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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
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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Journal of Information and Optimization Sciences
Open Access Research Article

The scalable cloud-based reinforcement learning for multimedia data in cognitive Neuroscience for secure healthcare analysis

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pp. 1793–1801Vol. 46Issue 6September 2025DOI: 10.47974/JIOS-2008XML
Received:
10 Dec 2024
Published Online:
30 Sep 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2008
Pages:
1793–1801

Abstract

Recently, the field of neuroscience and the complexity of brain research data have increased due to advancements in imaging technologies, electrophysiological recordings, and genetic sequencing. Managing and organizing this vast amount of data is a big challenge. Our research explores the integration or participation of cloud-based multimedia data systems in cognitive neuroscience and brain research, emphasizing their role in data storage, security, retrieval, encryption, and analysis.  Technology addresses these challenges, providing data storage and more efficient data analysis methods. As neuroscience continues to evolve, generating increasingly complex datasets, the role of multimedia data systems becomes crucial in unlocking brain functions and understanding neurological disorders. Additionally, cloud-based multimedia data systems offer scalable and cost-effective solutions for storing and processing large neuroscience datasets. Reinforcement learning (RL) approaches are gradually being explored for healthcare applications, especially neuroscience. The proposed RL algorithm is devised to learn and acquire optimal actions via a trial-and-error methodology steered by rewards or penalties. The proposed RL model transforms cognitive and neural procedures into formal scientific structures. This facilitates realizing and modeling how the brain can learn from events and generate decisions. Results and outcomes demonstrate that the proposed model outperforms the existing state-of-the-art models.

Keywords

Cognitive neuroscienceData analysisData validationElectrophysiologyImage processingMultimedia dataNeuroimagingReinforcement learning

Subject Classifications

68U1068M2092C55

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