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Computers
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Machine Learning and Statistical Learning with Applications (2nd Edition)
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Machine Learning and Statistical Learning with Applications (2nd Edition)

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2740

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 Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled “Machine Learning and Statistical Learning with Applications (2nd Edition)”, 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 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. 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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Related Special Issue

Published Papers (3 papers)

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Research

17 pages, 321 KB  
Article
Algorithmic Profiling of Operational Risk: A Data-Driven Predictive Model for Micro-Enterprise Solvency Assessment
by Jazmín Pérez-Salazar, Nicolás Márquez and Cristian Vidal-Silva
Computers 2026, 15(2), 135; https://doi.org/10.3390/computers15020135 - 22 Feb 2026
Viewed by 773
Abstract
The persistent financial exclusion of micro-enterprises is fundamentally driven by information asymmetry, as traditional credit scoring models rely heavily on audited financial statements that small entities rarely possess. To address this “thin-file” challenge, this study proposes a shift from asset-based valuation to behavioral [...] Read more.
The persistent financial exclusion of micro-enterprises is fundamentally driven by information asymmetry, as traditional credit scoring models rely heavily on audited financial statements that small entities rarely possess. To address this “thin-file” challenge, this study proposes a shift from asset-based valuation to behavioral algorithmic profiling, hypothesizing that high-frequency operational risk patterns can serve as informative proxies for solvency compared to static liquidity ratios. Using an Extreme Gradient Boosting (XGBoost) architecture on a synthetic dataset of 5000 micro-enterprise transaction logs, we develop a predictive framework that extracts latent features such as supply chain latency, inventory turnover consistency, and digital footprint intensity. The proposed model achieves an Area Under the Curve (AUC) of 0.94, outperforming traditional linear baselines and achieving performance levels above those commonly reported in micro-enterprise solvency prediction studies. The results indicate that operational stability emerges as a strong indicator of repayment capacity within the evaluated context, outperforming static liquidity-based measures. These findings suggest that computational intelligence approaches grounded in high-frequency operational data may contribute to mitigating information asymmetries in micro-enterprise credit assessment, particularly in environments characterized by limited financial disclosure, although further empirical validation is required prior to large-scale deployment. Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications (2nd Edition))
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24 pages, 3539 KB  
Article
Novel Approach Using Multi-Source Features and Attention Mechanism for Crude Oil Futures Price Prediction
by Xin-Ying Liu, Ming-Ge Yang, Xiao-Zhen Liang and Juan Zhang
Computers 2026, 15(2), 88; https://doi.org/10.3390/computers15020088 - 1 Feb 2026
Viewed by 599
Abstract
As an emerging trading market, the crude oil futures market has exhibited substantial uncertainty since its inception. Influenced by macroeconomic and geopolitical factors, its price movements are highly nonlinear and nonstationary, making accurate forecasting challenging. Therefore, it is vital to develop a powerful [...] Read more.
As an emerging trading market, the crude oil futures market has exhibited substantial uncertainty since its inception. Influenced by macroeconomic and geopolitical factors, its price movements are highly nonlinear and nonstationary, making accurate forecasting challenging. Therefore, it is vital to develop a powerful forecasting model for crude oil futures prices. However, conventional forecasting models rely solely on historical data and fail to capture the intrinsic patterns of complex sequences. This work presents a hybrid deep learning framework that incorporates multi-source features and a state-of-the-art attention mechanism. Specifically, search engine data were collected and integrated into the explanatory variables. By using lagged historical prices and search engine data to forecast future crude oil futures closing prices, the proposed framework effectively avoids lookahead bias. To reduce forecasting difficulty, the initial time series were then decomposed and reconstructed into several sub-sequences. Thereafter, traditional time series models (ARIMA) and attention-enhanced deep learning models were selected to forecast the reconstructed sub-sequences based on their distinct data features. The empirical study conducted on the INE crude oil futures price proves that the proposed model outperforms other benchmark models. The findings help fill the gap in the quantitative literature on crude oil futures price forecasting and offer valuable theoretical insights for affiliated policymakers, enterprises, and investors. Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications (2nd Edition))
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27 pages, 26025 KB  
Article
LFP-Mono: Lightweight Self-Supervised Network Applying Monocular Depth Estimation to Low-Altitude Environment Scenarios
by Hao Cai, Jiafu Liu, Jinhong Zhang, Jingxuan Xu, Yi Zhang and Qin Yang
Computers 2026, 15(1), 19; https://doi.org/10.3390/computers15010019 - 4 Jan 2026
Viewed by 931
Abstract
For UAVs, the industry currently relies on expensive sensors for obstacle avoidance. A significant challenge arises from the scarcity of high-quality depth estimation datasets tailored for low-altitude environments, which hinders the advancement of self-supervised learning methods in these settings. Furthermore, mainstream depth estimation [...] Read more.
For UAVs, the industry currently relies on expensive sensors for obstacle avoidance. A significant challenge arises from the scarcity of high-quality depth estimation datasets tailored for low-altitude environments, which hinders the advancement of self-supervised learning methods in these settings. Furthermore, mainstream depth estimation models capable of achieving obstacle avoidance through image recognition are built upon convolutional neural networks or hybrid Transformers. Their high computational costs make deployment on resource-constrained edge devices challenging. While existing lightweight convolutional networks reduce parameter counts, they struggle to simultaneously capture essential features and fine details in complex scenes. In this work, we introduce LFP-Mono as a lightweight self-supervised monocular depth estimation network. In the paper, we will detail the Pooling Convolution Downsampling (PCD) module, Continuously Dilated and Weighted Convolution (CDWC) module, and Cross-level Feature Integration (CFI) module. All results show that LFP-Mono outperforms existing lightweight methods on the KITTI benchmark, and by evaluating with the Make3D dataset, show that our method generalizes outdoors. Finally, by training and testing on the Syndrone dataset, baseline work shows that LFP-Mono exceeds state-of-the-art methods for low-altitude drone performance. Full article
(This article belongs to the Special Issue Machine Learning and Statistical Learning with Applications (2nd Edition))
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