Abstract
The need for optoelectronic devices is growing right now, but the production of these devices is having a difficult time keeping up with the advancement of the machinery, instruments, and manufacturing techniques they support. Pictures from big, unlabeled image collections are commonly retrieved using a technique called content-based image retrieval (CBIR). The availability of photographs is also growing as internet growth and transmission networks increase. This study suggests brand-new image retrieval methods for multispectral images used in Optoelectronic device monitoring that are based on image segmentation and classification methods with deep learningtechniques for failure management. Optoelectronic device Monitoring field-based multispectral images were used as the input, which was then processed for noise removal, resizing, and smoothing.Fuzzy c-means clustering-based image segmentation was used to divide up this processed image into its component parts. Following that, a hybrid multilayer transfer learning perception was used to classify the clustered segmented picture. The proposed technique has an accuracy of 95%, precision of 85%, recall of 75%, F-1 score of 63%, error rate of 51%, MAP of 55%; existing MRCN accuracy of 85%, precision of 75%, recall of 63%, F-1 score of 55%, error rate of 45%, MAP of 51%, OC-LBP accuracy of 89%, the precision of 79%, recall of 71%, F-1 score of 59%, error rate of 49%, MAP of 53%.
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References
Alla, H., Moumoun, L., Balouki, Y.: A multilayer perceptron neural network with selective-data training for flight arrival delay prediction. Sci. Programm. (2021)
Ding, C., Wang, M., Zhou, Z., Huang, T., Wang, X., Li, J.: Siamese transformer network-based similarity metric learning for cross-source remote sensing image retrieval. Neural Comput. Appl. 35(11), 8125–8142 (2023)
Elmahgary, M.G., Mahran, A.M., Ganoub, M., Abdellatif, S.O.: Optical investigation and computational modelling of BaTiO3 for optoelectronic devices applications. Sci. Rep. 13(1), 4761 (2023)
Feng, J., Yu, Y., Zhou, Z.H.: Multi-layered gradient boosting decision trees. arXiv preprint arXiv:1806.00007 (2018)
Gao, W., Yu, C., Chen, R.: Artificial intelligence accelerators based on graphene optoelectronic devices. Adv. Photon. Res. 2(6), 2100048 (2021)
Han, X.:. Design of general test system for optoelectronic equipment based on computer. In: Journal of Physics: Conference Series, vol. 2143, no. 1, p. 012020. IOP Publishing (2021, December).
Jeong, M., Joung, J.F., Hwang, J., Han, M., Koh, C.W., Choi, D.H., Park, S.: Deep learning for development of organic optoelectronic devices: efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs. NPJ Comput. Mater. 8(1), 147 (2022)
Li, X., Hoffman, J.M., Kanatzidis, M.G.: The 2D halide perovskite rulebook: how the spacer influences everything from the structure to optoelectronic device efficiency. Chem. Rev. 121(4), 2230–2291 (2021)
Mayr, F., Harth, M., Kouroudis, I., Rinderle, M., Gagliardi, A.: Machine learning and optoelectronic materials discovery: a growing synergy. J. Phys. Chem. Lett. 13(8), 1940–1951 (2022)
Meenakshi, A., Janani, A.P., Devi Mahalakshmi, S., Vanitha Sivagami, S.: A novel image recognition using fuzzy c-means and content-based fabric image retrieval. Imaging Sci. J. 1–13 (2023)
Mohammadpourfard, M., Weng, Y., Pechenizkiy, M., Tajdinian, M., Mohammadi-Ivatloo, B.: Ensuring cybersecurity of smart grid against data integrity attacks under concept drift. Int. J. Electr. Power Energy Syst. 119, 105947 (2020)
Padhy, R., Samantaray, L., Dash, S.K., Mishra, J.: Classification of high-resolution satellite image with content based image retrieval and local binary pattern. In: International Conference on Innovations in Intelligent Computing and Communications, pp. 409–416. Springer, Cham (2022, December)
Popescu, M.C., Balas, V.E., Perescu-Popescu, L., Mastorakis, N.: Multilayer perceptron and neural networks. WSEAS Trans. Circuits Syst. 8(7), 579–588 (2009)
Sathiyaprasad, B., & Kumar, B.S.: Multispectral image retrieval in remote sensing big data using fast recurrent convolutional neural network. In: 2022 International Conference for Advancement in Technology (ICONAT), pp. 1–7. IEEE (2022, January)
Shi, J., Zhang, J., Yang, L., Qu, M., Qi, D.C., Zhang, K.H.: Wide bandgap oxide semiconductors: from materials physics to optoelectronic devices. Adv. Mater. 33(50), 2006230 (2021)
Subramanian, M., Lingamuthu, V., Venkatesan, C., Perumal, S.: Content-based image retrieval using colour, grey, advanced texture, shape features, and random forest classifier with optimized particle swarm optimization. Int. J. Biomed. Imaging (2022)
Sumbul, G., Xiang, J., Demir, B.: Towards simultaneous image compression and indexing for scalable content-based retrieval in remote sensing. IEEE Trans. Geosci. Remote Sens. 60, 1–12 (2022)
Vharkate, M.N., Musande, V.B.: Fusion-based feature extraction and optimal feature selection in remote sensing image retrieval. Multimed. Tools Appl. 1–28 (2022)
Vharkate, M.N., Musande, V.B.: Remote sensing image retrieval using a hybrid visual geometry group network with relevance feedback. Int. J. Remote Sens. 42(14), 5540–5567 (2021)
Zaboon, K.H.: Determination of multi-modal dimension metric learning with application to web image retrieval. Iraqi J. Intell. Comput. Inform. (IJICI) 1(1), 34–40 (2022)
Zhang, C., Wei, G., Xie, M., Liu, P., Cao, Y., Lian, X., Huang, W., Liu, K.: Research on intelligent health management technology of opto-electronic equipment. In: 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), vol. 6, pp. 1961–1967. IEEE (2022, March)
Acknowledgements
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha, Kingdom of Saudi Arabia for funding this work through Large Groups RGP.2/170/1444.
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The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha, Kingdom of Saudi Arabia for funding this work through Large Groups RGP.2/170/1444.
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RB: Conceived and design the analysis. BA: Writing—Original draft preparation. NBT: Collecting the Data, TMS: Contributed data and analysis stools HE: Performed and analysis, Wrote the Paper SQ Editing and Figure Design.
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Bhukya, R., Arunsundar, B., Tatini, N.B. et al. Optoelectronic device based failure management using content based multispectral image retrieval and deep learning model. Opt Quant Electron 56, 239 (2024). https://doi.org/10.1007/s11082-023-05793-7
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DOI: https://doi.org/10.1007/s11082-023-05793-7