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Electronics
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Artificial Intelligence and Pattern Recognition for Intelligent Systems
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Artificial Intelligence and Pattern Recognition for Intelligent Systems

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 6086

Special Issue Editors

Dr. Xinyue Liu

E-Mail Website
Guest Editor
School of Software, Dalian University of Technology, Dalian 116024, China
Interests: deep learning; reinforcement learning; pattern recognition
Dr. Linlin Zong

E-Mail Website
Guest Editor
School of Software, Dalian University of Technology, Dalian 116024, China
Interests: multimodal learning; sentiment analysis; clustering analysis
Dr. Xiaowei Zhao

E-Mail Website
Guest Editor
School of Software, Dalian University of Technology, Dalian 116024, China
Interests: evolutionary game algorithm; deep learning; sentiment analysis

Special Issue Information

Dear Colleagues,

Artificial intelligence and pattern recognition are two closely related fields that have been hot topics in computer science and artificial intelligence research for the past few decades. Artificial intelligence aims to build intelligent systems that can understand, learn and reason, while pattern recognition focuses on identifying and classifying patterns in complex datasets. Through artificial intelligence and pattern recognition technology, computers have been able to accurately recognize objects, scenes and even emotions in images. This makes intelligent cameras, autonomous vehicles and other intelligent devices possible. In the field of intelligent healthcare, pattern recognition can help doctors make more accurate diagnoses and treatment plans by analyzing patient medical data. In the financial field, pattern recognition can identify abnormal patterns in transaction data, thereby helping prevent financial fraud. The integration of artificial intelligence and pattern recognition is leading human society toward an era of intelligent interactions. By intelligently recognizing images, sounds and data, we can create smarter products and services, greatly improving the quality of life. This Special Issue aims to introduce the latest breakthroughs in theoretical research, technological innovation and practical application regarding artificial intelligence and pattern recognition for intelligent systems. This Special Issue welcomes any original and high-quality papers including, but not limited to, the following:

  • Deep learning;
  • Machine learning;
  • Reinforcement learning;
  • Multimodal learning;
  • Computer vision;
  • Neural networks;
  • Knowledge graph;
  • Causal reasoning;
  • Diffusion model;
  • Large language model;
  • Sentiment analysis;
  • Embodied intelligence.

Dr. Xinyue Liu
Dr. Linlin Zong
Dr. Xiaowei Zhao
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

  • artificial intelligence
  • pattern recognition
  • deep learning
  • machine learning
  • multimodal learning

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Published Papers (7 papers)

