References:

Ackerman, S., Kour, G., & Farchi, E. (2023). Characterizing how’distributional’nlp corpora distance
metrics are. arXiv preprint arXiv:2310.14829.
Adamson, T., Mahmud, M., Wu, Y., Lin, N., Diao, F., Zhao, Y., & Balda, J. C. (2020). An 800-v
high-density traction inverter—electro-thermal characterization and low-inductance pcb bussing design. IEEE Journal of Emerging and Selected Topics in Power Electronics, 10(3),
3013–3023.
Adibhatla, V. A., Chih, H.-C., Hsu, C.-C., Cheng, J., Abbod, M. F., & Shieh, J.-S. (2021). Applying
deep learning to defect detection in printed circuit boards via a newest model of you-onlylook-once.
Ahsan, M. M., Mahmud, M. P., Saha, P. K., Gupta, K. D., & Siddique, Z. (2021). Effect of data
scaling methods on machine learning algorithms and model performance. Technologies, 9(3),
52.
Albee, A. J. (2013). The evolution of ict: Pcb technologies, test philosophies, and manufacturing business models are driving in-circuit test evolution and innovations. In Ipc apex expo
conference and exhibition (Vol. 1, pp. 381–401).
Ana-Maria, B., Georgiana, H. C., & Ioan, L. (2018). Stand for the functional testing automation of
the electronic modules. In 2018 international symposium on electronics and telecommunications (isetc) (pp. 1–4).
Anoop, K., Sarath, N., & Kumar, V. (2015). A review of pcb defect detection using image processing. Intern. Journal of Engineering and Innovative Technology (IJEIT), 4(11), 188–192.
Arik, S. O., & Pfster, T. (2021). Tabnet: Attentive interpretable tabular learning. In ¨ Proceedings of the aaai conference on artifcial intelligence (Vol. 35, pp. 6679–6687).
Bai, L., & Kalaj, D. (2021). Approximation of kolmogorov–smirnov test statistic. Stochastics,
93(7), 993–1027.
Bokaba, T., Doorsamy, W., & Paul, B. S. (2022). A comparative study of ensemble models for
predicting road traffc congestion. Applied Sciences, 12(3), 1337.
Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123–140.
Breiman, L. (2017). Classifcation and regression trees. Routledge.
Bullejos, M., Cabezas, D., Mart´ın-Mart´ın, M., & Alcala, F. J. (2022). A k-nearest neighbors ´
algorithm in python for visualizing the 3d stratigraphic architecture of the llobregat river
delta in ne spain. Journal of Marine Science and Engineering, 10(7), 986.
Cao, X. H., Stojkovic, I., & Obradovic, Z. (2016). A robust data scaling algorithm to improve
classifcation accuracies in biomedical data. BMC bioinformatics, 17, 1–10.
Chakraborty, M., Kettle, J., & Dahiya, R. (2022). Electronic waste reduction through devices and
printed circuit boards designed for circularity. IEEE Journal on Flexible Electronics, 1(1),
4–23.
Chaudhary, V., Dave, I. R., & Upla, K. P. (2017). Automatic visual inspection of printed circuit
board for defect detection and classifcation. In 2017 international conference on wireless
communications, signal processing and networking (wispnet) (pp. 732–737).
Chauhan, A. P. S., & Bhardwaj, S. C. (2011). Detection of bare pcb defects by image subtraction
method using machine vision. In Proceedings of the world congress on engineering (Vol. 2,
pp. 6–8).
Chen, I.-C., Hwang, R.-C., & Huang, H.-C. (2023). Pcb defect detection based on deep learning
algorithm. Processes, 11(3), 775.
Chen, J., Zhang, Z., & Wu, F. (2021). A data-driven method for enhancing the image-based
automatic inspection of ic wire bonding defects. International journal of production research,
59(16), 4779–4793.
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of
the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp.
785–794).
Chen, X., Tao, L., Shang, J., & Liu, D. (2022). Application research of ultrasonic testing in
microwave multilayer pcb. In Proceedings of the eighth asia international symposium on
mechatronics (pp. 1935–1943).
Corander, J., Remes, U., & Koski, T. (2021). On the jensen-shannon divergence and the variation
distance for categorical probability distributions. Kybernetika, 57(6), 879–907.
Cui, H., & Anderson, C. G. (2016). Literature review of hydrometallurgical recycling of printed
circuit boards (pcbs). J. Adv. Chem. Eng, 6(1), 142–153.
Cutler, A., Cutler, D. R., & Stevens, J. R. (2012). Random forests. Ensemble machine learning:
Methods and applications, 157–175.
