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

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.