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Advancing Smart Systems Through Deep Learning, Generative AI, and Big...
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Advancing Smart Systems Through Deep Learning, Generative AI, and Big Data Analytics

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

Deadline for manuscript submissions: 25 January 2027 | Viewed by 6994

Special Issue Editors

Dr. Xin Lu

E-Mail Website
Guest Editor
School of Computer Science, Leeds Trinity University, Leeds LS18 5HD, UK
Interests: deep learning; big data analytics; smart systems; human–robot collaboration; GenAI in higher education
Special Issues, Collections and Topics in MDPI journals
Dr. Jianhua Yang

E-Mail Website
Guest Editor
International Digital Laboratory, Warwick Manufacturing Group, Warwick University, Coventry CV4 7AL, UK
Interests: pedagogical research; artificial neural networks; evolutionary computing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoxia Li

E-Mail Website
Guest Editor
College of Informatics, Huazhong Agricultural University, No.1 Shizishan Street, Hongshan District, Wuhan 430070, China
Interests: intelligent optimization algorithm; sustainable manufacturing; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Dr. Dehao Wu

E-Mail Website
Guest Editor
Department of Computing & Informatics, Bournemouth University, Bournemouth BH12 5BB, UK
Interests: artificial intelligence for 5G verticals; AI in digital health; intelligent IoT and digital twin applications in beyond 5G; indoor positioning and sensing systems
Special Issues, Collections and Topics in MDPI journals
Dr. Wei Wang

E-Mail Website
Guest Editor
School of Engineering Science, University of Skövde, 54128 Skövde, Sweden
Interests: intelligent manufacturing; CAD/CAPP/CAM; human–robot collaboration; digital twins; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancements in deep learning, generative AI, and big data analytics have significantly transformed smart systems across various domains, including industrial applications, higher education, manufacturing, and agriculture. These technologies have revolutionized the way industries operate, enabling automation, intelligent decision-making, and enhanced productivity. In industrial applications, AI-driven predictive maintenance, quality control, and supply chain optimization have resulted in increased efficiency and reduced downtime. In higher education, generative AI is reshaping teaching methodologies by personalizing learning experiences, automating content generation, and providing AI-assisted tutoring. In manufacturing, the integration of deep learning and big data analytics has enabled smart factories, where real-time monitoring, predictive analytics, and adaptive control systems enhance production processes. In agriculture, the application of deep learning and big data analytics has enhanced our knowledge of agriculture in various aspects, such as protein-directed evolution, smart breeding, smart livestock farming, and so on. The convergence of these technologies is paving the way for more intelligent, data-driven, and autonomous systems that will continue to evolve across various sectors.

This Special Issue, titled ‘Advancing Smart Systems Through Deep Learning, Generative AI, and Big Data Analytics’, aims to bring together cutting-edge research that explores the integration of deep learning, generative AI, and reinforcement learning techniques to enhance smart manufacturing, Digital Intelligent Education, and smart agriculture. This Special Issue will focus on, but is not limited to, the following key areas:

  1. Deep learning for smart systems:
    • Advanced deep learning models for intelligent decision-making;
    • Deep learning-driven automation and optimization in smart environments;
    • Edge and fog computing for real-time deep learning applications.
  2. Generative AI and big data analytics in industrial applications:
    • AI-driven predictive maintenance and fault diagnosis;
    • Generative AI for industrial process optimization;
    • AI-powered digital twins for manufacturing systems;
    • Data-driven decision-making in Industry 4.0;
    • IoT and big data integration in smart factories;
    • Real-time data processing for predictive analytics in manufacturing.
  3. Generative AI in higher education:
    • AI-powered personalized learning and assessment tools;
    • GenAI for automated content generation in educational platforms;
    • AI-driven student engagement and feedback mechanisms.
  4. Human–robot collaboration with deep reinforcement learning:
    • Adaptive learning strategies for human–robot teaming;
    • Deep reinforcement learning for autonomous robotic systems;
    • Safety and trust in human–robot collaborative environments.
  5. Deep learning for smart agriculture:
    • Deep learning for smart breeding;
    • Deep learning for smart livestock farming;
    • Deep learning for protein directed evolution.
  6. Artificial intelligence for next-generation networks and digital ecosystems:
    • Artificial intelligence for 5G verticals;
    • Artificial intelligence in digital health;
    • Intelligent IoT and digital twin applications in beyond 5G systems.