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Research

18 pages, 898 KiB  
Article
Q-Learning Approach Applied to Network Security
by Zheni Utic and Ayomide Oyemaja
Electronics 2025, 14(10), 1996; https://doi.org/10.3390/electronics14101996 - 14 May 2025
Viewed by 240
Abstract
Network security and intrusion detection and response (IDR) are necessary issues nowadays. Enhancing our cyber defense by discovering advanced machine learning models, such as reinforcement learning and Q-learning, is a crucial security measure. This study proposes a novel intrusion response method by implementing [...] Read more.
Network security and intrusion detection and response (IDR) are necessary issues nowadays. Enhancing our cyber defense by discovering advanced machine learning models, such as reinforcement learning and Q-learning, is a crucial security measure. This study proposes a novel intrusion response method by implementing an off-policy Q-learning approach. We test the validity of our model by conducting a goodness-of-fit analysis and proving its efficiency. By performing sensitivity analysis, we prove that it is possible to protect our network successfully and establish an immediate response mechanism that could be successfully implemented in intrusion response (IR) systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Pattern Recognition for Intelligent Systems)
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25 pages, 4899 KiB  
Article
Development of Machine Learning-Based Indicators for Predicting Comeback Victories Using the Bounty Mechanism in MOBA Games
by Junhyuk Lee and Namhyoung Kim
Electronics 2025, 14(7), 1445; https://doi.org/10.3390/electronics14071445 - 3 Apr 2025
Viewed by 501
Abstract
Multiplayer Online Battle Arena (MOBA) games, exemplified by titles such as League of Legends and Dota 2, have attained global popularity and have been formally recognized as an official event in the 2022 Hangzhou Asian Games, thus establishing their significance in the esports [...] Read more.
Multiplayer Online Battle Arena (MOBA) games, exemplified by titles such as League of Legends and Dota 2, have attained global popularity and have been formally recognized as an official event in the 2022 Hangzhou Asian Games, thus establishing their significance in the esports industry. In this study, we proposed a machine learning-based model for predicting comeback victories by leveraging the object bounty mechanism, a critical yet underexplored aspect of previous research. By closely examining the game environment following the activation of the bounty system, we identified pivotal variables and constructed novel indicators that contribute to successful comebacks. Furthermore, an individualized case analysis based on SHapley Additive exPlanations (SHAP) provides new insights to support strategic in-game decision-making and enhance the player experience. The experimental results demonstrate that the indicators introduced in this study, such as the weighted team champion mastery and similarity in champion mastery among the team’s main champions, significantly influence the likelihood of a comeback victory. By capturing the intrinsic dynamism of MOBA games, the proposed model is expected to improve player engagement and satisfaction. Full article
(This article belongs to the Special Issue Artificial Intelligence and Pattern Recognition for Intelligent Systems)
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18 pages, 1833 KiB  
Article
Hybrid CNN-BiGRU-AM Model with Anomaly Detection for Nonlinear Stock Price Prediction
by Jiacheng Luo, Yun Cao, Kai Xie, Chang Wen, Yunzhe Ruan, Jinpeng Ji, Jianbiao He and Wei Zhang
Electronics 2025, 14(7), 1275; https://doi.org/10.3390/electronics14071275 - 24 Mar 2025
Viewed by 592
Abstract
To address challenges in stock price prediction including data nonlinearity and anomalies, we propose a hybrid CNN-BiGRU-AM framework integrated with deep learning-based anomaly detection. First, an anomaly detection module identifies irregularities in stock price data. The CNN component then extracts local features while [...] Read more.
To address challenges in stock price prediction including data nonlinearity and anomalies, we propose a hybrid CNN-BiGRU-AM framework integrated with deep learning-based anomaly detection. First, an anomaly detection module identifies irregularities in stock price data. The CNN component then extracts local features while filtering anomalous information, followed by nonlinear pattern modeling through BiGRU with attention mechanisms. Final predictions undergo secondary anomaly screening to ensure reliability. Experimental evaluation on Shanghai Composite (SSE) daily closing prices demonstrates superior performance with R2 = 0.9903, RMSE = 22.027, MAE = 19.043, and a Sharpe Ratio of 0.65. It is noteworthy that the MAE of this model is reduced by 14.7%, and the RMSE is decreased by 7.7% compared to its ablation model. The framework achieves multi-level feature extraction through convolutional operations and bidirectional temporal modeling, effectively enhancing model generalization via nonlinear mapping and anomaly correction. Comparative Sharpe Ratio analysis across models provides practical insights for investment decision-making. This dual-functional system not only improves prediction accuracy but also offers interpretable references for market mechanism analysis and regulatory policy formulation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Pattern Recognition for Intelligent Systems)
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30 pages, 7287 KiB  
Article
Context-Aware Tomato Leaf Disease Detection Using Deep Learning in an Operational Framework
by Divas Karimanzira
Electronics 2025, 14(4), 661; https://doi.org/10.3390/electronics14040661 - 8 Feb 2025
Cited by 1 | Viewed by 1092
Abstract
Tomato cultivation is a vital agricultural practice worldwide, yet it faces significant challenges due to various diseases that adversely affect crop yield and quality. This paper presents a novel tomato disease detection system within an operational framework that leverages an innovative deep learning-based [...] Read more.
Tomato cultivation is a vital agricultural practice worldwide, yet it faces significant challenges due to various diseases that adversely affect crop yield and quality. This paper presents a novel tomato disease detection system within an operational framework that leverages an innovative deep learning-based classifier, specifically a Vision Transformer (ViT) integrated with cascaded group attention (CGA) and a modified Focaler-CIoU (Complete Intersection over Union) loss function. The proposed method aims to enhance the accuracy and robustness of disease detection by effectively capturing both local and global contextual information while addressing the challenges of sample imbalance in the dataset. To improve interpretability, we integrate Explainable Artificial Intelligence (XAI) techniques, enabling users to understand the rationale behind the model’s classifications. Additionally, we incorporate a large language model (LLM) to generate comprehensive, context-aware explanations and recommendations based on the identified diseases and other relevant factors, thus bridging the gap between technical analysis and user comprehension. Our evaluation against state-of-the-art deep learning methods, including convolutional neural networks (CNNs) and other transformer-based models, demonstrates that the ViT-CGA model significantly outperforms existing techniques, achieving an overall accuracy of 96.5%, an average precision of 93.9%, an average recall of 96.7%, and an average F1-score of 94.2% for tomato leaf disease classification. The integration of CGA and Focaler-CIoU loss not only contributes to improved model interpretability and stability but also empowers farmers and agricultural stakeholders with actionable insights, fostering informed decision making in disease management. This research advances the field of automated disease detection in crops and provides a practical framework for deploying deep learning solutions in agricultural settings, ultimately supporting sustainable farming practices and enhancing food security. Full article
