Bipolar Neutrosophic Convolutional Neural Networks For Child Malnutrition Prediction Through Neutrosophic Set Domain.
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Abstract
Specifically, epistemic uncertainty, which reflects the model's lack of knowledge about the data, is the sort of uncertainty that has a significant impact on the performance of deep learning models employed for malnutrition prediction. The uncertainty in malnutrition dataset must be successfully resolved by enhancing deep learning architecture. To solve the issue of uncertainty information’s in malnutrition, Bipolar Neutrosophic Convolutional Neural Networks (BNCNN) is developed for extracting different deep features to generate predictive uncertainty estimates. A bipolar neutrosophic set is characterized by the positive-membership degree, where is a truth-membership function, indeterminacy-membership function, and falsity-membership function, and the negative-membership degree, where is a truth-membership function, indeterminacy-membership function, and falsity-membership function. Compared to Convolutional Neural Networks, the Bipolar neutrosophic is produced more accuracy results.
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References
Elyas Rashno; Ahmad Akbari; Babak Nasersharif,” A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition”, IEEE Xplore: 05 August 2019.
P. Bosc, O. Pivert, On a fuzzy bipolar relational algebra, Information Sciences, 219(2013) 1–16.
Nour Eldeen M. Khalifa1 · Florentin Smarandache2 · Gunasekaran Manogaran3,4 · Mohamed Loey5,” A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID‑19 Chest X‑ray Dataset” LLC part of Springer Nature 2021.
R. Sundareswaran, V. Mahesh, Said Broumi, R. Dhanush Babu, M Vahini, “Diabetic Neuropathy Severity Assessment: A Neutrosophic Approach”, International Journal of Neutrosophic Science (IJNS) Vol. 18, No. 04, PP. 291-300, 2022.
Gia Nhu Nguyen, Le Hoang Son, Amira S. Ashour, Nilanjan Dey,” A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses”, Springer-Verlag Berlin Heidelberg 2017.
Carlos Rosero Martínez1, German Acurio Hidalgo2, Marvelio Alfaro Matos3 and Florentin Smarandache4 “Neutrosophy for Survey Analysis in Social Sciences”, Neutrosophic Sets and Systems, Vol. 37, 2020.
Smritikana Ghosh,” Factors Responsible for Childhood Malnutrition: A Review of The Literature”, Current Research in Nutrition and Food Science, Vol. 08, No. (2) 2020, Pg. 360-370.
Elyas Rashno, Ahmad Akbari, Babak Nasersharif, “Uncertainty handling in convolutional neural networks”, Springer Nature 2022.
K. M. Lee, “Bipolar-valued fuzzy sets and their operations,” in Proceedings of the 2000 International Conference on Intelligent Technologies, pp. 307–312, Bangkok, 'ailand, 2000.
10. I. Deli, M. Ali, and F. Smarandache, “Bipolar neutrosophic sets and their application based on multi-criteria decision making problems,” in Proceedings of the 2015 International Conference on Advanced Mechatronic Systems (ICAMechS), Beijing, China, 2015.
D. Shen, G. Wu, and H.-I. Suk, "Deep learning in medical image analysis," Annual review of biomedical engineering, vol. 19, pp. 221-248, 2017.
A. Q. Ansari, R. Biswas, and S. Aggarwal, "Proposal for applicability of neutrosophic set theory in medical AI," International Journal of Computer Applications, vol. 27, pp. 5-11, 2011.
G. Deschrijver and E. E. Kerre, "On the relationship between some extensions of fuzzy set theory," Fuzzy sets and systems, vol. 133, pp. 227-235, 2003.
H. J. Zimmermann, "Fuzzy set theory," Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2, pp. 317-332, 2010.
W. Feller, An introduction to probability theory and its applications, vol 2: John Wiley & Sons, 2008.
Y. Yang, C. Zheng, and P. Lin, "Fuzzy C-means clustering algorithm with a novel penalty term for image segmentation," Optoelectronics Review, vol. 13, p. 309, 2005.
F. Smarandache, S. Broumi, P. K. Singh, C.-f. Liu, V. V. Rao, H.-L. Yang, et al., "Introduction to neutrosophy and neutrosophic environment," in Neutrosophic Set in Medical Image Analysis, ed: Elsevier, 2019, pp. 3-29.
F. Smarandache and N. P. Neutrosophy, "Set, and Logic, ProQuest Information & Learning," Ann Arbor, Michigan, USA, p. 105, 1998.
L. Zhang and Y. Zhang, "A novel region merge algorithm based on neutrosophic logic," International Journal of Digital Content Technology and its Applications, vol. 5, pp. 381-7, 2011.
J. Mohan, V. Krishnaveni, and Y. Guo, "A neutrosophic approach of MRI denoising," in 2011 International Conference on Image Information Processing, 2011, pp. 1-6.