Relationship Between BE4DBE2 and Variables n and z: A Comprehensive Analysis Using Linear Regression, Nonparametric Regression, Naive Bayes Classification, Decision Tree Analysis, SVM Analysis, K-Means Clustering, and Bayesian Regression
DOI:
10.29303/jppipa.v9i11.4483Published:
2023-11-25Issue:
Vol. 9 No. 11 (2023): NovemberKeywords:
Bayesian regression analysis, Decision tree analysis, K-means clustering, Naive bayes classification, SVM analysisResearch Articles
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Abstract
This research employed various statistical techniques, including linear regression, nonparametric regression, Naive Bayes classification, decision tree analysis, Support Vector Machine (SVM) analysis, k-means clustering, and Bayesian regression, to analyze nuclear data. The research aims to explore the relationships between variables, predict binding energy, classify nuclear data, and identify similar groups. The research results revealed that linear regression indicated a significant influence of the intercept and predictor variable 'n' on the variable 'BE4DBE2,' while the variable 'z' was not significant. However, the overall model had limited explanatory power. Nonparametric regression with smoothing functions effectively modeled the relationship between 'BE4DBE2' and variables 'n' and 'z,' explaining approximately 11% of the variability in the response variable. Classification using Naive Bayes successfully categorized nuclear data based on 'n' and 'z,' revealing their relationship. Decision tree analysis evaluated the performance of this classification model and provided insights into accuracy, agreement, sensitivity, specificity, precision, and negative predictive value. SVM analysis successfully built an accurate SVM model with a linear kernel, classifying nuclear data while depicting decision boundaries and support vectors. K-means clustering grouped nuclear data based on 'n' and 'z,' revealing distinct characteristics and enabling the identification of similar clusters. The Bayesian regression model predicted binding energy using 'n' and 'z' as independent variables, capturing the Gaussian distribution of 'BE4DBE2' and providing statistical measures for parameter estimation. Ccomprehensives nuclear data analysis using various statistical approaches provides valuable insights into relationships, predictions, classification, and clustering, contributing to the advancement of nuclear science and facilitating further research in this field.
References
Bashir, A. K., Khan, S., Prabadevi, B., Deepa, N., Alnumay, W. S., Gadekallu, T. R., & Maddikunta, P. K. R. (2021). Comparative analysis of machine learning algorithms for prediction of smart grid stability. International Transactions on Electrical Energy Systems, 31(9), e12706., https://doi.org/10.1002/2050-7038.12706.
Boehm, F. J., & Zhou, X. (2022). Statistical methods for Mendelian randomization in genome-wide association studies: A review. Computational and Structural Biotechnology Journal, 20, 2338–2351., https://doi.org/10.1016%2Fj.csbj.2022.05.015.
Demirhan, H. (2020). dLagM: An R package for distributed lag models and ARDL bounds testing. Plos One, 15(2). https://doi.org/10.1371/journal.pone.0228812.
Ghiasi, M. M., Zendehboudi, S., & Mohsenipour, A. A. (2020). Decision tree-based diagnosis of coronary artery disease: CART model. Computer Methods and Programs in Biomedicine, 192. https://doi.org/10.1016/j.cmpb.2020.105400.
Gomez-Fernandez, M., Higley, K., Tokuhiro, A., Welter, K., Wong, W.-K., & Yang, H. (2020). Status of research and development of learning-based approaches in nuclear science and engineering: A review. Nuclear Engineering and Design, 359, 110479. https://doi.org/10.1016/j.nucengdes.2019.110479
Gradojevic, N., & Yang, J. (2006). Nonâ€linear, nonâ€parametric, nonâ€fundamental exchange rate forecasting. Journal of Forecasting, 25(4), 227–245., Retrieved from https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%252Ffor.986;h=repec:jof:jforec:v:25:y:2006:i:4:p:227-245.
Igartua, J.-J., & Hayes, A. F. (2021). Mediation, moderation, and conditional process analysis: Concepts, computations, and some common confusions. The Spanish Journal of Psychology, 24, e49. Retrieved from https://psycnet.apa.org/doi/10.1017/SJP.2021.46
Juraku, K., & Sugawara, S.-E. (2021). Structural ignorance of expertise in nuclear safety controversies: Case analysis of post-Fukushima Japan. Nuclear Technology, 207(9), 1423–1441., https://doi.org/10.1080/00295450.2021.1908075.