Dr. Xin Lu
Dr. Jianhua Yang
Dr. Xiaoxia Li
Dr. Dehao Wu
Dr. Wei Wang
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

  • Artificial Intelligence (AI)
  • deep learning
  • Generative AI (GenAI)
  • big data analytics
  • smart systems
  • smart manufacturing
  • human–robot collaboration
  • deep reinforcement learning
  • digital twins
  • predictive maintenance
  • IoT in manufacturing
  • AI in higher education
  • smart agriculture

Benefits of Publishing in a Special Issue

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

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Research

23 pages, 35326 KB  
Article
An Automated Information Processing Framework for UAV-Based Detection and Spatial Mapping of Crop Damage Using Deep Learning
by Alejandro Carrillo-Gómez, Daniela Moctezuma and Enrique Camacho-Pérez
Information 2026, 17(6), 529; https://doi.org/10.3390/info17060529 - 27 May 2026
Viewed by 3528
Abstract
The early detection and spatial characterization of crop damage are critical for improving decision-making in precision agriculture, particularly in regions where traditional monitoring methods are limited in scalability and objectivity. This study presents an integrated information processing framework that couples UAV-based image acquisition, [...] Read more.
The early detection and spatial characterization of crop damage are critical for improving decision-making in precision agriculture, particularly in regions where traditional monitoring methods are limited in scalability and objectivity. This study presents an integrated information processing framework that couples UAV-based image acquisition, instance segmentation, slicing-aided inference of large orthomosaics, and georeferenced spatial analysis into a single reproducible pipeline for the detection and mapping of crop damage. The framework is applied to maize cultivated under traditional milpa systems in Yucatán, Mexico, a region characterized by intercropping, irregular plant spacing, and complex backgrounds rarely represented in mainstream agricultural deep learning benchmarks. High-resolution RGB images were systematically acquired over maize fields in Yucatán, Mexico, and curated into specialized datasets representing parcels, individual plants, and damaged vegetation. Instance segmentation models based on the YOLOv11 architecture were trained and evaluated to extract visual information related to crop condition, while the Slicing-Aided Hyper Inference (SAHI) method was integrated to enable efficient processing of large orthomosaic images. The proposed framework achieved high performance in detecting maize plants, with a precision of 92.9% and an mAP50 of 94.2%, and demonstrated reliable identification of damage patterns associated with Spodoptera frugiperda, reaching a precision of 79.2% and an mAP50 of 71.7%. The resulting georeferenced outputs provide spatially explicit information that supports quantitative analysis of crop health and damage distribution. The results indicate that the proposed framework constitutes a scalable and reproducible approach for UAV-based visual information extraction, with potential applicability to broader agricultural monitoring and data-driven decision support systems. Full article
(This article belongs to the Special Issue Advancing Smart Systems Through Deep Learning, Generative AI, and Big Data Analytics)
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14 pages, 684 KB  
Article
Comparison of a Linear Mixed Model and Tree-Based Machine Learning Models for Daily Milk Yield Prediction in Dairy Cows During Summer
by Babak Darabighane and Alberto Stanislao Atzori
Information 2026, 17(5), 415; https://doi.org/10.3390/info17050415 - 27 Apr 2026
Viewed by 514
Abstract
The expansion of digital technologies in dairy farming (precision dairy farming) has created new opportunities for the systematic use of data, which can lead to more efficient production processes. This study aimed to develop and evaluate models for predicting daily milk yield from [...] Read more.
