Ensemble-Based Machine Learning with PSO and GA for Ship Coating Detection Using Portable Vis/NIR
Keywords:
Classification, Ensemble model, Feature Selection, Non-destructive, Ship Coating, Vis/NIR spectroscopyAbstract
This study presents a method for detecting ship coating quality using a portable Vis/NIR spectroscopy system combined with machine learning. To improve accuracy, we integrated spectral transformation (Nippy), feature selection methods (PSO and GA), and ensemble learning models. The experiments involved four coating quality levels, producing 148 spectral samples. Results show that the proposed approach consistently outperforms single baseline models and traditional feature selection methods such as PCA and IFS. The best performance was achieved by combining Nippy with PSO, where the LDA algorithm reached 99.33% accuracy, while GA also showed strong results with both single and ensemble models. We also examined ensemble results at different stages of preprocessing and feature selection, showing that the ensemble maintained stable performance throughout the process. These findings demonstrate that the integration of spectral transformation and metaheuristic feature selection can enhance model robustness, providing more reliable and accurate coating quality detection for maritime applications.
References
H. S. Sultan Aljibori, A. A. H. Al-Amiery, and A. A. H. Kadhum, “Advances in corrosion protection coatings: A comprehensive review,” Int. J. Corros. Scale Inhib., vol. 12, no. 4, 2023, https://doi.org/10.17675/2305-6894-2023-12-4-6.
W. Pei, X. Pei, Z. Xie, and J. Wang, “Research progress of marine anti-corrosion and wear-resistant coating,” Tribol. Int., vol. 198, p. 109864, 2024, https://doi.org/10.1016/j.triboint.2024.109864.
L. Tremps, B. Yeter, and A. Kolios, “Review and analysis of the failure risk mitigation via monitoring for monopile offshore wind structures,” Energy Reports, vol. 11, pp. 5407-5420, 2024, https://doi.org/10.1016/j.egyr.2024.05.026.
Y. A. Ahmed, G. Theotokatos, I. Maslov, L. A. L. Wennersberg, and D. A. Nesheim, “Regulatory and legal frameworks recommendations for short sea shipping maritime autonomous surface ships,” Mar. Policy, vol. 166, p. 106226, 2024, https://doi.org/10.1016/j.marpol.2024.106226.
A. W. Momber, D. Langenkämper, T. Möller, and T. W. Nattkemper, “The exploration and annotation of large amounts of visual inspection data for protective coating systems on stationary marine steel structures,” Ocean Eng., vol. 278, p. 114337, 2023, https://doi.org/10.1016/j.oceaneng.2023.114337.
Z. Zhen-yu, Z. Qiu-yang, D. Cong, Y. Ju-yu, P. Guang-jian, and P. Zhong-yu, “Research on the promotion mechanism of surface burnishing process by two-dimensional ultrasonic vibration,” J. Mater. Res. Technol., vol. 13, pp. 1068-1082, 2021, https://doi.org/10.1016/j.jmrt.2021.05.038.
I. Stojanović, A. Farkas, V. Alar, and N. Degiuli, “Evaluation of the corrosion protection of two underwater coating systems in a simulated marine environment,” JOM, vol. 71, no. 12, pp. 4330-4338, 2019, https://doi.org/10.1007/s11837-019-03669-4.
N. Hütten, M. Alves Gomes, F. Hölken, K. Andricevic, R. Meyes, and T. Meisen, “Deep learning for automated visual inspection in manufacturing and maintenance: A survey of open- access papers,” Appl. Syst. Innov., vol. 7, no. 1, p. 11, 2024, https://doi.org/10.3390/asi7010011.
Z. Li, J. Wang, D. Xiao, Z. Gu, and H. Xie, “Iron ore rock classification and mine remote sensing inversion based on spectroscopy and improved extreme learning machine,” Infrared Phys. Technol., vol. 140, p. 105400, 2024, https://doi.org/10.1016/j.infrared.2024.105400.
R. Visnupriyan, B. M. Flanagan, K. J. Harper, and D. Cozzolino, “Near infrared spectroscopy combined with chemometrics as tool to monitor starch hydrolysis,” Carbohydr. Polym., vol. 324, p. 121469, 2024, https://doi.org/10.1016/j.carbpol.2023.121469.
M. Ceroni, F. S. Gobber, and M. Actis Grande, “Ultraviolet-Visible-Near InfraRed spectroscopy for assessing metal powder cross-contamination: A multivariate approach for a quantitative analysis,” Mater., vol. 242, p. 113023, 2024, https://doi.org/10.1016/j.matdes.2024.113023.
S. Kishigami, Y. Mizokami, T. Sakata, Y. Ogawa, D. Shiozawa, and T. Sakagami, “Detection of heavy-duty anticorrosion coating deterioration using near-infrared spectral characteristics,” NDT E Int., vol. 132, p. 102708, 2022, https://doi.org/10.1016/j.ndteint.2022.102708.
