670f86a979306.pdf
DOI:
Mavjud emas
Kuradusenge, M., Hitimana, E., Hanyurwimfura, D., Rukundo, P., Mtonga, K., Mukasine, A., Uwitonze, C., Ngabonziza, J., & Uwamahoro, A. (2023). Crop yield prediction using machine learning models: case of irish potato and maize. Agriculture, 13, 225. www.mdpi.com/2077-0472/13/1/225
Elbasi, E., Zaki, C., Topcu, A. E., Abdelbaki, W., Zreikat, A. I., Cina, E., Shdefat, A., & Saker, L. (2023). Crop prediction model using machine learning algorithms. Appl. Sci., 13, 9288. doi:10.3390/app13169288.
Tawseef, A.S., Tabasum, R., & Faisal, R.L. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Comput. Electron. Agric., 198, 107119.
Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021). A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE Access,9. 63406–63439. doi: 10.1109/ACCESS.2021.3075159
Evgeniou, T., & Pontil, M. (2001). Support Vector Machines: Theory and Applications. In G. Paliouras, V. Karkaletsis, C. D. Spyropoulos (Eds.) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science (vol. 2049). Springer, Berlin, Heidelberg. doi:10.1007/3-540-44673-7_12
Zhang, Y. (2012). Support Vector Machine Classification Algorithm and its Application. In Information Computing and Applications (ICICA 2012). Communications in Computer and Information Science, 308. Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-34041-3_27
Raximov, N., Kuvandikov, J., & Khasanov, D. (2022). The importance of loss function in artificial intelligence. Proceedings of the International Conference on Information Science and Communications Technologies (ICISCT 2022). doi:10.1109/ICISCT55600.2022.10146883
Rahimov, N., & Khasanov, D. (2022). The mathematical essence of logistic regression for machine learning. Computer Science and Engineering Technologies. Proceedings of the International scientific and technical conference (2022, October 14-15). doi:10.5281/zenodo.7239169
Khasanov D., Tojiyev M., & Primqulov, O. Gradient descent in machine. Proceedings of the International Conference on Information Science and Communications Technologies (ICISCT). https://ieeexplore.ieee.org/ document/9670169
Shai Sh.-Sh., & Shai B.-D. (2014). Understanding Machine Learning (pp. 46–85). Cambridge University press.
James, G., Witten, D. Hastie, T., & Tibshirani, R. (Eds.). (2013). An introduction to statistical learning: with applications in R. NY: Springer New York. doi:10.1007/978-1-4614-7138-7
Hastie, T., Tibshirani, R., & Friedman, J.H. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.). NY: New York Springer.
Theobald, O. (2017). Machine learning for absolute beginners (pp. 43-98). Scatterplot Press.
Tojiyev, M., Primqulov, O., & Xasanov, D. (2020). Image segmentation in OpenCV and Python. Academicia: an International Multidisciplinary Research Journal, 10 (12), 332–336. doi:10.5958/2249-7137.2020.01735.8
Raximov, N., Primqulov, O., & Daminova, B. (2021). Basic concepts and stages of research development on artificial intelligence. Proceedings of the International Conference on Information Science and Communications Technologies (ICISCT) (pp. 1–4). doi:10.1109/ICISCT52966.2021.9670085
Kelleher, J. D., Mac Namee, B., & D’Arcy, A. (2015). Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (pp. 16–102). Cambridge, Massachusetts London, England: the MIT Press. https://www.academia.edu/83021908/Fundamentals_of_Machine_Learning_for_ Predictive_Data_Analytics_Algorithms_Worked_Examples_and_Case_Studies
Tojiyev, M., Shirinboyev, R., & Bobolov, J. (2023). Image segmentation by otsu method. International Journal of Contemporary Scientific and Technical Research, spec. iss., 64–72. https://zenodo.org/record/7630893
Raximov, N., Doshchanova, M., Primqulov, O., & Quvondikov, J. (2022). Development of architecture of intellectual information system supporting decision-making for health of sportsmen. Proceedings of the 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA).
Biswas, P. (2021). Loss Function in Deep Learning and Python Implementation. http://www. towardsdatascience.com
Kumar, R., Singh, M., Kumar, P., & Singh, J. P. (2015). Crop selection method to maximize crop yield rate using machine learning technique. Proceedings of the 2015 International Conference on Smart technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM). IEEE.
Agalave, H., Mahavidyalaya, S., & Maharashtra M.N. (2017, January). Effect of environmental factors on productivity of crop. International Journal of Botany Studies, 2 (1), 14–16.
Irmak, A., Jones, J., Batchelor, W., Irmak, S., Boote, K., & Paz, P. (2006). Artificial neural network model as a data analysis tool in precision farming. TASABE, 49 (6).
Deshmukh, T., Rajawat, A., Goyal, S. B., Kumar, J., & Potgantwar, A. (2023). Analysis of machine learning technique for crop selection and prediction of crop cultivation. Proceedings of the 2023 International Conference on Inventive Computation Technologies (ICICT) (pp. 298–311). Lalitpur, Nepal. doi:10.1109/ ICICT57646.2023.10134371
Walker, S. M. (n.d.). What is a support vector machine? http://www.klu.ai/glossary/support-vectormachines
Chitrakumari. (2022). Smart Agricultural Production Optimizing Engine. Smart Agriculture is being improved by the Artificial Intelligence (AI). www.kaggle.com/datasets/chitrakumari25/smart-agriculturalproduction-optimizing-engine
Dean, J. (2020, February 16–20). The deep learning revolution and its implications for computer architecture and chip design. Proceedings of the IEEE International Solid-State Circuits Conference (ISSCC). San Francisco, CA, USA.
Saini, A. (2022, Aug 26). An introduction to random forest algorithm for beginners. In Data Science Blogathon. https://www.analyticsvidhya.com/blog/2021/10/an-introduction-to-random-forest-algorithm-forbeginners/#
Yehoshua, R. (2023, Mar 25). Random forests. https://medium.com/@roiyeho/random-forests98892261dc49