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BDCC
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Application of Pattern Recognition and Machine Learning
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Application of Pattern Recognition and Machine Learning

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289). This special issue belongs to the section "Data Mining and Machine Learning".

Deadline for manuscript submissions: 25 September 2026 | Viewed by 1758

Special Issue Editors

Prof. Dr. María del Carmen Pegalajar Jiménez

E-Mail Website
Guest Editor
Department of Computer Science and Artificial Intelligence, Universidad de Granada, 18014 Granada, Spain
Interests: machine learning; pattern recognition; neural networks; deep learning; metaheuristics algorithms; artificial intelligence; applied artificial intelligence; fuzzy logic; energy consumption modelling; psychology and artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Luis G. Baca Ruiz

E-Mail Website
Guest Editor
Department of Software Engineering, University of Granada, 18014 Granada, Spain
Interests: time series; data mining; artificial neural networks; energy efficiency; energy consumption modelling; soil science; unsupervised algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We can announce a new Special Issue entitled “Application of Pattern Recognition and Machine Learning” in the journal BDCC.

In today’s world, pattern recognition is transforming how we analyse the vast amount of data generated by uncountable sources. On the other hand, machine learning is speeding up that change and bringing new possibilities across many fields. Together, they are becoming essential tools for tackling complex scientific and practical problems.

We can see their effects in many areas. For example, in healthcare, where algorithms help to detect tumours in medical images; in language technologies, where chatbots and summarizers understand and generate text; in psychology, where it is possible to predict an individual’s personality through tweets or predict the estimation of psychological variables; in soil science, where they identify soil types and detect spatial patterns from observations; in autonomous systems, where perception and control are used in vehicles; in environmental monitoring, where models predict air quality and spot emerging issues; and in energy, where forecasting and fault detection help to keep grids running efficiently.

Motivated by these few examples, this Special Issue will collect novel research and real-world implementations that push pattern recognition and machine learning forward. We are looking for new algorithms, improved models and practical solutions that show innovation. Submissions can cover theory, experiments or applied work, especially when they demonstrate real impact.

We invite original research articles and critical reviews on topics related (but not limited) to the following:

  • Deep learning for images, speech and text;
  • Few-shot, zero-shot and transfer learning;
  • Anomaly and outlier detection in complex systems;
  • Time-series and sequential pattern analysis;
  • Data fusion and cross-modal learning;
  • Graph deep learning;
  • Federated learning;
  • Interpretive knowledge discovery;
  • Systems analytics;
  • Forecasting and fault detection;
  • Artificial neural networks.

Prof. Dr. María del Carmen Pegalajar Jiménez
Dr. Luis G. Baca Ruiz
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. Big Data and Cognitive Computing 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

  • pattern recognition
  • machine learning
  • deep learning
  • self‑supervised learning
  • explainability
  • interpretability
  • few‑shot learning
  • transfer learning
  • anomaly detection
  • time‑series analysis
  • multimodal fusion
  • graph learning
  • federated learning
  • artificial neural networks
  • analytics

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

Published Papers (3 papers)

