An intelligent decision support approach for quantified assessment of innovation ability via an improved BP neural network

Ming Chen; Yan Qi; Xinxing Zhang; Xueyong Jiang; Ming Chen; Yan Qi; Xinxing Zhang; Xueyong Jiang

doi:10.3934/mbe.2023677

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 15120-15134. doi: 10.3934/mbe.2023677

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Research article

An intelligent decision support approach for quantified assessment of innovation ability via an improved BP neural network

1.
Hebei Building Materials Vocational and Technical College, Qinhuangdao 066004, China
2.
Yanshan University, Qinhuangdao 066004, China

Academic Editor: Keping Yu

Received: 08 February 2023 Revised: 12 May 2023 Accepted: 24 May 2023 Published: 17 July 2023

In today's competitive and changing social environment, innovation and entrepreneurial ability have become important factors for the successful development of college students. However, relying solely on traditional evaluation methods and indicators cannot comprehensively and accurately evaluate the innovation and entrepreneurial potential and ability of college students. Therefore, developing a comprehensive evaluation model is urgently needed. To address this issue, this article introduces machine learning methods to explore the learning ability of subjective evaluation processes and proposes an intelligent decision support method for quantitatively evaluating innovation capabilities using an improved BP (Back Propagation) neural network. This article first introduces the current research status of evaluating the innovation and entrepreneurship ability of college students, and based on previous research, it has been found that inconsistent evaluation standards are one of the important issues at present. Then, based on different BP models and combined with the actual situation of college student innovation and entrepreneurship evaluation, we selected an appropriate input layer setting for the BP neural network and improved the setting of the middle layer (hidden layer). The identification of output nodes was also optimized by combining the current situation. Subsequently, the conversion function, initial value and threshold were determined. Finally, evaluation indicators were determined and an improved BP model was established which was validated using examples. The research results indicate that the improved BP neural network model has a low error rate, strong generalization ability and ideal prediction effect which can be effectively used to analyze problems related to intelligent evaluation of innovation ability.
- intelligent decision support,
- quantified assessment,
- BP neural network,
- deep learning
Citation: Ming Chen, Yan Qi, Xinxing Zhang, Xueyong Jiang. An intelligent decision support approach for quantified assessment of innovation ability via an improved BP neural network[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 15120-15134. doi: 10.3934/mbe.2023677

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Abstract

In today's competitive and changing social environment, innovation and entrepreneurial ability have become important factors for the successful development of college students. However, relying solely on traditional evaluation methods and indicators cannot comprehensively and accurately evaluate the innovation and entrepreneurial potential and ability of college students. Therefore, developing a comprehensive evaluation model is urgently needed. To address this issue, this article introduces machine learning methods to explore the learning ability of subjective evaluation processes and proposes an intelligent decision support method for quantitatively evaluating innovation capabilities using an improved BP (Back Propagation) neural network. This article first introduces the current research status of evaluating the innovation and entrepreneurship ability of college students, and based on previous research, it has been found that inconsistent evaluation standards are one of the important issues at present. Then, based on different BP models and combined with the actual situation of college student innovation and entrepreneurship evaluation, we selected an appropriate input layer setting for the BP neural network and improved the setting of the middle layer (hidden layer). The identification of output nodes was also optimized by combining the current situation. Subsequently, the conversion function, initial value and threshold were determined. Finally, evaluation indicators were determined and an improved BP model was established which was validated using examples. The research results indicate that the improved BP neural network model has a low error rate, strong generalization ability and ideal prediction effect which can be effectively used to analyze problems related to intelligent evaluation of innovation ability.

