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.

Summary

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.


Keywords
• 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
  • Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting
  • Abstract Despite the advancement within the last decades in the field of smart grids, energy consumption forecasting utilizing the metrological features is still challenging. This paper proposes a genetic algorithm-based adaptive error curve learning ensemble (GA-ECLE) model. The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach. A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy. This approach combines three models, namely CatBoost (CB), Gradient Boost (GB), and Multilayer Perceptron (MLP). The ensembled CB-GB-MLP model’s inner mechanism consists of generating… More
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  • 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:405       Downloads:385        Download PDF