References
Bernardino, S., & Santos, J. F. (2020). The impact of rewards on crowdfunding success: A case study analysis. Journal of Innovation & Knowledge, 5(2), 121-129.
Bishla, S., & Khosla, A. (2023). Enhanced chimp optimized self-tuned FOPR controller for battery scheduling using grid and solar PV sources. Journal of Energy Storage, 66, 107403. https://doi.org/10.1016/j.est.2023.107403
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.
Cheng, H., Zhu, Y., & Wang, Y. (2019). Multi-modal data fusion for crowdfunding success prediction: A case study on reward-based platforms. IET Software, 13(5), 229-238. https://doi.org/10.1049/iet-sen.2019.0119
Edward, N., Raflesia, E., & Christian, S. (2023). Sharia-compliant peer-to-peer lending: A machine learning approach to investor dynamics. Journal of Intelligent Information Systems, 49(3), 47-65. https://doi.org/10.1007/s10844-022-00731-9
Etter, V., Grossglauser, M., & Thiran, P. (2013). Launch hard or go home! Predicting the success of Kickstarter campaigns. In Proceedings of the first ACM conference on Online social networks (pp. 177-182).
Fernández Martínez, J. L., & García Gonzalo, E. (2009). The PSO family: Deduction, stochastic analysis and comparison. Swarm Intelligence, 3(4), 245–273. https://doi.org/10.1007/s11721-009-0034-8
Foster, J. (2019). Crowdfunding platforms and their role in supporting creative projects. Journal of Creative Industries, 12(3), 233-245.
García, S., Luengo, J., & Herrera, F. (2019). Data preprocessing in data mining. Springer.
Greenberg, M. D., Hui, J. S., & Gerber, E. M. (2013). Crowdfunding: A resource for creative entrepreneurs. The Information Society, 29(2), 92-104.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(Mar), 1157-1182.
Habibi Aghdam, H., & Jahani Heravi, E. (2017). Guide to convolutional neural networks. Springer International Publishing. https://doi.org/10.1007/978-3-319-57550-6
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
Jenei, Sz., Módosné Szalai, Sz., Leicht, F., Molnár, S., Varga, E., & Poór, J. (2024). The workforce challenges of disabled people in Hungary and Slovakia during the COVID-19 pandemic. Alter, 18(3), 23–49. https://doi.org/10.4000/alter.6467
Jenei, Sz., & Módosné Szalai, Sz. (2021). A koronavírus járvány hatásai a humánerőforrás-menedzsment különböző területeire 2020-ban. Új Munkaügyi Szemle, 2(2), 53–64.
Jiménez-Jiménez, D., Martínez-Caro, E., & Morales, A. I. (2021). The effects of crowdfunding success on subsequent financing rounds: Evidence from equity-based campaigns. Journal of Business Research, 123, 482-489.
Kashef, S., & Nezamabadi-pour, H. (2015). An advanced ACO algorithm for feature subset selection. Neurocomputing, 147, 271–279. https://doi.org/10.1016/j.neucom.2014.06.067
Ketkar, N., & Moolayil, J. (2021). Convolutional neural networks. In N. Ketkar & J. Moolayil (Eds.), Deep learning with Python: Learn best practices of deep learning models with PyTorch (pp. 197–242). Apress. https://doi.org/10.1007/978-1-4842-5364-9_6
Khishe, M., & Mosavi, M. (2020). Chimp optimization algorithm. Expert Systems with Applications, 149, 113338. https://doi.org/10.1016/j.eswa.2020.113338
Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on artificial intelligence (Vol. 2, pp. 1137-1143).
Kramer, O. (2017). Genetic algorithms. In O. Kramer (Ed.), Genetic algorithm essentials (pp. 11–19). Springer International Publishing. https://doi.org/10.1007/978-3-319-52156-5_2
Lavin, A., & Gray, S. (2016). Fast algorithms for convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4013–4021). https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Lavin_Fast_Algorithms_for_CVPR_2016_paper.html
McTeer, Q. (2021). Indiegogo crowdfunding campaigns [CSV]. Kaggle. https://www.kaggle.com/datasets/quentinmcteer/indiegogo-crowdfunding-data
Mehndy, A., Shamsuddin, S. M., & Yusof, R. (2020). Chimp optimization algorithm: A new metaheuristic optimizer. Journal of Computational Design and Engineering, 7(4), 390-410.