References

[1]	W. Ren, K. Wu, Q. Gu, Y. Hu, Intelligent decision making for service providers selection in maintenance service network: An adaptive fuzzy-neuro approach, Knowl. Based Syst., 190 (2020), 105263. https://doi.org/10.1016/j.knosys.2019.105263 doi: 10.1016/j.knosys.2019.105263
[2]	M. T. Quasim, A. Shaikh, M. Shuaib, A. Sulaiman, S. Alam, Y. Asiri, Smart healthcare management evaluation using fuzzy decision making method, preprint.
[3]	Y. Qian, S. Chen, J. Li, Q. Ren, J. Zhu, R. Yuan, et al., A decision-making model using machine learning for improving dispatching efficiency in Chengdu Shuangliu airport, Complexity, 2020 (2020), 6626937. https://doi.org/10.1155/2020/6626937 doi: 10.1155/2020/6626937
[4]	C. Liu, Y. Feng, Y. Wang, An innovative evaluation method for undergraduate education: an approach based on BP neural network and stress testing, Stud. Higher Edu., 47 (2022), 212–228. https://doi.org/10.1080/03075079.2020.1739013 doi: 10.1080/03075079.2020.1739013
[5]	A. Lauraitis, R. Maskeliūnas, R. Damaševičius, D. Połap, M. Woźniak, A smartphone application for automated decision support in cognitive task based evaluation of central nervous system motor disorders, IEEE J. Biomed. Health Inf., 23 (2019), 1865–1876. https://doi.org/10.1109/JBHI.2019.2891729 doi: 10.1109/JBHI.2019.2891729
[6]	N. Shahid, T. Rappon, W. Berta, Applications of artificial neural networks in health care organizational decision-making: A scoping review, PloS One, 14 (2019), e0212356. https://doi.org/10.1371/journal.pone.0212356 doi: 10.1371/journal.pone.0212356
[7]	Y. W. Li, K. Cao, Establishment and application of intelligent city building information model based on BP neural network model, Comput. Commun., 153 (2020), 382–389. https://doi.org/10.1016/j.comcom.2020.02.013 doi: 10.1016/j.comcom.2020.02.013
[8]	O. E. Bukharov, D. P. Bogolyubov, Development of a decision support system based on neural networks and a genetic algorithm, Expert Syst. Appl., 42 (2015), 6177–6183. https://doi.org/10.1016/j.eswa.2015.03.018 doi: 10.1016/j.eswa.2015.03.018
[9]	M. Yang, H. Zhu, K. Guo, Research on manufacturing service combination optimization based on neural network and multi-attribute decision making, Neural Comput. Appl., 32 (2020), 1691–1700. https://doi.org/10.1007/s00521-019-04241-6 doi: 10.1007/s00521-019-04241-6
[10]	M. Tkáč, R. Verner, Artificial neural networks in business: Two decades of research, Appl. Soft Comput., 38 (2016), 788–804. https://doi.org/10.1016/j.asoc.2015.09.040 doi: 10.1016/j.asoc.2015.09.040
[11]	X. Zhang, L. Xu, H. Zhang, Z. Jiang, W. Cai, Emergy based intelligent decision-making model for remanufacturing process scheme integrating economic and environmental factors, J. Cleaner Prod., 291 (2021), 125247. https://doi.org/10.1016/j.jclepro.2020.125247 doi: 10.1016/j.jclepro.2020.125247
[12]	J. M. Tien, Internet of things, real-time decision making, and artificial intelligence, Ann. Data Sci., 4 (2017), 149–178. https://doi.org/10.1007/s40745-017-0112-5 doi: 10.1007/s40745-017-0112-5
[13]	M. Collotta, G. Pau, An innovative approach for forecasting of energy requirements to improve a smart home management system based on BLE, IEEE Trans. Green Commun. Networking, 1 (2017), 112–120. https://doi.org/10.1109/TGCN.2017.2671407 doi: 10.1109/TGCN.2017.2671407
[14]	A. Rikalovic, I. Cosic, R. D. Labati, V. Piuri, Intelligent decision support system for industrial site classification: A GIS-based hierarchical neuro-fuzzy approach, IEEE Syst. J., 12 (2017), 2970–2981. https://doi.org/10.1109/JSYST.2017.2697043 doi: 10.1109/JSYST.2017.2697043
[15]	K. Hameed, I. S. Bajwa, S. Ramzan, W. Anwar, A. Khan, An intelligent IoT based healthcare system using fuzzy neural networks, Sci. Prog., 2020 (2020), 1–15. https://doi.org/10.1155/2020/8836927 doi: 10.1155/2020/8836927
[16]	T. M. Alabi, E. I. Aghimien, F. D. Agbajor, Z. Yang, L. Lu, A. R. Adeoye, et al., A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems, Renewable Energy, 194 (2022), 822–849. https://doi.org/10.1016/j.renene.2022.05.123 doi: 10.1016/j.renene.2022.05.123
