Abstract
Feature selection is critical in fields like data mining and pattern classification, as it eliminates irrelevant data and enhances the quality of highly dimensional datasets. This study explores the effectiveness of the Q-learning embedded sine cosine algorithm (QLESCA) for feature selection in industrial casting defect detection using the VGG19 model. QLESCA’s performance is compared to other optimization algorithms, with experimental results showing that QLESCA outperforms the other algorithms in terms of classification metrics. The best accuracy achieved by QLESCA is 97.0359%, with an average fitness value of − 0.99124. The proposed method provides a promising approach to improve the accuracy and reliability of industrial casting defect detection systems, which is essential for product quality and safety. Our findings suggest that using powerful optimization algorithms like QLESCA is crucial for obtaining the best subsets of information in feature selection and achieving optimal performance in classification tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Du W, Shen H, Fu J, Zhang G, He Q (2019) Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning. NDT Eng Int 107:102144. https://doi.org/10.1016/j.ndteint.2019.102144
Wang Y, Hu C, Chen K, Yin Z (2020) Self-attention guided model for defect detection of aluminium alloy casting on X-ray image. Comput Electr Eng 88:106821. https://doi.org/10.1016/j.compeleceng.2020.106821
Pastor-López I, Sanz B, Tellaeche A, Psaila G, de la Puerta JG, Bringas PG (2021) Quality assessment methodology based on machine learning with small datasets: industrial castings defects. Neurocomputing 456:622–628. https://doi.org/10.1016/j.neucom.2020.08.094
Wu B et al (2021) An ameliorated deep dense convolutional neural network for accurate recognition of casting defects in X-ray images. Knowl Based Syst 226:107096. https://doi.org/10.1016/j.knosys.2021.107096
Ji X et al (2021) Filtered selective search and evenly distributed convolutional neural networks for casting defects recognition. J Mater Process Technol 292:117064. https://doi.org/10.1016/j.jmatprotec.2021.117064
Jiang L, Wang Y, Tang Z, Miao Y, Chen S (2021) Casting defect detection in X-ray images using convolutional neural networks and attention-guided data augmentation. Measurement 170:108736. https://doi.org/10.1016/j.measurement.2020.108736
Han H, Gao C, Zhao Y, Liao S, Tang L, Li X (2020) Polycrystalline silicon wafer defect segmentation based on deep convolutional neural networks. Pattern Recognit Lett 130:234–241. https://doi.org/10.1016/j.patrec.2018.12.013
Bacanin N et al (2023) Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection. Heliyon 9(4):e15378. https://doi.org/10.1016/j.heliyon.2023.e15378
Zhong C, Li G, Meng Z, Li H, He W (2023) A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection. Comput Biol Med 153:106520. https://doi.org/10.1016/j.compbiomed.2022.106520
Houssein EH, Oliva D, Çelik E, Emam MM, Ghoniem RM (2023) Boosted sooty tern optimization algorithm for global optimization and feature selection. Exp Syst Appl 213:119015. https://doi.org/10.1016/j.eswa.2022.119015
Hamad QS, Samma H, Suandi SA (2023) Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study. Appl Intell 6:1–23. https://doi.org/10.1007/s10489-022-04446-8
Hamad QS, Samma H, Suandi SA, Mohamad-Saleh J (2022) Q-learning embedded sine cosine algorithm (QLESCA). Exp Syst Appl 193:116417. https://doi.org/10.1016/j.eswa.2021.116417
Dabhi R (2020) Casting product image data for quality inspection. https://www.kaggle.com/ravirajsinh45/real-life-industrial-dataset-of-casting-product
Mirjalili S (2016) SCA: a Sine Cosine Algorithm for solving optimization problems. Knowl Based Syst 96:120–133. https://doi.org/10.1016/j.knosys.2015.12.022
Hamad QS, Samma H, Suandi SA, Saleh JM (2022) A comparative study of sine cosine optimizer and its variants for engineering design problems, pp 1083–1089. https://doi.org/10.1007/978-981-16-8129-5_166
Abualigah L, Diabat A, Mirjalili S, Abd-Elaziz M, Gandomi AH (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609. https://doi.org/10.1016/j.cma.2020.113609
Acknowledgements
We extend our sincere appreciation to the Malaysia Ministry of Higher Education (MOHE) for their invaluable support through the Fundamental Research Grant Scheme (FRGS), under grant no. FRGS/1/2019/ICT02/USM/03/3.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hamad, Q.S., Saleh, S.A.M., Suandi, S.A., Samma, H., Hamad, Y.S., Al Amoudi, I. (2024). Optimizing Feature Selection for Industrial Casting Defect Detection Using QLESCA Optimizer. In: Ahmad, N.S., Mohamad-Saleh, J., Teh, J. (eds) Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. RoViSP 2021. Lecture Notes in Electrical Engineering, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-99-9005-4_61
Download citation
DOI: https://doi.org/10.1007/978-981-99-9005-4_61
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9004-7
Online ISBN: 978-981-99-9005-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)