Módosné Szalai, Sz., Kálmán, B. G., Tóth, A., Gyurián, N., Singh, D. P., Dávid, L. D., & Jenei, Sz. (2025). NUTS2 regions of the Visegrad countries during the Covid-19 pandemic and recovery. Regional Statistics, 15(3), 1–21. https://doi.org/10.15196/RS150302
Mura, L., Barcziová, A., Bálintová, M., Jenei, Sz., Molnár, S., & Módosné Szalai, Sz. (2022a). The effects of the COVID-19 pandemic on unemployment in Slovakia and Hungary. Vadyba: Journal of Management, 38(1), 25–35. https://doi.org/10.38104/vadyba.2022.1.03
Mura, L., Barcziová, A., Bálintová, M., Jenei, Sz., Molnár, S., & Módosné Szalai, Sz. (2022b). Economic measures to recover the area of entrepreneurship: A comparative analysis Slovakia-Hungary. Scientific Bulletin of Uzhhorod University. Series «Economics», (2(60)), 15–26.
Musheer, Z., Aslam, M., & Akhtar, N. (2019). Metaheuristic algorithms for feature selection: A review. IEEE Access, 7, 61248-61262.
O’Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks (Version 2). arXiv. https://doi.org/10.48550/ARXIV.1511.08458
Pashaei, E., & Pashaei, E. (2022). An efficient binary chimp optimization algorithm for feature selection in biomedical data classification. Neural Computing and Applications, 34(8), 6427–6451. https://doi.org/10.1007/s00521-021-06775-0
Poór, J., Módosné Szalai, Sz., Mura, L., Jenei, Sz., Varga, E., Szira, Z., & Hollósy-Vadász, G. (2023). The impact of the pandemic on the central and regional areas of Hungary: During the economic recovery following the global virus epidemic. AD ALTA: Journal of Interdisciplinary Research, 13(2), 207–212. https://doi.org/10.33543/1302
Poór, J., Módosné Szalai, Sz., & Jenei, Sz. (2021). Responsibility of the employers and employees in Hungary: The importance of hygiene during the pandemic. Acta Oeconomica Universitatis Selye, 10(2), 85–109.
Qian, L., Khishe, M., Huang, Y., & Mirjalili, S. (2024). SEB-ChOA: An improved chimp optimization algorithm using spiral exploitation behavior. Neural Computing and Applications, 36(9), 4763–4786. https://doi.org/10.1007/s00521-023-09236-y
Ralcheva, A., & Roosenboom, P. (2020). Forecasting the success of equity crowdfunding campaigns: Logistic regression analysis. Journal of Business Venturing, 35(3), 101484. https://doi.org/10.1016/j.jbusvent.2020.101484
Ramos, J. (2003). Using TF-IDF to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning (Vol. 242, No. 1, pp. 133-142).
Remsei, S., Módosné Szalai, Sz., & Jenei, Sz. (2023). Hungarian battery production – Public opinion on sustainability, labor market, and environmental protection. Chemical Engineering Transactions, 107, 691–696. https://doi.org/10.3303/CET2317116
Ryoba, J. D., Yusuf, M. B., & Tukur, Y. G. (2020). Metaheuristics and their applications: A brief review. Journal of Computer Science and Systems Biology, 13(2), 1-6.
Samsel, A., Stemler, A. R., & Evans, D. S. (2021). The determinants of crowdfunding success: Evidence from Kickstarter. Journal of Entrepreneurship and Innovation in Emerging Economies, 7(1), 35-48.
Silva, W., Felipe, I., Leal, C.C., & Aguiar, M.O. (2020). How the tone of mass media news affects pledge amounts in reward crowdfunding campaigns. Journal of Small Business Management, 62(2), 254-267. https://doi.org/10.1080/00472778.2020.1762787
Srinivasan, S. (2017). Kickstarter campaigns dataset [Dataset]. Kaggle. https://www.kaggle.com/datasets/sripaadsrinivasan/kickstarter-campaigns-dataset
Steinmo, M., & Rasmussen, E. (2018). The interplay of motivation and funding in science-based entrepreneurship. Research Policy, 47(1), 18-32.
Testa, S., Roma, P., & Scardigno, A. F. (2020). Crowdfunding: A new opportunity for small entrepreneurial ventures. Entrepreneurship Theory and Practice, 44(5), 929-955.
Ullah, I., & Zhou, J. (2020). Crowdfunding success factors: What we know and what we need to know. Journal of Business Research, 109, 246-258.
Webrobot. (2024). Indiegogo datasets [CSV /JSON]. https://webrobots.io/indiegogo-dataset/
Xu, A., Chen, X., & Zhu, J. (2014). Factors influencing success of crowdfunding projects in China. In Pacific Asia Conference on Information Systems (PACIS).
Yuan, Q., Wang, S., Hu, M., & Zeng, L. (2024). SLDChOA: A comprehensive and competitive multi-strategy-enhanced chimp algorithm for global optimization and engineering design. The Journal of Supercomputing, 80(3), 3589–3643. https://doi.org/10.1007/s11227-023-05617-1
Copyright (c) 2024 Zoltán Zéman, Botond Géza Kálmán, Szilárd Malatyinszki