AN EFFICIENT FRACTAL CARDIO DISEASES ANALYSIS USING OPTIMIZED DEEP LEARNING MODEL IN CLOUD OF THING CONTINUUM ARCHITECTURE

Manal Abdullah Alohali, Munya A. Arasi, Saad Alahmari, Asma Alshuhail, Wafa Sulaiman Almukadi, Bandar M. Alghamdi and Fouad Shoie Alallah
Additional contact information
Manal Abdullah Alohali: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Munya A. Arasi: ��Department of Computer Science, Applied College at Rijal Almaa, King Khalid University, Saudi Arabia
Saad Alahmari: ��Department of Computer Science, Applied College Northern Border University, Arar 91431, Saudi Arabia
Asma Alshuhail: �Department of Information Systems, College of Computer Sciences & Information Technology, King Faisal University, Saudi Arabia
Wafa Sulaiman Almukadi: �Department of Software Engineering, College of Engineering and Computer Science, University of Jeddah, Saudi Arabia
Bandar M. Alghamdi: ��Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Fouad Shoie Alallah: *Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-16

Abstract: Exercise has long been known to improve cardiovascular health, energy metabolism, and well-being. However, myocardial cell responses to exercise are complex and multifaceted due to their molecular pathways. To understand cardiac physiology and path physiology, one must understand these pathways, including energy autophagy. In recent years, deep learning techniques, IoT devices, and cloud computing infrastructure have enabled real-time, large-scale biological data analysis. The objective of this work is to extract and analyze autophagy properties in exercise-induced cardiac cells in a cloud-IoT context using deep learning, more especially an autoencoder. The Shanghai University of Sport Ethics Committee for Science Research gave its approval for the data collection, which involved 150 male Sprague–Dawley (SD) rats that were eight weeks old and in good health. The Z-score normalization method was used to standardize the data. Fractal optimization methods could be applied to these algorithms. For example, fractal-inspired optimization techniques might be used to analyze deep learning with Autoencoder, the autography energy of exercise myocardial cells within a cloud-IoT. To capture the intricate myocardial energy autophagy during exercise, we introduced the DMO-GCNN-Autoencoder, a Dwarf Mongoose Optimized Graph Convolutional Neural Network. The results showed that the proposed network’s performance matches that of the existing methods.

Keywords: Myocardial Cells; Energy Autophagy; Fractal Z-score Normalization; Dwarf Mongoose Optimized Graph Convolutional Neural Network (DMO-GCNN) with Autoencoder; Cloud-IoT (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X25400237
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:32:y:2024:i:09n10:n:s0218348x25400237

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X25400237

Access Statistics for this article

FRACTALS (fractals) is currently edited by Tara Taylor

More articles in FRACTALS (fractals) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().