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Electronics
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Computational Intelligence and Machine Learning: Models and Applications: 2nd Edition
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Computational Intelligence and Machine Learning: Models and Applications: 2nd Edition

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 3978

Special Issue Editors

Prof. Dr. Grzegorz Dudek

E-Mail Website
Guest Editor
Department of Automatic Control, Electrical Engineering and Optoelectronics, Faculty of Electrical Engineering, Częstochowa University of Technology, Al. Armii Krajowej 17, 42-200 Częstochowa, Poland
Interests: machine learning; evolutionary computation; artificial intelligence; pattern recognition; data mining and applications in forecasting, classification, regression, and optimization problems
Special Issues, Collections and Topics in MDPI journals
Dr. Arkadiusz Tomczyk

E-Mail Website
Guest Editor
Institute of Information Technology, Lodz University of Technology, al. Politechniki 8, 93-590 Lodz, Poland
Interests: image analysis; machine learning; computer vision; artificial intelligence; deep learning; structured prediction; pattern recognition; active contours
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) and machine learning (ML) are some of the most exciting fields in computing today. In recent decades, they have become an entrenched part of everyday life and have been successfully used to solve practical problems. The application area of CI and ML is very broad and includes engineering, industry, business, finance, medicine and many other domains. They cover a wide range of computational and learning algorithms, including classical ones such as linear regression, k-nearest neighbors and decision trees, as well as fuzzy systems, genetic, swarm and evolutionary algorithms, support vector machines and neural networks, and newly developed algorithms such as deep learning and boosted tree models. In practice, it is quite challenging to properly determine the appropriate architecture and parameters for CI and ML models so that the resulting model achieves a sound performance in both learning and generalization. Practical applications of CI and ML bring additional challenges, such as dealing with big, missing, distorted and uncertain data. In addition, interpretability is a paramount quality that CI and ML methods should achieve if they are to be applied in practice. Interpretability allows us to understand the model operation and raises confidence in its results.

This Special Issue focuses on CI and ML models and their applications in a diverse range of fields and problems. We welcome papers reporting substantive results on a wide range of computational and learning methods, discussing the conceptualization of a problem, data representation, feature engineering, CI and ML models, critical comparisons with existing techniques and interpretation of results. Specific attention will be given to recently developed CI and ML methods such as deep learning and boosted tree models.

Prof. Dr. Grzegorz Dudek
Dr. Arkadiusz Tomczyk
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. 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

  • computational intelligence
  • machine learning
  • artificial intelligence
  • soft computing
  • fuzzy logic
  • evolutionary computing
  • neural networks
  • decision trees
  • deep learning
  • expert systems
  • data mining
  • supervised learning
  • unsupervised learning
  • reinforcement learning
  • probabilistic methods
  • knowledge representation
  • forecasting
  • big data
  • pattern recognition
  • natural language processing
  • computer vision
  • bioinformatics
  • information retrieval
  • sentiment analysis
  • recommendation systems
  • speech recognition

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Related Special Issue

Published Papers (6 papers)

