Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/67646
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRaslapat Suteecaen_US
dc.contributor.authorPasit Chalernkhawnen_US
dc.contributor.authorKhawsroung Pakdeeen_US
dc.date.accessioned2020-04-02T14:58:47Z-
dc.date.available2020-04-02T14:58:47Z-
dc.date.issued2019-12-01en_US
dc.identifier.other2-s2.0-85082394602en_US
dc.identifier.other10.1109/TIMES-iCON47539.2019.9024510en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85082394602&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/67646-
dc.description.abstract© 2019 IEEE. The purpose of this research aims the development of a succulent image searching system. Based on deep learning technique provides information about succulent such as name, scientific name, family, characteristic, nursery, and breed by image searching and measuring the accuracy of models for predicting data in this development using web application to facilities for succulent image searching system. The Development of a succulent image searching system Based on deep learning technique and using Convolutional Neural Network (CNN) to create a model for a succulent image prediction. With adapted waterfall model of the software development Life Cycle (SDLC) to develop a succulent image searching system that has the efficacy of data and image prediction. The results from the independent study are predicting the succulent image searching system with more than 75% accuracy and meet the requirements of the system in all respects.en_US
dc.subjectBusiness, Management and Accountingen_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.titleDevelopment of Succulent Species Prediction System by Deep Learning Techniqueen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleTIMES-iCON 2019 - 2019 4th Technology Innovation Management and Engineering Science International Conferenceen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.


Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.