The Impact of Artificial Intelligence and Machine Learning on Sustainability for Complex Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Sustainable Processes".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3450

Special Issue Editors

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Guest Editor
Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, UK
Interests: artificial intelligence; cybersecurity; Internet of Things; cognitive radio; medical imaging
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
GMPR-Geometric Modelling & Pattern Recognition Group, Sheffield Hallam University, Sheffield, UK
Interests: artificial intelligence; pattern recognition; 3D imaging; data compression

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) techniques have attracted significant interest from researchers in the last 5 years, and huge progress is being made in this area. AI and ML are currently being used to solve a range of real-world problems, from object detection in autonomous vehicles through to machine translation services and code autocompletion in software development. PricewaterhouseCoopers (PwC) estimate that, as well as the obvious productivity benefits AI solutions can bring, harnessing AI to accelerate the move toward low-carbon industries can result in a 4% reduction in greenhouse gases globally when compared to business as usual. These potential gains are particularly prevalent in industries such as energy (where the potential for AI and IoT in smart grids can significantly improve operational efficiency) and transport (where autonomous and semi-autonomous vehicles can deliver improved fuel economy and allow for better traffic optimisation).

As well as direct reduction in greenhouse gases through process improvements, AI can also play a wider role in sustainability, with applications as diverse as monitoring and predicting water quality, analysing deforestation and illegal fishing from satellite images, and providing early warnings of poor air quality due to pollution. Worryingly, large AI models frequently require huge resources to train, with OpenAI’s GPT-3 large language model requiring an estimated 190,000 kWh of electricity. Therefore, best realising the opportunities for sustainability offered by AI models will require responsible research practices and careful decisions that consider long-term environmental and social impact.

This Special Issue focuses on the issues around artificial intelligence and machine learning in sustainability. Topics of interest for publication include but are not limited to the following:

  • Artificial intelligence applied to smart grid systems (e.g., expert systems, evolutionary computing, neural networks);
  • Artificial intelligence for the analysis and prediction of pollution;
  • Intelligent systems/artificial intelligence for enabling low-carbon industries;
  • Autonomous systems for sustainability;
  • Green artificial intelligence;
  • Data issues (such as federated data sharing, de-duplication, data privacy, and ethical concerns).

Dr. Alex Shenfield
Prof. Dr. Marcos Rodrigues
Dr. Bernardi Pranggono
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Processes is an international peer-reviewed open access monthly 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.


  • artificial intelligence
  • machine learning
  • sustainability
  • Industry 4.0
  • Internet of Things
  • green ICT
  • smart grid
  • data management

Published Papers (1 paper)

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27 pages, 2059 KiB  
Environmental Benefits of Sleep Apnoea Detection in the Home Environment
by Ragab Barika, Heather Elphick, Ningrong Lei, Hajar Razaghi and Oliver Faust
Processes 2022, 10(9), 1739; - 01 Sep 2022
Cited by 2 | Viewed by 2337
Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The [...] Read more.
Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment. Full article
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