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13 pages, 756 KB  
Article
H2Avatar: Expressive Whole-Body Avatars from Monocular Video via Hierarchical Geometry and Hybrid Rendering
by Jinsong Zhang, Cheng Guan, Zhihua Lin and Yuqin Lin
Big Data Cogn. Comput. 2026, 10(4), 105; https://doi.org/10.3390/bdcc10040105 - 1 Apr 2026
Viewed by 458
Abstract
Reconstructing photorealistic and animatable whole-body avatars from monocular videos is a hot topic in computer vision and computer graphics. However, existing methods still face challenges due to the limited frequency response of single-scale geometry encodings and the instability of appearance modeling without an [...] Read more.
Reconstructing photorealistic and animatable whole-body avatars from monocular videos is a hot topic in computer vision and computer graphics. However, existing methods still face challenges due to the limited frequency response of single-scale geometry encodings and the instability of appearance modeling without an explicit surface anchor. In this paper, we present H2Avatar, a real-time framework that builds on a mesh-embedded 3D Gaussian representation guided by SMPL-X and disentangles geometry and appearance into hierarchical and hybrid components. For geometry, we propose a semantic-aware hierarchical encoding based on a multi-scale tri-plane pyramid, where features at different resolutions capture both global structure and high-frequency surface details such as clothing wrinkles. For appearance, we introduce a hybrid rendering strategy that anchors canonical colors using a learnable UV texture map, and complements it with a neural residual color branch conditioned on tri-plane features, pose embedding, and surface normals to model pose- and view-dependent shading variations. This design improves temporal stability and preserves identity details while enhancing photorealism under complex motions. Experiments on the NeuMan dataset demonstrate that H2Avatar consistently outperforms representative baselines across multiple sequences, outperforming ExAvatar by up to 0.66 dB in PSNR and reducing LPIPS by up to 16.3%. These results validate the effectiveness of hierarchical geometry encoding and texture-anchored hybrid appearance modeling. Full article
(This article belongs to the Special Issue Application of Pattern Recognition and Machine Learning)
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27 pages, 6857 KB  
Article
A Convergent Method for Energy Optimization in Modern Hopfield Networks
by Yida Bao, Mohammad Arifuzzaman, Tran Duc Le, Tao Jiang, Jing Hou, Yuan Xing and Dongfang Hou
Big Data Cogn. Comput. 2026, 10(3), 71; https://doi.org/10.3390/bdcc10030071 - 28 Feb 2026
Viewed by 406
Abstract
Modern Hopfield networks are energy-based associative memory models whose performance critically depends on the structure and optimization of their energy functions. While recent formulations substantially improve storage capacity, the resulting non-convex energy landscapes are often optimized using heuristic update rules that can be [...] Read more.
Modern Hopfield networks are energy-based associative memory models whose performance critically depends on the structure and optimization of their energy functions. While recent formulations substantially improve storage capacity, the resulting non-convex energy landscapes are often optimized using heuristic update rules that can be sensitive to initialization and may not provide monotonic energy descent or rigorous convergence guarantees. In this work, we propose a new energy formulation for modern Hopfield networks together with a principled iterative optimization scheme. The proposed energy admits a natural decomposition that allows optimization via the concave–convex procedure (CCCP), yielding well-defined network dynamics with guaranteed energy descent beyond classical Hopfield updates. We establish fundamental theoretical properties of the proposed framework, including non-negativity, boundedness, and monotonic decrease in the energy along iterations. In particular, we prove that the induced dynamics converge to a stationary point of the energy function, providing explicit convergence guarantees for the resulting Hopfield-type model. We further evaluate the proposed approach on synthetic classification tasks and compare its optimization behavior with that of the original Hopfield network and several standard machine learning baselines. Experimental results demonstrate improved stability, convergence behavior, and competitive classification performance. We also validate the approach on real-world benchmark datasets to demonstrate utility beyond controlled experiments. Overall, this work provides a theoretically grounded energy-based optimization framework for modern Hopfield networks, clarifying the role of principled optimization in achieving stable and convergent associative memory dynamics. Full article
(This article belongs to the Special Issue Application of Pattern Recognition and Machine Learning)
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38 pages, 2848 KB  
Article
Efficient Time Series Visual Exploration for Insight Discovery
by Heba Helal and Mohamed A. Sharaf
Big Data Cogn. Comput. 2026, 10(2), 64; https://doi.org/10.3390/bdcc10020064 - 16 Feb 2026
Viewed by 437
Abstract
Visual exploration of time series data is essential for uncovering meaningful insights in domains such as healthcare monitoring and financial analysis, yet it remains computationally challenging due to the combinatorial explosion of potential subsequence comparisons. For long time series, an exhaustive comparison of [...] Read more.
Visual exploration of time series data is essential for uncovering meaningful insights in domains such as healthcare monitoring and financial analysis, yet it remains computationally challenging due to the combinatorial explosion of potential subsequence comparisons. For long time series, an exhaustive comparison of all possible subsequence pairs becomes prohibitively expensive, limiting interactive exploration. This paper presents the TiVEx (Time Series Visual Exploration) family of algorithms for efficiently discovering the top-k most dissimilar subsequence pairs in comparative time series analysis. TiVEx achieves scalability through three complementary strategies: TiVEx-sharing exploits computational reuse across overlapping subsequence windows, eliminating redundant distance calculations; TiVEx-pruning employs distance-based upper bounds to eliminate unpromising candidates without exhaustive evaluation; and TiVEx-hybrid integrates both mechanisms to maximize efficiency gains. The key observation is that overlapping subsequences share a substantial computational structure, which can be systematically exploited while maintaining result optimality through provably correct pruning bounds. Extensive experiments on six diverse datasets demonstrate that TiVEx-hybrid achieves up to 84% reduction in distance calculations compared to exhaustive search while producing identical top-k results. Compared to state-of-the-art subsequence comparison methods, TiVEx-hybrid achieves 2.3× improvement in computational efficiency. Our effectiveness analysis confirms that TiVEx achieves result quality within 5% of exhaustive search even when exploring only a subset of candidate positions, enabling scalable visual exploration without compromising insight quality. Full article
(This article belongs to the Special Issue Application of Pattern Recognition and Machine Learning)
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