Karunarasan, D., Sooriyarachchi, R., & Pinto, V. (2021). A comparison of Bayesian Markov chain Monte Carlo methods in a multilevel scenario. Communications in Statistics-Simulation and Computation, 1–17. https://doi.org/10.1080/03610918.2021.1967985.
Linardon, J., Tylka, T. L., & Fullerâ€Tyszkiewicz, M. (2021). Intuitive eating and its psychological correlates: A metaâ€analysis. International Journal of Eating Disorders, 54(7), 1073–1098. https://doi.org/10.1002/eat.23509.
Long, Y., Lv, Q., Wen, X., & Yan, S. (2023). Bayesian logistic regression in providing categorical streamflow forecasts using precipitation output from climate models. Stochastic Environmental Research and Risk Assessment, 37(2), 639–650. https://doi.org/10.1007/s00477-022-02295-y
Malerba, L., Al Mazouzi, A., Bertolus, M., Cologna, M., Efsing, P., Jianu, A., Kinnunen, P., Nilsson, K.-F., Rabung, M., & Tarantino, M. (2022). Materials for sustainable nuclear energy: A European strategic research and innovation agenda for all reactor generations. Energies, 15(5), 1845., https://doi.org/10.3390/en15051845.
Meuleman, B., Loosveldt, G., & Emonds, V. (2015). Regression analysis: Assumptions and diagnostics. Regression Analysis and Causal Inference, 83–110., Retrieved from https://methods.sagepub.com/book/regression-analysis-and-causal-inference/n5.xml.
Mohamed, M. A., & Kurnaz, S. (2023). Predicting and Analysis Electrical Energy Consumption by Using Data Mining Algorithms. International Journal of Scientific Trends, 2(7), 109–122., Retrieved from https://scientifictrends.org/index.php/ijst/article/download/116/101.
Papandrianos, N. I., Feleki, A., Moustakidis, S., Papageorgiou, E. I., Apostolopoulos, I. D., & Apostolopoulos, D. J. (2022). An explainable classification method of SPECT myocardial perfusion images in nuclear cardiology using deep learning and grad-CAM. Applied Sciences, 12(15), 7592. https://doi.org/10.3390/app12157592.
Pelz, M.-T., Schartau, M., Somes, C. J., Lampe, V., & Slawig, T. (2023). A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research. Geoscientific Model Development Discussions, 2023, 1–32. https://doi.org/10.5194/gmd-2023-17.
Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O., & Droit, A. (2021). Integration strategies of multi-omics data for machine learning analysis. Computational and Structural Biotechnology Journal, 19, 3735–3746. https://doi.org/10.1016/j.csbj.2021.06.030.
Purwanto, A. (2021). Education research quantitative analysis for little respondents: Comparing of Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS. Jurnal Studi Guru Dan Pembelajaran, 4(2). https://doi.org/10.30605/jsgp.4.2.2021.1326.
Ring, C., Blanchette, A., Klaren, W. D., Fitch, S., Haws, L., Wheeler, M. W., DeVito, M., Walker, N., & Wikoff, D. (2023). A multi-tiered hierarchical Bayesian approach to derive toxic equivalency factors for dioxin-like compounds. Regulatory Toxicology and Pharmacology, 143, 105464. https://doi.org/10.1016/j.yrtph.2023.105464.
Rosato, C., Devlin, L., Beraud, V., Horridge, P., Schön, T. B., & Maskell, S. (2022). Efficient learning of the parameters of non-linear models using differentiable resampling in particle filters. IEEE Transactions on Signal Processing, 70, 3676–3692. https://doi.org/10.48550/arXiv.2111.01409.
Ruso, L. A., Ankowski, A., Bacca, S., Balantekin, A., Carlson, J., Gardiner, S., Gonzalez-Jimenez, R., Gupta, R., Hobbs, T., & Hoferichter, M. (2022). Theoretical tools for neutrino scattering: Interplay between lattice QCD, EFTs, nuclear physics, phenomenology, and neutrino event generators. arXiv Preprint arXiv:2203.09030. https://doi.org/10.48550/arXiv.2203.09030.
