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Research Article

BEST PAPER 2021

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


Submission Deadline: 03 January 2022

Abstract

This article has no abstract.

Keywords

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
  • Research Article

    BEST PAPER 2021

    An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

    Abdelhameed Ibrahim1,*, Hattan F. Abutarboush2, Ali Wagdy Mohamed3,4, Mohamad Fouad1, El-Sayed M. El-kenawy5,6 CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 199-213, 2022, DOI:10.32604/cmc.2022.021886
    Abstract Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance. Antenna size affects the quality factor and the radiation loss of the antenna. Metamaterial antennas can overcome the limitation of bandwidth for small antennas. Machine learning (ML) model is recently applied to predict antenna parameters. ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna. The accuracy of the prediction depends mainly on the selected model. Ensemble models combine two or more base models to produce a better-enhanced model. In this paper, a weighted average… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification

    Mohd Anul Haq* CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1403-1425, 2022, DOI:10.32604/cmc.2022.021968
    Abstract Rapid industrialization and urbanization are rapidly deteriorating ambient air quality, especially in the developing nations. Air pollutants impose a high risk on human health and degrade the environment as well. Earlier studies have used machine learning (ML) and statistical modeling to classify and forecast air pollution. However, these methods suffer from the complexity of air pollution dataset resulting in a lack of efficient classification and forecasting of air pollution. ML-based models suffer from improper data pre-processing, class imbalance issues, data splitting, and hyperparameter tuning. There is a gap in the existing ML-based studies on air pollution due to improper data… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks

    Abdullah Ali Salamai1,*, Ather Abdulrahman Ageeli1, El-Sayed M. El-kenawy2 CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5091-5106, 2022, DOI:10.32604/cmc.2022.021268
    Abstract E-commerce refers to a system that allows individuals to purchase and sell things online. The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase. They will purchase the item online and have it delivered to their home within a few days. The goal of this research was to develop machine learning algorithms that might predict e-commerce platform sales. A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search (SFS) based on a Guided Whale Optimization Algorithm (WOA) to optimize the parameter… More >

    Graphic Abstract

  • Research Article

    BEST PAPER 2021

    Graph Transformer for Communities Detection in Social Networks

    G. Naga Chandrika1, Khalid Alnowibet2, K. Sandeep Kautish3, E. Sreenivasa Reddy4, Adel F. Alrasheedi2, Ali Wagdy Mohamed5,6,* CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5707-5720, 2022, DOI:10.32604/cmc.2022.021186
    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 >

    Graphic Abstract

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