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Information
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Machine Learning for Predictive Analytics: Models, Applications, and Challenges
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Machine Learning for Predictive Analytics: Models, Applications, and Challenges

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 6038

Special Issue Editors

Dr. Raza Hasan

E-Mail Website
Guest Editor
School of Technology and Maritime Industries, Southampton Solent University, Southampton SO140YN, UK
Interests: AI; machine learning; machine vision; LLM; data visualization
Special Issues, Collections and Topics in MDPI journals
Dr. Bacha Rehman

E-Mail Website
Guest Editor
Department of Science and Engineering, Southampton Solent University, Southampton SO14 0YN, UK
Interests: affective computing; investigating multimodal data; hybrid DNNs; applications of AI; data science; computer vision; time-series and financial market analysis; FinTech
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Xie

E-Mail Website
Guest Editor
Division of Information and Communication Engineering, Kitami Institute of Technology, Kitami 090-8507, Hokkaido, Japan
Interests: computer vision; control theory and application; artificial intelligence

Special Issue Information

Dear Colleagues,

The MDPI Information journal invites submissions to a Special Issue on "Machine Learning for Predictive Analytics: Models, Applications, and Challenges".

Machine learning (ML) continues to revolutionize predictive capabilities across scientific and industrial domains. While achieving remarkable success, ML-based prediction systems face persistent challenges in interpretability, generalization, computational efficiency, and ethical implementation. This Special Issue seeks to advance the field by publishing innovative research that bridges theoretical developments with practical solutions across the predictive analytics pipeline.

Contributions are invited across (but are not limited to) the following themes:

  1. Model Development and Innovation
  • Novel architectures (transformers, graph neural networks, neurosymbolic systems);
  • Time-series, spatial–temporal, and multimodal forecasting;
  • Uncertainty quantification and confidence calibration;
  • Federated and distributed learning approaches.
  1. Domain-Specific Applications
  • Healthcare: clinical outcome prediction and medical imaging analytics;
  • Cybersecurity: threat detection and adversarial attack forecasting;
  • Engineering: predictive maintenance and structural health monitoring;
  • Climate Science: extreme weather modeling and carbon emission prediction;
  • Finance (FinTech): algorithmic trading, fraud detection systems, and credit risk assessment;
  • Education (EdTech): learning outcome prediction, adaptive learning systems, student performance analytics, and educational resource optimization;
  • Smart Cities: traffic flow optimization;
  • Agriculture: precision farming and crop yield forecasting;
  • Social Good: poverty mapping, disaster response optimization, and computer forensic analytics.
  1. Critical Challenges and Solutions
  • Explainable AI (XAI) for high-consequence decisions;
  • Bias detection and fairness-aware modeling;
  • Edge deployment and resource-efficient inference;
  • Hybrid modeling;
  • Data scarcity solutions.
  1. Evaluation and Reproducibility
  • Benchmark datasets and metrics;
  • Reproducibility frameworks;
  • Real-world validation studies.

We welcome original research and reviews that demonstrate rigorous methodology with clear practical implications. Interdisciplinary contributions connecting ML theory with domain expertise are particularly encouraged. Join us in shaping the future of predictive analytics—submit your work to advance methodologies, tools, and applications that empower equitable and sustainable decision-making.

Dr. Raza Hasan
Dr. Bacha Rehman
Prof. Dr. Wei Xie
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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 AI
  • FinTech
  • cybersecurity
  • EduTech
  • healthcare analytics
  • forensic AI
  • hybrid deep learning
  • multimodal data fusion
  • predictive modeling
  • ethical AI
  • algorithmic fairness
  • adaptive learning systems
  • financial forecasting
  • threat intelligence

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

Published Papers (4 papers)

