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Electronics
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Green Artificial Intelligence: Theory and Applications
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Green Artificial Intelligence: Theory and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 2971

Special Issue Editors

Dr. Alexander Gegov

E-Mail Website
Guest Editor
School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UK
Interests: artificial intelligence; machine learning methods using fuzzy systems; neural networks and evolutionary algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Femi Isiaq

E-Mail Website
Guest Editor
Creative Computing Institute, University of the Arts London, 272 High Holborn, London WC1V 7EY, UK
Interests: artificial intelligence; machine learning; neural networks; data analytics
Dr. Raheleh Jafari

E-Mail Website
Guest Editor
School of Design, University of Leeds, Leeds LS2 9JT, UK
Interests: artificial intelligence; fuzzy logic; robotics and automation; fashion design
Special Issues, Collections and Topics in MDPI journals
Dr. Kalin Penev

E-Mail Website
Guest Editor
Department of Science and Engineering, Solent University, Southampton SO14 0YN, UK
Interests: artificial intelligence; evolutionary computation; particle swarm optimisation; adaptive systems

Special Issue Information

Dear Colleagues,

The environmental impact of artificial intelligence (AI) has been a hot topic as of late. In the continuously changing environment, AI technology can be applied to most industrial activities, from generating optimal schedules and minimizing energy consumption to recognizing faults.

Artificial intelligence can also boost green transition achievements. Green AI will make AI developments more sustainable. With the environmental applications for artificial intelligence, green AI can influence economic growth, reduce waste of resources, optimize energy generation and distribution, and reduce Co2 emissions.

This Special Issue aims to publish original research of the highest scientific quality on the theory and application of green AI. The scope includes theoretical and experimental studies that contribute to novel developments in fundamental research and its applications.

Potential topics for submissions include, but are not limited to, the following:

  • Green sustainable technologies;
  • Fuzzy systems for modelling and simulation;
  • Control and optimization;
  • Information processing;
  • Theories and applications;
  • Artificial intelligence;
  • Machine learning;
  • Machine reasoning;
  • Complex systems;
  • Smart power management and green technology;
  • Optimization in machine learning and applications;
  • Responsible and ethical AI;
  • Green energy efficient software

We look forward to receiving your contributions. 

Dr. Alexander Gegov
Dr. Femi Isiaq
Dr. Raheleh Jafari
Dr. Kalin Penev
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • green technologies
  • responsible artificial intelligence
  • ethical artificial intelligence

Published Papers (4 papers)

