Algorithms

22 pages, 3129 KiB

Open AccessArticle

A Novel Dual-Strategy Approach for Constructing Knowledge Graphs in the Home Appliance Fault Domain

by Daokun Zhang, Jian Zhang, Yanhe Jia and Mengjie Liao

Algorithms 2025, 18(8), 485; https://doi.org/10.3390/a18080485 - 5 Aug 2025

Knowledge graph technology holds significant importance for efficient fault diagnosis in household appliances. However, the scarcity of public fault diagnosis data and the lack of automated knowledge extraction pose major challenges to knowledge graph construction. To address issues such as ambiguous entity boundaries, [...] Read more.

Knowledge graph technology holds significant importance for efficient fault diagnosis in household appliances. However, the scarcity of public fault diagnosis data and the lack of automated knowledge extraction pose major challenges to knowledge graph construction. To address issues such as ambiguous entity boundaries, severe entity nesting, and poor entity extraction performance in fault diagnosis texts, this paper proposes a dual-strategy progressive knowledge extraction framework. First, to tackle the high complexity of fault diagnosis texts, an entity recognition model named RoBERTa-zh-BiLSTM-MUL-CRF is designed, improving the accuracy of nested entity extraction. Second, leveraging the semantic understanding capability of large language models, a progressive prompting strategy is adopted for ontology alignment and relation extraction, achieving automated knowledge extraction. Experimental results show that the proposed named entity recognition model outperforms traditional models, with improvements of 3.87%, 5.82%, and 2.05% in F1-score, recall, and precision, respectively. Additionally, the large language model demonstrates better performance in ontology alignment compared to traditional machine learning models. The constructed knowledge graph for household appliance fault diagnosis integrates structured fault diagnosis information. It effectively processes unstructured fault texts and supports visual queries and entity tracing. This framework can assist maintenance personnel in making rapid judgments, thereby improving fault diagnosis efficiency. Full article

(This article belongs to the Section Combinatorial Optimization, Graph, and Network Algorithms)

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15 pages, 834 KiB

Open AccessArticle

Tolerance Proportionality and Computational Stability in Adaptive Parallel-in-Time Runge–Kutta Methods

by Imre Fekete, Ferenc Izsák, Vendel P. Kupás and Gustaf Söderlind

Algorithms 2025, 18(8), 484; https://doi.org/10.3390/a18080484 - 5 Aug 2025

Abstract

In this paper, we investigate how adaptive time-integration strategies can be effectively combined with parallel-in-time numerical methods for solving systems of ordinary differential equations. Our focus is particularly on their influence on tolerance proportionality. We examine various grid-refinement strategies within the multigrid reduction-in-time [...] Read more.

In this paper, we investigate how adaptive time-integration strategies can be effectively combined with parallel-in-time numerical methods for solving systems of ordinary differential equations. Our focus is particularly on their influence on tolerance proportionality. We examine various grid-refinement strategies within the multigrid reduction-in-time (MGRIT) framework. Our results show that a simple adjustment to the original refinement factor can substantially improve computational stability and reliability. Through numerical experiments on standard test problems using the XBraid library, we demonstrate that parallel-in-time solutions closely match their sequential counterparts. Moreover, with the use of multiple processors, computing time can be significantly reduced. Full article

(This article belongs to the Section Analysis of Algorithms and Complexity Theory)

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22 pages, 398 KiB

Open AccessArticle

An Improved Convergence Analysis of a Multi-Step Method with High-Efficiency Indices

by Santhosh George, Manjusree Gopal, Samhitha Bhide and Ioannis K. Argyros

Algorithms 2025, 18(8), 483; https://doi.org/10.3390/a18080483 - 4 Aug 2025

Abstract

A multi-step method introduced by Raziyeh and Masoud for solving nonlinear systems with convergence order five has been considered in this paper. The convergence of the method was studied using Taylor series expansion, which requires the function to be six times differentiable. However, [...] Read more.

A multi-step method introduced by Raziyeh and Masoud for solving nonlinear systems with convergence order five has been considered in this paper. The convergence of the method was studied using Taylor series expansion, which requires the function to be six times differentiable. However, our convergence study does not depend on the Taylor series. We use the derivative of F up to two only in our convergence analysis, which is presented in a more general Banach space setting. Semi-local analysis is also discussed, which was not given in earlier studies. Unlike in earlier studies (where two sets of assumptions were used), we used the same set of assumptions for semi-local analysis and local convergence analysis. We discussed the dynamics of the method and also gave some numerical examples to illustrate theoretical findings. Full article

(This article belongs to the Special Issue Recent Advances in Numerical Algorithms and Their Applications)

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24 pages, 3291 KiB

Open AccessArticle

Machine Learning Subjective Opinions: An Application in Forensic Chemistry

by Anuradha Akmeemana and Michael E. Sigman

Algorithms 2025, 18(8), 482; https://doi.org/10.3390/a18080482 - 4 Aug 2025

Abstract

Simulated data created in silico using a previously reported method were sampled by bootstrapping to generate data sets for training multiple copies of an ensemble learner (i.e., a machine learning (ML) method). The posterior probabilities of class membership obtained by applying the ensemble [...] Read more.