The expansion of digital technologies in dairy farming (precision dairy farming) has created new opportunities for the systematic use of data, which can lead to more efficient production processes. This study aimed to develop and evaluate models for predicting daily milk yield from dairy cows during summer. This yield was modeled at the individual level, with days in milk and parity group included as baseline covariates in all analyses. Three feature-set scenarios were defined and evaluated, in which the temperature–humidity index (THI) and milk yield history were added to the baseline variables either separately (Scenarios 1 and 2) or jointly (Scenario 3). Performance was evaluated using walk-forward validation, and feature selection was nested within each iteration’s training window. The performance of the linear mixed model (LMM) was then compared with two machine learning models, random forest (RF) and gradient boosting machine (GBM), within the same experimental framework. In Scenario 3, all three models showed similar fits (R2 = 0.92 and concordance correlation coefficient = 0.96), although the GBM model yielded a smaller error (root mean square error [RMSE] = 2.07 ± 0.22, mean absolute error [MAE] = 1.39 ± 0.12) than the RF model (RMSE = 2.10 ± 0.23, MAE = 1.45 ± 0.13) and the LMM (RMSE = 2.15 ± 0.22, MAE = 1.41 ± 0.10). Overall, adding the THI and recent milk yield history to the baseline variables improved short-term prediction accuracy in this dataset, with the GBM model showing the smallest error. These results can support farmers and herd managers in predicting short-term milk yield under heat stress conditions and making timely management decisions. Full article
(This article belongs to the Special Issue Advancing Smart Systems Through Deep Learning, Generative AI, and Big Data Analytics)
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22 pages, 2073 KB  
Article
TVAE-GAN: A Generative Model for Providing Early Warnings to High-Risk Students in Basic Education and Its Explanation
by Chao Duan, Yiqing Wang, Wenlong Zhang, Zhongtao Yu, Yu Pei, Mingyan Zhang and Qionghao Huang
Information 2026, 17(4), 356; https://doi.org/10.3390/info17040356 - 8 Apr 2026
Viewed by 429
Abstract
The rapid development of intelligent learning guidance systems has created a favorable environment for personalized learning. By accurately predicting students’ future performance, education can be tailored and teaching strategies optimized. However, traditional prediction algorithms seldom account for highly imbalanced datasets in basic education, [...] Read more.
The rapid development of intelligent learning guidance systems has created a favorable environment for personalized learning. By accurately predicting students’ future performance, education can be tailored and teaching strategies optimized. However, traditional prediction algorithms seldom account for highly imbalanced datasets in basic education, overlook temporal factors, and lack further interpretability of the prediction results. To address these shortcomings, we propose Temporal Variational Autoencoder-Generative Adversarial Network (TVAE-GAN), a temporal variational autoencoder-generative adversarial network model aimed at providing early warnings for high-risk students in basic education, with in-depth interpretability analysis of the prediction results to suit the unique context of basic education. TVAE-GAN extracts features from real samples and introduces a Long Short-Term Memory (LSTM) network to capture dynamic features in time series, helping the model better understand temporal dependencies in the data, remember the sequential causal information of students’ online learning, and achieve better data generation performance. Using these features, the generative model generates new samples, and the discriminator model evaluates their quality, producing outputs that closely resemble real samples through training. The effectiveness of the TVAE-GAN model is validated on a collected online basic education dataset while also advancing the timing of interventions in predictions. The performance differences between the proposed method and classic resampling methods, as well as their impact in the educational field, are analyzed, highlighting that misclassification increases teacher workload and affects students’ emotions. Key influencing factors are identified using a decision-tree surrogate model, providing teachers with multidimensional references for academic assessment. Full article
(This article belongs to the Special Issue Advancing Smart Systems Through Deep Learning, Generative AI, and Big Data Analytics)
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24 pages, 1162 KB  
Article
A Study on Regional Disparities and Shifting Trends in Transportation Carbon Emissions in China
by Zhonghua Shen, Dehao Wu, Yuanchen Xu, Xin Lu and Leon Smalov
Information 2026, 17(3), 248; https://doi.org/10.3390/info17030248 - 2 Mar 2026
Viewed by 636
Abstract
