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Computers
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Machine Learning and Statistical Learning with Applications 2025
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Machine Learning and Statistical Learning with Applications 2025

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2319

Special Issue Editor

Dr. Yan Zhang

E-Mail Website
Guest Editor
School of Computer Science and Engineering, California State University San Bernardino, 5500 University Parkway, San Bernardino, CA 92407, USA
Interests: data analysis; machine learning; deep learning; natural language processing

Special Issue Information

Dear Colleagues,

This Special Issue entitled “Machine Learning and Statistical Learning with Applications 2025” aims to provide a platform for showcasing cutting-edge research and innovative applications of machine learning and statistical learning methodologies. As these fields continue to evolve, their integration into diverse domains has led to breakthroughs in solving complex problems, including classification, prediction, clustering, and decision making across industries such as healthcare, finance, marketing, and engineering.

This Special Issue welcomes contributions that advance the theoretical foundations of machine learning and statistical learning or introduce novel frameworks and algorithms. We are particularly interested in works that bridge the gap between theory and practice, highlighting real-world applications and demonstrating the impact of these methods on data-driven decision making.

Topics of interest include, but are not limited to, supervised and unsupervised learning, deep learning, reinforcement learning, statistical modeling, and hybrid approaches combining machine learning and traditional statistical methods. Studies focused on addressing challenges such as data imbalance, interpretability, scalability, and ethical considerations are also encouraged.

This Special Issue invites original research articles, review papers, and case studies that present novel findings or provide comprehensive insights into existing methodologies. We aim to foster an interdisciplinary exchange of ideas, pushing the boundaries of what machine learning and statistical learning can achieve.

Dr. Yan Zhang
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. 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

  • machine learning applications
  • statistical learning
  • deep learning
  • supervised learning
  • unsupervised learning
  • reinforcement learning
  • feature learning
  • hybrid machine learning approaches

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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