Dai, W., Mujeeb, A., Erdt, M., & Sourin, A. (2018). Towards automatic optical inspection of
soldering defects. In 2018 international conference on cyberworlds (cw) (pp. 375–382).
Dai, W., Mujeeb, A., Erdt, M., & Sourin, A. (2020). Soldering defect detection in automatic optical
inspection. Advanced Engineering Informatics, 43, 101004.
Dervisevi ˇ c, I., Mini ´ c, D., Kamberovi ´ c,´ Z., ˇ Cosovi ´ c, V., & Risti ´ c, M. (2013). Characterization of ´
pcbs from computers and mobile phones, and the proposal of newly developed materials for
substitution of gold, lead and arsenic. Environmental Science and Pollution Research, 20,
4278–4292.
Design mistakes that cause pcb assembly errors. (n.d.). Retrieved from https://
www.protoexpress.com/blog/design-mistakes-that-cause-pcb
-assembly-errors (accessed September 1, 2024)
Eom, G., & Byeon, H. (2023). Searching for optimal oversampling to process imbalanced data:
Generative adversarial networks and synthetic minority over-sampling technique. Mathematics, 11(16), 3605.
Erickson, B. J., & Kitamura, F. (2021). Magician’s corner: 9. performance metrics for machine
learning models (Vol. 3) (No. 3). Radiological Society of North America.
Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., & Smola, A. (2020). Autogluontabular: Robust and accurate automl for structured data. arXiv preprint arXiv:2003.06505.
Farkas, C., Geczy, A., Kov ´ acs, R., & Bony ´ ar, A. (2022). Biodegradable and nanocomposite sub- ´
strates: new prospects for sustainable electronics packaging. IEEE EPS eNews, 9, 1–9.
Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and
an application to boosting. Journal of computer and system sciences, 55(1), 119–139.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of
statistics, 1189–1232.
Gaber, L., Hussein, A. I., & Moness, M. (2021). Fault detection based on deep learning for digital
vlsi circuits. Procedia Computer Science, 194, 122–131.
Gaffet, A., Roa, N. B., Ribot, P., Chanthery, E., & Merle, C. (2022). A hierarchical xgboost early
detection method for quality and productivity improvement of electronics manufacturing systems. In Phm society european conference (Vol. 7).
Galetto, M., Verna, E., Genta, G., & Franceschini, F. (2020). Uncertainty evaluation in the prediction of defects and costs for quality inspection planning in low-volume productions. The
International Journal of Advanced Manufacturing Technology, 108, 3793–3805.
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63,
3–42.
Gore, P., Minami, T., Kundu, P., Lee, J., et al. (2022). A novel methodology for health assessment
in printed circuit boards. In Phm society european conference (Vol. 7, pp. 556–562).
Houdek, C., & Design, C. (2016). Inspection and testing methods for pcbs: An overview. Engineer/OwnerCaltronics Design & Assembly.
Huang, R., Gu, J., Sun, X., Hou, Y., & Uddin, S. (2019). A rapid recognition method for electronic
components based on the improved yolo-v3 network. Electronics, 8(8), 825.
Jemai, J., & Zarrad, A. (2023). Feature selection engineering for credit risk assessment in retail
banking. Information, 14(3), 200.
Jin, J., Feng, W., Lei, Q., Gui, G., Li, X., Deng, Z., & Wang, W. (2021). Defect detection of
printed circuit boards using effcientdet. In 2021 ieee 6th international conference on signal
and image processing (icsip) (pp. 287–293).
Jun, H., & Jung, I. Y. (2023). Enhancement of product-inspection accuracy using convolutional neural network and laplacian flter to automate industrial manufacturing processes. Electronics,
12(18), 3795.
Jurj, S. L., Rotar, R., Opritoiu, F., & Vladutiu, M. (2020). Affordable fying probe-inspired incircuit-tester for printed circuit boards evaluation with application in test engineering education. In 2020 ieee international conference on environment and electrical engineering and
2020 ieee industrial and commercial power systems europe (eeeic/i&cps europe) (pp. 1–6).
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., . . . Liu, T.-Y. (2017). Lightgbm: A
highly effcient gradient boosting decision tree. Advances in neural information processing
systems, 30.
Khalilian, S., Hallaj, Y., Balouchestani, A., Karshenas, H., & Mohammadi, A. (2020). Pcb defect
detection using denoising convolutional autoencoders. In 2020 international conference on
machine vision and image processing (mvip) (pp. 1–5).
