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Computers
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Advances in Explainable and Multimodal AI for Intelligent Systems and...
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Advances in Explainable and Multimodal AI for Intelligent Systems and Medical Applications

A Special Issue of Computers (ISSN 2073-431X) belonging to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 1029

Editors

Dr. Naeem Ullah

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
Interests: explainable AI; multimodal learning; deep learning; machine learning; medical imaging; time series analysis
Dr. Javed Ali Khan

E-Mail Website
Guest Editor
Department of Computer Science, University of Hertfordshire, Hatfield, UK
Interests: software engineering; health informatics; mining software repositories; CrowdRE; applied deep learning; large language models
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Yaqoob

E-Mail Website
Guest Editor
Department of Computer Science, University of Hertfordshire, Hatfield, UK
Interests: computational science; optimization; artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

Recent breakthroughs in artificial intelligence (AI) have significantly advanced intelligent systems and data-driven applications across multiple domains, particularly in healthcare and medical imaging. Deep learning and multimodal learning frameworks have enabled the integration of heterogeneous data sources such as images, signals, text, and structured data, leading to improved performance and richer representations. However, the increasing complexity of these models has raised critical concerns regarding interpretability, transparency, robustness, and trustworthiness.

Explainable artificial intelligence (XAI) has emerged as a crucial paradigm to address these challenges by providing insights into model decisions and enhancing human understanding of AI systems. At the same time, multimodal AI has demonstrated remarkable potential in improving decision-making by leveraging complementary information from multiple data modalities. The convergence of explainability and multimodality represents a key step toward the development of reliable, transparent, and deployable intelligent systems.

This Special Issue aims to provide a comprehensive platform for presenting state-of-the-art research on explainable and multimodal AI methodologies, theoretical foundations, and real-world applications. It seeks to bridge methodological advances with practical implementations in intelligent systems and medical applications while encouraging interdisciplinary contributions from computer science, engineering, and healthcare. This Special Issue is designed to be sufficiently broad to attract diverse contributions while maintaining a coherent focus on explainability, multimodal learning, and intelligent computing.

We invite original research articles, review papers, and methodological contributions that explore innovative AI models, interpretability techniques, multimodal fusion strategies, and domain-specific applications.

Research areas may include (but are not limited to) the following:

  • Explainable artificial intelligence (XAI) and interpretable models;
  • Multimodal and multisource learning;
  • Deep learning and representation learning;
  • Hybrid AI and neuro-symbolic approaches;
  • Knowledge distillation and model compression;
  • Foundation models and large multimodal models;
  • Model interpretability, transparency, and accountability;
  • Post hoc and intrinsic explainability methods;
  • Fairness, robustness, and ethical AI;
  • Uncertainty estimation and reliability analysis;
  • Human-centered and interactive AI;
  • Cross-modal and multi-view learning;
  • Sensor fusion and data integration;
  • Vision–language models and multimodal transformers;
  • Self-supervised and contrastive multimodal learning;
  • Medical imaging and clinical decision support;
  • Biomedical signal processing and healthcare analytics;
  • Smart cities and IoT-based intelligent systems;
  • Robotics and autonomous systems;
  • Remote sensing and geospatial analytics;
  • Industrial AI and digital twins;
  • Cybersecurity and intelligent networks;
  • Bioinformatics and computational biology.

We look forward to receiving your contributions.

Dr. Naeem Ullah
Dr. Javed Ali Khan
Dr. Muhammad Yaqoob
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly 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 1800 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

  • explainable artificial intelligence
  • multimodal learning
  • interpretable deep learning
  • intelligent systems
  • medical applications
  • data fusion
  • trustworthy AI
  • machine learning
  • computer vision
  • hybrid AI