C. R. Petersen et al., “Non-destructive subsurface inspection of marine and protective coatings using near- and mid-infrared optical coherence tomography,” Coatings, vol. 11, no. 8, p. 877, 2021, https://doi.org/10.3390/coatings11080877.
E. Konadu-Yiadom, E. Bontrager, and A. Staerz, “The usefulness of infrared spectroscopy for elucidating the degradation mechanism of metal industrial heritage coatings,” Surfaces, vol. 7, no. 4, pp. 846-863, 2024, https://doi.org/10.3390/surfaces7040056.
A. Khumaidi, Y. A. Purwanto, H. Sukoco, and S. H. Wijaya, “Using fuzzy logic to increase accuracy in mango maturity index classification: Approach for developing a portable near-infrared spectroscopy device,” Sensors, vol. 22, no. 24, p. 9704, 2022, https://doi.org/10.3390/s22249704.
P. Koziol, K. Kosowska, P. Korecki, and T. P. Wrobel, “Scattering correction for samples with cylindrical domains measured with polarized infrared spectroscopy,” Anal. Chim. Acta, vol. 1278, p. 341722, 2023, https://doi.org/10.1016/j.aca.2023.341722.
S. Chakraborty, I. Patra, A. Pal, K. Mondal, and M. Pradhan, “Development of a semi-supervised machine learning based noise filter for quantum cascade laser-coupled mid-infrared spectrometer,” Infrared Phys. Technol., vol. 141, p. 105452, 2024, https://doi.org/10.1016/j.infrared.2024.105452.
W. Zhang, L. C. Kasun, Q. J. Wang, Y. Zheng, and Z. Lin, “A review of machine learning for near-infrared spectroscopy,” Sensors, vol. 22, no. 24, p. 9764, 2022, https://doi.org/10.3390/s22249764.
J. Torniainen, I. O. Afara, M. Prakash, J. K. Sarin, L. Stenroth, and J. Töyräs, “Open-source python module for automated preprocessing of near infrared spectroscopic data,” Anal. Chim. Acta, vol. 1108, pp. 1-9, 2020, https://doi.org/10.1016/j.aca.2020.02.030.
A. Khumaidi and R. Raafi'udin, “Effects of oversampling smote and spectral transformations in the classification of mango cultivars using near-infrared spectroscopy,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 3, p. 1047, 2022, https://doi.org/10.18517/ijaseit.12.3.16001.
S. Ren and Y. Jia, “Near-Infrared data classification at phone terminal based on the combination of PCA and CS-RBFSVC algorithms,” Spectrochim. Acta Part A Mol. Biomol. Spectrosc., vol. 287, p. 122080, 2023, https://doi.org/10.1016/j.saa.2022.122080.
R. Raafi'udin, Y. A. Purwanto, I. S. Sitanggang, and D. A. Astuti, “Feature selection model development on near-infrared spectroscopy data case study of beef freshness quality prediction,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 1, pp. 645-653, 2024. https://doi.org/10.14569/IJACSA.2024.0150163.
S. Zhang, Y. Yin, C. Liu, J. Li, X. Sun, and J. Wu, “Discrimination of wheat flour grade based on PSO-SVM of hyperspectral technique,” Spectrochim. Acta Part A Mol. Biomol. Spectrosc., vol. 302, p. 123050, 2023, https://doi.org/10.1016/j.saa.2023.123050.
Y. Qin, K. Song, N. Zhang, M. Wang, M. Zhang, and B. Peng, “Robust NIR quantitative model using MIC-SPA variable selection and GA-ELM,” Infrared Phys. Technol., vol. 128, p. 104534, 2023, https://doi.org/10.1016/j.infrared.2022.104534.
A. Khumaidi, R. Raafi'udin, and N. S. Triastuti, “Enhancing ship coating quality detection via machine learning-optimized visible near-infrared spectroscopy,” Instrum. Mes. Métrologie, vol. 23, no. 6, pp. 441-450, 2024, https://doi.org/10.18280/i2m.230604.
H. Jiang, S. Zhang, Z. Yang, L. Zhao, Y. Zhou, and D. Zhou, “Quality classification of stored wheat based on evidence reasoning rule and stacking ensemble learning,” Comput. Electron. Agric., vol. 214, p. 108339, 2023, https://doi.org/10.1016/j.compag.2023.108339.
A. K. Biswas, R. Seethalakshmi, P. Mariappan, and D. Bhattacharjee, “An ensemble learning model for predicting the intention to quit among employees using classification algorithms,” Decis. Anal. J., vol. 9, p. 100335, 2023, https://doi.org/10.1016/j.dajour.2023.100335.
D. H. Parekh and V. Dahiya, “Early detection of breast cancer using machine learning and ensemble techniques,” Int. J. Comput., pp. 231-237, 2023, https://doi.org/10.47839/ijc.22.2.3093.