Kim, J., Ko, J., Choi, H., & Kim, H. (2021). Printed circuit board defect detection using deep
learning via a skip-connected convolutional autoencoder. Sensors, 21(15), 4968.
Laria, H., Wang, Y., van de Weijer, J., & Raducanu, B. (2022). Transferring unconditional to conditional gans with hyper-modulation. In Proceedings of the ieee/cvf conference on computer
vision and pattern recognition (pp. 3840–3849).
Li, Y.-T., Kuo, P., & Guo, J.-I. (2020). Automatic industry pcb board dip process defect detection with deep ensemble method. In 2020 ieee 29th international symposium on industrial
electronics (isie) (pp. 453–459).
Lin, C.-H., Wang, S.-H., & Lin, C.-J. (2019). Using convolutional neural networks for character
verifcation on integrated circuit components of printed circuit boards. Applied Intelligence,
49(11), 4022–4032.
Ling, Q., & Isa, N. A. M. (2023). Printed circuit board defect detection methods based on image
processing, machine learning and deep learning: A survey. IEEE Access.
Liu, X., & Hsieh, C.-J. (2019). Rob-gan: Generator, discriminator, and adversarial attacker. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition (pp. 11234–
11243).
Luque-Rodriguez, M., Molina-Baena, J., Jimenez-Vilchez, A., & Arauzo-Azofra, A. (2022). Initialization of feature selection search for classifcation. Journal of Artifcial Intelligence Research, 75, 953–983.
Mashohor, S., Evans, J. R., & Erdogan, A. T. (2006). Automatic hybrid genetic algorithm based
printed circuit board inspection. In First nasa/esa conference on adaptive hardware and
systems (ahs’06) (pp. 390–400).
A measurement method that solves problems in coplanarity inspection. (n.d.). Retrieved
from https://www.keyence.com/ss/products/microscope/measurement
-solutions/coplanarity.jsp ([Online])
Mendikowski, M., & Hartwig, M. (2022). Creating customers that never existed: Synthesis of ecommerce data using ctgan. In 18th international conference on machine learning and data
mining (mldm-22). new york, us: Ibai publishing (pp. 91–105).
Mescheder, L., Geiger, A., & Nowozin, S. (2018). Which training methods for gans do actually
converge? In International conference on machine learning (pp. 3481–3490).
Mirzaei, M. (2023). Automating fault detection and quality control in pcbs: A machine learning
approach to handle imbalanced data (Unpublished doctoral dissertation). Concordia University.
Neubauer, C. (1997). Intelligent x-ray inspection for quality control of solder joints. IEEE Transactions on Components, Packaging, and Manufacturing Technology: Part C, 20(2), 111–120.
Neubauer, C., & Hanke, R. (1993). Improving x-ray inspection of printed circuit boards by integration of neural network classifers. In Proceedings of 15th ieee/chmt international electronic
manufacturing technology symposium (pp. 14–18).
Ng, A., et al. (2011). Sparse autoencoder. CS294A Lecture notes, 72(2011), 1–19.
Nguyen, V.-T., & Bui, H.-A. (2022). A real-time defect detection in printed circuit boards applying
deep learning. EUREKA: Physics and Engineering,(2), 143–153.
Nkikabahizi, C., Cheruiyot, W., & Kibe, A. (2022). Chaining zscore and feature scaling methods
to improve neural networks for classifcation. Applied Soft Computing, 123, 108908.
Ong, A. T., Mustapha, A., Ibrahim, Z. B., Ramli, S., & Eong, B. C. (2015). Real-time automatic
inspection system for the classifcation of pcb fux defects. American Journal of Engineering
and Applied Sciences, 8(4), 504.
Park, J., Kwon, S., & Jeong, S.-P. (2023). A study on improving turnover intention forecasting by
solving imbalanced data problems: focusing on smote and generative adversarial networks.
Journal of Big Data, 10(1), 36.
Parlak, I. E., & Emel, E. (2023). Deep learning-based detection of aluminum casting defects and
their types. Engineering Applications of Artifcial Intelligence, 118, 105636.
Patki, N., Wedge, R., & Veeramachaneni, K. (2016, Oct). The synthetic data vault. In Ieee international conference on data science and advanced analytics (dsaa) (p. 399-410). doi:
10.1109/DSAA.2016.49
Paul, S. D., & Bhunia, S. (2021). Silverin: Systematic integrity verifcation of printed circuit board
using jtag infrastructure. ACM Journal on Emerging Technologies in Computing Systems
(JETC), 17(3), 1–28.
