TITLE:
Arab Region Intelligent Medical: Using Secure IoT Protocols and Deep Convolutional Neural Networks for Healthcare Optimization
AUTHORS:
Mohamed Sayed, Hady Mohamed
KEYWORDS:
Artificial Intelligence of Things, Deep Convolutional Neural Networks, E-Health Care Systems, Internet of Things, Reading Complex Data
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.14 No.3,
August
18,
2026
ABSTRACT: Recent advances in intelligent healthcare have led to the development of deep convolutional neural networks and standardized rules for connected devices, two key building blocks for automated healthcare diagnosis and remote patient monitoring systems. This paper proposes an integrated healthcare framework supported by the Internet of Things, leveraging smart medical devices, secure communication protocols, and deep learning. The framework processes medical images and physiological data using IoT-enabled devices, supported by robust, lightweight transmission protocols. This study focuses on medical care systems in the Arabic-speaking world, where an inadequate supply of healthcare professionals and the remote locations of healthcare facilities pose challenges to efficient diagnosis and continuous care monitoring. To address communication problems in IoT healthcare networks, the study proposes an intelligent routing algorithm that detects network failures and prioritizes critical medical information during transmission. The proposed framework addresses IoT problems such as cyberattacks, congestion, delays, jitter, and limited bandwidth while preserving secure data communication. Additionally, the text addresses cultural and societal concerns specific to Arab countries. It emphasizes the importance of balancing transparency in medical practices with the advancement of IoT technologies to safeguard patient privacy. Experimental results on the transmission of medical images and healthcare information using IoT communication protocols indicate high training and validation accuracies and a low mean squared error for the deep learning algorithm.