Artificial Neural Network (ANN) Classification: Titanic Passenger Safety
Abstract
Scientific and technological innovation has always been the main driver of economic growth and social progress. The rapid development of technology and advances in the internet have made it possible to disseminate information and interact more easily. With the rapid development of technology, a lot of information is shared every second, resulting in big data in terms of different, complex variables. ANN is the result of work in the computer field that is inspired by the capabilities of the human brain which consists of biological neural networks. In recent years, the use of artificial neural networks (ANN) has increased. The research carried out aims to analyze the survival capabilities of Titanic passengers who experienced an accident while sailing and sank. This research uses initial data of 1309 observations with 14 variables. From the research results, 2 hidden variables are the most accurate with an accuracy of 80.5%, compared to the number of hidden variables of 3 (79%) and 4 (79%). So it can be concluded that the number of hidden variables with the same number of hidden screens does not have a significant difference in accuracy
References
Baygin, M. (2021). An accurate automated schizophrenia detection using TQWT and statistical moment based feature extraction. Biomedical Signal Processing and Control, 68(April), 102777. https://doi.org/10.1016/j.bspc.2021.102777
Betiku, E., Okunsolawo, S. S., Ajala, S. O., & Odedele, O. S. (2015). Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter. Renewable Energy, 76, 408–417. https://doi.org/10.1016/j.renene.2014.11.049
Chaki, S., & Biswas, T. K. (2023). An ANN-entropy-FA model for prediction and optimization of biodiesel-based engine performance. Applied Soft Computing, 133, 109929. https://doi.org/10.1016/j.asoc.2022.109929
Choi, H. Y., & Park, J. (2022). Do data-driven CSR initiatives improve CSR performance? The importance of big data analytics capability. Technological Forecasting and Social Change, 182(July 2021), 121802. https://doi.org/10.1016/j.techfore.2022.121802
Cui, Y., Ma, Z., Wang, L., Yang, A., Liu, Q., Kong, S., & Wang, H. (2022). A Survey on Big Data-Enabled Innovative Online Education Syst ems During the COVID-19 Pandemic. Journal of Innovation & Knowledge, 100295. https://doi.org/10.1016/j.jik.2022.100295
Custers, B. (2022). New digital rights: Imagining additional fundamental rights for the digital era. Computer Law and Security Review, 44, 105636. https://doi.org/10.1016/j.clsr.2021.105636
Esfe, M. H., Toghraie, D., & Amoozad, F. (2022). Optimization and design of artificial neural network with Levenberg-Marquardt algorithm to increase accuracy in predicting the dynamic viscosity of SAE40 oil-based hybrid nano-lubricant. Powder Technology, 415(October 2022), 118097. https://doi.org/10.1016/j.powtec.2022.118097
Geerts, G. L., & O’Leary, D. E. (2022). V-Matrix: A wave theory of value creation for big data. International Journal of Accounting Information Systems, 47(January), 100575. https://doi.org/10.1016/j.accinf.2022.100575
Management, D., & Homes, S. (2019). Analytics-Assisted Smart Power Meters Considering. Sensors, 19(9), 1–26.
Nikoo, M., Abbasi Malekabadi, R., & Hafeez, G. (2023). Estimating the mechanical properties of Heat-Treated woods using Optimization Algorithms-Based ANN. Measurement: Journal of the International Measurement Confederation, 207(March 2022), 112354. https://doi.org/10.1016/j.measurement.2022.112354
Ouyang, X., Sun, Z., & Xu, X. (2023). Patent system in the digital era - Opportunities and new challenges. Journal of Digital Economy. https://doi.org/10.1016/j.jdec.2022.12.003
Pei, Y., Tang, X., Zhang, Y., Huang, Y., & Dong, L. (2022). Brief introduction of machine learning on cSDH patients. Clinical Neurology and Neurosurgery, 213(98), 106982. https://doi.org/10.1016/j.clineuro.2021.106982
Teke, C., Akkurt, I., Arslankaya, S., Ekmekci, I., & Gunoglu, K. (2023). Prediction of gamma ray spectrum for 22Na source by feed forward back propagation ANN model. Radiation Physics and Chemistry, 202(October 2022), 110558. https://doi.org/10.1016/j.radphyschem.2022.110558
Yang, Z., Tang, R., Zeng, W., Lu, J., & Zhang, Z. (2021). Short-term prediction of airway congestion index using machine learning methods. Transportation Research Part C: Emerging Technologies, 125(September 2020), 103040. https://doi.org/10.1016/j.trc.2021.103040
