Emerging Technologies in Intelligent Software Methodologies, Tools, and Techniques (Selected Papers from SoMeT 2023)

Dear Colleagues,

We invite contributions to our Special Issue on "Emerging Technologies in Intelligent Software Methodologies, Tools, and Techniques". This issue aims to explore the latest advancements in deep learning and reinforcement learning, with a specific focus on their applications in industrial production and supply chain management.

As software methodologies continue to evolve, the integration of intelligent technologies has become crucial for enhancing efficiency, accuracy, and decision making in various domains. This Special Issue seeks to bridge the gap between emerging technologies, such as deep learning and reinforcement learning, and their practical applications in the fields of industrial production and supply chain management.

Topics of interest include, but are not limited to:

1. Deep Learning: We encourage submissions that explore novel deep learning algorithms, architectures, and applications, with a particular emphasis on how these techniques can revolutionize software methodologies.
2. Reinforcement Learning: Papers discussing innovative reinforcement learning approaches and their integration into software tools and methodologies are welcomed.
3. Applications to Industrial Production: We invite research that showcases the implementation of intelligent methodologies to optimize production processes, increase automation, and reduce costs within industrial settings.
4. Applications to Supply Chain Management: Manuscripts addressing the use of intelligent techniques to enhance supply chain planning, logistics, and decision-making are of great interest.

This Special Issue builds upon the existing body of literature on intelligent software methodologies and their applications. It seeks to provide a platform for researchers to share their insights and discoveries while contributing to the ongoing discourse in the field. By highlighting the intersection of deep learning, reinforcement learning, and industrial production/supply chain management, we aim to foster interdisciplinary discussions and advance the state-of-the-art in intelligent software methodologies.

Prof. Dr. Hamido Fujita
Prof. Dr. Guido Guizzi
Dr. Maria Grazia Marchesano
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. Electronics is an international peer-reviewed open access semimonthly 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.

• intelligent software
• deep learning
• reinforcement learning
• emerging technologies
• industrial production
• supply chain management
• software methodologies
• automation
• decision support
• optimization
• artificial intelligence
• industrial applications
• software tools
• advanced algorithms
• intelligent decision-making