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Research

23 pages, 578 KiB  
Article
Distributed Partial Label Multi-Dimensional Classification via Label Space Decomposition
by Zhen Xu and Sicong Chen
Electronics 2025, 14(13), 2623; https://doi.org/10.3390/electronics14132623 - 28 Jun 2025
Viewed by 86
Abstract
Multi-dimensional classification (MDC), in which the training data are concurrently associated with numerous label variables across many dimensions, has garnered significant interest recently. Most of the current MDC methods are based on the framework of supervised learning, which induces a predictive model from [...] Read more.
Multi-dimensional classification (MDC), in which the training data are concurrently associated with numerous label variables across many dimensions, has garnered significant interest recently. Most of the current MDC methods are based on the framework of supervised learning, which induces a predictive model from a large amount of precisely labeled data. So, they are challenged to obtain satisfactory learning results in the situation where the training data are not annotated with precise labels but assigned with ambiguous labels. Besides, the current MDC algorithms only consider the scenario of centralized learning, where all training data are handled at a single node for the purpose of classifier induction. However, in some real applications, the training data are not consolidated at a single fusion center, but rather are dispersedly distributed among multiple nodes. In this study, we focus on the problem of decentralized classification involving partial multi-dimensional data that have partially accessible candidate labels, and develop a distributed method called dPL-MDC for learning with these partial labels. In this algorithm, we conduct one-vs.-one decomposition on the originally heterogeneous multi-dimensional output space, such that the problem of partial MDC can be transformed into the issue of distributed partial multi-label learning. Then, by using several shared anchor data to characterize the global distribution of label variables, we propose a novel distributed approach to learn the label confidence of the training data. Under the supervision of recovered credible labels, the classifier can be induced by exploiting the high-order label dependencies from a common low-dimensional subspace. Experiments performed on various datasets indicate that our proposed method is capable of achieving learning performance in distributed partial MDC. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning: Models and Applications: 2nd Edition)
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16 pages, 2163 KiB  
Article
AgriTransformer: A Transformer-Based Model with Attention Mechanisms for Enhanced Multimodal Crop Yield Prediction
by Luis Jácome Galarza, Miguel Realpe, Marlon Santiago Viñán-Ludeña, María Fernanda Calderón and Silvia Jaramillo
Electronics 2025, 14(12), 2466; https://doi.org/10.3390/electronics14122466 - 18 Jun 2025
Viewed by 357
Abstract
A more accurate crop yield estimation is essential for optimizing agricultural productivity and resource management. Traditional machine learning models, such as linear regression and convolutional neural networks (CNNs), often struggle to integrate multimodal data sources effectively, limiting their predictive accuracy. In this study, [...] Read more.
A more accurate crop yield estimation is essential for optimizing agricultural productivity and resource management. Traditional machine learning models, such as linear regression and convolutional neural networks (CNNs), often struggle to integrate multimodal data sources effectively, limiting their predictive accuracy. In this study, we propose the AgriTransformer model, a transformer-based model that enhances crop yield prediction by leveraging attention mechanisms for multimodal data fusion. The AgriTransformer model incorporates tabular agricultural data and vegetation indices (VI), allowing dynamic feature interaction and improved interpretability. Experimental results have demonstrated that AgriTransformer significantly outperforms conventional approaches, achieving an R2 of 0.919, compared to 0.884 for the best-performing linear regression model. The findings highlight the importance of structured tabular data in yield estimation, while VI serves as a complementary feature that increases the prediction capability and confidence. This study highlights the potential of transformer-based architectures in precision agriculture, offering a scalable and adaptable framework for crop yield forecasting. The AgriTransformer model enhances predictive accuracy and generalization across diverse agricultural conditions by prioritizing relevant features through attention mechanisms. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning: Models and Applications: 2nd Edition)
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15 pages, 456 KiB  
Article
Enhancing Basketball Team Strategies Through Predictive Analytics of Player Performance
by Roshan Chandru, Abhishek Kaushik and Pranay Jaiswal
Electronics 2025, 14(11), 2177; https://doi.org/10.3390/electronics14112177 - 27 May 2025
Viewed by 985
Abstract
This study explores the application of predictive analytics in evaluating player performance in the National Basketball Association (NBA), focusing on rebounds per game (REB), an essential component for better performance and results in basketball. The research employs a comparative analysis of machine learning [...] Read more.
This study explores the application of predictive analytics in evaluating player performance in the National Basketball Association (NBA), focusing on rebounds per game (REB), an essential component for better performance and results in basketball. The research employs a comparative analysis of machine learning (ML) models by leveraging a detailed NBA dataset. A key novelty lies in integrating advanced hyperparameter tuning and feature selection, enabling these models to capture complex relationships within the dataset. The Gradient Boosting Regressor demonstrated superior predictive performance, achieving an R² score of 0.8749 after tuning, with Linear Regression following closely at 0.8668. This study also highlights the importance of model interpretability and scalability, emphasizing the balance between predictive accuracy and usability for real-world decision-making. By offering actionable insights for optimizing player strategies and team performance, this research contributes to the growing body of knowledge in data-driven sports analytics and paves the way for more advanced applications in professional basketball management. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning: Models and Applications: 2nd Edition)
