Application of neural network in the discovery of functional knowledge based on the rational education of Avicenna and Kant

Samadi, Elham; Bakhtiyar Nasrabadi, Hasanali; Saadatmand, Zohreh

doi:10.22034/kps.2024.424645.1167

Application of neural network in the discovery of functional knowledge based on the rational education of Avicenna and Kant

Document Type : Original Research Manuscripts

Authors

Elham Samadi ¹

Hasanali Bakhtiyar Nasrabadi ²

Zohreh Saadatmand ³

¹ Ph.D. Student, Department of Educational Science, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.

² Professor, Department of Educational Science, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.

³ Associate Professor, Department of Educational Science, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.

https://doi.org/10.22034/kps.2024.424645.1167

Abstract

The aim of this research is to employ neural networks in discovering functional knowledge based on the rational training of Avicenna and Kant. The methodology of this study is based on deep learning neural networks, making it an exploratory research. Given the practicality of functional knowledge, this research is applied in nature. To assess the significance of components and evaluation indicators of functional knowledge, text mining and the frequency of related symbols have been used. In order to utilize data mining techniques in this research, the WEKA software has been employed. The algorithms considered for implementation in this study are MLP, SVR, AdaBoost.R, Bagged Trees (BAGTREE), Linear Regression (LR), and Least Squares Support Vector Regression (LSSVR). According to the results obtained for functional knowledge, the LSSVR and SVR methods outperform the others, indicating their superiority. As the charts illustrate, there is significant volatility in this dataset, making prediction challenging. Furthermore, the R2 value is very close to one, indicating relatively accurate predictions by the methods. Neural networks can serve as powerful tools to aid in rational thinking, logical decision-making, and better understanding of the surrounding world, in line with the perspectives of Avicenna and Kant. These tools can assist in analyzing and interpreting complex data in these fields and strive for rationality and human excellence.

Keywords

Artificial neural network

Text Analysis

Functional knowledge

Subjects

Knowledge Extraction

Alsayat, A., & Ahmadi, H. (2023). A hybrid method using ensembles of neural network and text mining for learner satisfaction analysis from big datasets in online learning platform. Neural Processing Letters, 55(3), 3267-3303. https://doi.org/10.1007/s11063-022-11009-y

Alvandian, M., & Badeshti. A., (2018).Avencia’s anthropology. scientific-research quarterly, religious and theological research. 9(33), 73-90

Alvargonzález, D. (2022). Modes and Dimensions of Being. Axiomathes, 32(Suppl 2), 241-261. https://doi.org/10.1007/s10516-021-09596-x

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., ... & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data, 8, 1-74. https://doi.org/10.1186/s40537-021-00444-8

Aprison, W. (2021). The Analysis of Educational Thought According To Ibn Sina And Its Relevance In Islamic Education In The Modern Era. International Journal Of Humanities Education and Social Sciences (IJHESS), 1(3). https://doi.org/10.55227/ijhess.v1i3.62

Attar, S. (2012). Suppressed or Falsified History? The Untold Story of Arab-Islamic Rationalist Philosophy. In The Role of the Arab-Islamic World in the Rise of the West: Implications for Contemporary Trans-Cultural Relations (pp. 116-143). London: Palgrave Macmillan UK. https://doi.org/10.1057/9780230393219_6

Båve, A. (2019). Concept designation. American Philosophical Quarterly, 56(4), 331-344. https://doi.org/10.2307/48563047

Benna, P. (2023). Avicenna or pseudo-Avicenna? Acta Neurologica Belgica, 1-1. https://doi.org/10.1007/s13760-023-02307-w

CheshmehSohrabi, M., & Mashhadi, A. (2023). Using data mining, text mining, and bibliometric techniques to the research trends and gaps in the field of language and linguistics. Journal of Psycholinguistic Research, 52(2), 607-630. https://doi.org/10.1007/s10936-022-09911-6

