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Advances in Data-Driven Artificial Intelligence, 2nd Edition
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Advances in Data-Driven Artificial Intelligence, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 September 2026 | Viewed by 1069

Editors

Dr. Shuo Yu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Dalian University of Technology, Dalian 116078, China
Interests: data science; network science; knowledge science; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Dr. Shuai Xu

E-Mail Website
Guest Editor
School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: Social network analysis and mining; spatio-temporal data mining; smart cities
Special Issues, Collections and Topics in MDPI journals
Dr. Minghui Qian

E-Mail Website
Guest Editor
School of Information Resource Management, Renmin University of China, Beijing 100872, China
Interests: Information analysis; brand analysis; decision-making
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data-driven artificial intelligence (AI) leverages vast amounts of data and deep learning techniques and can be employed in varied domains, including healthcare, commerce, transportation, etc. The integration of numerous data with advanced AI techniques has enabled the development of innovative solutions that aid decision-making processes and provide personalized recommendations. Based on a mixture of analysis, modeling, computation, and learning, data-driven AI techniques enable us to enhance the efficiency, accuracy, and scope of scientific research. Simultaneously, combining data science technology and these new artificial intelligence paradigms will facilitate the application of AI in many application scenarios.

Although the application of data-driven AI technologies has advanced in various engineering applications, many challenges and problems remain to be addressed by researchers and practitioners. Therefore, this Special Issue will address recent advances and ongoing improvements in data-driven AI to promote the continuous development of real-world applications. Specifically, this Special Issue will attempt to answer the following questions. (1) How can the boundaries among disciplines, methodologies, and theories be broken to further promote data-driven AI technologies? (2) What will be the new paradigm of data-driven AI? (3) How can data-driven AI further benefit real-world applications?

LIST OF POTENTIAL TOPICS:

  • The use of data-driven AI techniques in various domains, including intelligent network architecture, intelligent network data management and analysis technology, the cleaning and repairing of inferior data, methods and standards for evaluating data quality, etc.;
  • Multimodal data aggregation, including intelligent methods for multi-source heterogeneous data fusion, such as text, images, audio, video, 3D, GIS, etc.;
  • Explainable and interpretable methods for artificial intelligence;
  • Representation learning methods for image, language or other modalities;
  • Intelligent computations such as deep graph learning, lifelong learning, etc.;
  • Data-driven methods for industrial system data analysis, information fusion, pattern recognition, and trend analysis;
  • Reliability, effectiveness, and security evaluation methods/mechanisms for data-driven AI techniques;
  • Generative AI such as large language models (LLMs) for smart education;
  • The application of artificial intelligence technologies such as deep transfer learning, meta learning, life-long learning and graph neural networks in intelligent perception, manufacturing, operation, maintenance, etc.;

Dr. Shuo Yu
Dr. Shuai Xu
Dr. Minghui Qian
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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

  • artificial intelligence
  • deep learning
  • big data
  • data fusion
  • generative AI
  • AI management

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Related Special Issue

Published Papers (2 papers)

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Research

15 pages, 515 KB  
Article
Expert-Transformer with Prototype-Aware Contrastive Learning for Semi-Supervised Time-Series Classification
by Zhen Huang, Fei Peng, Kaiyuan Hou, Deming Xia and Tianyu An
Electronics 2026, 15(11), 2303; https://doi.org/10.3390/electronics15112303 - 26 May 2026
Viewed by 208
Abstract
Semi-supervised time-series classification (TSC) faces challenges in handling intra-class variability and distribution shifts, which limit the effectiveness of standard contrastive learning methods. To address these limitations, we propose the Expert-Transformer with Prototype-Aware Contrastive Learning (ExT-PACL), a novel framework that integrates an uncertainty-guided Mixture-of-Experts [...] Read more.
Semi-supervised time-series classification (TSC) faces challenges in handling intra-class variability and distribution shifts, which limit the effectiveness of standard contrastive learning methods. To address these limitations, we propose the Expert-Transformer with Prototype-Aware Contrastive Learning (ExT-PACL), a novel framework that integrates an uncertainty-guided Mixture-of-Experts (MoE) module within a Transformer encoder to dynamically capture diverse temporal patterns. An expert balancing strategy ensures all experts contribute meaningfully, preventing collapse and enhancing representation robustness. In addition, a prototype-aware contrastive learning loss guides both labeled and high-confidence unlabeled samples toward class prototypes, improving discriminative power and reducing reliance on large negative sample sets. Extensive experiments on multiple benchmark datasets demonstrate that ExT-PACL achieves superior generalization and state-of-the-art performance. Full article
(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence, 2nd Edition)
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25 pages, 7549 KB  
Article
Unseen-Crop Plant Disease Classification via Disentangled Representation Learning
by Zhenzhen Wu, Jianli Guo, Wei Hou, Kun Zhou, Kerang Cao and Hoekyung Jung
Electronics 2026, 15(8), 1553; https://doi.org/10.3390/electronics15081553 - 8 Apr 2026
Cited by 1 | Viewed by 566
Abstract
Deep learning has accelerated progress in plant disease recognition, providing strong technical support for early diagnosis and precision management. However, models often lack robustness and generalization when confronted with novel crops absent from the training set, leading to a marked performance drop in [...] Read more.
Deep learning has accelerated progress in plant disease recognition, providing strong technical support for early diagnosis and precision management. However, models often lack robustness and generalization when confronted with novel crops absent from the training set, leading to a marked performance drop in cross-unseen-crop scenarios. Cross-crop generalization for plant disease recognition requires models to identify known disease categories in crop domains never observed during training. A central challenge is that disease symptoms are strongly coupled with crop-specific appearance cues, which severely degrades generalization. Here, TDC (Text-guided feature Disentanglement Contrast) is introduced as a feature-disentanglement framework for cross-crop plant disease recognition. The proposed method employs a dual-branch visual encoder to separately capture disease semantic representations and crop-domain representations, and it leverages a frozen CLIP text encoder to use disease and crop prompts for text-guided semantic anchoring. A semantic-anchor-only contrastive disentanglement strategy is further formulated under a hybrid label space, where crop-branch features are incorporated as stop-gradient hard negatives to suppress semantic–domain information leakage and strengthen the intra-class aggregation of the same disease across crops. Residual domain-discriminative cues are mitigated via domain-adversarial learning. During inference, only the disease branch is retained for classification, improving generalization while reducing deployment overhead. Experiments demonstrate that under the PlantVillage cross-crop setting, the method achieves 98.04% and 74.29% Top-1 accuracy on seen and unseen crop domains, respectively. Moreover, it attains 81.99% on a real-world field dataset of strawberry powdery mildew and 76.31% on a low-illumination degradation set, validating robustness under realistic imaging distribution shifts. Full article
(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence, 2nd Edition)
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