Green AI Algorithms, Methods and Technologies for Smart Cities

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 5914

Special Issue Editors

School of Architecture and Built Environment, Queensland University of Technology, Brisbane, Australia
Interests: smart technologies, communities, cities and urbanism; knowledge-based development of cities and innovation districts; sustainable and resilient cities; communities and urban ecosystems
Special Issues, Collections and Topics in MDPI journals
BISITE Research Group, Edificio Multiusos I+D+I, University of Salamanca, 37007 Salamanca, Spain
Interests: artificial Intelligence; machine learning; edge computing; distributed computing; Blockchain; consensus model; smart cities; smart grid
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cities and societies are underpinned by our ability to engage with our environments and analyse and make efficient, sustainable, and equitable decisions [1], [2]. The fundamental fabric of these smart cities and societies is built on smart devices, mobile and other device apps, digital systems, and platforms. The lifeblood of these smart devices and systems are algorithms increasingly powered by artificial intelligence (AI). These algorithms make decisions about virtually everything individuals, organisations, and governments do, such as finding the love of our life; recruiting employees; identifying immoral, illegal, and criminal behaviours in individuals and communities; and so on indefinitely. The decisions of these algorithms relate to many core concepts that characterise us humans and our morals, ethics, principles, laws. These algorithms make decisions about our everyday routines that impact—every second or less—innumerable design and process efficiencies, energy usage, planet environment, and individual and national economies. There is an urgent need to develop green AI algorithms, methods, and technologies for smart cities and societies.               

We call for contributions from scientists and engineers to develop green AI algorithms and technologies for smart applications, devices, digital systems, and platforms.

The topics include but are not limited to the development of green AI in the following application domains:

  • Green computing
  • Green data
  • Green communications and networking
  • Green manufacturing and Industry 5.0
  • Green food sciences and technologies
  • Green robotics and cobotics
  • Green land transportation and mobility
  • Green aerospace engineering and transportation
  • Green precision agriculture
  • Green energy
  • Green devices, systems, and platforms
  • Green environmental technologies
  • Green healthcare technologies

References

[1] T. Yigitcanlar, R. Mehmood, and J. M. Corchado, “Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures,” Sustain. 2021, Vol. 13, Page 8952, vol. 13, no. 16, p. 8952, Aug. 2021, doi: 10.3390/SU13168952.

[2] S. Alotaibi, R. Mehmood, I. Katib, O. Rana, and A. Albeshri, “Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning,” Appl. Sci., vol. 10, no. 4, p. 1398, Feb. 2020, doi: 10.3390/app10041398.

Prof. Dr. Rashid Mehmood
Prof. Dr. Tan Yigitcanlar
Prof. Dr. Juan M. Corchado
Guest Editors

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Published Papers (1 paper)

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Research

33 pages, 9745 KiB  
Article
Smart Robotic Strategies and Advice for Stock Trading Using Deep Transformer Reinforcement Learning
Appl. Sci. 2022, 12(24), 12526; https://doi.org/10.3390/app122412526 - 07 Dec 2022
Cited by 3 | Viewed by 4233
Abstract
The many success stories of reinforcement learning (RL) and deep learning (DL) techniques have raised interest in their use for detecting patterns and generating constant profits from financial markets. In this paper, we combine deep reinforcement learning (DRL) with a transformer network to [...] Read more.
The many success stories of reinforcement learning (RL) and deep learning (DL) techniques have raised interest in their use for detecting patterns and generating constant profits from financial markets. In this paper, we combine deep reinforcement learning (DRL) with a transformer network to develop a decision transformer architecture for online trading. We use data from the Saudi Stock Exchange (Tadawul), one of the largest liquid stock exchanges globally. Specifically, we use the indices of four firms: Saudi Telecom Company, Al-Rajihi Banking and Investment, Saudi Electricity Company, and Saudi Basic Industries Corporation. To ensure the robustness and risk management of the proposed model, we consider seven reward functions: the Sortino ratio, cumulative returns, annual volatility, omega, the Calmar ratio, max drawdown, and normal reward without any risk adjustments. Our proposed DRL-based model provided the highest average increase in the net worth of Saudi Telecom Company, Saudi Electricity Company, Saudi Basic Industries Corporation, and Al-Rajihi Banking and Investment at 21.54%, 18.54%, 17%, and 19.36%, respectively. The Sortino ratio, cumulative returns, and annual volatility were found to be the best-performing reward functions. This work makes significant contributions to trading regarding long-term investment and profit goals. Full article
(This article belongs to the Special Issue Green AI Algorithms, Methods and Technologies for Smart Cities)
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