Investigation of deep learning optimizer for water pipe leaking detection

Simple item page
dc.contributor.authorArunsuriyasak, Peerachai
dc.contributor.authorBoonme, Phattraporn
dc.contributor.authorPhasukkit, Pattarapong
dc.date.accessioned2026-08-06T10:24:59Z
dc.date.available2026-08-06T10:24:59Z
dc.date.issued2019-07-01
dc.description.abstractNowadays, Deep learning plays an important role in complex problems. Thus, one of important algorithm part is an optimizer. This paper aims to improve algorithm using optimizers. Adam optimizer, a powerful and effective optimizer, was used to adjust parameters in Deep Neural Networks model. Which, object datasets consist leaking water pipe, non-leaking water pipe are used to classify 2 object labels. Nevertheless, RMSprop and Adadelta are alternative optimizers that can be used in Deep Neural Network. Other than that, this experiment has been shown Adam gave an accuracy at 98.973% for leaking water pipe and 97.466% for non-leaking water pipe. While, Adadelta gave 76.755% and 70.448%. And RMSprop gave 98.973% and 97.466%.
dc.identifier.citationProceedings of the 16th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2019, 85-88, 2019
dc.identifier.doi10.1109/ECTI-CON47248.2019.8955355
dc.identifier.other2-s2.0-85078838383
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/9969
dc.sourceProceedings of the 16th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2019
dc.subjectAdadelta
dc.subjectAdam
dc.subjectDeep learning
dc.subjectNeural networks
dc.subjectOptimizer
dc.subjectRmsprop
dc.subjectWater pipe leaking
dc.titleInvestigation of deep learning optimizer for water pipe leaking detection
dc.typeConference Paper

Files

Collections

Research