In order to achieve the carbon peaking and carbon neutrality goals in China’s transportation sector, this paper examines the regional data in transportation carbon emissions across China, investigates the shifting trends of the carbon emission centroid over time, and proposes a novel representation [...] Read more.
In order to achieve the carbon peaking and carbon neutrality goals in China’s transportation sector, this paper examines the regional data in transportation carbon emissions across China, investigates the shifting trends of the carbon emission centroid over time, and proposes a novel representation using fuzzy set theory and rough set theory for carbon emission prediction. This paper employs the ESDA model to analyze the regional distribution of carbon emissions in the transportation sector across 30 provinces in China for the years 2005, 2010, 2015, and 2020. Utilizing the economic centroid model and standard deviation ellipse, the trend of carbon emission centroid shifts in China’s transportation sector is examined, revealing that the carbon emission centroid for all four time points is located in Henan Province. Subsequently, focusing on Henan Province, ridge regression analysis is conducted to identify the driving factors influencing carbon emissions in the transportation sector from 2005 to 2020. Lastly, a combined approach integrating scenario analysis and the STIRPAT model is employed to forecast carbon emissions in the transportation sector of Henan Province for the period 2021–2035. The findings suggest that high-carbon-emission regions in China’s transportation sector gradually extend from the eastern coastal areas to the southwestern regions, with an overall trend of the carbon emission centroid shifting northward. The carbon emission centroid for the years 2005, 2010, 2015, and 2020 is consistently located in Henan Province. Ridge regression analysis indicates that population size, transportation energy consumption intensity, energy structure, transportation economic share, and per capita GDP all have promoting effects on carbon emissions in Henan Province’s transportation sector. Based on the combined approach of scenario analysis and the STIRPAT model, it is predicted that the transportation sector in Henan Province may reach its carbon peak between 2027 and 2029. These conclusions facilitate the formulation of region-specific emission reduction policies and measures tailored to the transportation sector. Full article
(This article belongs to the Special Issue Advancing Smart Systems Through Deep Learning, Generative AI, and Big Data Analytics)
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20 pages, 1925 KB  
Article
Improving Construction Site Safety with Large Language Models: A Performance Analysis
by Concetta Manuela La Fata, Gianfranco Barone and Marco Cammarata
Information 2026, 17(2), 210; https://doi.org/10.3390/info17020210 - 17 Feb 2026
Viewed by 989
Abstract
Hazard recognition on construction sites is crucial for ensuring worker safety. Traditional methods widely rely on expert assessments, on-site inspections, and checklists, which can be time-consuming and susceptible to human error. The integration of multimodal Large Language Models (LLMs), such as GPT-based systems, [...] Read more.
Hazard recognition on construction sites is crucial for ensuring worker safety. Traditional methods widely rely on expert assessments, on-site inspections, and checklists, which can be time-consuming and susceptible to human error. The integration of multimodal Large Language Models (LLMs), such as GPT-based systems, offers a promising opportunity to overcome these limitations. Therefore, this study evaluates the effectiveness of GPT-4o in recognizing workplace hazards from image inputs, with a specific focus on construction sites. The results indicate that the model can serve as a valuable decision-support tool for safety professionals by providing scalable and real-time insights. However, the study also highlights key limitations, including the model’s reliance on general visual features rather than domain-specific safety knowledge, and the continued need for human supervision. Additionally, ethical concerns, including bias in AI-generated hazard assessments, data privacy, and the risk of over-reliance on AI, must be carefully managed to ensure these tools contribute responsibly and effectively to proactive risk management strategies. Full article
(This article belongs to the Special Issue Advancing Smart Systems Through Deep Learning, Generative AI, and Big Data Analytics)
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