Arithmetic Optimization with Deep Learning Enabled Anomaly Detection in Smart City

作     者：Mahmoud Ragab Maha Farouk S.Sabir 

作者机构：Information Technology DepartmentFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision MedicinesKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Information Systems DepartmentFaculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第10期

页      面：381-395页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020205[经济学-产业经济学] 

基　　金：This project was supported financially by Institution Fund projects under grant no.(IFPIP-1308-612-1442) 

主　　题：Object detection anomaly detection smart city infrastructure deep learning parameter tuning 

摘      要：In recent years,Smart City Infrastructures(SCI)have become familiar whereas intelligent models have been designed to improve the quality of living in smart ***,anomaly detection in SCI has become a hot research topic and is widely explored to enhance the safety of *** increasing popularity of video surveillance system and drastic increase in the amount of collected videos make the conventional physical investigation method to identify abnormal actions,a laborious *** this background,Deep Learning(DL)models can be used in the detection of anomalies found through video surveillance *** current research paper develops an Internet of Things Assisted Deep Learning Enabled Anomaly Detection Technique for Smart City Infrastructures,named(IoTAD-SCI)*** aim of the proposed IoTAD-SCI technique is to mainly identify the existence of anomalies in smart city ***,IoTAD-SCI technique involves Deep Consensus Network(DCN)model design to detect the anomalies in input video *** addition,Arithmetic Optimization Algorithm(AOA)is executed to tune the hyperparameters of the DCN ***,ID3 classifier is also utilized to classify the identified objects in different *** experimental analysis was conducted for the proposed IoTADSCI technique upon benchmark UCSD anomaly detection dataset and the results were inspected under different *** simulation results infer the superiority of the proposed IoTAD-SCI technique under different metrics.