Improved Motion Planning Algorithms Based on Rapidly-exploring Random Tree: A Review

Anak Agung Putri Ratna, Prima Dewi Purnamasari, Nadhifa Khalisha Anandra, Dyah Lalita Luhurkinanti

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This paper discusses the development of an Automatic Essay Grading System (SIMPLE-O) designed using hybrid CNN and Bidirectional LSTM and Manhattan Distance for Japanese language course essay grading. The most stable and best model is trained using hyperparameters with kernel sizes of 5, filters or CNN outputs of 64, a pool size of 4, Bidirectional LSTM units of 50, and a batch size of 64. The deep learning model is trained using the Adam optimizer with a learning rate of 0.001, an epoch of 25, and using an L1 regularization of 0.01. The average error obtained is 29%.

Original languageEnglish
Title of host publicationICCIP 2022 - 2022 8th International Conference on Communication and Information Processing
PublisherAssociation for Computing Machinery
Pages22-27
Number of pages6
ISBN (Electronic)9781450397100
DOIs
Publication statusPublished - 3 Nov 2022
Event8th International Conference on Communication and Information Processing, ICCIP 2022 - Virtual, Online, China
Duration: 27 Oct 202229 Oct 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference8th International Conference on Communication and Information Processing, ICCIP 2022
Country/TerritoryChina
CityVirtual, Online
Period27/10/2229/10/22

Keywords

  • Automated Short Answer Grading
  • BiLSTM
  • CNN

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