Sustainable Applications for Machine Learning

Dear Colleagues,

The smart-everything wave and advancements in AI have caused a paradigm shift in every aspect of today's human life. Additionally, the pervasive nature of information systems has resulted in the generation of a variety of voluminous data, which should 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 as its influence has enabled advanced solutions in a wide range of applications, such as autonomous systems, medical/satellite image processing, chatbots, robotics, and financial technology. Considering ML governance in numerous domains, its sustainability inevitably should be taken into consideration, now more than ever. This becomes more critical as sensitive businesses and big players such as governments, banks, giant tech, and smart factories are increasingly using ML.

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

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but 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 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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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