25 pages, 8763 KiB  
Article
Forecasting the Unseen: Enhancing Tsunami Occurrence Predictions with Machine-Learning-Driven Analytics
by Snehal Satish, Hari Gonaygunta, Akhila Reddy Yadulla, Deepak Kumar, Mohan Harish Maturi, Karthik Meduri, Elyson De La Cruz, Geeta Sandeep Nadella and Guna Sekhar Sajja
Computers 2025, 14(5), 175; https://doi.org/10.3390/computers14050175 - 4 May 2025
Viewed by 360
Abstract
This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. The primary goal is to develop a predictive framework that integrates a wide range of data sources, including seismic, geospatial, and ecological data, toward improving [...] Read more.
This research explores the improvement of tsunami occurrence forecasting with machine learning predictive models using earthquake-related data analytics. The primary goal is to develop a predictive framework that integrates a wide range of data sources, including seismic, geospatial, and ecological data, toward improving the accuracy and lead times of tsunami occurrence predictions. The study employs machine learning methods, including Random Forest and Logistic Regression, for binary classification of tsunami events. Data collection is performed using a Kaggle dataset spanning 1995–2023, with preprocessing and exploratory analysis to identify critical patterns. The Random Forest model achieved superior performance with an accuracy of 0.90 and precision of 0.88 compared to Logistic Regression (accuracy: 0.89, precision: 0.87). These results underscore Random Forest’s effectiveness in handling imbalanced data. Challenges such as improving data quality and model interpretability are discussed, with recommendations for future improvements in real-time warning systems. Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications 2025)
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15 pages, 387 KiB  
Article
Analyzing Digital Political Campaigning Through Machine Learning: An Exploratory Study for the Italian Campaign for European Union Parliament Election in 2024
by Paolo Sernani, Angela Cossiri, Giovanni Di Cosimo and Emanuele Frontoni
Computers 2025, 14(4), 126; https://doi.org/10.3390/computers14040126 - 30 Mar 2025
Viewed by 546
Abstract
The rapid digitalization of political campaigns has reshaped electioneering strategies, enabling political entities to leverage social media for targeted outreach. This study investigates the impact of digital political campaigning during the 2024 EU elections using machine learning techniques to analyze social media dynamics. [...] Read more.
The rapid digitalization of political campaigns has reshaped electioneering strategies, enabling political entities to leverage social media for targeted outreach. This study investigates the impact of digital political campaigning during the 2024 EU elections using machine learning techniques to analyze social media dynamics. We introduce a novel dataset—Political Popularity Campaign—which comprises social media posts, engagement metrics, and multimedia content from the electoral period. By applying predictive modeling, we estimate key indicators such as post popularity and assess their influence on campaign outcomes. Our findings highlight the significance of micro-targeting practices, the role of algorithmic biases, and the risks associated with disinformation in shaping public opinion. Moreover, this research contributes to the broader discussion on regulating digital campaigning by providing analytical models that can aid policymakers and public authorities in monitoring election compliance and transparency. The study underscores the necessity for robust frameworks to balance the advantages of digital political engagement with the challenges of ensuring fair democratic processes. Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications 2025)
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20 pages, 2914 KiB  
Article
Cross-Dataset Data Augmentation Using UMAP for Deep Learning-Based Wind Speed Prediction
by Eder Arley Leon-Gomez, Andrés Marino Álvarez-Meza and German Castellanos-Dominguez
Computers 2025, 14(4), 123; https://doi.org/10.3390/computers14040123 - 27 Mar 2025
Viewed by 411
Abstract
Wind energy has emerged as a cornerstone in global efforts to transition to renewable energy, driven by its low environmental impact and significant generation potential. However, the inherent intermittency of wind, influenced by complex and dynamic atmospheric patterns, poses significant challenges for accurate [...] Read more.
Wind energy has emerged as a cornerstone in global efforts to transition to renewable energy, driven by its low environmental impact and significant generation potential. However, the inherent intermittency of wind, influenced by complex and dynamic atmospheric patterns, poses significant challenges for accurate wind speed prediction. Existing approaches, including statistical methods, machine learning, and deep learning, often struggle with limitations such as non-linearity, non-stationarity, computational demands, and the requirement for extensive, high-quality datasets. In response to these challenges, we propose a novel neighborhood preserving cross-dataset data augmentation framework for high-horizon wind speed prediction. The proposed method addresses data variability and dynamic behaviors through three key components: (i) the uniform manifold approximation and projection (UMAP) is employed as a non-linear dimensionality reduction technique to encode local relationships in wind speed time-series data while preserving neighborhood structures, (ii) a localized cross-dataset data augmentation (DA) approach is introduced using UMAP-reduced spaces to enhance data diversity and mitigate variability across datasets, and (iii) recurrent neural networks (RNNs) are trained on the augmented datasets to model temporal dependencies and non-linear patterns effectively. Our framework was evaluated using datasets from diverse geographical locations, including the Argonne Weather Observatory (USA), Chengdu Airport (China), and Beijing Capital International Airport (China). Comparative tests using regression-based measures on RNN, GRU, and LSTM architectures showed that the proposed method was better at improving the accuracy and generalizability of predictions, leading to an average reduction in prediction error. Consequently, our study highlights the potential of integrating advanced dimensionality reduction, data augmentation, and deep learning techniques to address critical challenges in renewable energy forecasting. Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications 2025)
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23 pages, 428 KiB  
Article
EnterpriseAI: A Transformer-Based Framework for Cost Optimization and Process Enhancement in Enterprise Systems
by Shinoy Vengaramkode Bhaskaran
Computers 2025, 14(3), 106; https://doi.org/10.3390/computers14030106 - 16 Mar 2025
Viewed by 309
Abstract
Coordination among multiple interdependent processes and stakeholders and the allocation of optimal resources make enterprise systems management a challenging process. Even for experienced professionals, it is not uncommon to cause inefficiencies and escalate operational costs. This paper introduces EnterpriseAI, a novel transformer-based framework [...] Read more.
Coordination among multiple interdependent processes and stakeholders and the allocation of optimal resources make enterprise systems management a challenging process. Even for experienced professionals, it is not uncommon to cause inefficiencies and escalate operational costs. This paper introduces EnterpriseAI, a novel transformer-based framework designed to automate enterprise system management. This transformer model has been designed and customized to reduce manual effort, minimize errors, and enhance resource allocation. Moreover, it assists in decision making by incorporating all interdependent and independent variables associated with a matter. All of these together lead to significant cost savings across organizational workflows. A unique dataset has been derived in this study from real-world enterprise scenarios. Using the transfer learning approach, the EnterpriseAI transformer has been trained to analyze complex operational dependencies and deliver context-aware solutions related to enterprise systems. The experimental results demonstrate EnterpriseAI’s effectiveness, achieving an accuracy of 92.1%, a precision of 92.5%, and a recall of 91.8%, with a perplexity score of 14. These results represent the ability of the EnterpriseAI to accurately respond to queries. The scalability and resource utilization tests reflect the astonishing factors that significantly reduce resource consumption while adapting to demand. Most importantly, it reduces the operational cost while enhancing the operational flow of business. Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications 2025)
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