Approaches of Artificial Intelligence and Machine Learning in Smart Cities: Critical Review

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%.

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Artificial Intelligence

Advances in Human and Social Aspects of Technology - Responsible AI and Ethical Issues for Businesses and Governments ◽

10.4018/978-1-7998-4285-9.ch001 ◽

2021 ◽

pp. 1-18

Author(s):

Bistra Konstantinova Vassileva

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Future Development ◽

Critical Review ◽

Data Science ◽

Daily Lives ◽

Societal Relevance ◽

Responsible Manner

In recent years, artificial intelligence (AI) has gained attention from policymakers, universities, researchers, companies and businesses, media, and the wide public. The growing importance and relevance of artificial intelligence (AI) to humanity is undisputed: AI assistants and recommendations, for instance, are increasingly embedded in our daily lives. The chapter starts with a critical review on AI definitions since terms such as “artificial intelligence,” “machine learning,” and “data science” are often used interchangeably, yet they are not the same. The first section begins with AI capabilities and AI research clusters. Basic categorisation of AI is presented as well. The increasing societal relevance of AI and its rising inburst in our daily lives though sometimes controversial are discussed in second section. The chapter ends with conclusions and recommendations aimed at future development of AI in a responsible manner.

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Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities

10.1007/978-3-030-84459-2 ◽

2022 ◽

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Smart Cities

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A Review on Classifying Object through Machine Learning: International conference on Recent Trends in Artificial Intelligence, IOT, Smart Cities & Applications (ICAISC-2020)

SSRN Electronic Journal ◽

10.2139/ssrn.3611523 ◽

2020 ◽

Author(s):

Arijit Dutta ◽

Abhay Kumar Das ◽

Ajay Gope ◽

Akash Kumar ◽

Akash Kumar Saw ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Smart Cities ◽

International Conference ◽

Recent Trends

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Applications of Artificial Intelligence and Machine learning in smart cities

Computer Communications ◽

10.1016/j.comcom.2020.02.069 ◽

2020 ◽

Vol 154 ◽

pp. 313-323 ◽

Cited By ~ 31

Author(s):

Zaib Ullah ◽

Fadi Al-Turjman ◽

Leonardo Mostarda ◽

Roberto Gagliardi

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Smart Cities

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Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review

Process Safety and Environmental Protection ◽

10.1016/j.psep.2019.01.013 ◽

2019 ◽

Vol 123 ◽

pp. 229-252 ◽

Cited By ~ 17

Author(s):

Majid Bagheri ◽

Ali Akbari ◽

Sayed Ahmad Mirbagheri

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Membrane Fouling ◽

Critical Review ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Advanced Control

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Artificial intelligence and machine learning approaches for smart transportation in smart cities using blockchain architecture

10.1016/b978-0-12-824446-3.00009-0 ◽

2021 ◽

pp. 35-56

Author(s):

S. Sundaresan ◽

K. Suresh Kumar ◽

R. Nishanth ◽

Y. Harold Robinson ◽

A. Jaya Kumar

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Smart Cities ◽

Learning Approaches ◽

Smart Transportation

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Deep Learning

Artificial Neural Network Applications in Business and Engineering - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3238-6.ch008 ◽

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.

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