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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

Efficient resource allocation in healthcare systems through deep learning and optimization science

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pp. 1277–1288Vol. 46Issue 4-BMay 2025DOI: 10.47974/JIOS-1989XML
Received:
15 Oct 2024
Published Online:
31 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1989
Pages:
1277–1288

Abstract

Efficient resource allotment in healthcare frameworks could be a basic challenge, particularly given the expanding request for quality care and constrained assets. This term paper investigates the integration of Deep learning and optimization science as a vigorous approach to address this challenge. Deep learning methods give precise expectations of healthcare requests, whereas optimization models help allocate resources powerfully and effectively. This cooperative energy empowers healthcare frameworks to play down costs, move forward understanding results, and improve generally framework proficiency. The paper talks about key strategies, case thinks about, and future inquire about bearings in applying Deep learning and optimization science to healthcare asset allotment.

Keywords

Subject Classifications

68M07

References

[1] H. K. Tripathy, S. Mishra, and Abhishek, A Succinct Analytical Study of the Usability of Encryption Methods in Healthcare Data Security. Singapore: Springer Nature Singapore, pp. 105-120 (2022).
[2] D. M. Bavkar, R. Kashyap, and V. Khairnar, “Multimodal sarcasm detection via hybrid classifier with optimistic logic,” Journal of Telecommunications and Information Technology, vol. 3, pp. 97-114 (2022).
[3] S. Suman, S. Mishra, and H. K. Tripathy, “A Support Vector Machine Approach for Effective Bicycle Sharing in Urban Zones,” in Cognitive Informatics and Soft Computing: Proceeding of CISC 2020, pp. 73-83 (2021).
[4] D. Pathak, R. Kashyap, and S. Rahamatkar, “A study of deep learning approach for the classification of Electroencephalogram (EEG) brain signals,” in Artificial Intelligence and Machine Learning for EDGE Computing, pp. 133-144 (2022).
[5] R. Golcha, P. Khobragade and A. Talekar, “Multimodal Deep Learning for Advanced Health Monitoring A Comprehensive Approach for Enhanced Precision and Early Disease Detection,” 2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India, pp. 1-6 (2024), doi: 10.1109/ICITIIT61487.2024.10580622.
[6] H. Halabian, “Distributed resource allocation optimization in 5G virtualized networks,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 3, pp. 627-642 (2019).
[7] H. Sahu, R. Kashyap, and B. K. Dewangan, “Hybrid Deep Learning-Based Semi-supervised Model for Medical Imaging,” in 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON), 2023, pp. 1-6.
[8] E. L. Huamani and L. Ocares-Cunyarachi, “Analysis and prediction of recorded COVID-19 infections in the constitutional departments of Peru using specialized machine learning techniques,” International Journal of Emerging Technology and Advanced Engineering, vol. 11, no. 11, pp. 39-47 (2021).
[9] Z. M. Zabidi, A. N. Alias, N. A. Zakaria, Z. S. Mahmud, R. Ali, M. K. Yaakob, and S. Masrom, “Machine learning predictor models in the electronic properties of alkanes based on degree-topology indices,” International Journal of Emerging Technology and Advanced Engineering, vol. 11, no. 11, pp. 1-14 (2021).
[10] L. Liu and Z. Li, “Permissioned blockchain and deep reinforcement learning enabled security and energy efficient healthcare Internet of Things,” IEEE Access, vol. 10, pp. 53640-53651 (2022).
[11] Z. Chkirbene, R. Hamila, and A. Erbad, “Secure medical data sharing for healthcare system,” in Proceedings of the IEEE 33rd Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 641-647 (2022).
[12] Lu-Cheng Zhu, Yun-Liang Ye, Wen-Hua Luo, Meng Su, Hang-Ping Wei, Xue-Bang Zhang, Juan Wei, and Chang-Lin Zou, “A model to discriminate malignant from benign thyroid nodules using artificial neural network,” PloS One, vol. 8, no. 12, pp. e82211 (2013).
[13] S. Sarraf and G. Tofighi, “Classification of Alzheimer’s Disease using fMRI Data and Deep Learning Convolutional Neural Networks,” (2016).

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