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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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Abstract

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models.

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Data sharing not applicable to this article as no datasets were generated during the current study.

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Funding

This project is sponsored by Prince Sattam Bin Abdulaziz University (PSAU) as part of funding for its SDG Roadmap Research Funding Programme project number PSAU-2023- SDG-21.

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Authors and Affiliations

  1. Department of Computer Science,College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, 16273, Saudi Arabia

    Mesfer Al Duhayyim

  2. Department of Computer Science, Faculty of Computer Science and Information Technology, Omdurman Islamic University, Omdurman, Sudan

    Mahir Mohammed Sharif

  3. Computer Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Muskaan Munjal

  4. Department of Information System, Faculty of Computer Science and Information Technology, Omdurman Islamic University, Omdurman, Sudan

    Anwer A. Hilal

Authors
  1. Mesfer Al Duhayyim
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  2. Mahir Mohammed Sharif
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  3. Muskaan Munjal
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  4. Anwer A. Hilal
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Contributions

Mesfer Al Duhayyim, Mahir Mohammed Sharif, is responsible for designing the framework, analyzing the performance, validating the results, and writing the article. Muskaan Munjal, Anwer A. Hilal, is responsible for collecting the information required for the framework, provision of software, critical review, and administering the process.

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Correspondence to Mesfer Al Duhayyim.

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The authors declare that they have no Conflict of interest. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

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Duhayyim, M.A., Sharif, M.M., Munjal, M. et al. Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition. SN COMPUT. SCI. 5, 564 (2024). https://doi.org/10.1007/s42979-024-02894-2

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