Abstract
The Earthquake is an essential problem in human life, by using machine learning techniques in earthquake prediction, we can save humankind. Using the successful application of machine learning techniques indicates that it would be possible to use them to make accurate forecasts to avoid short-term earthquake damage. In this paper, with the first aim, we have applied seven machine learning techniques, namely, Artificial Neural Network (ANN), Decision Tree (DT), Logistic Regression, Random Forest Classification, Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) to reach the best technique for prediction. The second aim used the methods of two learning styles, surface and deep learning, in training students with programming skills to use the seven techniques. Through two experimental groups, one of them used the method of surface learning (collective), and the other used the method of deep learning (individual).This is to determine the best learning style to teach students programming skills.
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References
Murwantara, M., Yugopuspito, P., Hermawan,R. (2014). Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data. Neurocomputing, 135, 273–28.
Ruano, A., Madureira, G., Barros, O., Khosravani, H., Ruano, M., Ferreira, P. (2020, June). Seismic detection using support vector machines, TELKOMNIKA Telecommunication, Computing, Electronics and Control, 18, 1332.
Snyder, R. F. (2000). The relationship between learning styles/multiple intelligences and academic achievement of high school students. High School Journal, 83(2), 11–21.
Asim, K., Idris, A., Iqbal, T., Martínez-Álvarez, F. (2018). Earthquake prediction model using support vector regressor and hybrid neural networks, open access. PLOS ONE, 13, 1–22.
Asim, K., Martínez-Álvarez, F., Basit, A., & Iqbal, T. (2017). Earthquake magnitude prediction in Hindukush region using machine learning techniques. Natural Hazards, 85, 471–486.
Asim, K., Martínez-Álvarez, F., Idris, A., & Iqbal, T. (2016). Short term earthquake prediction in Hindukush Region using tree based ensemble learning. IEEE, 1, 365–370.
Chaurasia, K., Kanse, S., Yewale, A., Singh, V., Sharma, B., & Dattu, B. R. (2019). Predicting damage to buildings caused by earthquakes using machine learning techniques. IEEE, 7, 81–86.
Mallouhy, R., Abou Jaoude, C.,Guyeux, C., & Makhoul, A. (2019). Major earthquake event prediction using various machine-learning algorithms. IEEE, 2, 1–7.
Ismail, N., & Abdulwareth, S. (2012). The contributions of students learning styles, thinking styles and their attitudes towards studying their specializations in the English Language to their achievement at the college level. Journal of the Faculty of Education, Zagazig University, 77, 333–374.
Al-omari, E. (2018). The big five personality factors and learning environment as predictors for the surface and deep learning styles among a sample of Yarmouk University Students. PH.D Dissertation, Yarmouk University, pp. 1–151.
Hasan, A., & Al-Zahrany, A. (2019). The effect of augmented reality and learning style (deep- surface) in the development of web-design skills among educational technology students in the Faculty of Education of the University of Jeddah. Journal of the Faculty of Education, Sohag University, 68, 1563–1591.
Alves, E. I. (2006). Earthquake forecasting using neural networks: Results and future work. Nonlinear Dynamics, 44, 341–349.
Alves, E. I. (1995). A simple seismic computational model. Memórias e Notícias 37–42.
Gu, G., He, Q., Wang, B., & Dai, B. (2018). Comparison of machine learning techniques for the detection of the electricity theft. IEEE, 2, 116–119.
Li, A., & Li, K. (2009). KNN-based modeling and its application in aftershock prediction. In 2009 International Asia Symposium on Intelligent Interaction and Affective Computing, IEEE, pp. 83–86.
Cover, T., Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13, 21–27.
Wahbeh, A., Al-Radaideh, Q., Al-Kabiand, M., et al. (2011). A comparison study between data mining tools over some classification methods. International Journal of Advanced Computer Science and Applications, 35, 18–26.
Greenlan, S. (1994). Alternative models for ordinal logistic regression. Statistics in Medicine, 13, 1665–1677.
Manning, C., Raghavan, P., Sch¨utze, H. (2010). Introduction to information retrieval. Natural Language Engineering, 16, 100–103.
Vapnik, V. (1995). The nature of statistical learning theory. Springer.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.
Rokach, L., Maimon, O. (2005). Decision trees. In Data mining and knowledge discovery handbook, pp. 165–192. Springer.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Asim, K., Idris, A., Alvarez, F., Iqbal, T. (2016). Short term earthquake prediction in Hindukush Region using tree based ensemble learning. In Conference: International Conference on Frontiers of Information Technology (FIT), vol. 1, pp. 365–370.
Backer, R., & Yelich, S. (2002). Comparison of learning styles and student achievement of aviation students, San Jose. San Jose State University.
Acknowledgements
This work was supported by the Deanship of Scientific Research, King Faisal University, Saudi Arabia [grant number NA000175].
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Mohamed El Koshiry, A., Gomaa, M.M., Hamed Ibraheem Eliwa, E., Abd El-Hafeez, T. (2023). Using Machine Learning Techniques for Earthquake Prediction Through Student Learning Styles. In: Pati, B., Panigrahi, C.R., Mohapatra, P., Li, KC. (eds) Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering. Lecture Notes in Networks and Systems, vol 428. Springer, Singapore. https://doi.org/10.1007/978-981-19-2225-1_42
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