A Newborn Screening System Based on Service-Oriented Architecture Embedded Support Vector Machine

2008 ◽  
Author(s):  
Sung-Huai Hsieh ◽  
Sheau-Ling Hsieh ◽  
Yin-Hsiu Chien ◽  
Zhenyu Wang ◽  
Yung-Ching Weng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Newborn Screening ◽  
Service Oriented Architecture ◽  
Support Vector ◽  
Screening System ◽  
Service Oriented

A Newborn Screening System Based on Service-Oriented Architecture Embedded Support Vector Machine

2009 ◽  
Vol 34 (5) ◽  
pp. 899-907 ◽  
Author(s):  
Kai-Ping Hsu ◽  
Sung-Huai Hsieh ◽  
Sheau-Ling Hsieh ◽  
Po-Hsun Cheng ◽  
Yung-Ching Weng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Newborn Screening ◽  
Service Oriented Architecture ◽  
Support Vector ◽  
Screening System ◽  
Service Oriented

Pricing Method for Service-Oriented Manufacturing With Support Vector Machine

2017 ◽  
Author(s):  
Qiunan Meng ◽  
Jian Lou ◽  
Xun Xu ◽  
Shiqiang Yu
Keyword(s):  
Support Vector Machine ◽  
Value Chain ◽  
Back Propagation ◽  
Influential Factors ◽  
Support Vector ◽  
Small Scale ◽  
Product Price ◽  
Price Prediction ◽  
Service Oriented

To evaluate the effects of customers’ participation levels in various business activities on pricing in service-oriented manufacturing, the indices of pricing are proposed through extracting the influential factors in the four stages (i.e., design, manufacturing, production and services) from the whole value chain to comprehensively reflect customers’ demands. A new pricing model based on these indices is formulated by Support Vector Machine (SVM). It can predict a more accurate product price regarding the products’ similarity by the values of the influential factors that are determined in terms of business activities participated by customers. Finally, a case study from a molding company in China is conducted to verify the effectiveness of this pricing methodology. The results indicate that the model by SVM fares better in comparison with that by Back Propagation Neural Networks in small scale samples, especially in the performances of generalization and robustness. The outcomes also testify that this price prediction methodology can increase the accuracy of a product’s price as well as the customer’s satisfaction.

Leveraging Incrementally Enriched Domain Knowledge to Enhance Service Categorization

2012 ◽  
Vol 9 (3) ◽  
pp. 43-66 ◽  
Author(s):  
Jia Zhang ◽  
Jian Wang ◽  
Patrick Hung ◽  
Zheng Li ◽  
Neng Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Structural Feature ◽  
Text Classification ◽  
Domain Knowledge ◽  
Support Vector ◽  
Iterative Approach ◽  
Classification Technique ◽  
Unique Structural Feature ◽  
Service Oriented ◽  
Open Service

This paper reports the authors’ study over an open service and mashup repository, ProgrammableWeb, which groups stored services into predefined categories. Leveraging such a unique structural feature and hidden domain knowledge of the service repository, they extend the Support Vector Machine (SVM)-based text classification technique to enhance service-oriented categorization. An iterative approach is presented to automatically verify and adjust service categorization, which will incrementally enrich domain ontology and in turn enhance the accuracy of service categorization.

Newborn Screening Healthcare Information System Based on Service-Oriented Architecture

2009 ◽  
Vol 34 (4) ◽  
pp. 519-530 ◽  
Author(s):  
Sung-Huai Hsieh ◽  
Sheau-Ling Hsieh ◽  
Yin-Hsiu Chien ◽  
Yung-Ching Weng ◽  
Kai-Ping Hsu ◽  
...  
Keyword(s):  
Information System ◽  
Newborn Screening ◽  
Service Oriented Architecture ◽  
Healthcare Information ◽  
Healthcare Information System ◽  
Service Oriented

scholarly journals Automated Feature Extraction from UML Images to Measure SOA Size

2020 ◽  
Vol 9 (2) ◽  
pp. 1132-1137
Keyword(s):  
Deep Learning ◽  
Architectural Design ◽  
Service Oriented Architecture ◽  
Short Term Memory ◽  
Image Data ◽  
Support Vector ◽  
Sequence Diagrams ◽  
Service Oriented ◽  
Design Documents ◽  
Automatic Tool

Enormous development has been experiences in the field of text and image extraction and classification. This is due to large amount of image data that is generated as a result of document sharing for collaborative software development and electronic storage of design documents. One of the recent technique for analyzing large dataset and discover underlying patterns is Deep learning technique. Deep learning is a branch of Machine learning inspired by human brain functionality for the purpose of analyzing unstructured data including images, sound and text. Unified Model Language (UML) is an architectural design which provides developers with a view of software components and scope. UML contain texts and notations which are mostly analyzed and interpreted manually for the purpose of system implementation and scope or size measurement. Consequently, manual processing of electronic design artifacts is prone to bias, errors and time consuming. Various researchers have attempted to automate the process of reading and interpreting design artifacts but still there is a challenge due to varying style of designing these artifacts. This study propose an automatic tool based on existing deep learning algorithms including ResNet50 CNN to read UML interface and sequence diagrams images to detect UML arrows, EAST test detector to detect text, Tesseract OCR with Long Short-Term Memory (LSTM) to recognize text and Multi-class Support Vector Machine to classify text for the purpose of measuring Service Oriented Architecture size. We subjected the tool to accuracy tests which returned encouraging results.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector
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