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Research

19 pages, 6211 KiB  
Article
Energy Efficient Graph-Based Hybrid Learning for Speech Emotion Recognition on Humanoid Robot
by Haowen Wu, Hanyue Xu, Kah Phooi Seng, Jieli Chen and Li Minn Ang
Electronics 2024, 13(6), 1151; https://doi.org/10.3390/electronics13061151 - 21 Mar 2024
Viewed by 483
Abstract
This paper presents a novel deep graph-based learning technique for speech emotion recognition which has been specifically tailored for energy efficient deployment within humanoid robots. Our methodology represents a fusion of scalable graph representations, rooted in the foundational principles of graph signal processing [...] Read more.
This paper presents a novel deep graph-based learning technique for speech emotion recognition which has been specifically tailored for energy efficient deployment within humanoid robots. Our methodology represents a fusion of scalable graph representations, rooted in the foundational principles of graph signal processing theories. By delving into the utilization of cycle or line graphs as fundamental constituents shaping a robust Graph Convolution Network (GCN)-based architecture, we propose an approach which allows the capture of relationships between speech signals to decode intricate emotional patterns and responses. Our methodology is validated and benchmarked against established databases such as IEMOCAP and MSP-IMPROV. Our model outperforms standard GCNs and prevalent deep graph architectures, demonstrating performance levels that align with state-of-the-art methodologies. Notably, our model achieves this feat while significantly reducing the number of learnable parameters, thereby increasing computational efficiency and bolstering its suitability for resource-constrained environments. This proposed energy-efficient graph-based hybrid learning methodology is applied towards multimodal emotion recognition within humanoid robots. Its capacity to deliver competitive performance while streamlining computational complexity and energy efficiency represents a novel approach in evolving emotion recognition systems, catering to diverse real-world applications where precision in emotion recognition within humanoid robots stands as a pivotal requisite. Full article
(This article belongs to the Special Issue Green Artificial Intelligence: Theory and Applications)
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17 pages, 3223 KiB  
Article
Taking Flight for a Greener Planet: How Swarming Could Help Monitor Air Pollution Sources
by Jan Baumgart, Dariusz Mikołajewski and Jacek M. Czerniak
Electronics 2024, 13(3), 577; https://doi.org/10.3390/electronics13030577 - 31 Jan 2024
Viewed by 426
Abstract
As the world grapples with the pressing challenge of environmental sustainability, the need for innovative solutions to combat air pollution has become paramount. Air pollution is a complex issue that necessitates real-time monitoring of pollution sources for effective mitigation. This paper explores the [...] Read more.
As the world grapples with the pressing challenge of environmental sustainability, the need for innovative solutions to combat air pollution has become paramount. Air pollution is a complex issue that necessitates real-time monitoring of pollution sources for effective mitigation. This paper explores the potential of swarm algorithms applied as a novel and efficient approach to address this critical environmental concern. Swarm algorithms offer a promising framework for coordinating fleets of drones to collaboratively monitor and analyze air pollution sources. The unique capabilities of drones, including their agility, accessibility, and versatility, make them ideal candidates for aerial data collection. When harnessed in a swarm, these drones can create a dynamic and adaptable network that provides a more comprehensive and fine-grained understanding of air pollution dynamics. This paper delves into the conceptual foundations of using swarm algorithms in drone-based air pollution monitoring. Full article
(This article belongs to the Special Issue Green Artificial Intelligence: Theory and Applications)
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20 pages, 6266 KiB  
Article
Integrating Sensor Systems and Signal Processing for Sustainable Production: Analysis of Cutting Tool Condition
by Edward Kozłowski, Katarzyna Antosz, Jarosław Sęp and Sławomir Prucnal
Electronics 2024, 13(1), 185; https://doi.org/10.3390/electronics13010185 - 31 Dec 2023
Cited by 1 | Viewed by 575
Abstract
This research focuses on the crucial role of monitoring tool conditions in milling to improve workpiece quality, increase production efficiency, and reduce costs and environmental impact. The goal was to develop predictive models for detecting tool condition changes. Data from a sensor-equipped research [...] Read more.
This research focuses on the crucial role of monitoring tool conditions in milling to improve workpiece quality, increase production efficiency, and reduce costs and environmental impact. The goal was to develop predictive models for detecting tool condition changes. Data from a sensor-equipped research setup were used for signal analysis during different machining stages. The study applied logistic regression and a gradient boosting classifier for material layer identification, with the latter achieving an impressive 97.46% accuracy. Additionally, the effectiveness of the classifiers was further confirmed through the analysis of ROC (Receiver Operating Characteristic) curves and AUC (Area Under the Curve) values, demonstrating their high quality and precise identification capabilities. These findings support the classifiers’ utility in predicting the condition of cutting tools, potentially reducing raw material consumption and environmental impact, thus promoting sustainable production practices. Full article
(This article belongs to the Special Issue Green Artificial Intelligence: Theory and Applications)
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21 pages, 2476 KiB  
Article
A Multi-Robot-Based Architecture and a Trust Model for Intelligent Fault Management and Control Systems
by Atef Gharbi and Saleh M. Altowaijri
Electronics 2023, 12(17), 3679; https://doi.org/10.3390/electronics12173679 - 31 Aug 2023
Cited by 1 | Viewed by 818
Abstract
One of the most important challenges in robotics is the development of a Multi-Robot-based control system in which the robot can make intelligent decisions in a changing environment. This paper proposes a robot-based control approach for dynamically managing robots in such a widely [...] Read more.
One of the most important challenges in robotics is the development of a Multi-Robot-based control system in which the robot can make intelligent decisions in a changing environment. This paper proposes a robot-based control approach for dynamically managing robots in such a widely distributed production system. A Multi-Robot-based control system architecture is presented, and its main features are described. Such architecture facilitates the reconfiguration (either self-reconfiguration ensured by the robot itself or distributed reconfiguration executed by the Multi-Robot-based system). The distributed reconfiguration is facilitated through building a trust model that is based on learning from past interactions between intelligent robots. The Multi-Robot-based control system architecture also addresses other specific requirements for production systems, including fault flexibility. Any out-of-control fault occurring in a production system results in the loss of production time, resources, and money. In these cases, robot trust is critical for successful job completion, especially when the work can only be accomplished by sharing knowledge and resources among robots. This work introduces research on the construction of trust estimation models that experimentally calculate and evaluate the trustworthiness of robots in a Multi-Robot system where the robot can choose to cooperate and collaborate exclusively with other trustworthy robots. We compare our proposed trust model with other models described in the literature in terms of performance based on four criteria, which are time steps analysis, RMSD evaluation, interaction analysis, and variation of total feedback. The contribution of the paper can be summarized as follows: (i) the Multi-Robot-based Control Architecture; (ii) how the control robot handles faults; and (iii) the trust model. Full article
(This article belongs to the Special Issue Green Artificial Intelligence: Theory and Applications)
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