research-article

Feature Selection for Automated Grading of Edible Birds Nest with ANFIS

Authors:

Mei Yuan Koay,

Selina Xin Ci Loh,

Kam Meng Goh,

Weng Kin LaiAuthors Info & Claims

ICBBT '18: Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology

Pages 25 - 32

https://doi.org/10.1145/3232059.3232075

Published: 16 May 2018 Publication History

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Abstract

Edible bird nest (EBN) is an expensive animal bio-product produced by swiftlets which is beneficial to humans. The conventional way of grading the EBN is carried out manually by human experts. However this approach is time consuming, cost ineffective with inconsistencies occurring due to human fatigue. This paper proposes a method of using machine vision to automatically grade EBN with a novel set of features. Classification of the EBN has been with Adaptive Neuro Fuzzy Inference (ANFIS) and the results were compared with those obtained from the very popular k-Nearest Neighbour (kNN). An accuracy of nearly 90% is achieved on the datasets used with our proposed method.

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Shuyuti NSalami EDahari MArof HRamiah H(2024)Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A SurveyIEEE Access10.1109/ACCESS.2024.335185812(31498-31514)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3351858
Nge BTan CWong YChiong CChong CLim MLai W(2023)Preprocessing Variations for Classification in Smart ManufacturingProceedings of the 5th ACM International Conference on Multimedia in Asia Workshops10.1145/3611380.3629545(1-7)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3611380.3629545
Goh KLim LKrishnamoorthy SLai WMaul TChaw J(2023)Recent advancement of intelligent-systems in edible birds nest: A review from production to processingMultimedia Tools and Applications10.1007/s11042-023-17490-483:17(51159-51209)Online publication date: 9-Nov-2023
https://doi.org/10.1007/s11042-023-17490-4
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Index Terms

Feature Selection for Automated Grading of Edible Birds Nest with ANFIS
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition

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ICBBT '18: Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology

May 2018

93 pages

ISBN:9781450363662

DOI:10.1145/3232059

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 16 May 2018

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ICBBT '18: 2018 10th International Conference on Bioinformatics and Biomedical Technology

May 16 - 18, 2018

Amsterdam, Netherlands

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Shuyuti NSalami EDahari MArof HRamiah H(2024)Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A SurveyIEEE Access10.1109/ACCESS.2024.335185812(31498-31514)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3351858
Nge BTan CWong YChiong CChong CLim MLai W(2023)Preprocessing Variations for Classification in Smart ManufacturingProceedings of the 5th ACM International Conference on Multimedia in Asia Workshops10.1145/3611380.3629545(1-7)Online publication date: 6-Dec-2023
https://dl.acm.org/doi/10.1145/3611380.3629545
Goh KLim LKrishnamoorthy SLai WMaul TChaw J(2023)Recent advancement of intelligent-systems in edible birds nest: A review from production to processingMultimedia Tools and Applications10.1007/s11042-023-17490-483:17(51159-51209)Online publication date: 9-Nov-2023
https://doi.org/10.1007/s11042-023-17490-4
El Sheikha A(2021)Why the importance of geo-origin tracing of edible bird nests is arising?Food Research International10.1016/j.foodres.2021.110806150(110806)Online publication date: Dec-2021
https://doi.org/10.1016/j.foodres.2021.110806
Lai WGan JKoh P(2019)Artificial Honey Bee Swarm Intelligence for the Autograding of EBNAdvances in Natural Computation, Fuzzy Systems and Knowledge Discovery10.1007/978-3-030-32456-8_51(472-480)Online publication date: 7-Nov-2019
https://doi.org/10.1007/978-3-030-32456-8_51

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Time series prediction using ensembles of ANFIS models with genetic optimization of interval type-2 and type-1 fuzzy integrators

Application of the fuzzy Failure Mode and Effect Analysis methodology to edible bird nest processing

Optimization of Interval Type-2 and Type-1 Fuzzy Integrators in Ensembles of ANFIS Models with Genetic Algorithms

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