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Applied Sciences
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Big Data Driven Machine Learning and Deep Learning
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Big Data Driven Machine Learning and Deep Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1563

Special Issue Editors

Dr. Linzi Yin

E-Mail Website
Guest Editor
School of Physics and Electronics, Central South University, Changsha 410017, China
Interests: big data; machine learning; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Anca Udristoiu

E-Mail Website
Guest Editor
Faculty of Automation, Computers and Electronics, University of Craiova, 200440 Craiova, Romania
Interests: artificial intelligence; computer vision; software engineering; algorithm design; big data; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on Big Data-Driven Machine Learning and Deep Learning.

Machine learning constitutes the fundamental cornerstone of artificial intelligence (AI). Despite the remarkable achievements attained by contemporary generative deep learning across a spectrum of application scenarios, this does not signify that it represents the sole pathway for the advancement of AI. Presently, machine learning and deep learning methodologies are undergoing continuous evolution, and their irreplaceable role in specific specialized domains remains unchallenged. In particular, the integration with big data emerges as a pivotal direction in the future development of AI and has evolved into one of the prominent research frontiers in the current academic landscape.

The main topics include computing models, Algorithms, framework and related applications and so on, as well as optimization and application of machine learning theory and big data.

Dr. Linzi Yin
Dr. Anca Udristoiu
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. 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

  • big data-driven machine learning
  • big data-driven decision modeling
  • big data and data mining
  • deep learning and applications
  • artificial intelligence and applications

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Published Papers (2 papers)

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Research

27 pages, 2342 KB  
Article
Attention-Based Deep Learning Hybrid Model for Cash Crop Price Forecasting: Evidence from Global Futures Markets with Implications for West Africa
by Mohammed Gadafi Tamimu, Shurong Zhao, Qianwen Xu and Jie Zhang
Appl. Sci. 2026, 16(3), 1600; https://doi.org/10.3390/app16031600 - 5 Feb 2026
Viewed by 729
Abstract
Accurate forecasting of agricultural commodity prices is essential for managing market volatility, improving supply chain coordination, and supporting food security-related decision-making. Recent advances in deep learning have demonstrated strong potential for capturing nonlinear and temporal dependencies in commodity price dynamics. In this study, [...] Read more.
Accurate forecasting of agricultural commodity prices is essential for managing market volatility, improving supply chain coordination, and supporting food security-related decision-making. Recent advances in deep learning have demonstrated strong potential for capturing nonlinear and temporal dependencies in commodity price dynamics. In this study, we propose a hybrid long short-term memory–multi-head attention (LSTM–MHA) framework for agricultural commodity price forecasting using global futures market data. The model is trained and evaluated on multivariate global commodity futures prices, reflecting internationally traded benchmark markets rather than region-specific domestic prices. While the empirical analysis is based on global data, the study is motivated by the relevance of international price movements for import-dependent regions, particularly West Africa, where global price transmission plays a critical role in domestic market dynamics. The experimental results demonstrate that the proposed model effectively captures short-term temporal dependencies and provides interpretable attention-based insights into lag relevance. An ablation study further highlights the trade-offs between forecasting accuracy and interpretability across different model configurations. The hybrid architecture combines the time-based pattern identification and weighting capabilities of multi-head attention with the sequential learning capabilities of LSTM. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the model’s performance. With an MSE of 0.0124, an RMSE of 0.1114, and an MAE of 0.1097, the model outperformed conventional models like ARIMA and standalone LSTM by three to four times in error reduction. The findings suggest that attention-enhanced deep learning models can serve as valuable analytical tools for understanding global price dynamics and informing policy analysis and risk management in West African agricultural markets. Full article
(This article belongs to the Special Issue Big Data Driven Machine Learning and Deep Learning)
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28 pages, 12374 KB  
Article
A Distributed Instance Selection Algorithm Based on Cognitive Reasoning for Regression Tasks
by Linzi Yin, Wendi Cai, Zhanqi Li and Xiaochao Hou
Appl. Sci. 2026, 16(2), 913; https://doi.org/10.3390/app16020913 - 15 Jan 2026
Viewed by 406
Abstract
Instance selection is a critical preprocessing technique for enhancing data quality and improving machine learning model efficiency. However, existing algorithms for regression tasks face a fundamental trade-off: non-heuristic methods offer high precision but suffer from sequential dependencies that hinder parallelization, while heuristic methods [...] Read more.
Instance selection is a critical preprocessing technique for enhancing data quality and improving machine learning model efficiency. However, existing algorithms for regression tasks face a fundamental trade-off: non-heuristic methods offer high precision but suffer from sequential dependencies that hinder parallelization, while heuristic methods support parallelization but often yield coarse-grained results susceptible to local optima. To address these challenges, we propose CRDISA, a novel distributed instance selection algorithm driven by a formalized cognitive reasoning logic. Unlike traditional approaches that evaluate subsets, CRDISA transforms each instance into an independent “Instance Expert” capable of reasoning about the global data distribution through a unique difference knowledge base. For regression tasks with continuous outputs, we introduce a soft partitioning strategy to define adaptive error boundaries and a bidirectional voting mechanism to robustly identify high-quality instances. Although the fine-grained reasoning implies high computational complexity, we implement CRDISA on Apache Spark using an optimized broadcast mechanism. This architecture provides linear scalability in wall-clock time, enabling scalable processing without sacrificing theoretical rigor. Experiments on 22 datasets demonstrate that CRDISA achieves an average compression rate of 31.7% while maintaining predictive accuracy (R2=0.681) comparable to or better than state-of-the-art methods, proving its superiority in balancing selection granularity and distributed efficiency. Full article
(This article belongs to the Special Issue Big Data Driven Machine Learning and Deep Learning)
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