Deep Learning and Machine Learning in Information Systems

Dear Colleagues,

Deep Learning (DL) and Machine Learning (ML) continue to redefine the landscape of Information Systems (IS) research and practice. DL and ML have become key drivers of innovation and competitiveness in the digital economy, from intelligent decision support and predictive analytics to automated business processes and adaptive digital platforms. This Special Issue focuses on the growing demand for integrating DL and ML techniques in business operations that increasingly rely on data-driven decision-making and intelligent automation. DL and ML have become essential for enhancing system efficiency, improving predictive accuracy, and enabling real-time analytics. DL and ML techniques are transforming the core components of Information Systems, including data management, information retrieval, decision support, system optimisation, and intelligent applications across various sectors. We are pleased to invite contributions that explore new and improved models, algorithms, frameworks, architectures, and evaluation strategies that advance the field and offer practical, scalable value for industry and academia. This Special Issue aims to bridge the gap between computational advances and information systems, and also to advance knowledge through cutting-edge research, theoretical advancements, empirical studies, and innovative applications. This can help explore how DL and ML algorithms and models can create business value, enhance system intelligence, and support decision making in dynamic, complex digital environments.

We welcome high-quality original research articles and reviews that address, but are not limited to, the following topics:

1. Systematic reviews synthesising recent trends, challenges, and opportunities in domains.
2. Deep learning architectures and models for Information Systems.
3. Machine learning-based decision support, optimisation, and prediction.
4. Explainable, interpretable, and ethical ML/DL approaches in Information Systems.
5. System-level integration, deployment strategies, and MLOps in Information Systems.
6. Natural language processing and text analytics for business intelligence.
7. Image and video analysis for digital platforms and customer experience.
8. Predictive and prescriptive analytics for enterprise systems.
9. ML-driven business process optimisation and automation.
10. Data-driven decision making and adaptive systems in organisations.
11. Integrating DL/ML with Information Systems theories and models.
12. Explainable AI (XAI) and interpretability in business contexts.

Dr. Elliot Mbunge
Guest Editor

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. Applied Sciences 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.

• deep learning
• machine learning
• information systems
• intelligent systems
• predictive analytics
• data-driven decision making
• computational models