Special Issue "Artificial Intelligence and Machine Learning Algorithms in Real-World Applications and Theories"

Submission Deadline: 03 January 2022
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Guest Editors
Dr. Elsayed Elknawy, Delta Higher Institute of Engineering and Technology, Egypt.
Dr. Abdelhameed Ibrahim, Mansoura University, Egypt.
Dr. Marwa M. Eid, Delta Higher Institute for Engineering & amp; Technology, Egypt.
Dr. Ali Wagdy Mohamed, Cairo University& Nile University, Egypt.


Artificial intelligence and machine learning are the hot research topics in recent years, both in theory and applications. However, machine learning models are quick to slip into overfitting issues because machine learning systems have uncertainty or backbox problems; it is challenging to consider how a given algorithm makes a judgment, which is essential in certain fields, especially real-world applications and theories.


This special issue focuses on the latest development in the Artificial Intelligence and Machine Learning foundation of real-world applications and theories. We welcome the new research ideas and developments in mathematics and computing relevant to real-world applications and theories from a machine learning perspective, including foundation, systems, innovative application, and other research contributions.

Modeling real-world problems
Medical diagnosis
Statistical arbitrage
Predictive analytics
Image recognition
Speech recognition
Artificial intelligence applications
Internet of Things (IoT), IoMT, AIoT & AIoMT
eBusiness, eCommerce, eHealth, eLearning
Deep learning
Computer-based algorithms
Time Series and Forecasting
Smart City
Smart Traffic
Swarm Intelligence
Evolutionary Algorithms

Published Papers

  • Graph Transformer for Communities Detection in Social Networks
  • Abstract Graphs are used in various disciplines such as telecommunication, biological networks, as well as social networks. In large-scale networks, it is challenging to detect the communities by learning the distinct properties of the graph. As deep learning has made contributions in a variety of domains, we try to use deep learning techniques to mine the knowledge from large-scale graph networks. In this paper, we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs. The advantages of neural attention are widely seen in the field of NLP and computer vision, which has… More
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