Psychological Support in Student Entrepreneurship

1. N. A. Ozcan, S. Sahin, and B. Cankir, "The validity and reliability of thriving scale in academic context: Mindfulness, GPA, and entrepreneurial intention among university students," Current Psychol., vol. 42, no. 7, pp. 5200, 5211, Mar. 2023.
2. U. Stephan, A. Rauch, and I. Hatak, "Happy entrepreneurs? Everywhere? A meta-analysis of entrepreneurship and wellbeing," Entrepreneurship Theory Pract., vol. 47, no. 2, pp. 553, 593, Mar. 2023.
3. J. M. Martins, M. F. Shahzad, and S. Xu, "Factors influencing entrepreneurial intention to initiate new ventures: Evidence from university students," J. Innov. Entrepreneurship, vol. 12, no. 1, p. 63, Sep. 2023.
4. Y. Zang, S. Hu, B. B. Zhou, L. Lv, and X. Sui, "Entrepreneurship and the formation mechanism of taobao villages: Implications for sustainable development in rural areas," J. Rural Stud., vol. 100, May 2023, Art. no. 103030.
5. J. Zhuang and H. Sun, "Impact of institutional environment on entrepreneurial intention: The moderating role of entrepreneurship education," Int. J. Manage. Educ., vol. 21, no. 3, Nov. 2023, Art. no. 100863.
6. T. S. C. Poon, C. H. Wu, and M. C. Liu, "Developing entrepreneurial ecosystem: A case of unicorns in China and its innovation policy implications," Asian J. Technol. Innov., vol. 32, no. 1, pp. 20, 36, Jan. 2024.
7. A. De Los Reyes, M. Wang, M. D. Lerner, B. A. Makol, O. M. Fitzpatrick, and J. R. Weisz, "The operations triad model and youth mental health assessments: Catalyzing a paradigm shift in measurement validation," J. Clin. Child Adolescent Psychol., vol. 52, no. 1, pp. 19, 54, Jan. 2023.
8. A. C. Timmons, J. B. Duong, N. Simo Fiallo, T. Lee, H. P. Q. Vo, M. W. Ahle, J. S. Comer, L. C. Brewer, S. L. Frazier, and T. Chaspari, "A call to action on assessing and mitigating bias in artificial intelligence applications for mental health," Perspect. Psychol. Sci., vol. 18, no. 5, pp. 1062, 1096, Sep. 2023.
9. O. Higgins, B. L. Short, S. K. Chalup, and R. L. Wilson, "Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review," Int. J. Mental Health Nursing, vol. 32, no. 4, pp. 966, 978, Aug. 2023.
10. K. Nova, "Machine learning approaches for automated mental disorder classification based on social media textual data," Contemp. Issues Behav. Soc. Sci., vol. 7, no. 1, pp. 70, 83, 2023.
11. B. Wang, "Exploration of the path of innovation and entrepreneurship education for college students from the perspective of mental health education," J. Healthcare Eng., vol. 2022, no. 1, Apr. 2022, Art. no. 2659160.
12. C. Margaça, B. R. Hernández-Sánchez, J. C. Sánchez-García, and G. M. Cardella, "The roles of psychological capital and gender in university students' entrepreneurial intentions," Frontiers Psychol., vol. 11, Jan. 2021, Art. no. 615910.
13. S. Siyal, C. Xin, W. A. Umrani, S. Fatima, and D. Pal, "How do leaders influence innovation and creativity in employees? The mediating role of intrinsic motivation," Admin. Soc., vol. 53, no. 9, pp. 1337, 1361, Oct. 2021.
14. N. K. Iyortsuun, S. H. Kim, M. Jhon, H. J. Yang, and S. Pant, "A review of machine learning and deep learning approaches on mental health diagnosis," Healthcare, vol. 11, no. 3, p. 285, Jan. 2023.
15. Z. Elyoseph, I. Levkovich, and S. Shinan-Altman, "Assessing prognosis in depression, Comparing perspectives of AI models, mental health professionals and the general public," Family Med. Community Health, vol. 12, Jan. 2024, Art. no. e002583.
16. R. Tornero-Costa, A. Martinez-Millana, N. Azzopardi-Muscat, L. Lazeri, V. Traver, and D. Novillo-Ortiz, "Methodological and quality flaws in the use of artificial intelligence in mental health research, Systematic review," JMIR Mental Health, vol. 10, no. 1, Feb. 2023, Art. no. e42045.
17. M. Obschonka and D. B. Audretsch, "Artificial intelligence and big data in entrepreneurship, A new era has begun," Small Bus. Econ., vol. 55, no. 3, pp. 529, 539, Oct. 2020.