Chia, P. S. (2022). The Divine Knowledge in Relation to Determinism in the Philosophy of Avicenna. Sophia, 61(2), 319-329. https://doi.org/10.1007/s11841-021-00876-y

Dalfardi, B., Esnaashary, M. H., & Yarmohammadi, H. (2014). Rabies in medieval Persian literature–the Canon of Avicenna (980–1037 AD). Infectious diseases of poverty, 3, 1-6. https://doi.org/10.1186/2049-9957-3-7

Forouzian, M., Rasekhi, F., & Nazarnejad, N. (2021). Comparing the Consolations of Ibn Sina and Kant in the Face of Financial Poverty. Journal of Philosophical Theological Research, 23(4), 143-164. doi: 10.22091/jptr.2021.6736.2507

Ghaffari, I., (2015). Examining avencia’s educational opinions regarding the goals of education.The third international conference on modern researches in management.economics and human sciences, 13-5. (In Persian)

Gharayaq Zandi, D. (2020). The Relationship Between Knowledge And Leadership In Ibn Sina’s Thought. Journal of Islamic Political Studies, 2(3), 143-168.

Hajar, R. (2013). The air of history (part V) Ibn Sina (Avicenna): the great physician and philosopher. Heart views: the official journal of the Gulf Heart Association, 14(4), 196. https://doi.org/10.4103/1995-705X.126893

Kaukua, J. (2020). Avicenna on negative judgement. Topoi, 39(3), 657-666. https://doi.org/10.1007/s11245-016-9380-5

Lombardi, F., & Marinai, S. (2020). Deep learning for historical document analysis and recognition—A survey. Journal of Imaging, 6(10), 110. https://doi.org/10.3390/jimaging6100110

Masjedy, H., Adel, S. M. R., Amirian, S. M. R., & Zareian, G. (2022). An Overview of Text Mining in Language Studies: The Computational Approach to Text Analytics. Language Related Research, 12(6), 499-531.

Prusa, J. D., & Khoshgoftaar, T. M. (2017). Improving deep neural network design with new text data representations. Journal of Big Data, 4, 1-16. https://doi.org/10.1186/s40537-017-0065-8

Putri, Y., & Nurhuda, A. (2023). IBN SINA'S THOUGHTS RELATED TO ISLAMIC EDUCATION. JURNAL HURRIAH: Jurnal Evaluasi Pendidikan dan Penelitian, 4(1), 140-147. https://doi.org/10.56806/jh.v4i1.121

Rafiee, M., & Keramatfar, A. (2022). Analytical Comparison of Iranian Scientific Documents in Text Mining. Caspian Journal of Scientometrics, 9(1), 104-116.

Salehi, P. (2021). Text mining an Effective Tool for Documenting and Completing Oral History interviews. Biannual Journal of Oral History, 13(1), 69-82.

Salleh, S., & Embong, R. (2017). Educational views of Ibnu Sina: Pemikiran pendidikan Ibnu Sina. Al-Irsyad: Journal of Islamic and Contemporary Issues, 2(1), 13-24. https://doi.org/10.53840/alirsyad.v2i1.23

Schierbaum, S., & Perälä, M. (2020). Introduction: Negative Judgement: Ancient, Medieval, and Modern Perspectives. Topoi, 39, 639-643. https://doi.org/10.1007/s11245-020-09690-6

Tayarani, N., & Jalali, M. (2019). Presenting a Novel Hybrid Approach of Text Mining Sentiment Analysis in Twitter Using CART Decision Tree. Engineering Management and Soft Computing, 5(1), 210-227.

Wang, L. N., Zheng, Y., Wei, H., Dong, J., & Zhong, G. (2023). Stretching Deep Architectures: A Deep Learning Method without Back-Propagation Optimization. Electronics, 12(7), 1537. https://doi.org/10.3390/electronics12071537

Woleński, J. (2019). Truth from Anselm of Canterbury to Kant. In: Semantics and Truth. Logic, Epistemology, and the Unity of Science, Springer, Cham. 45. https://doi.org/10.1007/978-3-030-24536-8