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Improved Feature Selection Based on Chaos Game Optimization for Social Internet of Things with a Novel Deep Learning Model

Author

Listed:
  • Abdelghani Dahou

    (Faculty of Computer Sciences and Mathematics, Ahmed Draia University, Adrar 01000, Algeria)

  • Samia Allaoua Chelloug

    (Information Technology Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Mai Alduailij

    (Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Mohamed Abd Elaziz

    (Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
    Department of Artificial Intelligence Science and Engineering, Galala University, Suze 435611, Egypt
    Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
    Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon)

Abstract

The Social Internet of Things (SIoT) ecosystem tends to process and analyze extensive data generated by users from both social networks and Internet of Things (IoT) systems and derives knowledge and diagnoses from all connected objects. To overcome many challenges in the SIoT system, such as big data management, analysis, and reporting, robust algorithms should be proposed and validated. Thus, in this work, we propose a framework to tackle the high dimensionality of transferred data over the SIoT system and improve the performance of several applications with different data types. The proposed framework comprises two parts: Transformer CNN (TransCNN), a deep learning model for feature extraction, and the Chaos Game Optimization (CGO) algorithm for feature selection. To validate the framework’s effectiveness, several datasets with different data types were selected, and various experiments were conducted compared to other methods. The results showed that the efficiency of the developed method is better than other models according to the performance metrics in the SIoT environment. In addition, the average of the developed method based on the accuracy, sensitivity, specificity, number of selected features, and fitness value is 88.30%, 87.20%, 92.94%, 44.375, and 0.1082, respectively. The mean rank obtained using the Friedman test is the best value overall for the competitive algorithms.

Suggested Citation

  • Abdelghani Dahou & Samia Allaoua Chelloug & Mai Alduailij & Mohamed Abd Elaziz, 2023. "Improved Feature Selection Based on Chaos Game Optimization for Social Internet of Things with a Novel Deep Learning Model," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:1032-:d:1072611
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    References listed on IDEAS

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    1. Hadeer Adel & Abdelghani Dahou & Alhassan Mabrouk & Mohamed Abd Elaziz & Mohammed Kayed & Ibrahim Mahmoud El-Henawy & Samah Alshathri & Abdelmgeid Amin Ali, 2022. "Improving Crisis Events Detection Using DistilBERT with Hunger Games Search Algorithm," Mathematics, MDPI, vol. 10(3), pages 1-22, January.
    2. Xuechen Li & Xinfang Ma & Fengchao Xiao & Fei Wang & Shicheng Zhang, 2020. "Application of Gated Recurrent Unit (GRU) Neural Network for Smart Batch Production Prediction," Energies, MDPI, vol. 13(22), pages 1-22, November.
    3. Jiang, Ping & Liu, Zhenkun & Wang, Jianzhou & Zhang, Lifang, 2021. "Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm," Resources Policy, Elsevier, vol. 73(C).
    4. Ibrahim Alsaidan & Mohamed A. M. Shaheen & Hany M. Hasanien & Muhannad Alaraj & Abrar S. Alnafisah, 2021. "Proton Exchange Membrane Fuel Cells Modeling Using Chaos Game Optimization Technique," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    5. Abderrahim Zannou & Abdelhak Boulaalam & El Habib Nfaoui, 2020. "SIoT: A New Strategy to Improve the Network Lifetime with an Efficient Search Process," Future Internet, MDPI, vol. 13(1), pages 1-23, December.
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    Cited by:

    1. Nour Elhouda Chalabi & Abdelouahab Attia & Abderraouf Bouziane & Mahmoud Hassaballah & Abed Alanazi & Adel Binbusayyis, 2023. "An Archive-Guided Equilibrium Optimizer Based on Epsilon Dominance for Multi-Objective Optimization Problems," Mathematics, MDPI, vol. 11(12), pages 1-30, June.

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