Appraisal of Predictive Techniques using Computational Methods
Keywords:
Predictive models, Computational methods, Behavioral pattern, Machine learning, Evaluation.Abstract
This research focuses on theorems governing computational methods. Computational methods are a group of techniques that acts on big content with data to get outcomes with the prospect of obtaining a decision-making process. The theorems considered in this research are mathematical methods. The predictive techniques covered consist of mathematical analysis of solving problems, which are computational methods. These theorems encompass predictive values, which come about being considered. These methods are based on these computational methods. The major objective of this research is to utilize quantitative techniques in solving complex challenges. The techniques are enumerated in this research. The evaluation of these methods is also investigated. The technique that is more accurate and reliable is identified and selected when tested on data.
Downloads
References
Allison, J. R. (2020). Computational methods for exploring protein conformations. Biochemical Society Transactions, 48(4), 1707-1724.
Agamah, F. E., Mazandu, G. K., Hassan, R., Bope, C. D., Thomford, N. E., Ghansah,A. and Chimusa, E. R. (2020). Computational/in silico methods in drug target and lead prediction. Briefings in bioinformatics, 21(5), 1663-1675.
Arroyo-Marioli, F., Bullano, F., Kucinskas, S. and Rondón-Moreno, C. (2021). Tracking R of COVID-19: A new real-time estimation using the Kalman filter. PloS one, 16(1), e0244474.
Alfian, R. I., Ma'arif A. and Sunardi, S. (2021). Noise Reduction in the Accelerometer and Gyroscope Sensor with the Kalman Filter Algorithm. Journal of Robotics and Control (JRC), 2(3): 180-189.
Asamoah, D., Annan, J., & Arthur, Y. (2012). Time Series Analysis of Electricity Meter Supply in Ghana. International Journal of Business ans Social Science, 3(19), 16– 22.
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., and Baker, D. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373(6557), 871-876.
Chaabouni, R., Kharitonov, E., Dupoux, E. and Baroni, M. (2021). Communicating artificial neural networks develop efficient color-naming systems. Proceedings of the National Academy of Sciences, 118(12).
Chancellor, S. and De Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media: a critical review. NPJ digital medicine, 3(1): 1-11.
Chen, B. W., Xu, L. and Mavrikakis, M. (2020). Computational methods in heterogeneous catalysis. Chemical Reviews, 121(2), 1007-1048.
Chen, Z., Zhao, P., Li, F., Wang, Y., Smith, A. I., Webb, G. I. and Song, J. (2020). Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences. Briefings in bioinformatics, 21(5): 1676-1696.
Efremova, M. and Teichmann, S. A. (2020). Computational methods for single-cell omics across modalities. Nature methods, 17(1), 14-17.
Hewing, L., Wabersich, K. P., Menner, M. and Zeilinger, M. N. (2020). Learning-based model predictive control: Toward safe learning in control. Annual Review of Control, Robotics, and Autonomous Systems, 3, 269-296.
Kang, J., Chen, T., Luo, H., Luo, Y., Du, G. and Jiming-Yang, M. (2021). Machine learning predictive model for severe COVID-19. Infection, Genetics and Evolution 90: 104737.
Khoo, Y., Lu, J. and Ying, L. (2021). Solving parametric PDE problems with artificial neural networks. European Journal of Applied Mathematics, 32(3): 421-435.
Kumar, G., Jain, S. and Singh, U. P. (2021). Stock market forecasting using computational intelligence: A survey. Archives of Computational Methods in Engineering, 28(3): 1069-1101.
Lai, X., Yi, W., Cui, Y., Qin, C., Han, X., Sun, T. and Zheng, Y. (2021). Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter. Energy, 216, 119233.
Lin, X., Li, X. and Lin, X. (2020). A review on applications of computational methods in drug screening and design. Molecules, 25(6), 1375.
Lin, X., Li, W., Li S., Ye J., Yao C. and He, Z. (2021). Combined adaptive robust Kalman filter algorithm. Measurement Science and Technology, 32(7): 075015.
Madhavan, P. G. (2021). Stochastic Formulation of Causal Digital Twin: Kalman Filter Algorithm arXiv preprint arXiv:2105.05236.
Open Data Kit (2021). A post by ODK available at https://getodk.org/
Ozoh, P., Abd-Rahman, S; Labadin, J; Apperley, M. (2014). Modelling Electricity Consumption Using Modified Newton’s Method, International Journal of Computer Applications, 86(13): 27-31.
Wu, C. (2018). Regression Technique. Retrieved from http://www.historyofinformation.com/expanded.php?id=2706.
Wu, Z., Lv, H., Meng, Y., Guan, X. and Zang, Y. (2021). The determination of flood damage curve in areas lacking disaster data based on the optimization principle of variation coefficient and beta distribution. Science of The Total Environment, 750: 142277.
Yang, X., Guan, J., Ding, L., You, Z., Lee, V. C., Hasan, M. R. M. and Cheng, X. (2021). Research and applications of artificial neural network in pavement engineering: a state-of-the-art review. Journal of Traffic and Transportation Engineering (English Edition).
Zhao J., Cao Y. and Zhang, L. (2020). Exploring the computational methods for protein-ligand binding site prediction. Computational and structural biotechnology journal, 18: 417-426.
Zhong, K., Wang, Y., Pei, J., Tang, S. and Han, Z. (2021). Super efficiency SBM-DEA and neural network for performance evaluation. Information Processing & Management, 58(6), 102728.