(This article belongs to the Special Issue Artificial Intelligence and Pattern Recognition for Intelligent Systems)
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17 pages, 2479 KiB  
Article
A Study on the Factors Influencing Rank Prediction in PlayerUnknown’s Battlegrounds
by Ji-Na Lee and Ji-Yeoun Lee
Electronics 2025, 14(3), 626; https://doi.org/10.3390/electronics14030626 - 5 Feb 2025
Cited by 1 | Viewed by 831
Abstract
This study analyzes the key factors influencing player rank prediction in PlayerUnknown’s Battlegrounds (PUBG), using machine learning models to evaluate in-game performance. By examining variables such as “walkDistance”, “boosts”, and “weaponsAcquired”, the study identifies these as critical predictors, with “walkDistance” emerging [...] Read more.
This study analyzes the key factors influencing player rank prediction in PlayerUnknown’s Battlegrounds (PUBG), using machine learning models to evaluate in-game performance. By examining variables such as “walkDistance”, “boosts”, and “weaponsAcquired”, the study identifies these as critical predictors, with “walkDistance” emerging as the most significant across all match types. Utilizing models including random forest (RF), gradient descent (GD), extreme gradient boosting (XGBoost), and feedforward neural network (FNN), the analysis reveals performance variation by match type: XGBoost achieves the highest accuracy in solo matches (88.07%), GD performs best in duo matches (84.75%), and RF records the highest accuracy in squad matches (78.21%). These findings provide valuable insights for game developers in balancing gameplay and offer personalized strategic recommendations for players. Future research may enhance predictive performance by incorporating additional variables and exploring alternative models applicable to PUBG and similar battle royale games. Full article
(This article belongs to the Special Issue Artificial Intelligence and Pattern Recognition for Intelligent Systems)
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25 pages, 5363 KiB  
Article
Power-Optimized Field-Programmable Gate Array Implementation of Neural Activation Functions Using Continued Fractions for AI/ML Workloads
by Chanakya Hingu, Xingang Fu, Taofiki Saliyu, Rui Hu and Ramkrishna Mishan
Electronics 2024, 13(24), 5026; https://doi.org/10.3390/electronics13245026 - 20 Dec 2024
Viewed by 709
Abstract
The increasing demand for energy-efficient hardware platforms to support artificial intelligence (AI) and machine learning (ML) algorithms in edge computing has driven the adoption of system-on-chip (SoC) architectures. Implementing neural network (NN) activation functions, such as the hyperbolic tangent (tanh), on hardware presents [...] Read more.
The increasing demand for energy-efficient hardware platforms to support artificial intelligence (AI) and machine learning (ML) algorithms in edge computing has driven the adoption of system-on-chip (SoC) architectures. Implementing neural network (NN) activation functions, such as the hyperbolic tangent (tanh), on hardware presents challenges due to computational complexity, high resource requirements, and power consumption. This paper aims to optimize the hardware implementation of the tanh function using continued fraction and polynomial approximations to minimize resource consumption and power usage while preserving computational accuracy. Five models of the tanh function, including continued fraction and quadratic approximations, were implemented on Intel field-programmable gate arrays (FPGAs) using VHDL and Intel’s ALTFP toolbox, with 32-bit floating-point outputs validated against MATLAB’s 64-bit floating-point results. Detailed analyses of resource utilization, power optimization, clock latency, and bit-level accuracy were conducted, focusing on minimizing logic elements and digital signal processing (DSP) blocks while achieving high precision and low power consumption. The most optimized model was further integrated into a four-input, two-output recurrent neural network (RNN) structure to assess real-time performance. Experimental results demonstrate that the continued fraction-based models significantly reduce resource usage, computation time, and power consumption, enhancing FPGA performance for AI/ML applications in resource-constrained and power-sensitive environments. Full article
(This article belongs to the Special Issue Artificial Intelligence and Pattern Recognition for Intelligent Systems)
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24 pages, 2131 KiB  
Article
Improving Text Classification in Agricultural Expert Systems with a Bidirectional Encoder Recurrent Convolutional Neural Network
by Xiaojuan Guo, Jianping Wang, Guohong Gao, Li Li, Junming Zhou and Yancui Li
Electronics 2024, 13(20), 4054; https://doi.org/10.3390/electronics13204054 - 15 Oct 2024
Cited by 2 | Viewed by 1249
Abstract
With the rapid development of internet and AI technologies, Agricultural Expert Systems (AESs) have become crucial for delivering technical support and decision-making in agricultural management. However, traditional natural language processing methods often struggle with specialized terminology and context, and they lack the adaptability [...] Read more.
With the rapid development of internet and AI technologies, Agricultural Expert Systems (AESs) have become crucial for delivering technical support and decision-making in agricultural management. However, traditional natural language processing methods often struggle with specialized terminology and context, and they lack the adaptability to handle complex text classifications. The diversity and evolving nature of agricultural texts make deep semantic understanding and integration of contextual knowledge especially challenging. To tackle these challenges, this paper introduces a Bidirectional Encoder Recurrent Convolutional Neural Network (AES-BERCNN) tailored for short-text classification in agricultural expert systems. We designed an Agricultural Text Encoder (ATE) with a six-layer transformer architecture to capture both preceding and following word information. A recursive convolutional neural network based on Gated Recurrent Units (GRUs) was also developed to merge contextual information and learn complex semantic features, which are then combined with the ATE output and refined through max-pooling to form the final feature representation. The AES-BERCNN model was tested on a self-constructed agricultural dataset, achieving an accuracy of 99.63% in text classification. Its generalization ability was further verified on the Tsinghua News dataset. Compared to other models such as TextCNN, DPCNN, BiLSTM, and BERT-based models, the AES-BERCNN shows clear advantages in agricultural text classification. This work provides precise and timely technical support for intelligent agricultural expert systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Pattern Recognition for Intelligent Systems)
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