Artificial Intelligence and Machine Learning for Ensuring Security in Smart Cities

2021 ◽  
pp. 23-47
Author(s):  
Sabbir Ahmed ◽  
Md. Farhad Hossain ◽  
M. Shamim Kaiser ◽  
Manan Binth Taj Noor ◽  
Mufti Mahmud ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Smart Cities

scholarly journals Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2025 ◽  
Author(s):  
Jun Hong Park ◽  
Seunggi Lee ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Won-Tae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Transfer ◽  
Detection System ◽  
Smart Cities ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Fire Detection System

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

Applications of Artificial Intelligence and Machine learning in smart cities

2020 ◽  
Vol 154 ◽  
pp. 313-323 ◽  
Author(s):  
Zaib Ullah ◽  
Fadi Al-Turjman ◽  
Leonardo Mostarda ◽  
Roberto Gagliardi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Smart Cities

Deep Learning

2021 ◽  
pp. 171-196
Author(s):  
Semra Erpolat Taşabat ◽  
Olgun Aydin
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Smart Cities ◽  
Key Technology ◽  
Smart Systems ◽  
Advantages And Disadvantages

Deep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities, security, automated machines. In this chapter, brief information about DL theory is given, advantages and disadvantages of deep learning are discussed, most used types of DNN are mentioned, popular DL architectures and frameworks are glanced and aimed to build smart systems for the finance and real estate domains. Finally, a case study about image recognition using transfer learning is developed.

scholarly journals Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

2020 ◽  
Author(s):  
Damminda Alahakoon ◽  
Rashmika Nawaratne ◽  
Yan Xu ◽  
Daswin De Silva ◽  
Uthayasankar Sivarajah ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics
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