Automatic alert generation in a surveillance systems for smart city environment using deep learning algorithm

Author(s):  
B. Janakiramaiah ◽  
G. Kalyani ◽  
A. Jayalakshmi
2021 ◽  
Author(s):  
L. M. I. Leo Joseph ◽  
Pankaj Goel ◽  
Ashish Jain ◽  
K Rajyalakshmi ◽  
Kamal Gulati ◽  
...  

2021 ◽  
Author(s):  
Revathy jayabaskar ◽  
SELVAKUMAR k

Abstract Wireless networks the mobile user to establish a wireless connection with the central server in the fixed network. On-line activities such as transaction and querying information can be conducted by mobile user. Mobile user queries are location dependent in mobile environment. Mobile user wants to move to hospital or hypermarket, before moving to the location, mobile user wants to know the crowd at the destination , Based on the number of people present, the mobile user can choose to go to another branch. At present a mobile user cannot get the details about the crowd in the particular location. Recent researches are the vehicle tracking, taxi services, road traffic and location-based queries. A proposed research idea is to identify the number of people present in a given area in a city. We use the Heatmap layer tool to find out the crowd intensity. Heatmap layer will indicate crowd intensity of a particular area, Heatmap layer show the color on the map, red colour in the particular location is represent about high intensity of crowd and the green colour is represent lower intensity of crowd, Heatmap shows crowd intensity through mobile signal, more people present more mobile signal in that location. In our research proposal ,we calculate the people crowd using deep learning algorithm. This research application can be used in smart city for the school fees payment , Ministry office , hospital (out Patient not to wait more time in hospital) , hypermarket and cinema theater. Numerical method is obtained by using the fuzzy inference model.


2021 ◽  
Author(s):  
Inam Ullah Khan ◽  
Arsin Abdollahi ◽  
Muhammad Asghar Khan ◽  
Irfan Uddin ◽  
Insaf Ullah

Abstract Due to the limited computational resources of small unmanned aerial vehicles (UAVs), the Internet of flying things (IoFT) is vulnerable to cybersecurity attacks, particularly Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. In addition, the transfer of reliable information from source UAV to destination UAV is another big challenge in IoFT networks. Therefore, this article aims to address the security deficiency by proposing an experience-based deep learning algorithm to cater to the DoS, D-DoS and a special kind of threat covering ping-of-death attacks. The proposed scheme uses the notion of the intrusion detection system (IDS). In addition, for reliable communication, a nature-based control routing algorithm AntHocNet is investigated with other contemporary protocols. The proposed approach is implemented in a smart city environment as a case study. The result authenticates the superiority of the proposed schemes in terms of security and QoS requirement from its counterparts.


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