Table of Content

Intelligent Computational Models based on Machine Learning and Deep Learning for Diagnosis System

Submission Deadline: 15 January 2023 (closed) Submit to Special Issue

Guest Editors

Dr. Ahmed Mostafa Khalil, Al-Azhar University, Egypt.
Dr. Hager Saleh, South Valley University, Egypt.
Dr. Rana Muhammad Zulqarnain, University of the Management and Technology, Pakistan.
Dr. Hu Zhao, Xi'an Polytechnic University, China.


Recently, Intelligent Computational Models have demonstrated remarkable results in many applications such as engineering and smart healthcare. In addition, the volume of healthcare data is steadily increasing at an unprecedented pace across various disparate and incompatible data sources. For example, wearable devices generate a massive amount of healthcare data, and extracting useful information from data and analyzing data to provide a fast and accurate diagnosis is challenging. Also, social media platforms are rich in medical knowledge that is utilized increasingly for health and medicinal objectives, including sharing data about diabetes, determining the potential adverse drug, diagnosing breast cancer, and others. Also, Artificial Intelligence (AI), including deep learning and machine learning models, is adopted in the healthcare industry to provide health systems with higher accuracy with time-sensitive data processing. Additionally, they improve healthcare professionals' ability to understand better the day-to-day habits and desires of the people they care about, allowing them to provide better input, advice, and encouragement to keep healthy. Therefore, there is a need for an intelligent model based on machine learning and deep learning that can automatically handle the enormous data, analyze and extract the hidden knowledge from data, predict a patient's health condition and develop a diagnosis system.


This Special Issue aims to address the areas of deep learning models and machine learning models for intelligent healthcare
• Deep learning models for healthcare applications
• Machine learning models for healthcare applications
• Ensemble Deep learning models in healthcare
• Applications of natural language processing in healthcare
• Intelligent Computational Models in healthcare
• Wearable sensors in healthcare monitoring systems
• AI and ML decision-making in medical expert models
• Swarm Intelligence
• Evolutionary Algorithms
• Artificial Intelligence Applications
• Deep Learning
• Time Series and Forecasting

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