A Physics-Guided Deep Learning Predictive Model for Robust Production Forecasting and Diagnostics in Unconventional Wells

2021 ◽  
Author(s):  
Syamil Mohd Razak ◽  
Jodel Cornelio ◽  
Young Cho ◽  
Hui-Hai Liu ◽  
Ravimadhav Vaidya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Predictive Model ◽  
Production Forecasting

scholarly journals HetEnc: a deep learning predictive model for multi-type biological dataset

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Leihong Wu ◽  
Xiangwen Liu ◽  
Joshua Xu
Keyword(s):  
Deep Learning ◽  
Predictive Model ◽  
Biological Dataset

scholarly journals DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sofia B. Dias ◽  
Sofia J. Hadjileontiadou ◽  
José Diniz ◽  
Leontios J. Hadjileontiadis
Keyword(s):  
Online Learning ◽  
Deep Learning ◽  
Predictive Model ◽  
Short Term Memory ◽  
Ground Truth ◽  
Face To Face ◽  
Learning Techniques ◽  
Learning Plans ◽  
First Time

AbstractCoronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner’s behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users’ interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) $$<0.009$$ < 0.009 , and average correlation coefficient between ground truth and predicted QoI values $$r\ge 0.97$$ r ≥ 0.97 $$(p<0.05)$$ ( p < 0.05 ) , when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user’s online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners’ motivation and participation in the learning process.

scholarly journals Deep Learning-based Production Forecasting in Manufacturing: a Packaging Equipment Case Study

2019 ◽  
Vol 38 ◽  
pp. 248-255 ◽  
Author(s):  
Luca Brunelli ◽  
Chiara Masiero ◽  
Diego Tosato ◽  
Alessandro Beghi ◽  
Gian Antonio Susto
Keyword(s):  
Deep Learning ◽  
Production Forecasting

Oil production forecasting using deep learning for shale oil wells under variable gas-oil and water-oil ratios

2021 ◽  
pp. 1-24
Author(s):  
Pedram Mahzari ◽  
Mehryar Emambakhsh ◽  
Cenk Temizel ◽  
Adrian P. Jones
Keyword(s):  
Deep Learning ◽  
Oil Production ◽  
Oil Wells ◽  
Shale Oil ◽  
Gas Oil ◽  
Production Forecasting

Deep-Learning Based Analysis of Preoperative MRI Predicts Microvascular Invasion and Outcome in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Bao-Ye sun ◽  
Pei-Yi Gu ◽  
Ruo-Yu Guan ◽  
Cheng Zhou ◽  
Jian-Wei Lu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Predictive Model ◽  
Clinical Outcomes ◽  
Area Under The Curve ◽  
Microvascular Invasion ◽  
Dce Mri ◽  
Preoperative Prediction ◽  
Model Combining ◽  
Contrast Enhanced

Abstract Background & Aims: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict the MVI status and clinical outcomes in patients with HCC. Methods We retrospectively included a total of 321 HCC patients with pathologically confirmed MVI status. Preoperative DCE-MRI of these patients were collected, annotated and further analyzed by DL in this study. A predictive model for MVI integrating DL-predicted MVI status (DL-MVI) and clinical parameters was constructed with multivariate logistic regression. Results Of 321 HCC patients, 136 patients were pathologically MVI absent and 185 patients were MVI present. Recurrence-free survival (RFS) and overall survival (OS) were significantly different between the DL-predicted MVI-absent and MVI-present. Among all clinical variables, only DL-predicted MVI status and AFP were independently associated with MVI: DL-MVI (odds ratio [OR]=35.738; 95% confidence interval [CI]: 14.027-91.056; p<0.001), AFP (OR=4.634, 95% CI: 2.576-8.336; p<0.001). To predict the presence of MVI, DL-MVI combined with AFP achieved an area under the curve (AUC) of 0.824. Conclusions Our predictive model combining DL-MVI and AFP achieved good performance for predicting MVI and clinical outcomes in patients with HCC.

scholarly journals Deep Learning-Based Security Verification for a Random Number Generator Using White Chaos

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1134
Author(s):  
Cai Li ◽  
Jianguo Zhang ◽  
Luxiao Sang ◽  
Lishuang Gong ◽  
Longsheng Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Predictive Model ◽  
Random Number ◽  
Random Number Generator ◽  
External Cavity ◽  
Number Generator ◽  
Output Data ◽  
Final Output ◽  
Linear Congruential

In this paper, a deep learning (DL)-based predictive analysis is proposed to analyze the security of a non-deterministic random number generator (NRNG) using white chaos. In particular, the temporal pattern attention (TPA)-based DL model is employed to learn and analyze the data from both stages of the NRNG: the output data of a chaotic external-cavity semiconductor laser (ECL) and the final output data of the NRNG. For the ECL stage, the results show that the model successfully detects inherent correlations caused by the time-delay signature. After optical heterodyning of two chaotic ECLs and minimal post-processing are introduced, the model detects no patterns among corresponding data. It demonstrates that the NRNG has the strong resistance against the predictive model. Prior to these works, the powerful predictive capability of the model is investigated and demonstrated by applying it to a random number generator (RNG) using linear congruential algorithm. Our research shows that the DL-based predictive model is expected to provide an efficient supplement for evaluating the security and quality of RNGs.

scholarly journals US-Based Deep Learning Model for Differentiating Hepatocellular Carcinoma (HCC) From Other Malignancy in Cirrhotic Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Hang Zhou ◽  
Tao Jiang ◽  
Qunying Li ◽  
Chao Zhang ◽  
Cong Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Deep Learning ◽  
Predictive Model ◽  
Clinical Information ◽  
Learning Model ◽  
Lower Sensitivity ◽  
Liver Imaging ◽  
The Us ◽  
Cirrhotic Patients ◽  
Deep Learning Model

The aim was to build a predictive model based on ultrasonography (US)-based deep learning model (US-DLM) and clinical features (Clin) for differentiating hepatocellular carcinoma (HCC) from other malignancy (OM) in cirrhotic patients. 112 patients with 120 HCCs and 60 patients with 61 OMs were included. They were randomly divided into training and test cohorts with a 4:1 ratio for developing and evaluating US-DLM model, respectively. Significant Clin predictors of OM in the training cohort were combined with US-DLM to build a nomogram predictive model (US-DLM+Clin). The diagnostic performance of US-DLM and US-DLM+Clin were compared with that of contrast enhanced magnetic resonance imaging (MRI) liver imaging and reporting system category M (MRI LR-M). US-DLM was the best independent predictor for evaluating OMs, followed by clinical information, including high cancer antigen 199 (CA199) level and female. The US-DLM achieved an AUC of 0.74 in the test cohort, which was comparable with that of MRI LR-M (AUC=0.84, p=0.232). The US-DLM+Clin for predicting OMs also had similar AUC value (0.81) compared with that of LR-M+Clin (0.83, p&gt;0.05). US-DLM+Clin obtained a higher specificity, but a lower sensitivity, compared to that of LR-M +Clin (Specificity: 82.6% vs. 73.9%, p=0.007; Sensitivity: 78.6% vs. 92.9%, p=0.006) for evaluating OMs in the test set. The US-DLM+Clin model is valuable in differentiating HCC from OM in the setting of cirrhosis.

Sign in / Sign up

Export Citation Format

Share Document