UGLEVODORODLAR O‘QITISHDA SUN’IY INTELLEKTNING O‘RNI: INTERAKTIV O‘QUV JARAYONINI YARATISH
Abstract
Mazkur maqolada uglevodorodlar mavzusini o‘qitishda sun’iy intellekt texnologiyalarining o‘rni va ulardan foydalanishning afzalliklari tahlil qilinadi. Interaktiv o‘quv jarayonlarini tashkil etish orqali talabalarning bilim olishga bo‘lgan qiziqishini oshirish, dars samaradorligini yaxshilash va murakkab kimyoviy jarayonlarni oson va tushunarli tarzda o‘rgatish imkoniyatlari muhokama qilinadi. Sun’iy intellekt yordamida yaratilgan platformalar va vositalar uglevodorodlar kimyosi bo‘yicha nazariy bilimlarni mustahkamlash, laboratoriya ishlari simulyatsiyasi va masalalar yechimini avtomatlashtirishda foydalaniladi. Maqola innovatsion yondashuvlarning ta’lim jarayonidagi ahamiyatini yoritishga qaratilgan.
References
Ponce, H., and P. Ponce. "Artificial hydrocarbon networks: A new algorithm bio-inspired on organic chemistry." International Journal of Artificial Intelligence. 2012.
DOI: https://doi.org/10.1016/j.neucom.2012.12.007
Gyasi, H. "Computer-assisted instruction (CAI) towards effective teaching and learning of nomenclature of cyclic hydrocarbons." University of Education, Winneba. 2023. PDF
DOI: https://doi.org/10.1016/j.neucom.2023.01.005
Koroteev, D., and Z. Tekic. "Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future." Energy and AI. 2021.
DOI: https://doi.org/10.1016/j.egyai.2021.100091
Amghizar, I., and G.B. Marin. "Artificial intelligence in steam cracking modeling: A deep learning algorithm for detailed effluent prediction." Engineering. 2019.
DOI: https://doi.org/10.1016/j.eng.2019.11.001
Wiyarsi, A., N. Fitriyana, and J. Ikhsan. "Using technology in hydrocarbon topics: A profile on students' self-regulated learning." Journal for the Education of Gifted Young Scientists. 2019.
DOI: https://doi.org/10.17478/jegys.616947
Fitriyana, N., and A. Wiyarsi. "Fostering of students' self-regulated learning and achievement: A study on hydrocarbon hybrid-learning and android-based-game." Journal of Physics: Conference Series. 2018.
DOI: https://doi.org/10.1088/1742-6596/1097/1/012064
Souza, P., H. Ponce, and E. Lughofer. "Evolving fuzzy neural hydrocarbon networks: A model based on organic compounds." Knowledge-Based Systems. 2020.
DOI: https://doi.org/10.1016/j.knosys.2020.105122
Zheng, P., R. Zubatyuk, and O. Isayev. "Artificial intelligence-enhanced quantum chemical method with broad applicability." Nature Communications. 2021.
DOI: https://doi.org/10.1038/s41467-021-27340-2
Mohammadi, M.R., and F. Hadavimoghaddam. "Application of robust machine learning methods to modeling hydrogen solubility in hydrocarbon fuels." International Journal of Hydrogen Energy. 2022.
DOI: https://doi.org/10.1016/j.ijhydene.2021.11.014
Staszak, M., K. Staszak, and K. Wieszczycka. "Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship." Wiley Interdisciplinary Reviews: Computational Molecular Science. 2022.