B. K. Indonesia., “Guidelines for condition assessment program,” vol. 11 Guidel. Cond. Assess. Progr. 2015 Ed., 2015.
SparkFun, “AS7265x Datasheet,” AMS Datasheet, vol. 1, pp. 1-63, 2018.
J. Mankar, C. Darode, K. Trivedi, M. Kanoje, and P. Shahare, “Review of I2C protocol,” Int. J. Res. Advent Technol., vol. 2, no. 1, pp. 2321-9637, 2014.
C. Rajathi and P. Rukmani, “Hybrid learning model for intrusion detection system: A combination of parametric and non-parametric classifiers,” Alexandria Eng. J., vol. 112, pp. 384-396, 2025, https://doi.org/10.1016/j.aej.2024.10.101.
M. T R, V. K. V, D. K. V, O. Geman, M. Margala, and M. Guduri, “The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification,” Healthc. Anal., vol. 4, p. 100247, 2023, https://doi.org/10.1016/j.health.2023.100247.
L. A. C. Ribeiro, G. L. Menezes, T. Bresolin, S. I. A. Apelo, and J. R. R. Dórea, “Near-infrared spectroscopy analysis to predict urinary allantoin in dairy cows,” JDS Commun., vol. 6, pp. 212-216, 2024, https://doi.org/10.3168/jdsc.2024-0641.
W. Yang, Y. Xiong, Z. Xu, L. Li, and Y. Du, “Piecewise preprocessing of near-infrared spectra for improving prediction ability of a PLS model,” Infrared Phys. Technol., vol. 126, p. 104359, 2022, https://doi.org/10.1016/j.infrared.2022.104359.
J. Scheffel and K. Lindvall, “Temporal smoothing – A step forward for time-spectral methods,” Comput. Phys. Commun., vol. 270, p. 108173, 2022, https://doi.org/10.1016/j.cpc.2021.108173.
X. Chen and D. Wang, “Baseline correction of near-fault ground motion records based on the Hilbert spectral analysis,” Soil Dyn. Earthq. Eng., vol. 154, p. 107162, 2022, https://doi.org/10.1016/j.soildyn.2022.107162.
Y. Wang and X.-D. Zhang, “On weighted spectral radius of unraveled balls and normalized Laplacian eigenvalues,” Discrete Math., vol. 345, no. 12, p. 113173, 2022, https://doi.org/10.1016/j.disc.2022.113173.
N. McArdle, S. Cournane, J. McCavana, J. Lucey, and L. León Vintró, “Development of a scatter correction technique for planar 99mTc-MAA imaging to improve accuracy in lung shunt fraction estimation,” Phys. Medica, vol. 99, pp. 94-101, 2022, https://doi.org/10.1016/j.ejmp.2022.05.017.
M. Muljono and N. Adhipurna, “A weighted majority voting ensemble model for disease prediction boosted by PSO: The case of type 2 diabetes,” Int. J. Comput., vol. 22, issue 4, pp. 475-484, 2023, https://doi.org/10.47839/ijc.22.4.3354.
C. Shambare, Y. Sun, and O. Imoru, “An improved QPSK based on a hybrid genetic algorithm for efficient OFDM transmission,” Int. J. Comput., vol. 23, issue 4, pp. 517-525, 2025, https://doi.org/10.47839/ijc.23.4.3751.
N. Singh et al., “Integrating NIR spectroscopy with machine learning and heuristic algorithm-assisted wavelength selection algorithms for protein content prediction in rice bean (Vigna umbellata L.),” Food Humanit., vol. 3, p. 100399, 2024, https://doi.org/10.1016/j.foohum.2024.100399.
K. Zhang et al., “Optimization of visible transmission and NIR reflection in multilayer via PSO: Simulation and experimental validation,” Renew. Energy, vol. 237, p. 121913, 2024, https://doi.org/10.1016/j.renene.2024.121913.
B. Ferreiro, R. Leardi, E. Farinini, and J. M. Andrade, “Supervised classification combined with genetic algorithm variable selection for a fast identification of polymeric microdebris using infrared reflectance,” Mar. Pollut. Bull., vol. 195, p. 115540, 2023, https://doi.org/10.1016/j.marpolbul.2023.115540.
X. Hao, Z. Chen, S. Yi, and J. Liu, “Application of improved Stacking ensemble learning in NIR spectral modeling of corn seed germination rate,” Chemom. Intell. Lab. Syst., vol. 243, p. 105020, 2023, ttps://doi.org/10.1016/j.chemolab.2023.105020.
A. M. Peco Chacón, I. Segovia Ramírez, and F. P. García Márquez, “K-nearest neighbour and K-fold cross-validation used in wind turbines for false alarm detection,” Sustain. Futur., vol. 6, p. 100132, 2023, https://doi.org/10.1016/j.sftr.2023.100132.
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