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Research

20 pages, 2397 KB  
Article
Towards Sustainable AI: Benchmarking Energy Efficiency of Deep Neural Networks for Resource-Constrained Edge Devices
by Rohail Qamar, Raheela Asif and Syed Muslim Jameel
Information 2026, 17(4), 380; https://doi.org/10.3390/info17040380 - 17 Apr 2026
Abstract
Deep learning models represent one of the most advanced and effective approaches in predictive modeling. Their hierarchical architectures enable the extraction of complex, non-linear feature relationships and the identification of latent patterns within data, making them highly suitable for tasks involving high-dimensional or [...] Read more.
Deep learning models represent one of the most advanced and effective approaches in predictive modeling. Their hierarchical architectures enable the extraction of complex, non-linear feature relationships and the identification of latent patterns within data, making them highly suitable for tasks involving high-dimensional or unstructured inputs. However, these models are computationally demanding, requiring significant processing resources and time. Furthermore, their predictive performance is largely contingent upon the availability of large-scale datasets. In this study, a Deep Green Framework is employed for the prediction of two computer vision tasks. CIFAR-10 and CIFAR-00 have been taken for image classification. Fifteen convolutional neural network (CNN) variants categorized into light-weight and heavy-weight are trained for the prediction of these two datasets. Based on energy footprint, time, memory usage, Top-1 accuracy, Top-3 accuracy, model size, and model parameters. The study highlights that MobileNetV3-Small produces the best outcomes when compared to other trained models having low task latency and higher efficiency, making it highly suitable for edge environments where resources are scarce. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Analytics: Models, Applications, and Challenges)
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33 pages, 3715 KB  
Article
Enhancing Multi-Level Spatio-Temporal Forecasting of Adjudicated Crime Occurrence Trends in Indonesia
by Firman Arifman, Teddy Mantoro and Media Anugerah Ayu
Information 2026, 17(4), 331; https://doi.org/10.3390/info17040331 - 1 Apr 2026
Viewed by 328
Abstract
Indonesia faces persistent challenges in crime forecasting and judicial resource management, compounded by chronic underreporting and inconsistent spatial resolution in official crime statistics. In this study, a multi-level spatio-temporal machine learning framework is developed and applied to 95,666 adjudicated crime records from the [...] Read more.
Indonesia faces persistent challenges in crime forecasting and judicial resource management, compounded by chronic underreporting and inconsistent spatial resolution in official crime statistics. In this study, a multi-level spatio-temporal machine learning framework is developed and applied to 95,666 adjudicated crime records from the Supreme Court of Indonesia spanning January 2023 to June 2024. Following the CRISP-DM methodology, a hybrid STL-XGBoost v. 3.2.0 model is trained on a chronological split to forecast daily judicial caseloads, achieving an R2 of 0.8070, MAE of 16.52, and sMAPE of 9.76% on the held-out test set. DBSCAN spatial clustering, parameterized via k-distance plot analysis (ϵ=0.3∘, minPts = 3) and validated through Jaccard Similarity Index sensitivity analysis, identifies 29 distinct adjudicated crime hubs concentrated along Java and Sumatra’s urban and transit corridors. Comparative analysis of reported versus adjudicated crime data reveals systematic judicial funnel attrition ranging from 199.12% in Riau to 2436.02% in Papua, establishing that adjudicated crime records provide a reliable indicator of judicial workload rather than a comprehensive measure of social deviance. Key limitations, including the 18-month observation window that may not capture long-term policy shifts and the use of city centroids as spatial proxies that introduces a degree of ecological fallacy, are acknowledged. The framework offers a scalable, interpretable decision support tool for evidence-based judicial resource planning across national, provincial, and city scales in Indonesia. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Analytics: Models, Applications, and Challenges)
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32 pages, 2526 KB  
Article
HSE-GNN-CP: Spatiotemporal Teleconnection Modeling and Conformalized Uncertainty Quantification for Global Crop Yield Forecasting
by Salman Mahmood, Raza Hasan and Shakeel Ahmad
Information 2026, 17(2), 141; https://doi.org/10.3390/info17020141 - 1 Feb 2026
Viewed by 608
Abstract
Global food security faces escalating threats from climate variability and resource constraints. Accurate crop yield forecasting is essential; however, existing methods frequently overlook complex spatial dependencies driven by climate teleconnections, such as the ENSO, and lacks rigorous uncertainty quantification. This paper presents HSE-GNN-CP, [...] Read more.
Global food security faces escalating threats from climate variability and resource constraints. Accurate crop yield forecasting is essential; however, existing methods frequently overlook complex spatial dependencies driven by climate teleconnections, such as the ENSO, and lacks rigorous uncertainty quantification. This paper presents HSE-GNN-CP, a novel framework integrating heterogeneous stacked ensembles, graph neural networks (GNNs), and conformal prediction (CP). Domain-specific features are engineered, including growing degree days and climate suitability scores, and explicitly model spatial patterns via rainfall correlation graphs. The ensemble combines random forest and gradient boosting learners with bootstrap aggregation, while GNNs encode inter-regional climate dependencies. Conformalized quantile regression ensures statistically valid prediction intervals. Evaluated on a global dataset spanning 15 countries and six major crops from 1990 to 2023, the framework achieves an R2 of 0.9594 and an RMSE of 4882 hg/ha. Crucially, it delivers calibrated 80% prediction intervals with 80.72% empirical coverage, significantly outperforming uncalibrated baselines at 40.03%. SHAP analysis identifies crop type and rainfall as dominant predictors, while the integrated drought classifier achieves perfect accuracy. These contributions advance agricultural AI by merging robust ensemble learning with explicit teleconnection modeling and trustworthy uncertainty quantification. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Analytics: Models, Applications, and Challenges)
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21 pages, 1753 KB  
Article
A Personality-Informed Candidate Recommendation Framework for Recruitment Using MBTI Typology
by Hamza Wazir Khan, Mian Usman Sattar, Samreen Noor and Muna I. Alyousef
Information 2025, 16(10), 863; https://doi.org/10.3390/info16100863 - 5 Oct 2025
Cited by 1 | Viewed by 4325
Abstract
In many developing regions, recruitment still relies heavily on traditional methods that often ignore the importance of aligning a candidate’s personality with the job role. This mismatch can lead to poor performance, dissatisfaction, and high turnover. To address this, the study presents a [...] Read more.
In many developing regions, recruitment still relies heavily on traditional methods that often ignore the importance of aligning a candidate’s personality with the job role. This mismatch can lead to poor performance, dissatisfaction, and high turnover. To address this, the study presents a personality-aware recommendation system that combines the Myers–Briggs Type Indicator (MBTI) with machine learning to support smarter hiring decisions. The system is tailored for the South Asian job market and includes two main components: a web-based MBTI assessment for applicants and a dashboard for HR professionals powered by a XGBoost classifier. This model was trained on a dataset correlating applicant profiles and the flagged preferences of MBTI with the job. Experience and the number of skills, education level, and encoded MBTI types were the key features, and the SMOTE method was employed to balance the dataset. The model attained an accuracy of 74.30%, having balanced precision and recall measures. It was also discriminative, the ROC AUC was 0.84, and the precision–recall AUC was 0.85. One example of utilizing the Software Developer position in real life demonstrated the success of the system to filter and rank candidates at the same time according to both technical and personality-specific criteria. Overall, this study emphasizes the worth of combining insights from psychological profiling with machine learning in order to develop a more holistically, fair, and efficient hiring process. Full article
(This article belongs to the Special Issue Machine Learning for Predictive Analytics: Models, Applications, and Challenges)
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