DeepCys : Structure‐based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains

2021 ◽  
Author(s):  
Vamsi Nallapareddy ◽  
Shubham Bogam ◽  
Himaja Devarakonda ◽  
Shubham Paliwal ◽  
Debashree Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Prediction Method ◽  
Function Prediction

Energy Consumption Forecasting Using United Grey System–Bayesian Regularization Neural Network Model

2012 ◽  
Vol 524-527 ◽  
pp. 3087-3092 ◽  
Author(s):  
Xiao Hui Hu ◽  
Lv Jun Zhan ◽  
Yun Xue ◽  
Gui Xi Liu ◽  
Zhe Fan
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Regularization Parameter ◽  
Prediction Method ◽  
Neural Model ◽  
Bayesian Regularization ◽  
Grey System ◽  
Bayesian Regularization Neural Network ◽  
Energy Consumption Prediction

The energy consumption of the enterprise is subject to various factors. To solve the problem, a new grey-neural model is proposed which effectively combines the grey system and Bayesian-regularization neural network and avoids the disadvantages of each other. The case study indicates that the prediction method is not only reasonable in theory but also owns good application value in the energy consumption prediction. Meanwhile, results also exhibit that G-BRNN model has the automated regularization parameter selection capability and may ensure the excellent adaptability and robustness.

scholarly journals Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS

2020 ◽  
Vol 12 (2) ◽  
pp. 316
Author(s):  
Vesta Afzali Gorooh ◽  
Subodh Kalia ◽  
Phu Nguyen ◽  
Kuo-lin Hsu ◽  
Soroosh Sorooshian ◽  
...  
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Deep Neural Network ◽  
Temporal Frequency ◽  
Cloud Type ◽  
Multispectral Data ◽  
Precipitation Estimation ◽  
High Clouds ◽  
Type Classification

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation.

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