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19 pages, 24296 KiB  
Article
LocRecNet: A Synergistic Framework for Table Localization and Rectification
by Zefeng Cai, Jie Feng, Zhaokun Hou, Haixiang Zhang and Hanjie Ma
Electronics 2025, 14(10), 1920; https://doi.org/10.3390/electronics14101920 - 9 May 2025
Viewed by 264
Abstract
This paper introduces LocRecNet, a deformation-aware network for table localization and correction, aimed at improving the recognition accuracy of complex table data. Conventional algorithms typically depend on table cell or line features for model training but exhibit limitations when processing real-world deformed table [...] Read more.
This paper introduces LocRecNet, a deformation-aware network for table localization and correction, aimed at improving the recognition accuracy of complex table data. Conventional algorithms typically depend on table cell or line features for model training but exhibit limitations when processing real-world deformed table data. LocRecNet addresses these challenges by correcting deformations prior to table structure recognition, significantly enhancing model performance. The proposed network employs a novel keypoint detection method to precisely locate table edge points, enabling the efficient correction of deformed tables. Experimental results reveal that integrating LocRecNet substantially improves table recognition algorithms in terms of various key performance metrics, with recall rates increasing by up to 10% and F1 scores nearing 90%. Tests conducted on real-world datasets further validate its effectiveness, demonstrating a reasonable trade-off between computational cost and performance gains. Additionally, LocRecNet enhances performance even on standard table data, highlighting its strong generalizability and potential for broader application. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning: Models and Applications: 2nd Edition)
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31 pages, 1002 KiB  
Article
Distributed Partial Label Learning for Missing Data Classification
by Zhen Xu and Zushou Chen
Electronics 2025, 14(9), 1770; https://doi.org/10.3390/electronics14091770 - 27 Apr 2025
Viewed by 247
Abstract
Distributed learning (DL), in which multiple nodes in an inner-connected network collaboratively induce a predictive model using their local data and some information communicated across neighboring nodes, has received significant research interest in recent years. Yet, it is challenging to achieve excellent performance [...] Read more.
Distributed learning (DL), in which multiple nodes in an inner-connected network collaboratively induce a predictive model using their local data and some information communicated across neighboring nodes, has received significant research interest in recent years. Yet, it is challenging to achieve excellent performance in scenarios when training data instances have incomplete features and ambiguous labels. In such cases, it is essential to develop an efficient method to jointly perform the tasks of missing feature imputation and credible label recovery. Considering this, in this article, a distributed partial label missing data classification (dPMDC) algorithm is proposed. In the proposed algorithm, an integrated framework is formulated, which takes the ideas of both generative and discriminative learning into account. Firstly, by exploiting the weakly supervised information of ambiguous labels, a distributed probabilistic information-theoretic imputation method is designed to distributively fill in the missing features. Secondly, based on the imputed feature vectors, the classifier modeled by the random feature map of the χ2 kernel function can be learned. Two iterative steps constitute the dPMDC algorithm, which can be used to handle dispersed, distributed data with partially missing features and ambiguous labels. Experiments on several datasets show the superiority of the suggested algorithm from many viewpoints. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning: Models and Applications: 2nd Edition)
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15 pages, 639 KiB  
Article
From AI Knowledge to AI Usage Intention in the Managerial Accounting Profession and the Role of Personality Traits—A Decision Tree Regression Approach
by Lavinia Denisia Cuc, Dana Rad, Teodor Florin Cilan, Bogdan Cosmin Gomoi, Cristina Nicolaescu, Robert Almași, Simona Dzitac, Florin Lucian Isac and Ionut Pandelica
Electronics 2025, 14(6), 1107; https://doi.org/10.3390/electronics14061107 - 11 Mar 2025
Viewed by 1424
Abstract
This study examines the key drivers behind the adoption of artificial intelligence (AI) in the accounting profession, emphasizing the influence of AI-related knowledge, personality traits, and professional roles. By applying Decision Tree Regression analysis to survey data from accounting professionals, our research identifies [...] Read more.
This study examines the key drivers behind the adoption of artificial intelligence (AI) in the accounting profession, emphasizing the influence of AI-related knowledge, personality traits, and professional roles. By applying Decision Tree Regression analysis to survey data from accounting professionals, our research identifies AI knowledge as the strongest determinant of AI adoption, underscoring the importance of expertise in technology acceptance. While personality traits play a secondary role, extraversion and openness emerge as significant factors influencing adoption intentions. The study further explores AI applications in financial auditing, tax compliance, and fraud detection, clarifying the specific accounting domains impacted by AI integration. These findings offer valuable guidance for policymakers, educators, and business leaders aiming to equip the accounting workforce with the necessary skills and mindset to navigate the AI-driven transformation of the profession. Full article
(This article belongs to the Special Issue Computational Intelligence and Machine Learning: Models and Applications: 2nd Edition)
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