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

26 pages, 1343 KB  
Article
A Three-Phase Explainable Deep Learning Approach for Reliable Wrist Fracture Identification from X-Ray Images
by Naeem Ullah, Muhammad Hassan, Rahman Ullah and Javed Ali Khan
Computers 2026, 15(9), 585; https://doi.org/10.3390/computers15090585 - 4 Sep 2026
Viewed by 101
Abstract
Wrist fractures present significant challenges in clinical diagnosis, often leading to treatment delays and compromised recoveries. Manual diagnosis is resource-intensive and error-prone. To address these challenges, we develop DeepWristFNet, a compact convolutional architecture designed for end-to-end wrist fracture classification using a small dataset [...] Read more.
Wrist fractures present significant challenges in clinical diagnosis, often leading to treatment delays and compromised recoveries. Manual diagnosis is resource-intensive and error-prone. To address these challenges, we develop DeepWristFNet, a compact convolutional architecture designed for end-to-end wrist fracture classification using a small dataset of 193 wrist X-ray images. The DeepWristFNet architecture integrates multi-scale convolutional operations with Fire and Shuffle modules within a compact network design, followed by fully connected layers for binary classification. We applied data pre-processing techniques such as data augmentation, image enhancement, and image resizing to increase the number of images, improve image quality, and resize images to match the DeepWristFNet input size. The proposed method comprised three phases. In the first phase, we trained, validated, and tested end-to-end and achieved validation and testing accuracies of 99.04% and 87.93%, respectively. Testing was performed on a hold-out subset of image instances that was kept separate from model development. The evaluated hold-out images originated from the same dataset distribution and included the corresponding augmented variants. In the second phase, we further evaluated the learned representation by extracting deep features from the first fully connected layer of DeepWristFNet. ReliefF was then used to select informative features, which were subsequently evaluated using 10 conventional machine learning classifiers. Out of 10 classifiers, 5 classifiers, i.e., Efficient linear SVM, quadratic SVM, Narrow NN, wide NN, and medium NN, achieved 100% testing accuracy on unseen samples. In the third phase, an auxiliary Fuzzy Inference System provides an intensity-based foreground-background representation of the X-ray images. This representation provides complementary visual information for interpretation but is not intended to directly classify or localize fractures. Grad-CAM is additionally used to visualize image regions contributing to the DeepWristFNet predictions, providing a model-specific explanation of the classification decision. Additionally, we evaluated how well the proposed DeepWristFNet approach performed against cutting-edge deep transfer learning models. In the evaluated experiments, DeepWristFNet outperformed the compared pre-trained deep learning architectures on the unseen hold-out subset from the same dataset distribution (test set). This study demonstrates the potential of DeepWristFNet for wrist fracture classification under a small-data setting. However, further evaluation on larger, independently collected clinical datasets is required to establish its robustness, generalizability, and suitability for clinical decision support. Full article
(This article belongs to the Special Issue Advances in Explainable and Multimodal AI for Intelligent Systems and Medical Applications)
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22 pages, 7546 KB  
Article
Attn-ChurnNet: A Transformer-Based Sequential Framework for Customer Churn Prediction in Subscription Platforms with Focal Loss Training and Conformal Uncertainty Quantification
by Didar Hossain, Mohiuddin Mehedi, Khandakar Rabbi Ahmed, Md Rafiul Mahmud, Mainul Islam Khan and Sakib Salam Jamee
Computers 2026, 15(8), 502; https://doi.org/10.3390/computers15080502 - 4 Aug 2026
Viewed by 360
Abstract
Customer churn is a critical challenge for subscription-based digital platforms, as customer activity patterns change dynamically over time. Traditional churn modeling methods fail to account for sequential dependencies across customer interaction histories. This paper presents Attn-ChurnNet, a novel attention-based Transformer architecture that effectively [...] Read more.
Customer churn is a critical challenge for subscription-based digital platforms, as customer activity patterns change dynamically over time. Traditional churn modeling methods fail to account for sequential dependencies across customer interaction histories. This paper presents Attn-ChurnNet, a novel attention-based Transformer architecture that effectively predicts customer churn by modeling sequential customer contact histories. The proposed methodology leverages multi-head self-attention with sinusoidal positional encodings and Pre-LN residual connections to highlight key interaction sequences and interpret the temporal dynamics of customer activity in subscription platforms. Experiments are conducted on the large-scale WSDM–KKBox Customer Churn Prediction dataset using a temporal train/validate/test split, incorporating transaction records, usage logs, and customer demographic information. Comprehensive comparison against established baselines—Logistic Regression (LR), Random Forest (RF), XGBoost, and Gated Recurrent Unit (GRU)—demonstrates that Attn-ChurnNet achieves a macro-averaged classification accuracy of 97.03% (±0.41%), precision of 95% (±0.5%), recall of 96% (±0.6%), F1-score of 95.50% (±0.5%), AUC of 0.98 (±0.004), Average Precision of 0.963, and log-loss of 0.15 (±0.007) under five-fold stratified cross-validation, outperforming all competing approaches with statistical significance (p<0.01, McNemar’s test). A comprehensive two-part ablation study (22 variants), calibration analysis (ECE = 0.031; Ts* = 1.08), attention entropy analysis with Jensen–Shannon divergence and two-sample t-test (t=18.4, p<0.001), Integrated Gradients attribution, conformal prediction (91.4% coverage, 88% singleton efficiency), precision–recall analysis, and computational complexity evaluation further validate the model’s design and production readiness. Full article
(This article belongs to the Special Issue Advances in Explainable and Multimodal AI for Intelligent Systems and Medical Applications)
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