Advanced Deep Learning Methods for Large-Scale Food Distribution

Dear Colleagues,

This Special Issue welcomes paper submissions from all areas of deep learning applications, with a special focus on the research articles showing the development of advanced bio-inspired deep solutions and algorithms for addressing the main issues of large-scale food distribution (LSFD).

There is growing interest in applying bio-inspired mathematical models and recent machine learning algorithms to address different issues on food distribution activities, especially with regard to the large quantity of multi-modal data that these algorithms will have to analyze in real time.

This Special Issue brings together research papers that report new theoretical or applied algorithms employing mathematical modeling and/or machine learning in a variety of LSFD issues. We strongly encourage the submission of papers that explore new research perspectives in different areas of LSFD including, but not limited to, deep learning for shelf availability monitoring in retails stores, supervised deep solutions for shelf availability forecasting retail stores, unsupervised approaches and reinforcement learning for shelf availability forecasting retails stores, retail sentiment analysis, etc.

Main topics include the following:

• Artificial intelligence for addressing LSFD main issues;
• Out-of-stocks prediction algorithms in retail stores;
• Deep learning for shelf availability monitoring in retails stores;
• Supervised deep solutions for shelf availability forecasting retail stores;
• Unsupervised approaches and reinforcement learning for shelf availability forecasting retail stores;
• Intelligent sentiment analysis;
• Deep learning approaches for out-of-stocks management in retail stores;
• Mathematical modeling of client behaviors in retail stores;
• Benefits of adaptive shelf management analysis and the use of real-time deep systems for retail store management.

In light of these, the Special Issue is also highly interested in publishing papers in which novel bio-inspired approaches are highlighted for addressing classical LSFD issues. They include bio-inspired predictive algorithms, advanced reinforcement learning, evolutionary algorithms, advanced genetic programming, and heuristic approaches.

The Special Issue also welcomes replication and/or past published studies in any area of LSFD with the foresight that they are re-evaluated using alternative methods.

Dr. Francesco Rundo
Guest Editor

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