Construction of Support System for Demand Driven Design of Cocktail Recipes by Deep Learning

2019 ◽  
pp. 92-108
Author(s):  
Soichiro Ota ◽  
Kohei Otake ◽  
Takashi Namatame
Keyword(s):  
Deep Learning ◽  
Support System

Using Deep Learning in The Development of A Welding Practice Training Support System

2021 ◽  
Author(s):  
Pham-Tuan-Anh Phung ◽  
Ngoc-Thien Tran ◽  
Vu-Hoang Tran ◽  
Ton-Nghia Huynh
Keyword(s):  
Deep Learning ◽  
Support System ◽  
Training Support

scholarly journals Deep learning-based decision support system for the diagnosis of neoplastic gallbladder polyps on ultrasonography: Preliminary results

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Younbeom Jeong ◽  
Jung Hoon Kim ◽  
Hee-Dong Chae ◽  
Sae-Jin Park ◽  
Jae Seok Bae ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Gallbladder Polyps ◽  
Preliminary Results

scholarly journals Extraction of ionosphere parameters in ionograms using deep learning

2019 ◽  
Vol 127 ◽  
pp. 01004 ◽  
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Support System ◽  
Deep Neural Networks ◽  
Intelligent Support

Based on a new developed author’s method for recognition traces of reflections from different layers of the ionosphere in ionograms, the ionosphere parameters are extracted. The method is based on the use of deep neural networks (DNN). The rules for extracting the ionosphere parameters in ionograms are given. Based on the results obtained by the authors, an intelligent support system for ionogram analysis is being developed.

scholarly journals Interpretation Support System for Classification Patterns in Deep Learning with Texts

2019 ◽  
Vol 31 (4) ◽  
pp. 779-787
Author(s):  
Masayuki ANDO ◽  
Yoshinobu KAWAHARA ◽  
Wataru SUNAYAMA ◽  
Yuji HATANAKA
Keyword(s):  
Deep Learning ◽  
Support System

scholarly journals A data-driven approach for chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea

2020 ◽  
Author(s):  
Jinhyeok Park ◽  
Kang-Yoon Lee ◽  
Jeong-Heum Baek ◽  
Youngho Lee
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Cancer Treatment ◽  
Support System ◽  
Treatment Guideline ◽  
Concordance Rate ◽  
Actual Data ◽  
Nccn Guidelines

Abstract Background: Recently, the Clinical Decision Support System (CDSS) has attracted attention as a method for minimizing medical errors. To overcome the limitation that existing CDSS does not reflect actual data, we proposed CDSS based on deep learning. Methods: We proposed Colorectal Cancer Chemotherapy Recommender (C3R), a deep learning-based chemotherapy recommendation model. This supplements the limitation that the existing CDSS is difficult to support data-based decision making. It is configured to study the clinical data generated at Gachon Gil Medical Center and recommend appropriate chemotherapy. To validate the model, we compared the diagnosis concordance rate with the NCCN Guidelines, a representative cancer treatment guideline, and the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). Results: The diagnosis concordance rates of the C3R model with the NCCN guidelines were 70.5% for the Top-1 Accuracy and 84% for the Top-2 Accuracy. Also, the diagnosis concordance rate with the GCCTP were 57.9% for the Top-1 Accuracy and 77.8 for the Top-2 Accuracy. Conclusions: This model is meaningful in that it is Korea’s first colon cancer treatment method decision support system that reflects actual data. In the future, if sufficient data is secured through multi-organization, more reliable results can be obtained.

scholarly journals Research on Security Architecture of Global Energy Internet Standard Support System

2019 ◽  
Vol 118 ◽  
pp. 01011
Author(s):  
Pei Shen ◽  
Sining Wang ◽  
Nie Ling
Keyword(s):  
Deep Learning ◽  
Support System ◽  
Security Policy ◽  
Learning Algorithm ◽  
Policy Framework ◽  
Security Architecture ◽  
Global Energy ◽  
Energy Internet ◽  
Network Intrusion ◽  
Standard Support

The global energy Internet standard support system plays a vital role in global energy interconnection. However, the system brings convenience to the global energy interconnection and introduces new security issues. Due to the diversity of users, the complexity of the environment and the internationalization, there will be more and more malicious network intrusions from outside and inside. The security system of the global energy Internet standard support system faces great challenges. To this end, this paper proposes a security architecture that combines active and passive, establishing a chain of trust between the active security architecture and the passive security architecture, ensuring a new security architecture, a security organization framework, a security policy framework, a security operation framework, and a closed-loop system that integrates the security technology framework. A deep learning algorithm was introduced for network intrusion learning to achieve sustainable management of the information security life cycle. The combination of active and passive security systems and continuous monitoring strategies based on deep learning fully ensure the security of the global energy Internet standard support system.

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