Machine Learning Applications and Artificial Intelligence for Sustainable Development

Dear Colleagues,

Information invites submissions to a Special Issue on " Machine Learning Applications and Artificial Intelligence for Sustainable Development".

Recently, artificial intelligence has been developing at an increasingly rapid pace. AI machines are capable of sorting and interpreting large amounts of data from a variety of sources to perform a wide variety of tasks. At the same time, the transforming power of artificial intelligence creates problems ranging from issues of transparency, confidence and security to the problems of job losses and worsening inequality.

The current proliferation of artificial intelligence is the result of advances in an area known as machine learning. Machine learning uses algorithms that allows computers to learn on their own by looking at data and performing tasks based on examples. A machine learning technique called deep learning discovers and remembers schemas in large amounts of data. Deep learning systems perform tasks by looking at examples, usually without programming, and outperform traditional machine learning algorithms.

This Special Issue focuses on breakthrough results in machine learning applications and artificial intelligence and addresses the global challenges and obstacles in achieving sustainable development.

Potential Topics

• Bias in machine learning
• Explainable ML and AI
• Deep Continuous Discrete Machine Learning
• Ethical Machine Learning and Artificial Intelligence
• Machine Learning and AI applications
• Fundamentals and applications of AI
• Tensor Methods for Deep Learning
• Software Engineering challenges for Machine Learning
• Human-Interpretable Machine Learning
• Human-Centered AI crowd Computing
• Problem-solving and planning
• Reasoning and inference
• Deep inference, learning, and architectures
• Combinatorial optimization
• Constraint processing and learning
• AutoML and AI
• Probabilistic (logic) programming
• Statistical Relational AI
• Tractable Inference and Learning
• Learning to infer and to learn
• Learning to understand non-standard data

Dr. Rahul Reddy Nadikattu
Dr. Roman Tsarev
Dr. Pawan Whig
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. Information 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 1600 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 leaning
• artificial intelligence
• tensor methods
• deep learning
• software engineering