[17]	B. Hamrouni, A. Bourouis, A. Korichi, M. Brahmi, Explainable ontology-based intelligent decision support system for business model design and sustainability, Sustainability, 13 (2021), 9819. https://doi.org/10.3390/su13179819 doi: 10.3390/su13179819
[18]	O. Karountzos, G. Kagkelis, K. Kepaptsoglou, A decision support GIS framework for establishing zero-emission maritime networks: The case of the greek coastal shipping network, J. Geovisualization Spat. Anal., 7 (2023), 145–158. https://doi.org/10.1007/s41651-023-00145-1 doi: 10.1007/s41651-023-00145-1
[19]	P. Hajek, R. Henriques, Modelling innovation performance of European regions using multi-output neural networks, PloS One, 12 (2017), e0185755. https://doi.org/10.1371/journal.pone.0185755 doi: 10.1371/journal.pone.0185755
[20]	A. K. Kar, A hybrid group decision support system for supplier selection using analytic hierarchy process, fuzzy set theory and neural network, J. Comput. Sci., 6 (2015), 23–33. https://doi.org/10.1016/j.jocs.2014.11.002 doi: 10.1016/j.jocs.2014.11.002
[21]	R. J. Kuo, Y. S. Tseng, Z. Y. Chen, Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data, J. Intell. Manuf., 27 (2016), 1191–1207. https://doi.org/10.1007/s10845-014-0944-1 doi: 10.1007/s10845-014-0944-1
[22]	H. He, H. Yan, W. Liu, Intelligent teaching ability of contemporary college talents based on BP neural network and fuzzy mathematical model, J. Intell. Fuzzy Syst., 39 (2020), 4913–4923. https://doi.org/10.3233/JIFS-179977 doi: 10.3233/JIFS-179977
[23]	C. Zeng, K. Yan, Z. Wang, Y. Yu, S. Xia, N. Zhao, Abs-CAM: A gradient optimization interpretable approach for explanation of convolutional neural networks, Signal Image Video Process., 17 (2023), 1069–1076. https://doi.org/10.1007/s11760-022-02313-0 doi: 10.1007/s11760-022-02313-0
[24]	H. K. Lee, T. G. Puranik, D. N. Mavris, Deep spatio-temporal neural networks for risk prediction and decision support in aviation operations, J. Comput. Inf. Sci. Eng., 21 (2021), 041013. https://doi.org/10.1115/1.4049992 doi: 10.1115/1.4049992
[25]	Y. Hao, The innovation and entrepreneurship education model of college students based on the comprehensive participation of the society, Agro Food Ind. Hi Tech., 28 (2017), 357–361.
[26]	H. K. Lee, T. G. Puranik, D. N. Mavris, Deep spatio-temporal neural networks for risk prediction and decision support in aviation operations, J. Comput. Inf. Sci. Eng., 21 (2021), 041013. https://doi.org/10.1115/1.4049992 doi: 10.1115/1.4049992
[27]	K. Wang, R. Tan, Q. Peng, F. Wang, P. Shao, Z. Gao, A holistic method of complex product development based on a neural network-aided technological evolution system, Adv. Eng. Inf., 48 (2021), 101294. https://doi.org/10.1016/j.aei.2021.101294 doi: 10.1016/j.aei.2021.101294
[28]	L. I. Yan-Kun, L. I. Ling-Li, D. O. Physics, and T. N. University. Study on the flipped teaching mode of college students' innovation and entrepreneurship based on wechat, J. Tangshan Normal Univ., 2018 (2018).
[29]	Y. B. Park, S. J. Yoon, J. S. Yoo, Development of a knowledge-based intelligent decision support system for operational risk management of global supply chains, Eur. J. Ind. Eng., 12 (2018), 93–115. https://doi.org/10.1504/EJIE.2018.089878 doi: 10.1504/EJIE.2018.089878
[30]	A. P. Marugán, F. P. G. Márquez, J. M. P. Perez, D. Ruiz-Hernández, A survey of artificial neural network in wind energy systems, Appl. Energy, 228 (2018), 1822–1836. https://doi.org/10.1016/j.apenergy.2018.07.084 doi: 10.1016/j.apenergy.2018.07.084
[31]	M. A. Mohammed, M. K. Abd Ghani, N. Arunkumar, R. I. Hamed, S. A. Mostafa, M. Khir Abdullah, et al. Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network, J. Supercomput., 76 (2020), 1086–1104. https://doi.org/10.1007/s11227-018-2587-z doi: 10.1007/s11227-018-2587-z
[32]	S. Mi, Y. Feng, H. Zheng, Y. Wang, Y. Gao, J. Tan, Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework, J. Manuf. Syst., 58 (2021), 329–345. https://doi.org/10.1016/j.jmsy.2020.08.001 doi: 10.1016/j.jmsy.2020.08.001

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