Perdigones, F., & Quero, J. M. (2022). Printed circuit boards: The layers’ functions for electronic
and biomedical engineering. Micromachines, 13(3), 460.
PHM Society. (2022). data challenge: 7th european conference of the prognostics and health
management society 2022. Retrieved 18.06.2022, from https://phm-europe.org/
data-challenge
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). Catboost: unbiased boosting with categorical features. Advances in neural information processing systems,
31.
Putera, S. I., & Ibrahim, Z. (2010). Printed circuit board defect detection using mathematical
morphology and matlab image processing tools. In 2010 2nd international conference on
education technology and computer (Vol. 5, pp. V5–359).
Pycaret — pycaret 2.3.5 documentation. (n.d.). Retrieved from https://pycaret
.readthedocs.io/en/stable/ (Accessed: 2022-11-10)
Pytorch. (n.d.). Retrieved from https://pytorch.org/ (Accessed: 2022-11-10)
Regol, F., Kroon, A., & Coates, M. (2023). Evaluation of categorical generative models-bridging
the gap between real and synthetic data. In Icassp 2023-2023 ieee international conference
on acoustics, speech and signal processing (icassp) (pp. 1–5).
Reitermanova, Z., et al. (2010). Data splitting. In Wds (Vol. 10, pp. 31–36).
Sankar, V. U., Lakshmi, G., & Sankar, Y. S. (2022). A review of various defects in pcb. Journal of
Electronic Testing, 38(5), 481–491.
Sarawade, A. A., & Charniya, N. N. (2019). Detection of faulty integrated circuits in pcb with
thermal image processing. In 2019 international conference on nascent technologies in engineering (icnte) (pp. 1–6).
Schmidt, I., Dingeldein, L., Hunemohr, D., Simon, H., & Weigert, M. (2022). Application of ¨
machine learning methods to predict the quality of electric circuit boards of a production line.
In Phm society european conference (Vol. 7, pp. 550–555).
Shah, C., Du, Q., & Xu, Y. (2022). Enhanced tabnet: Attentive interpretable tabular learning for
hyperspectral image classifcation. Remote Sensing, 14(3), 716.
Shashidhara, H., Yellampalii, S., & Goudanavar, V. (2014). Board level jtag/boundary scan test
solution. In International conference on circuits, communication, control and computing (pp.
73–76).
Silva, L. H. d. S., Azevedo, G. O. d. A., Fernandes, B. J., Bezerra, B. L., Lima, E. B., & Oliveira,
S. C. (2019). Automatic optical inspection for defective pcb detection using transfer learning.
In 2019 ieee latin american conference on computational intelligence (la-cci) (pp. 1–6).
Singh, K., Kharche, S., Chauhan, A., & Salvi, P. (2024). Pcb defect detection methods: A review of
existing methods and potential enhancements. Journal of Engineering Science & Technology
Review, 17(1).
Sundaram, S., & Zeid, A. (2023). Artifcial intelligence-based smart quality inspection for manufacturing. Micromachines, 14(3), 570.
Ta, V.-C., Hoang, T.-L., Doan, N.-S., Nguyen, V.-T., Nguyen Dieu, N., Pham, T. T. T., &
Nguyen Dang, N. (2023). Tabnet effciency for facies classifcation and learning feature
embedding from well log data. Petroleum Science and Technology, 1–16.
Tang, H., Tian, Y., Dai, J., Wang, Y., Cong, J., Liu, Q., . . . Fu, Y. (2022). Prediction of production
line status for printed circuit boards. In Phm society european conference (Vol. 7, pp. 563–
570).
Taunk, K., De, S., Verma, S., & Swetapadma, A. (2019). A brief review of nearest neighbor
algorithm for learning and classifcation. In 2019 international conference on intelligent
computing and control systems (iccs) (pp. 1255–1260).
Thiyam, B., & Dey, S. (2024). Ciir: an approach to handle class imbalance using a novel feature
selection technique. Knowledge and Information Systems, 1–34.
Tsai, D.-M., & Huang, C.-K. (2018). Defect detection in electronic surfaces using template-based
fourier image reconstruction. IEEE Transactions on Components, Packaging and Manufacturing Technology, 9(1), 163–172.
Ural, D. I., & Sezen, A. (2024). Research on pcb defect detection using artifcial intelligence: a
systematic mapping study. Evolutionary Intelligence, 1–11.
Vafeiadis, T., Dimitriou, N., Ioannidis, D., Wotherspoon, T., Tinker, G., & Tzovaras, D. (2018).
A framework for inspection of dies attachment on pcb utilizing machine learning techniques.
Journal of Management Analytics, 5(2), 81–94.
