Image
10.33826/ijmras/v05i04.1

ANALOGIC NON-PROPERLY PREPARED TEACHERS VERSUS NOISY CONTAMINATED OPTICAL CHARACTER RECOGNITION REGARDING STUDENTS’ ACADEMIC PERFORMANCE, ADOPTING ARTIFICIAL NEURAL NETWORKS’ MODELING

Abstract

This Research paper tackles an important and interestingly complex, and challenging educational problematic phenomenon. Specifically, it addresses two analogously interrelated issues namely: the non-properly prepared teachers that are characterized by the undesirable impact on students' academic achievement inside classrooms. Additionally, Herein, this issue is shown to be analogous to the recognition process of noisy contaminated Optical Character Recognition (OCR). Briefly, this comparative study objectively illustrates the analogous relationship between contaminated noisy information provided by the non-properly prepared teaching process versus the noisy contaminated (OCR) process.

In more detail, various noisy power level values which changed in the learning environment resulted in considerable correspondence with different learning rate values. The unfavorable amount of teacher’s improperness is mapped similar to well-known communication technology term namely signal to noise (S/N) ratio. Which quantitatively measures the clarity degree related to received desired learning/teaching signal across the educational communication channel. In other words, it illustrates simulated outcomes presented as a percentage of lessons’ focusing degree versus # Neurons for different learning rate values.  More properly. The performance of non-properly prepared teachers results in noisy information submitted to children’s brains in classrooms. Accordingly. it observed annoyance in the learning environment and negatively affects the quality of children’s learning performance. Herein, this research work illustrates specifically the analogy between learning under a noisy data environment in Artificial Neural Networks (ANNS) models versus the effect of the physical environment on the quality of education in classrooms. The observed non-properly prepared teachers' phenomenon in classrooms was observed to have a negatively undesired effect on the evaluated educational process performance. Analogously, the observed effect of additively contaminating noise power on any map size made with the resolution of (3x3) pixels.  These pixels were associated with diverse three English clear characters (T&L, or H) which were originally written over (3x3) binary (black & white) digitized retina. Herein; obtained interesting findings shaded light over more complex challenging research directions towards future more elaborated investigational study for such interdisciplinary observed educational phenomena

