A novel metaheuristics with adaptive neuro-fuzzy inference system for decision making on autonomous unmanned aerial vehicle systems

Mahmoud Ragab; Ehab Bahaudien Ashary; Wajdi H Aljedaibi; Ibrahim R Alzahrani; Anil Kumar; Deepak Gupta; Romany F Mansour

doi:10.1016/j.isatra.2022.04.006

A novel metaheuristics with adaptive neuro-fuzzy inference system for decision making on autonomous unmanned aerial vehicle systems

ISA Trans. 2023 Jan:132:16-23. doi: 10.1016/j.isatra.2022.04.006. Epub 2022 Apr 13.

Authors

Mahmoud Ragab¹, Ehab Bahaudien Ashary², Wajdi H Aljedaibi³, Ibrahim R Alzahrani⁴, Anil Kumar⁵, Deepak Gupta⁶, Romany F Mansour⁷

Affiliations

¹ Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia; Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt. Electronic address: mragab@kau.edu.sa.
² Electrical and Computer Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: eashary@kau.edu.sa.
³ Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia. Electronic address: waljedaibi@kau.edu.sa.
⁴ Computer Science and Engineering Department, College of Computer Science and Engineering, University of Hafr Al Batin, Al Jamiah, Hafar Al Batin 39524, Saudi Arabia. Electronic address: ialzahrani@uhb.edu.sa.
⁵ Data Science Research Group, School of Computing, DIT University, Dehradun, India. Electronic address: dahiyaanil@yahoo.com.
⁶ Department of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India. Electronic address: deepakgupta@mait.ac.in.
⁷ Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt. Electronic address: romanyf@sci.nvu.edu.eg.

PMID: 35523604
DOI: 10.1016/j.isatra.2022.04.006

Abstract

Recently, autonomous systems have received considerable attention amongst research communities and academicians. Unmanned aerial vehicles (UAVs) find useful in several applications like transportation, surveillance, disaster management, and wildlife monitoring. One of the important issues in the UAV system is energy efficiency, which can be resolved by the use of clustering approaches. In addition, high resolution remote sensing images need to be classified for effective decision making using deep learning (DL) models. Though several models are available in the literature, only few approaches have focused on the clustering and classification processes in UAV networks. In this aspect, this paper designs a novel metaheuristic with an adaptive neuro-fuzzy inference system for decision making named MANFIS-DM technique on autonomous UAV systems. The proposed MANFIS-DM technique intends to effectively organize the UAV networks into clusters and then classify the images into appropriate class labels. The proposed MANFIS-DM technique encompasses two major stages namely quantum different evolution based clustering (QDE-C) technique and ANFIS based classification technique. Primarily, the QDE-C technique involves the design of a fitness function involving three parameters namely average distance, distance to UAVs, and UAV degree. Besides, the image classification model involves a set of subprocesses namely DenseNet based feature extraction, Adadelta based hyperparameter optimization, and ANFIS based classification. The design of QDE-C algorithm with classification model for autonomous UAV systems show the novelty of the work. The experimental result analysis of the MANFIS-DM method is carried out against benchmark dataset and the results ensured the enhanced performance of the MANFIS-DM technique over the other methods with the maximum accu_y of 99.13%.

Keywords: Autonomous systems; Clustering; Decision making; Hyperparameter optimization; Metaheuristics; Unmanned aerial vehicles.