based Condition Monitoring System

2021 ◽  
Author(s):  
Bara Alzawaideh ◽  
Payam Teimourzadeh Baboli ◽  
Davood Babazadeh ◽  
Susanne Horodyvskyy ◽  
Isabel Koprek ◽  
...  
Keyword(s):  
Prediction Model ◽  
Wind Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Failure Prediction ◽  
Condition Monitoring System

GPS failure prediction model based on improved grey dynamic forecasting

2015 ◽  
pp. 1193-1197
Author(s):  
Li-Dong Wang ◽  
Qing Gao ◽  
Xian-Quan Luo ◽  
Guan-Hui Liang
Keyword(s):  
Prediction Model ◽  
Failure Prediction ◽  
Model Based ◽  
Dynamic Forecasting
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