Hybrid Recommender System for Educational Resources to the Smart University Campus Domain

Maruyama, Martin; Silveira, Luan; Oliveira, José Martins de; Gasparini, Isabela; Maran, Vinícius

doi:10.5220/0011841900003470

Cited by 2 publications

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“…Fourth, the applications layer is where the action is on the Internet of Things since that's where all the cool stuff occurs, including information processing, analytics, security, and device administration. The challenges associated with implementing IoT in healthcare systems were analyzed by Faisal et al [43] and discussed further.…”

Section: Literature Reviewmentioning

confidence: 99%

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Smart Patient Monitoring and Recommendation (SPMR) Using Cloud Analytics and Deep Learning

Sundas,

Badotra,

Shahi

et al. 2024

IEEE Access

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The escalating global prevalence of chronic and lifestyle-related illnesses presents substantial societal and economic challenges. This work delves into an extensive review of healthcare monitoring systems tailored for chronic and lifestyle disorders. Subsequently, we propose a pioneering Smart Patient Monitoring and Recommendation (SPMR) framework, leveraging Deep Learning (DL) and cloud-based analytics. SPMR ensures continuous monitoring and predictive insights into a patient's authentic health status using data from vital signs and contextual activities collected via Ambient Assisted Living devices. Within the predictive DL component of the LIP module, we employ Categorical Cross Entropy (CCE) Optimization to forecast real-world health conditions using unbalanced datasets derived from Chronic Blood Pressure Disorder case studies. Significantly, SPMR's capability to deliver real-time preventive measures and treatments persists even without Internet or cloud connectivity. This circumvents the need to replicate Machine Learning (ML) models and associated procedures in local setups, thus streamlining operations. Comparative analysis against analogous models showcases the considerable effectiveness of our proposed model, notably enhancing accuracy by up to 8 to 18 percent. Moreover, both the overall F-score and the emergency class F-score exhibit marked improvements of 17% and 36%, respectively. These outcomes underscore SPMR's pivotal role, especially during crises, emphasizing its significance in healthcare monitoring systems.

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Section: Literature Reviewmentioning

confidence: 99%

“…Average CE loss is calculated by removing the chance of an event that is much more likely than the average CE loss. When fewer epochs are used, the DL algorithms achieve their goal quicker [42][43][44].…”

Section: F Proposed Cce (Categorical Cross Entropy) Optimization Algo...mentioning

confidence: 99%

Smart Patient Monitoring and Recommendation (SPMR) Using Cloud Analytics and Deep Learning

Sundas,

Badotra,

Shahi

et al. 2024

IEEE Access

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Towards a Software Architecture to Provide Hybrid Recommendations for Smart Campuses

Maruyama,

Silveira,

Dorneles

et al. 2024

Communications in Computer and Information Science

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Hybrid Recommender System for Educational Resources to the Smart University Campus Domain

Cited by 2 publications

References 0 publications

Smart Patient Monitoring and Recommendation (SPMR) Using Cloud Analytics and Deep Learning

Smart Patient Monitoring and Recommendation (SPMR) Using Cloud Analytics and Deep Learning

Towards a Software Architecture to Provide Hybrid Recommendations for Smart Campuses

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