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Deep Learning and Machine Learning in Information Systems
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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: 20 July 2026 | Viewed by 1417

Special Issue Editor

Dr. Elliot Mbunge

E-Mail Website
Guest Editor
Department of Applied Information Systems, College of Business and Economics, University of Johannesburg, Auckland Park Campus, Johannesburg, South Africa
Interests: machine learning; information technology; information systems; healthcare optimisation; health informatics; digital technologies; ICT for development (ICT4D)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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 250 words) can be sent to the Editorial Office for assessment.

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

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

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Published Papers (2 papers)

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Research

35 pages, 2740 KB  
Article
Prediction of Depression Risk on Social Media Using Natural Language Processing and Explainable Machine Learning
by Ronewa Mabodi, Elliot Mbunge, Tebogo Makaba and Nompumelelo Ndlovu
Appl. Sci. 2026, 16(7), 3489; https://doi.org/10.3390/app16073489 - 3 Apr 2026
Viewed by 572
Abstract
Major Depressive Disorder (MDD) is a significant global health burden that contributes to disability and reduced quality of life. Its impact extends beyond individuals, placing emotional, social, and economic strain on families and healthcare systems worldwide. Despite its prevalence, MDD remains widely misunderstood, [...] Read more.
Major Depressive Disorder (MDD) is a significant global health burden that contributes to disability and reduced quality of life. Its impact extends beyond individuals, placing emotional, social, and economic strain on families and healthcare systems worldwide. Despite its prevalence, MDD remains widely misunderstood, with limited mental health literacy and persistent stigma often preventing individuals from seeking help. This research explored the prediction of MDD utilising social media data via Natural Language Processing (NLP), Machine Learning (ML), and explainable Machine Learning (xML) techniques. The research aimed at identifying depressive indicators on X (formerly Twitter) and developing interpretable models for depression risk detection. The study’s methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to ensure a systematic approach to data analysis. Data was collected via X’s API and processed using regex-based noise removal, normalisation, tokenisation, and lemmatisation. Symptoms were mapped to DSM-5-TR criteria at the post-level, with user-level MDD risk assessed based on symptom persistence over a two-week period. Risk levels were classified as No Risk, Monitor, and High Risk to facilitate early intervention. Six ML models were trained and tested, while the Synthetic Minority Over-sampling Technique (SMOTE) was applied to mitigate class imbalance. The dataset was partitioned into training and testing sets using an 80:20 split. ML models were evaluated, and the Extreme Gradient Boosting model outperformed the others. Extreme Gradient Boosting achieved an accuracy of 0.979, F1-score of 0.970, and ROC-AUC of 0.996, surpassing benchmark results reported in prior studies. Explainability techniques, such as LIME and tree-based feature importance, enhance model transparency and clinical interpretability. Depressed mood consistently emerged as the highest-weighted predictor across different models. The findings highlight the value of aligning ML models with validated diagnostic frameworks to improve trustworthiness and reduce false positives. Future research can expand beyond text-based analysis by incorporating multimodal features to broaden diagnostic depth. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Information Systems)
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26 pages, 2806 KB  
Article
A Deep Learning-Based Decision Support System for Sensory Evaluation: A Predictive Framework for Functional Product Taste Assessment in Neuromarketing
by Jesús Jaime Moreno Escobar, Verónica de Jesús Pérez Franco, Mauro Daniel Castillo Pérez, Ana Lilia Coria Páez, Erika Yolanda Aguilar del Villar and Hugo Quintana Espinosa
Appl. Sci. 2026, 16(5), 2368; https://doi.org/10.3390/app16052368 - 28 Feb 2026
Viewed by 467
Abstract
This study aims to investigate the relationship between consumer neuroscience and neuromarketing using a multivariate methodology. Tools such as Principal Component Analysis (PCA) and deep learning neural networks were employed to interpret consumer responses to functional products. To this end, EEG signals were [...] Read more.
This study aims to investigate the relationship between consumer neuroscience and neuromarketing using a multivariate methodology. Tools such as Principal Component Analysis (PCA) and deep learning neural networks were employed to interpret consumer responses to functional products. To this end, EEG signals were collected, recorded, and analyzed from 83 participants aged 20 to 29 to identify significant neural markers related to food consumption decisions. Key factors influencing decision making were identified, including low beta and gamma frequency bands. Participants’ levels of attention and reflection also played a role. The findings validate the effectiveness of the proposed method, demonstrating its applicability in various fields requiring accurate and reliable classification. Furthermore, some possible applications of this topic are mentioned in the food industry section, with the aim of enabling them to develop personalized nutritional strategies based on the results obtained from the brain activity of consumers. Full article
(This article belongs to the Special Issue Deep Learning and Machine Learning in Information Systems)
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