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Machine Learning Applications in Computer Vision, Data Modeling, and Natural...
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Machine Learning Applications in Computer Vision, Data Modeling, and Natural Language Processing

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

Deadline for manuscript submissions: 15 October 2026 | Viewed by 5413

Editors

Prof. Dr. Mayra Antonio-Cruz

E-Mail Website
Guest Editor
Sección de Estudios de Posgrado e Investigación, Unidad Profesional Interdisciplinaria de Ingeniería y Ciencias Sociales y Administrativas (SEPI-UPIICSA), Instituto Politécnico Nacional (IPN), Mexico City 08400, Mexico
Interests: machine learning; automatic control; underactuated mechanical systems; mobile robots; power electronic systems
Prof. Dr. Carlos Alejandro Merlo-Zapata

E-Mail Website
Guest Editor
Sección de Estudios de Posgrado e Investigación, Unidad Profesional Interdisciplinaria de Ingeniería y Ciencias Sociales y Administrativas (SEPI-UPIICSA), Instituto Politécnico Nacional (IPN), Mexico City 08400, Mexico
Interests: machine learning applications in built cultural heritage; automatic control applications in underactuated mechanical systems; mobile robots; power electronic systems

Special Issue Information

Dear Colleagues,

Over the last several years, the accessibility and availability of massive volumes of data have led to the widespread adoption of machine learning algorithms. Hence, the application of machine learning in industry, healthcare, education, transportation, and food is transforming the world. Among the wide range of research fields advanced by machine learning, computer vision, data modeling, and natural language processing are the most prominent. This can be attributed to their rich background in extracting patterns, supporting decision-making, and interpreting unstructured data, with pivotal importance in different complex applications for autonomous systems, healthcare diagnostics, human–computer interaction, and predictive analytics.

We welcome original contributions to this Special Issue dedicated to exploring the latest developments and innovative applications of machine learning in computer vision (e.g., image classification, object detection and tracking, semantic segmentation, 3D reconstruction, facial recognition); data modeling (e.g., time series forecasting, anomaly detection, generative modeling, feature engineering, clustering and dimensionality reduction); and natural language processing (e.g. information retrieval, information extraction, text classification, text generation, summarization, question answering, machine translation, sentiment analysis).

In this Special Issue, reviews, theoretical and practical research articles, short communications, and letters are welcome. Research areas may include (but are not limited to) the following:

  • Supervised learning;
  • Multi-instance learning;
  • Transductive learning;
  • Active learning;
  • Meta learning;
  • Multitask learning;
  • Unsupervised learning;
  • Self-supervised learning;
  • Constructive learning;
  • Association-rule learning;
  • Reinforcement learning;
  • Forecasting;
  • Prediction models;
  • Stationary and non-stationary data;
  • Linear modeling;
  • Deep learning;
  • Automated machine learning;
  • Language modeling and word embeddings;
  • Morphology;
  • Parsing;
  • Semantics;
  • Autonomous systems;
  • Human–computer interaction;
  • Predictive analytics.

Prof. Dr. Mayra Antonio-Cruz
Prof. Dr. Carlos Alejandro Merlo-Zapata
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

  • machine learning
  • computer vision
  • data modeling
  • natural language processing
  • segmentation
  • classification
  • object detection
  • forecasting
  • prediction
  • computational linguistics

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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

