Українська
Zarichuk, O.
(2023).
Hybrid approaches to machine learning in software development: Applying artificial intelligence to automate and improve processes.
Development Management,
22(4),
53-60.
https://doi.org/10.57111/devt/4.2023.53
English
Article
Hybrid approaches to machine learning in software development: Applying artificial intelligence to automate and improve processes
Received 11.05.2023, Revised 12.10.2023, Accepted 29.11.2023
Abstract
The study on hybrid machine learning approaches is relevant because these approaches have great potential to improve predictive accuracy and software automation, and their use is becoming more widespread. The purpose of this study was to provide recommendations for the use of hybrid machine learning methods and analyse the areas of application of artificial intelligence, which is used to automate and improve processes. Problems related to hybrid approaches to machine learning were identified using the analytical method. The use of the statistical method allowed assessing the development of stability and performance of hybrid machine learning approaches. Features and differences of machine learning in the field of software development are noted. Errors and reasons that are made when improving development processes are analysed. It is established that a comprehensive analysis of the functioning of artificial intelligence is important to assess its effectiveness, development, and complexity of work in automation and improvement of development. The issues of evaluating the work of this type of approach, the expediency of their use, limitations in the process, and the impact of restrictions on the result are considered. It is determined that the use of artificial intelligence in the process of automation and improvement of development processes will improve the quality of resource optimisation. The study offers recommendations that will contribute to the effective regulation of this issue. The practical value of the study lies in the possibility of applying the results obtained to eliminate errors in the development and improvement of hybrid approaches, investigating the reliability of using artificial intelligence, considering various factors that serve as the basis for recommendations on appropriate use
Keywords:
predictive accuracy; resource optimisation; information protection; reduced development time; cybersecurity
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References
[1] Arpaci, I., & Bahari, M. (2023). Investigating the role of psychological needs in predicting the educational sustainability of Metaverse using a deep learning-based hybrid SEM-ANN technique. Interactive Learning Environments. doi: 10.1080/10494820.2022.2164313.
[2] Baduge, S.K., Thilakarathna, S., Perera, J.S., Arashpour, M., Sharafi, P., Teodosio, B., Shringi, A., & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, article number 104440. doi: 10.1016/j.autcon.2022.104440.
[3] Banna, M.H.A., Ghosh, T., Nahian, M.J.A., Kaiser, M.S., Mahmud, M., Taher, K.A., Hossain, M.S., & Andersson, K. (2023). A hybrid deep learning model to predict the impact of COVID-19 on mental health from social media big data. IEEE Access, 11, 77009-77022. doi: 10.1109/ACCESS.2023.3293857.
[4] Beerbaum, D.O. (2022). Artificial intelligence ethics taxonomy-robotic process automation (RPA) as business case. SSRN. doi: 10.2139/ssrn.4165048.
[5] Bharadiya, J.P. (2023). Machine learning and AI in business intelligence: Trends and opportunities. International Journal of Computer (IJC), 48(1), 123-134.
[6] Byelov, D.M., & Bielova, M.V. (2023). Artificial intelligence in judicial proceedings and court decisions, potential and risks. Scientific Bulletin of the Uzhhorod National University, 2(78), 315-320. doi: 10.24144/2307-3322.2023.78.2.50.
[7] Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, 616-630. doi: 10.1007/s11528-022-00715-y.
[8] Ivanova, I., Borovyk, T., Zalozna, T., & Rudenko, A. (2023). Use of artificial intelligence for marketing. Marketing and Digital Technologies, 7(2), 32-42. doi: 10.15276/mdt.7.2.2023.3.
[9] Jarrahi, M.H., Askay, D., Eshraghi, A., & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99. doi: 10.1016/j.bushor.2022.03.002.
[10] Javaid, M., Haleem, A., Singh, R.P., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(1), 83-111. doi: 10.1142/S2424862221300040.
[11] Kochkodan, V., Petryna, M., & Stankovska, I. (2023). Application of machine learning and artificial intelligence in oilfield development. Scientific Bulletin of Ivano-Frankivsk National Technical University of Oil and Gas, 1(27), 16-26. doi: 10.31471/2409-0948-2023-1(27)-16-26.
[12] Lareyre, F., Behrendt, C.-A., Chaudhuri, A., Lee, R., Carrier, M., Adam, C., Duy Lê, C., & Raffort, J. (2023). Applications of artificial intelligence for patients with peripheral artery disease. Journal of Vascular Surgery, 77(2), 650-658. doi: 10.1016/j.jvs.2022.07.160.
[13] Nazarova, K., Parasii-Verhunenko, I., & Ostapets, A. (2023). Risk classification of IT industry companies. Bulletin of the Kyiv National University of Trade and Economics, 150(4), 120-137. doi: 10.31617/1.2023(150)08.
[14] Povolotskyi, B. (2019). Mobile application with Telegram-bot of the Konotop Industrial Pedagogical Professional College of Sumy State University. (Master thesis, Sumy State University, Sumy, Ukraine).
[15] Rajagopal, B.R., Anjanadevi, B., Tahreem, M., Kumar, S., Debnath, M., & Tongkachok, K. (2022). Comparative analysis of blockchain technology and artificial intelligence and its impact on open issues of automation in workplace. In 2022 2nd international conference on advance computing and innovative technologies in engineering (ICACITE) (pp. 288-292). Greater Noida: IEEE. doi: 10.1109/ICACITE53722.2022.9823792.
[16] Ramachandran, K.K., Mary, A.A.S., Hawladar, S., Asokk, D., Bhaskar, B., & Pitroda, J.R. (2022). Machine learning and role of artificial intelligence in optimizing work performance and employee behavior. Materials Today: Proceedings, 51(8), 2327-2331. doi: 10.1016/j.matpr.2021.11.544.
[17] Ray, S.S., Verma, R.K., Singh, A., Ganesapillai, M., & Kwon, Y.N. (2023). A holistic review on how artificial intelligence has redefined water treatment and seawater desalination processes. Desalination, 546, article number 116221. doi: 10.1016/j.desal.2022.116221.
[18] Smyrnov, O., & Borysenko, A. (2023). Trend in software-defined vehicles. Bulletin of Mechanical Engineering and Transport, 17(1), 163-169. doi: 10.31649/2413-4503-2023-17-1-163-169.
[19] Talukder, A., Hasan, K.F., Islam, M., Uddin, A., Akhter, A., Yousuf, M.A., Alhabri, F., & Moni, M.A. (2023). A dependable hybrid machine learning model for network intrusion detection. Journal of Information Security and Applications, 72, article number 103405. doi: 10.1016/j.jisa.2022.103405.
[20] Yarovoy, T. (2023). Opportunities and risks of the use of artificial intelligence in public administration. Economic Synergy, 2, 36-47. doi: 10.53920/ES-2023-2-3.