Recent Advances in Deep Learning and Machine Learning in Information Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 December 2025 | Viewed by 6
Special Issue Editors
Interests: data analytics; information visualization; uncertainty quantification in deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: big data analytics; deep learning acceleration; GPU-based design of parallel applications
Interests: data analytics; visualization; management information systems; HCI
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue explores the transformative impact of deep learning and machine learning technologies on information systems across diverse domains. It aims to bridge theoretical advancements with practical applications, focusing on how these technologies enhance decision-making, automate processes, and create intelligent systems that adapt to complex organizational needs. This issue welcomes research that addresses challenges in implementing ML/DL within information systems, including ethical considerations, interpretability, data quality, and integration with existing enterprise architectures. We particularly encourage submissions that demonstrate novel applications, methodological innovations, and empirical results that advance our understanding of how these technologies reshape organizational capabilities and information management paradigms.
Suggested topics:
- Neural networks in enterprise systems;
- Explainable AI for business intelligence;
- Deep learning for knowledge management;
- Natural language processing in information retrieval;
- Computer vision in business processes;
- Reinforcement learning for decision support;
- Federated learning for distributed information systems;
- Transfer learning in enterprise applications;
- AI governance and information security;
- Responsible AI in organizational contexts;
- ML/DL model deployment in IS infrastructures;
- Predictive analytics for business forecasting;
- Generative AI for content management systems;
- Anomaly detection in information systems;
- Human–AI collaboration in enterprise environments;
- Multimodal learning for information integration;
- Sentiment analysis for customer relationship management;
- Big data analytics with deep learning;
- Self-supervised learning for knowledge discovery.
Dr. Dong Hyun Jeong
Prof. Dr. Jeong-Gun Lee
Dr. Bong-Keun Jeong
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. 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.
Keywords
- enterprise neural networks
- explainable AI
- knowledge management AI
- business NLP
- corporate computer vision
- decision support systems
- federated learning
- AI governance
- responsible AI
- IS model deployment
- business predictive analytics
- generative business AI
- information anomaly detection
- human–AI collaboration
- multimodal information systems
- customer sentiment analysis
- big data analytics
- knowledge discovery
- intelligent information systems
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