21 pages, 2658 KB  
Article
CNN-Based Acoustic Gait Recognition: A Benchmarking Framework
by Ilaisaane Tilisa Fonua and Shahram Latifi
Electronics 2026, 15(12), 2658; https://doi.org/10.3390/electronics15122658 - 16 Jun 2026
Viewed by 540
Abstract
Acoustic gait recognition is an emerging passive biometric modality that identifies individuals by unique walking sound patterns. This work presents a reproducible benchmarking framework for convolutional neural network (CNN)-based acoustic gait recognition, providing a systematic evaluation methodology across varying identity pool sizes. Raw [...] Read more.
Acoustic gait recognition is an emerging passive biometric modality that identifies individuals by unique walking sound patterns. This work presents a reproducible benchmarking framework for convolutional neural network (CNN)-based acoustic gait recognition, providing a systematic evaluation methodology across varying identity pool sizes. Raw footstep recordings from the AFPILD dataset were converted into 128-bin mel-spectrograms and used to train a compact CNN across identity pool sizes from 10 to 40 subjects. To ensure statistical reliability, a three-times-repeated five-fold stratified cross-validation protocol was implemented. Experimental results demonstrate strong discriminative capability, with validation accuracy reaching 94.92% and Equal Error Rate (EER) of 1.31% for the 40-subject configuration. A multi-seed subset validation experiment across five independent random subject draws per pool size confirmed that the observed scaling trend is consistent across subset compositions rather than an artifact of a single subject selection. Additional analysis confirmed the framework’s resilience to moderate environmental noise and its superiority over classical Mel-Frequency Cepstral Coefficients paired with a Support Vector Machine (MFCC-SVM) and Convolutional Recurrent Neural Network (CRNN) baselines, supporting the feasibility of acoustic gait recognition as a passive biometric modality. Full article
(This article belongs to the Special Issue Machine Learning Applications in Computer Vision, Data Modeling, and Natural Language Processing)
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18 pages, 1322 KB  
Article
MP-CNER: A Course Named Entity Recognition Model Based on Multi-Dimensional Position Features
by Feiyue Qiu, Hao Xiao, Kangping Yu and Qicang Qiu
Electronics 2026, 15(11), 2396; https://doi.org/10.3390/electronics15112396 - 1 Jun 2026
Viewed by 287
Abstract
In the context of the rapid development of intelligent education and personalized learning, constructing high-quality curriculum knowledge graphs has become a core task. As a pivotal link in knowledge extraction, named entity recognition (NER) is essential for the construction of knowledge graphs. However, [...] Read more.
In the context of the rapid development of intelligent education and personalized learning, constructing high-quality curriculum knowledge graphs has become a core task. As a pivotal link in knowledge extraction, named entity recognition (NER) is essential for the construction of knowledge graphs. However, because curriculum texts exhibit deep complexity in type, structure, and semantics, current research faces two critical challenges: recognizing fuzzy entity boundaries and accurately capturing long-distance semantic dependencies. To address these issues, this study proposes MP-CNER, a curriculum-oriented NER model that integrates multi-dimensional position features. The model adopts a dual-branch parallel architecture: one branch utilizes the Chinese-RoBERTa-wwm-ext pre-trained model to extract deep character-level semantic vectors, while the other branch incorporates an innovatively designed multi-dimensional position feature (MDPF) fusion module. Combined with domain dictionaries, this module significantly enhances the model’s perception accuracy regarding entity boundary features. Building on this, a selective contextual linker (SCL) is introduced to leverage boundary signals as guidance, effectively capturing logical associations within long-span entities and across entities. Comprehensive experimental evaluations on three datasets demonstrate that the proposed MP-CNER significantly outperforms existing baseline models in key metrics such as F1 score, fully validating its effectiveness and versatility in processing curriculum texts. Full article
(This article belongs to the Special Issue Machine Learning Applications in Computer Vision, Data Modeling, and Natural Language Processing)
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26 pages, 1366 KB  
Article
Dual-Smoothing over Manifold and Parameter for Partial-Label Unsupervised Domain Adaptation
by Yifan Pan and Yuesheng Zhu
Electronics 2026, 15(7), 1488; https://doi.org/10.3390/electronics15071488 - 2 Apr 2026
Viewed by 379
Abstract
