Sustainable Applications for Machine Learning—2nd Edition

Dear Colleagues,

The smart-everything wave and advancements in AI have caused a paradigm shift across every aspect of modern life. Additionally, the pervasive nature of information systems has generated voluminous data that must be processed, analyzed, and interpreted. While earlier approaches are no longer effective in dealing with such a sheer amount of digital data, AI offers many opportunities and solutions.

Within the realm of AI, machine learning (ML) is playing a pivotal role, enabling advanced solutions across a wide range of applications, including autonomous systems, medical/satellite image processing, chatbots, robotics, and financial technology. Given ML's governance across numerous domains, its sustainability should be a top priority now more than ever. This becomes more critical as sensitive businesses and major players such as governments, banks, giant tech companies, and smart factories increasingly use ML.

This Special Issue aims to collate the latest findings on the challenges and state-of-the-art solutions for the sustainability of ML and its applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Dependability of machine learning models;
• Acceleration of deep neural networks;
• Privacy-preserving aspects of machine learning;
• Reliability assessment of deep learning systems;
• Multi-agent systems in reinforcement learning;
• Privacy concerns in federated learning approaches;
• Artificial neural network applications in a circular economy;
• Sustainability of natural language processing models;
• Optimization in machine learning;
• Recommender systems;
• Graph neural network analysis;
• Reliability in ensemble learning;
• Security aspects of generative models;
• Ethical issues with AI/ML;
• Machine learning applications in healthcare;
• Computer vision applications in smart cities;
• Machine learning for business continuity;
• Machine learning for sustainable supply chains;
• The role of ML/DL in Industry 4.0.

We look forward to receiving your contributions.

Dr. Danial Javaheri
Prof. Dr. Hassan Chizari
Prof. Dr. Amir Masoud Rahmani
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 250 words) can be sent to the Editorial Office for assessment.

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. Machine Learning and Knowledge Extraction 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 1800 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.

• machine learning
• deep learning
• artificial neural networks
• reinforcement learning
• sustainable computing
• big data analytics
• optimization
• data mining

• Sustainable Applications for Machine Learning in Machine Learning and Knowledge Extraction (10 articles)