Special Issue "Emerging Trends in Artificial Intelligence and Machine Learning"

Submission Deadline: 30 April 2021 (closed)
Guest Editors
Dr. Mohammad Tabrez Quasim, University of Bisha, Saudi Arabia.
Dr. Kapal Dev, Trinity College Dublin, Ireland.
Dr. Surbhi Bhatia, King Faisal University, Saudi Arabia.
Dr. Rihem Farkh, King Saud university, Saudi Arabia.


Artificial Intelligence and Deep Learning are offering practical tools for many engineering applications. Computer learning, artificial intelligence and its learning, adaptation paradigms are able to improve engineering applications. This covers topics like logic, evolutionary algorithms, neural networks, and DNA computation. These methods can be very effective in dealing with uncertainties and contextual vagueness inherent in the decisions. The computer science study is able to lift the convergence on machine learning and artificial intelligence computing. This is possible to apply machine learning and artificial intelligence for data processing and engineering applications.

This special issue will focus on the problems that can be quickly addressed by using machine learning, deep learning, AI techniques and optimization algorithms.

• Artificial Intelligence for Engineering Application
• Machine Learning for Data Science
• Soft Computing for Emerging Applications
• Optimization Algorithms
• Genetic Algorithms
• Swarm Optimization
• Deep Learning
• Data Analytics

Published Papers
  • Intelligent Multiclass Skin Cancer Detection Using Convolution Neural Networks
  • Abstract The worldwide mortality rate due to cancer is second only to cardiovascular diseases. The discovery of image processing, latest artificial intelligence techniques, and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality rate. Efficiently applying these latest techniques has increased the survival chances during recent years. The research community is making significant continuous progress in developing automated tools to assist dermatologists in decision making. The datasets used for the experimentation and analysis are ISBI 2016, ISBI 2017, and HAM 10000. In this work pertained models are used to extract the efficient feature. The… More
  •   Views:140       Downloads:115        Download PDF