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Entropy
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Methods in Artificial Intelligence and Information Processing, Third Edition
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Methods in Artificial Intelligence and Information Processing, Third Edition

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 15 January 2026 | Viewed by 4008

Special Issue Editors

Prof. Dr. Zoran H. Perić

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Guest Editor
Faculty of Electronic Engineering, University of Nis, 18106 Nis, Serbia
Interests: digital telecommunication; quantization; compression; machine learning; coding
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vlado Delić

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Guest Editor
Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia
Interests: ICT; speech technologies; HCI
Special Issues, Collections and Topics in MDPI journals
Dr. Vladimir Despotovic

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Guest Editor
Bioinformatics Platform, Luxembourg Institute of Health, 1445 Strassen, Luxembourg
Interests: speech processing; vocal biomarkers; machine learning; medical image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Zoran Ognjanović

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Guest Editor
Mathematical Institute of the Serbian Academy of Sciences and Arts, 11000 Belgrade, Serbia
Interests: nonclassical logic; applications of mathematical logic in computer science; artificial intelligence and uncertain reasoning; automated theorem proving; applications of heuristics to satisfiability problems and digitization of cultural and scientific heritage
Special Issues, Collections and Topics in MDPI journals
Dr. Marko S. Andjelković

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Guest Editor
System Architectures, IHP—Institute for High Performance Microelectronics, Frankfurt, Germany
Interests: radiation effects; rad-hard design; radiation sensors

Special Issue Information

Dear Colleagues,

The area of artificial intelligence (AI), though introduced many years ago, has received considerable attention more recently. This can be explained by the necessity to process a large amount of data, where efficient methods and algorithms are desirable. Most AI methods encountered in the literature are based on the mathematical theory developed before AI occurred. Further research in this area will result in better understanding the AI and will also provide its simplification with corresponding approximations. Namely, such a simplification will provide the base for practical implementation, which is of crucial interest for engineers, researchers, and scientists dealing with the transfer of scientific research results into commercial products and other applications. On the other hand, designing and analyzing processing algorithms using only very complex mathematical theory in AI and information processing (IP) would result in a loss of wide applicability (e.g., possibility of hardware implementation).

Modern technology relies on research in IP and AI, and a number of methods have been developed with the aim of solving problems in pattern recognition in signals (speech, image, audio, biomedical signals), recognition of emotions, signal quality enhancement, detection of signals in the presence of noise, methods and algorithms in wireless sensor networks, deep neural networks (DNN), data compression, quantization in neural networks (NN), and learning representations.

Implementation of DNN on devices with constrained resources (edge devices, microcontrollers, tiny ML, tiny AI, etc.) is very important today. Therefore, new solutions emerge in the field of normalization and coding as well as in the compression of DNN parameters.

This Special Issue will focus not only on the application of methods but on the development of these two fields independently and combined.

Potential topics include but are not limited to the following:

  • Parametric and non-parametric machine learning algorithms;
  • Deep learning algorithms;
  • Entropy coding;
  • Compression methods in neural networks (pruning and quantization);
  • Acceleration of computing;
  • Tiny AI;
  • Speech and image processing;
  • Biomedical signal/image processing;
  • Object detection and face recognition;
  • Formal reasoning about neuro-symbolic AI and entropy.

Prof. Dr. Zoran H. Perić
Prof. Dr. Vlado Delić
Dr. Vladimir Despotovic
Dr. Zoran Ognjanović
Dr. Marko S. Andjelković
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. Entropy 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 2600 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

  • deep learning
  • neural networks
  • compression
  • entropy coding
  • speech and image processing
  • biomedical signal processing

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Related Special Issues

Published Papers (3 papers)