Şahin, M., & Aybek, E. (2019). Jamovi: An easy to use statistical software for the social scientists. International Journal of Assessment Tools in Education, 6(4), 670–692. https://doi.org/10.21449/ijate.661803.
Seki, T., Hamazaki, K., Natori, T., & Inadera, H. (2019). Relationship between internet addiction and depression among Japanese university students. Journal of Affective Disorders, 256, 668–672. https://doi.org/10.1016/j.jad.2019.06.055.
Shu, X., & Ye, Y. (2023). Knowledge Discovery: Methods from data mining and machine learning. Social Science Research, 110, 102817. https://doi.org/10.1016/j.ssresearch.2022.102817.
Stott, N., & Bosman, I. (2021). Nuclear Science and Technology: Driving Africa’s Development. Retrieved from https://saiia.org.za/research/nuclear-science-and-technology-driving-africas-development/.
Taylor, J. W. (2000). A quantile regression neural network approach to estimating the conditional density of multiperiod returns. Journal of Forecasting, 19(4), 299–311. https://doi.org/10.1002/1099-131X(200007)19:4%3C299::AID-FOR775%3E3.0.CO;2-V.
Vatturi, P., & Wong, W. K. (2009, June). Category detection using hierarchical mean shift. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 847-856). https://doi.org/10.1145/1557019.1557112.
Wang, X., & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and recommendations. Chest, 158(1), S65–S71., https://doi.org/10.1016/j.chest.2020.03.012.
Wiedermann, W., & Li, X. (2018). Direction dependence analysis: A framework to test the direction of effects in linear models with an implementation in SPSS. Behavior Research Methods, 50, 1581–1601., Retrieved from https://link.springer.com/article/10.3758/s13428-018-1031-x.
Wongso, E., Nateghi, R., Zaitchik, B., Quiring, S., & Kumar, R. (2020). A dataâ€driven framework to characterize stateâ€level water use in the United States. Water Resources Research, 56(9), e2019WR024894., https://doi.org/10.1029/2019WR024894.
Xu, N., Lovreglio, R., Kuligowski, E. D., Cova, T. J., Nilsson, D., & Zhao, X. (2023). Predicting and Assessing Wildfire Evacuation Decision-Making Using Machine Learning: Findings from the 2019 Kincade Fire. Fire Technology, 59(2), 793–825., Retrieved from https://www.frames.gov/catalog/67471.
Ylönen, M., & Björkman, K. (2023). Integrated management of safety and security (IMSS) in the nuclear industry–Organizational culture perspective. Safety Science, 166, 106236., https://doi.org/10.1016/j.ssci.2023.106236.
Zhao, X., Kim, J., Warns, K., Wang, X., Ramuhalli, P., Cetiner, S., Kang, H. G., & Golay, M. (2021). Prognostics and health management in nuclear power plants: An updated method-centric review with special focus on data-driven methods. Frontiers in Energy Research, 9, 696785. https://doi.org/10.3389/fenrg.2021.696785.
Zhong, W., Liu, Y., & Zeng, P. (2023). A model-free variable screening method based on leverage score. Journal of the American Statistical Association, 118(541), 135–146. https://doi.org/10.1080/01621459.2021.1918554.
Author Biographies
Budiman Nasution, Universitas Negeri Medan, Medan
Winsyahputra Ritonga, Universitas Negeri Medan, Medan
Ruben Cornelius Siagian, Departemen of Physics, Universitas Negeri Medan
Paulus Dolfie Pandara, Universitas Sam Ratulangi, Manado
Lulut Alfaris, Department of Marine Technology, Pangandaran
Aldi Cahya Muhammad, Islamic University of Technology, Dhaka
Arip Nurahman, Indonesian Institute of Education, Garut
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Copyright (c) 2023 Budiman Nasution, Winsyahputra Ritonga, Ruben Cornelius Siagian, Paulus Dolfie Pandara, Lulut Alfaris, Aldi Cahya Muhammad, Arip Nurahman
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