In real-world machine learning scenarios, training data are frequently weakly annotated and distributionally misaligned with deployment environments. Specifically, label ambiguity may arise when each instance is associated with a set of candidate labels, and distribution shifts between training and testing are common in [...] Read more.
In real-world machine learning scenarios, training data are frequently weakly annotated and distributionally misaligned with deployment environments. Specifically, label ambiguity may arise when each instance is associated with a set of candidate labels, and distribution shifts between training and testing are common in practice. Although Partial Label Learning (PLL) and Unsupervised Domain Adaptation (UDA) have been extensively studied individually, they frequently co-occur in practice. For instance, in cross-hospital medical image analysis, datasets may exhibit both inconsistent diagnostic labels due to variations in expert interpretation (label ambiguity) and significant differences in imaging equipment or patient demographics (distribution shift). However, Partial-Label Unsupervised Domain Adaptation (PLUDA) has received limited attention as a unified problem. In this paper, a unified generalization bound is established for Partial-Label Unsupervised Domain Adaptation (PLUDA) and three critical limitations causing existing approaches to fail: ambiguity degree, ideal joint error, and model complexity remain uncontrolled. Motivated by these theoretical insights, we propose Dual-Smoothing over Manifold and Parameter (DSMP) to control all three factors. DSMP employs manifold-based representation smoothing via Laplacian smoothing based on adaptive multi-kernel RKHS similarity and candidate set refinement to address the three critical limitations. Moreover, DSMP leverages sharpness-aware parameter smoothing to ensure stable optimization under weak supervision through loss landscape flattening. Extensive experiments demonstrate that DSMP outperforms existing baselines, achieving superior cross-domain generalization from weakly labeled sources. This work provides theoretical insights and a principled solution to the previously underexplored yet practically important PLUDA problem. Full article
(This article belongs to the Special Issue Machine Learning Applications in Computer Vision, Data Modeling, and Natural Language Processing)
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35 pages, 2974 KB  
Article
Multi-Agent Coordination Strategies vs. Retrieval-Augmented Generation in LLMs: A Comparative Evaluation
by Irina Radeva, Ivan Popchev, Lyubka Doukovska and Miroslava Dimitrova
Electronics 2025, 14(24), 4883; https://doi.org/10.3390/electronics14244883 - 11 Dec 2025
Cited by 1 | Viewed by 3645
Abstract
This paper evaluates multi-agent coordination strategies against single-agent retrieval-augmented generation (RAG) for open-source language models. Four coordination strategies (collaborative, sequential, competitive, hierarchical) were tested across Mistral 7B, Llama 3.1 8B, and Granite 3.2 8B using 100 domain-specific question–answer pairs (3100 total evaluations). Performance [...] Read more.
This paper evaluates multi-agent coordination strategies against single-agent retrieval-augmented generation (RAG) for open-source language models. Four coordination strategies (collaborative, sequential, competitive, hierarchical) were tested across Mistral 7B, Llama 3.1 8B, and Granite 3.2 8B using 100 domain-specific question–answer pairs (3100 total evaluations). Performance was assessed using Composite Performance Score (CPS) and Threshold-aware CPS (T-CPS), aggregating nine metrics spanning lexical, semantic, and linguistic dimensions. Under the tested conditions, all 28 multi-agent configurations showed degradation relative to single-agent baselines, ranging from −4.4% to −35.3%. Coordination overhead was identified as a primary contributing factor. Llama 3.1 8B tolerated Sequential and Hierarchical coordination with minimal degradation (−4.9% to −5.3%). Mistral 7B with shared context retrieval achieved comparable results. Granite 3.2 8B showed degradation of 14–35% across all strategies. Collaborative coordination exhibited the largest degradation across all models. Study limitations include evaluation on a single domain (agriculture), use of 7–8B parameter models, and homogeneous agent architectures. These findings suggest that single-agent RAG may be preferable for factual question-answering tasks in local deployment scenarios with computational constraints. Future research should explore larger models, heterogeneous agent teams, role-specific prompting, and advanced consensus mechanisms. Full article
(This article belongs to the Special Issue Machine Learning Applications in Computer Vision, Data Modeling, and Natural Language Processing)
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