Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

被引:0
|
作者
Ragab, Mahmoud [1 ,2 ]
Khadidos, Adil O. [1 ]
Alshareef, Abdulrhman M. [3 ]
Alyoubi, Khaled H. [3 ]
Hamed, Diaa [4 ,5 ]
Khadidos, Alaa O. [3 ,6 ]
机构
[1] King Abdulaziz Univ, Fac Comp & Informat Technol, Informat Technol Dept, Jeddah 21589, Saudi Arabia
[2] Al Azhar Univ, Fac Sci, Dept Math, Nasr City 11884, Egypt
[3] King Abdulaziz Univ, Fac Comp & Informat Technol, Informat Syst Dept, Jeddah 21589, Saudi Arabia
[4] King Abdulaziz Univ, Fac Earth Sci, Jeddah 21589, Saudi Arabia
[5] Al Azhar Univ, Fac Sci, Geol Dept, Nasr City 11884, Egypt
[6] King Abdulaziz Univ, Ctr Res Excellence Artificial Intelligence & Data, Jeddah 21589, Saudi Arabia
关键词
agricultural residues; biofuel classification; solid fuel; deep learning; sailfish optimizer; IoT environment; smart cities;
D O I
10.3390/su151612523
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Solid biofuels and Internet of Things (IoT) technologies play a vital role in the development of smart cities. Solid biofuels are a renewable and sustainable source of energy obtained from organic materials, such as wood, agricultural residues, and waste. The integration of IoT technology with solid biofuel classification can improve the performance, quality control, and overall management of biofuel production and usage. Recently, machine learning (ML) and deep learning (DL) models can be applied for the solid biofuel classification process. Therefore, this article develops a novel solid biofuel classification using sailfish optimizer hybrid deep learning (SBFC-SFOHDL) model in the IoT platform. The proposed SBFC-SFOHDL methodology focuses on the identification and classification of solid biofuels from agricultural residues in the IoT platform. To achieve this, the SBFC-SFOHDL method performs IoT-based data collection and data preprocessing to transom the input data into a compatible format. Moreover, the SBFC-SFOHDL technique employs the multihead self attention-based convolutional bidirectional long short-term memory model (MSA-CBLSTM) for solid biofuel classification. For improving the classification performance of the MSA-CBLSTM model, the SFO algorithm is utilized as a hyperparameter optimizer. The simulation results of the SBFC-SFOHDL technique are tested and the results are examined under different measures. An extensive comparison study reported the betterment of the SBFC-SFOHDL technique compared to recent DL models.
引用
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页数:17
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