18. A. Al-Okaily, A. P. Teoh, and M. Al-Okaily, "Evaluation of data analytics oriented business intelligence technology effectiveness, An enterprise level analysis," Bus. Process Manage. J., vol. 29, no. 3, pp. 777, 800, May 2023.
19. F. Kitsios and M. Kamariotou, "Digital innovation and entrepreneurship transformation through open data hackathons, Design strategies for successful start-up settings," Int. J. Inf. Manage., vol. 69, Apr. 2023, Art. no. 102472.
20. H. Chen, Y. Wang, C. Xu, C. Xu, and D. Tao, "Learning Student networks via feature embedding," IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 1, pp. 25, 35, Jan. 2021.
21. J. Gu and S. Lu, "An effective intrusion detection approach using SVM with naive Bayes feature embedding," Comput. Secur., vol. 103, Apr. 2021, Art. no. 102158.
22. M. Xu, "Understanding graph embedding methods and their applications," SIAM Rev., vol. 63, no. 4, pp. 825, 853, Jan. 2021.
23. Y. Sui, X. Cheng, G. Zhang, and H. Wang, "Flow2Vec, Value-flow-based precise code embedding," Proc. ACM Program. Lang., vol. 4, pp. 1, 27, Nov. 2020.
24. T. Zhang and X. Zhang, "A polarization fusion network with geometric feature embedding for SAR ship classification," Pattern Recognit., vol. 123, Mar. 2022, Art. no. 108365.
25. J. Wang, R. Huang, S. Guo, L. Li, Z. Pei, and B. Liu, "Hyper LiteNet, Extremely lightweight non-deep parallel network for hyperspectral image classification," Remote Sens., vol. 14, no. 4, p. 866, Feb. 2022.
26. M. D. Kremantzis, P. Beullens, L. S. Kyrgiakos, and J. Klein, "Measurement and evaluation of multi-function parallel network hierarchical DEA systems," Socio-Econ. Planning Sci., vol. 84, Dec. 2022, Art. no. 101428.
27. A. Khanda, S. Srinivasan, S. Bhowmick, B. Norris, and S. K. Das, "A parallel algorithm template for updating single-source shortest paths in large-scale dynamic networks," IEEE Trans. Parallel Distrib. Syst., vol. 33, no. 4, pp. 929, 940, Apr. 2022.
28. X. Jiang, Y. Jin, and Y. Yao, "Low-dose CT lung images denoising based on multiscale parallel convolution neural network," Vis. Comput., vol. 37, no. 8, pp. 2419, 2431, Aug. 2021.
29. M. C. Gonzalez, A. Mehrnezhad, N. Razaviarab, T. G. Barbosa-Silva, and S. B. Heymsfield, "Calf circumference, Cutoff values from the NHANES 1999, 2006," Amer. J. Clin. Nutrition, vol. 113, no. 6, pp. 1679, 1687, Jun. 2021.
30. T. Oh, D. Kim, S. Lee, C. Won, S. Kim, J. S. Yang, J. Yu, B. Kim, and J. Lee, "Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES," Sci. Rep., vol. 12, no. 1, p. 2250, Feb. 2022.
31. D. Kee, J. Wisnivesky, and M. S. Kale, "Lung cancer screening uptake, Analysis of BRFSS 2018," J. Gen. Internal Med., vol. 36, no. 9, pp. 2897, 2899, Sep. 2021.
32. J. Chung and J. Teo, "Single classifier vs. Ensemble machine learning approaches for mental health prediction," Brain Informat., vol. 10, no. 1, p. 1, Dec. 2023.
33. Vandana, N. Marriwala, and D. Chaudhary, "A hybrid model for depression detection using deep learning," Meas., Sensors, vol. 25, Feb. 2023, Art. no. 100587.
34. [N. A. A. Abdelwahed, "Recognizing the role of ChatGPT in decision making and recognition of mental health disorder among entrepreneurs," OBM Neurobiology, vol. 8, no. 3, pp. 1, 16, 2024.
35. Z. Elyoseph, T. Gur, Y. Haber, T. Simon, T. Angert, Y. Navon, A. Tal, and O. Asman, "An ethical perspective on the democratization of mental health with generative AI," JMIR Mental Health, vol. 11, Oct. 2024, Art. no. e58011.
36. G. Castaneda-Garza, H. G. Ceballos, and P. G. Mejia-Almada, "Artificial intelligence for mental health, A review of AI solutions and their future," in What AI Can Do. Berlin, Germany, Springer, Jun. 2023, pp. 373, 399