Keywords
  • Performance evaluation,
  • Optical character recognition,
  • Artificial neural networks models,
  • Student's learning performance.
References
  • Muna Ahmed Awel, Ali Imam Abidi “REVIEW ON OPTICAL CHARACTER RECOGNITION” International Research Journal of
  • Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06| June 2019 www.irjet.net p-ISSN: 2395-0072© 2019,
  • IRJET| Impact Factor value: 7.211| ISO 9001:2008 Certified Journal| Page 3666 Available online at:
  • https://www.researchgate.net/publication/334162853_review_on_optical_character_recognition
  • Abhishek Das, Mihir Narayan Mohanty “A Useful Review on Optical Character Recognition for Smart Era Generation” Source Title:
  • Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality.Available online at: https://www.igi-
  • global.com/chapter/an-useful-review-on-optical-character-recognition-for-smart-era-generation/268532 Copyright: © 2021
  • |Pages: 1-41 DOI: 10.4018/978-1-7998-4703-8.ch001
  • White House OSTP Issues Decade of the Brain Report, Maximizing Human Potential: 1990-2000. Available online at:
  • https://www.loc.gov/loc/brain/proclaim.html
  • Alicia Puglionesi A Reborn 'Decade of the Brain' Could Cost America More Than Money. Available online on April 9, 2013 // 09:00
  • AM EST at: https://www.vice.com/en/article/ezzwzw/a-reborn-decade-of-the-brain-could-cost-america-more-than-money
  • Summary of that project is available online at: http://www.loc.gov/loc/brain/summary2.html
  • Kelly Morris Advances in the “brain decade” bring new challenges published at LACENT Journal. 2000;355(9197):45. Available:
  • http://www.thelancet.com/pdfs/journals/lancet/PIIS0140673699902222. pdf
  • Kardan AA, Sadeghi H, Ghidary SS, Sani MR. Prediction of student course selection in online higher education institutes using
  • neural networks. Computers & Education. 2013;65:1-1.
  • Lau ET, Sun L, Yang Q. Modelling, prediction and classification of student academic performance using artificial neural networks.
  • SN Applied Sciences. 2019;1(9):1-0.
  • Binh HT, Duy BT. Predicting students' performance based on learning style by using artificial neural networks. In2017 9th
  • international conference on knowledge and systems engineering (KSE). 2017;48-53. IEEE.
  • Bernard J, Chang TW, Popescu E, Graf S. Using artificial neural networks to identify learning styles. An International Conference on
  • Artificial Intelligence in Education. Springer, Cham. 2015;541-544.
  • Signal to noise ratio definition, posted by Margaret Rouse. Available: http://searchnetworking.techtarget.com/definition/signal-to-
  • noise-ratio
  • Mariale Hardiman posted online at:
  • http://www.mona.uwi.edu/cop/sites/default/files/resource/files/The%20Brain%20Targeted%20Teaching%20Model.pdf
  • Kohonen T. "self-organization and Associative Memory": New York, Springer; 1984.
  • Haykin S., Neural Networks, Englewood Cliffs, NJ: Prentice-Hall; 1999.
  • Jayanta Kumar Basu, Debnath Bhattacharyya, Tai-hoon Kim "Use of Artificial Neural Network in Pattern Recognition" International
  • Journal of Software Engineering and Its Applications. 2010;4(2).
  • Hassan MH. Mustafa1, Mahmoud S2, Ibrahim H Assaf3, Ayoub Al- Hamadi4, Zedan M. Abdulhamid5. The comparative analogy of
  • overcrowded effects in classrooms versus solving 'cocktail party problem' (Neural Networks Approach). International Journal of
  • Engineering Science and Innovative Technology (IJESIT) ISSN: 2319- 5967 ISO 9001:2008. 2014;3(2). Published at INTED2014
  • Proceedings. Available: http://library.iated.org/view/MUSTAFA2013ONQ
  • Mustafa H, Mahmoud S, Al-Hamadi A, Abdulhamid ZM, Al- Bassiouni AM. On the quantified evaluation of noisy data impact on
  • children's mental development using artificial neural networks. Published at ICERI2013 Proceedings. Available:
  • http://library.iated.org/view/MUSTAFA2013ONQ
  • Kandel ER. Small systems of neurons. Scientific American, Sept. 1979;224:67-79.
  • Marr D. A theory of cerebellar cortex. T. Physiol. (London). 1969; 202:437-470.
  • Grossberg S. (ED.). Neural networks and natural intelligence. The MIT Press. 1988;1-5.
  • Hassan HM, Ayoub Al-Hamadi, Al-Mohaya F. On quantifying learning creativity using artificial neural networks (A Nero-
  • physiological Cognitive Approach). Published at National Conference on Applied Cognitive Psychology held in India, Calcutta; 2007.
  • Hassan HM. In the simulation of adaptive learner control considering students' cognitive styles using artificial neural networks
  • (ANNs). Published at CIMCA, Austria; 2005.
  • Zhang K, Genzburg I, Sejnowski TJ. Interpreting neuronal population activity by reconstruction. Journal of Neurophysiology. 1998;
  • :1017-44.
  • Brownlee J. Clever algorithms: Nature-inspired programming recipes. Available: http://www.cleveralgorithms.com/nature-
  • inspired/swarm/ant_colony_system.html
  • Vartika Sharma, Susheva Sharma, Noor Danish Ahrar Mundari “Neural Network a Supervised Machine Learning Algorithm “.
  • Published at International Journal of Engineering Development and Research © 2015 IJEDR | Volume 3, Issue 2 | ISSN: 2321-9939 ©
  • , pp.662-668
  • Hassan HM, et.al. Towards the evaluation of phonics method for the teaching of reading using artificial neural networks (A
  • Cognitive Modeling Approach). Published at IEEE Symposium on Signal Processing and Information Technology Seventh
  • Symposium held in Cairo-Egypt; 2007.
  • Fukaya M. Two-level neural networks: Learning by interaction with the environment. 1st ICNN, San Diego; 1988.
  • Jeffheaton. In introducing the Kohonen neural network. On sun. 2007; 21:48. Available:
  • http://www.heatonresearch.com/articles/6/page2.html
  • Jochen Fröhlich, "Neural Networks with Java: Neural Net Components in an Object-Oriented Class Structure", Fachhochschule
  • Regensburg, Department of Computer Science, 1997, available online at address: http://fbim.fh
  • regensburg.de/~saj39122/jfroehl/diplom/e-idex.html.
  • Ghonaimy MA, Al–Bassiouni AM, Hassan HM. Leaning of neural networks using noisy data. Second
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

M. H. Mustafa1, H., Mohamed I. A. Ibrahim, & Hany S. Ramzy. (2022). ANALOGIC NON-PROPERLY PREPARED TEACHERS VERSUS NOISY CONTAMINATED OPTICAL CHARACTER RECOGNITION REGARDING STUDENTS’ ACADEMIC PERFORMANCE, ADOPTING ARTIFICIAL NEURAL NETWORKS’ MODELING. International Journal of Multidisciplinary Research and Studies, 5(04), 01–14. https://doi.org/10.33826/ijmras/v05i04.1

Download Citation

Downloads

Download data is not yet available.

Similar Articles

You may also start an advanced similarity search for this article.