Verma, S., Arora, V., & Perumal, T. (2023). Art generation ai model for low-end devices. In
2023 international conference on advances in computation, communication and information
technology (icaiccit) (pp. 30–35).
Verna, E., Genta, G., Galetto, M., & Franceschini, F. (2023). Zero defect manufacturing: a selfadaptive defect prediction model based on assembly complexity. International Journal of
Computer Integrated Manufacturing, 36(1), 155–168.
Verna, E., Puttero, S., Genta, G., & Galetto, M. (2023). Toward a concept of digital twin for
monitoring assembly and disassembly processes. Quality Engineering, 1–18.
Wan, J., Chen, H., Yuan, Z., Li, T., Yang, X., & Sang, B. (2021). A novel hybrid feature selection method considering feature interaction in neighborhood rough set. Knowledge-Based
Systems, 227, 107167.
Wang, F., Yue, Z., Liu, J., Qi, H., Sun, W., Chen, M., . . . Yue, H. (2022). Quantitative imaging
of printed circuit board (pcb) delamination defects using laser-induced ultrasound scanning
imaging. Journal of Applied Physics, 131(5).
Wang, S., Luo, H., Huang, S., Li, Q., Liu, L., Su, G., & Liu, M. (2023). Counterfactual-based
minority oversampling for imbalanced classifcation. Engineering Applications of Artifcial
Intelligence, 122, 106024.
Wei, A., Yu, K., Dai, F., Gu, F., Zhang, W., & Liu, Y. (2022). Application of tree-based ensemble
models to landslide susceptibility mapping: A comparative study. Sustainability, 14(10),
6330.
Wolpert, D. H. (1992). Stacked generalization. Neural networks, 5(2), 241–259.
Worden, J. C. (2024). Elevating excellence in high-reliability electronics: The personal and lasting
legacy of clean pcbs. In 2024 pan pacifc strategic electronics symposium (pan pacifc) (pp.
1–2).
Wu, C. (2020). Research on design and application of brand vision inspection and sorting system
based on image processing. In 2020 2nd international conference on machine learning, big
data and business intelligence (mlbdbi) (pp. 568–571).
Wu, C., Awasthi, A. K., Qin, W., Liu, W., & Yang, C. (2022). Recycling value materials from waste
pcbs focus on electronic components: technologies, obstruction and prospects. Journal of
Environmental Chemical Engineering, 10(5), 108516.
Wu, H., Lei, R., & Peng, Y. (2022). Pcbnet: A lightweight convolutional neural network for
defect inspection in surface mount technology. IEEE Transactions on Instrumentation and
Measurement, 71, 1–14.
Xu, K., Li, C., Zhu, J., & Zhang, B. (2019). Understanding and stabilizing gans’ training dynamics
with control theory. arXiv preprint arXiv:1909.13188.
Xu, L., Skoularidou, M., Cuesta-Infante, A., & Veeramachaneni, K. (2019). Modeling tabular data
using conditional gan. Advances in neural information processing systems, 32.
Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., & Tang, S. (2020). Using deep learning to detect
defects in manufacturing: a comprehensive survey and current challenges. Materials, 13(24),
5755.
Ye, D., Wang, X., & Chen, X. (2023). Lightweight generative joint source-channel coding for semantic image transmission with compressed conditional gans. In 2023 ieee/cic international
conference on communications in china (iccc workshops) (pp. 1–6).
Yeom, T., Gu, C., & Lee, M. (2024). Dudgan: Improving class-conditional gans via dual-diffusion.
IEEE Access.
You, S. (2022). Pcb defect detection based on generative adversarial network. In 2022 2nd international conference on consumer electronics and computer engineering (iccece) (pp. 557–560).
Zbikowski, K., & Antosiuk, P. (2021). A machine learning, bias-free approach for predicting busi- ˙
ness success using crunchbase data. Information Processing & Management, 58(4), 102555.
Zhang, C., Shi, W., Li, X., Zhang, H., & Liu, H. (2018). Improved bare pcb defect detection
approach based on deep feature learning. The Journal of Engineering, 2018(16), 1415–1420.
Zhou, Y., Yuan, M., Zhang, J., Ding, G., & Qin, S. (2023). Review of vision-based defect detection
research and its perspectives for printed circuit board. Journal of Manufacturing Systems, 70,
557–578.
Zingade, D. S., Deshmukh, R. K., & Kadam, D. B. (2023). Sentiment analysis using multiobjective optimization-based feature selection approach. In 2023 4th international conference
for emerging technology (incet) (pp. 1–6).