Neural Prediction in Industry: Increasing Reliability through Use of Confidence Measures and Model Combination

2001 ◽  
pp. 147-175
Author(s):  
P. J. Edwards ◽  
G. Papadopoulos ◽  
A. F. Murray
Keyword(s):  
Model Combination ◽  
Confidence Measures

scholarly journals Anticancer effects of the combined Thai noni juice ethanolic extracts and 5-fluorouracil against cholangiocarcinoma cells in vitro and in vivo

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeerati Prompipak ◽  
Thanaset Senawong ◽  
Banchob Sripa ◽  
Albert J. Ketterman ◽  
Suppawit Utaiwat ◽  
...  
Keyword(s):  
Nude Mice ◽  
Combination Treatment ◽  
Synergistic Effects ◽  
Single Agent ◽  
Xenograft Model ◽  
Ethanolic Extract ◽  
Model Combination ◽  
Mouse Xenograft Model ◽  
Ethanolic Extracts

AbstractApplication of 5-fluorouracil (5-FU) in cholangiocarcinoma (CCA) is limited by adverse side effects and chemoresistance. Therefore, the combination therapy of 5-FU with other substances, especially natural products may provide a new strategy for CCA treatment. The aim of this study was to evaluate the combination effects of 5-FU and two ethanolic extracts of Thai noni juice (TNJ) products on CCA cell lines and nude mice xenografts. The results of antiproliferative assay showed the combination treatment of 5-FU and each TNJ ethanolic extract exerted more cytotoxicity on CCA cells than either single agent treatment. Synergistic effects of drug combinations can enable the dose reduction of 5-FU. The mechanism underlying a combination treatment was apoptosis induction through an activation of p53 and Bax proteins. In the nude mouse xenograft model, combination treatments of 5-FU with each TNJ ethanolic extract suppressed the growth of CCA cells implanted mice more than single agent treatments with no effects on mouse body weight, kidney, and spleen. Moreover, low doses of TNJ ethanolic extracts reduced the hepatotoxicity of 5-FU in nude mice. Taken together, these data suggested that the ethanolic extracts of TNJ products can enhance the anti-CCA effect and reduce toxicity of 5-FU.

scholarly journals The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients

2021 ◽  
Vol 7 (2) ◽  
pp. 17
Author(s):  
Michael Baine ◽  
Justin Burr ◽  
Qian Du ◽  
Chi Zhang ◽  
Xiaoying Liang ◽  
...  
Keyword(s):  
Clinical Features ◽  
Dna Methyltransferase ◽  
Predictive Ability ◽  
Post Treatment ◽  
Model Combination ◽  
Methylguanine Dna Methyltransferase ◽  
Mri Scans ◽  
Area Under Roc Curve ◽  
True Progression ◽  
Potential Use

Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs.

Confidence measures for large vocabulary continuous speech recognition

2001 ◽  
Vol 9 (3) ◽  
pp. 288-298 ◽  
Author(s):  
F. Wessel ◽  
R. Schluter ◽  
K. Macherey ◽  
H. Ney
Keyword(s):  
Speech Recognition ◽  
Continuous Speech ◽  
Continuous Speech Recognition ◽  
Confidence Measures ◽  
Large Vocabulary

Research on non-intrusive load disaggregation method based on multi-model combination

2021 ◽  
Vol 200 ◽  
pp. 107472
Author(s):  
Yi Guo ◽  
Xuejun Xiong ◽  
Qi Fu ◽  
Liang Xu ◽  
Shi Jing
Keyword(s):  
Model Combination ◽  
Load Disaggregation

scholarly journals Incorporating confidence measures into fuzzy classifiers

1988 ◽  
Vol 2 (2) ◽  
pp. 104-105
Author(s):  
Asghar Nafarieh ◽  
James M. Keller
Keyword(s):  
Confidence Measures ◽  
Fuzzy Classifiers
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