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Research

22 pages, 944 KiB  
Article
A Dual-Encoder Contrastive Learning Model for Knowledge Tracing
by Yanhong Bai, Xingjiao Wu, Tingjiang Wei and Liang He
Entropy 2025, 27(7), 685; https://doi.org/10.3390/e27070685 - 26 Jun 2025
Viewed by 639
Abstract
Knowledge tracing (KT) models learners’ evolving knowledge states to predict future performance, serving as a fundamental component in personalized education systems. However, existing methods suffer from data sparsity challenges, resulting in inadequate representation quality for low-frequency knowledge concepts and inconsistent modeling of students’ [...] Read more.
Knowledge tracing (KT) models learners’ evolving knowledge states to predict future performance, serving as a fundamental component in personalized education systems. However, existing methods suffer from data sparsity challenges, resulting in inadequate representation quality for low-frequency knowledge concepts and inconsistent modeling of students’ actual knowledge states. To address this challenge, we propose Dual-Encoder Contrastive Knowledge Tracing (DECKT), a contrastive learning framework that improves knowledge state representation under sparse data conditions. DECKT employs a momentum-updated dual-encoder architecture where the primary encoder processes current input data while the momentum encoder maintains stable historical representations through exponential moving average updates. These encoders naturally form contrastive pairs through temporal evolution, effectively enhancing representation capabilities for low-frequency knowledge concepts without requiring destructive data augmentation operations that may compromise knowledge structure integrity. To preserve semantic consistency in learned representations, DECKT incorporates a graph structure constraint loss that leverages concept–question relationships to maintain appropriate similarities between related concepts in the embedding space. Furthermore, an adversarial training mechanism applies perturbations to embedding vectors, enhancing model robustness and generalization. Extensive experiments on benchmark datasets demonstrate that DECKT significantly outperforms existing state-of-the-art methods, validating the effectiveness of the proposed approach in alleviating representation challenges in sparse educational data. Full article
(This article belongs to the Special Issue Methods in Artificial Intelligence and Information Processing, Third Edition)
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20 pages, 359 KiB  
Article
A Reinforcement Learning-Based Generative Approach for Event Temporal Relation Extraction
by Zhonghua Wu, Wenzhong Yang, Meng Zhang, Fuyuan Wei and Xinfang Liu
Entropy 2025, 27(3), 284; https://doi.org/10.3390/e27030284 - 9 Mar 2025
Viewed by 1092
Abstract
Event temporal relation extraction is a crucial task in natural language processing, aimed at recognizing the temporal relations between event triggers in a text. Despite extensive efforts in this area, the existing methods face two main issues. Firstly, the previous models for event [...] Read more.
Event temporal relation extraction is a crucial task in natural language processing, aimed at recognizing the temporal relations between event triggers in a text. Despite extensive efforts in this area, the existing methods face two main issues. Firstly, the previous models for event temporal relation extraction mainly rely on a classification framework, which fails to output the crucial contextual words necessary for predicting the temporal relations between two event triggers. Secondly, the prior research that formulated natural language processing tasks as text generation problems usually trained the generative models by maximum likelihood estimation. However, this approach encounters potential difficulties when the optimization objective is misaligned with the task performance metrics. To resolve these limitations, we introduce a reinforcement learning-based generative framework for event temporal relation extraction. Specifically, to output the important contextual words from the input sentence for temporal relation identification, we introduce dependency path generation as an auxiliary task to complement event temporal relation extraction. This task is solved alongside temporal relation prediction to enhance model performance. To achieve this, we reformulate the event temporal relation extraction task as a text generation problem, aiming to generate both event temporal relation labels and dependency path words based on the input sentence. To bridge the gap between the optimization objective and task performance metrics, we employ the REINFORCE algorithm to optimize our generative model, designing a novel reward function to simultaneously capture the accuracy of temporal prediction and the quality of generation. Lastly, to mitigate the high variance issue encountered when using the REINFORCE algorithm in multi-task generative model training, we propose a baseline policy gradient algorithm to improve the stability and efficiency of the training process. Experimental results on two widely used datasets, MATRES and TB-DENSE, show that our approach exhibits competitive performance. Full article
(This article belongs to the Special Issue Methods in Artificial Intelligence and Information Processing, Third Edition)
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17 pages, 399 KiB  
Article
Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles
by Evans Teiko Tetteh and Beata Zielosko
Entropy 2025, 27(1), 35; https://doi.org/10.3390/e27010035 - 4 Jan 2025
Viewed by 1657
Abstract
This study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments. Decision rules, known for their transparency, provide an accessible method for knowledge extraction from data, facilitating decision-making processes across diverse [...] Read more.
This study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments. Decision rules, known for their transparency, provide an accessible method for knowledge extraction from data, facilitating decision-making processes across diverse fields. Traditional decision tree algorithms, such as CART and ID3, are employed to induce decision trees from bootstrapped datasets, which represent distributed data sources. Subsequently, a greedy algorithm is applied to derive decision rules that are true across multiple decision trees. Experiments are performed, taking into account knowledge representation and discovery perspectives. They show that, as the value of α, 0≤α<1, increases, shorter rules are obtained, and also it is possible to improve the classification accuracy of rule-based models. Full article
(This article belongs to the Special Issue Methods in Artificial Intelligence and Information Processing, Third Edition)
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