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Intelligent Energy-Aware Thermal Exchange Optimization with Deep Learning Model for IoT-Enabled Smart Healthcare.

Mahmoud Ragab1,2,3, Sami Saeed Binyamin4

  • 1Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces an intelligent energy-aware thermal exchange optimization with deep learning (IEA-TEODL) model for Internet of Things (IoT)-enabled smart healthcare. The IEA-TEODL model enhances disease diagnosis and optimizes IoT device clustering for improved healthcare services.

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • The integration of Internet of Things (IoT) and advanced sensors is revolutionizing healthcare by connecting devices, patients, and professionals.
  • Smart healthcare systems improve patient safety, accessibility, and operational efficiency while reducing costs.

Purpose of the Study:

  • To develop an intelligent energy-aware thermal exchange optimization with deep learning (IEA-TEODL) model for IoT-enabled smart healthcare.
  • To effectively cluster IoT devices and enhance disease diagnosis within the smart healthcare sector.

Main Methods:

  • The proposed IEA-TEODL model utilizes an energy-aware chaotic thermal exchange optimization-based clustering (EACTEO-C) scheme for device grouping.
  • Disease classification is performed using preprocessing, K-medoid clustering for outlier removal, Multihead Attention Bidirectional Long Short-Term Memory (MHA-BLSTM), and Weighted Salp Swarm Algorithm (WSSA) for hyperparameter tuning.

Main Results:

  • The IEA-TEODL approach demonstrated enhanced classification outcomes through outlier removal and WSSA-based hyperparameter optimization.
  • Extensive simulations on benchmark datasets confirmed the superior performance of the IEA-TEODL technique compared to existing methods across various evaluation metrics.

Conclusions:

  • The developed IEA-TEODL model offers a significant advancement in IoT-enabled smart healthcare systems.
  • The proposed technique effectively optimizes IoT device clustering and improves the accuracy of disease diagnosis, paving the way for more efficient and cost-effective healthcare solutions.