Simulated data created in silico using a previously reported method were sampled by bootstrapping to generate data sets for training multiple copies of an ensemble learner (i.e., a machine learning (ML) method). The posterior probabilities of class membership obtained by applying the ensemble of ML models to previously unseen validation data were fitted to a beta distribution. The shape parameters for the fitted distribution were used to calculate the subjective opinion of sample membership into one of two mutually exclusive classes. The subjective opinion consists of belief, disbelief and uncertainty masses. A subjective opinion for each validation sample allows identification of high-uncertainty predictions. The projected probabilities of the validation opinions were used to calculate log-likelihood ratio scores and generate receiver operating characteristic (ROC) curves from which an opinion-supported decision can be made. Three very different ML models, linear discriminant analysis (LDA), random forest (RF), and support vector machines (SVM) were applied to the two-state classification problem in the analysis of forensic fire debris samples. For each ML method, a set of 100 ML models was trained on data sets bootstrapped from 60,000 in silico samples. The impact of training data set size on opinion uncertainty and ROC area under the curve (AUC) were studied. The median uncertainty for the validation data was smallest for LDA ML and largest for the SVM ML. The median uncertainty continually decreased as the size of the training data set increased for all ML.The AUC for ROC curves based on projected probabilities was largest for the RF model and smallest for the LDA method. The ROC AUC was statistically unchanged for LDA at training data sets exceeding 200 samples; however, the AUC increased with increasing sample size for the RF and SVM methods. The SVM method, the slowest to train, was limited to a maximum of 20,000 training samples. All three ML methods showed increasing performance when the validation data was limited to higher ignitable liquid contributions. An ensemble of 100 RF ML models, each trained on 60,000 in silico samples, performed the best with a median uncertainty of 1.39 ×

10^{- 2}

and ROC AUC of 0.849 for all validation samples. Full article

(This article belongs to the Special Issue Artificial Intelligence in Modeling and Simulation (2nd Edition))

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13 pages, 7106 KiB

Open AccessArticle

Multi-Scale Universal Style-Transfer Network Based on Diffusion Model

by Na Su, Jingtao Wang and Yun Pan

Algorithms 2025, 18(8), 481; https://doi.org/10.3390/a18080481 - 4 Aug 2025

Abstract

Artistic style transfer aims to transfer the style of an artwork to a photograph while maintaining its original overall content. Although current style-transfer methods have achieved promising results when processing photorealistic images, they often struggle with brushstroke preservation in artworks, especially in styles [...] Read more.

Artistic style transfer aims to transfer the style of an artwork to a photograph while maintaining its original overall content. Although current style-transfer methods have achieved promising results when processing photorealistic images, they often struggle with brushstroke preservation in artworks, especially in styles such as oil painting and pointillism. In such cases, the extracted style and content features tend to include redundant information, leading to issues such as blurred edges and a loss of fine details in the transferred images. To address this problem, this paper proposes a multi-scale general style-transfer network based on diffusion models. The proposed network consists of a coarse style-transfer module and a refined style-transfer module. First, the coarse style-transfer module is designed to perform mainstream style-transfer tasks more efficiently by operating on downsampled images, enabling faster processing with satisfactory results. Next, to further enhance edge fidelity, a refined style-transfer module is introduced. This module utilizes a segmentation component to generate a mask of the main subject in the image and performs edge-aware refinement. This enhances the fusion between the subject’s edges and the target style while preserving more detailed features. To improve overall image quality and better integrate the style along the content boundaries, the output from the coarse module is upsampled by a factor of two and combined with the subject mask. With the assistance of ControlNet and Stable Diffusion, the model performs content-aware edge redrawing to enhance the overall visual quality of the stylized image. Compared with state-of-the-art style-transfer methods, the proposed model preserves more edge details and achieves more natural fusion between style and content. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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19 pages, 455 KiB

Open AccessArticle

A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud

by Rafik Hamza

Algorithms 2025, 18(8), 480; https://doi.org/10.3390/a18080480 - 4 Aug 2025

Abstract

The advent of quantum computing poses an existential threat to the security of cloud services that handle sensitive visual data. Simultaneously, the need for computational privacy requires the ability to process data without exposing it to the cloud provider. This paper introduces and [...] Read more.

The advent of quantum computing poses an existential threat to the security of cloud services that handle sensitive visual data. Simultaneously, the need for computational privacy requires the ability to process data without exposing it to the cloud provider. This paper introduces and evaluates a hybrid quantum-resistant framework that addresses both challenges by integrating NIST-standardized post-quantum cryptography with optimized fully homomorphic encryption (FHE). Our solution uses CRYSTALS-Kyber for secure channel establishment and the CKKS FHE scheme with SIMD batching to perform image processing tasks on a cloud server without ever decrypting the image. This work provides a comprehensive performance analysis of the complete, end-to-end system. Our empirical evaluation demonstrates the framework’s practicality, detailing the sub-millisecond PQC setup costs and the amortized transfer of 33.83 MB of public FHE materials. The operational performance shows remarkable scalability, with server-side computations and client-side decryption completing within low single-digit milliseconds. By providing a detailed analysis of a viable and efficient architecture, this framework establishes a practical foundation for the next generation of privacy-preserving cloud applications. Full article

(This article belongs to the Special Issue Secure and Explainable AI: Enhancing Trust, Resilience, and Efficiency in Machine Learning Models)

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17 pages, 3816 KiB

Open AccessArticle

Charging Station Siting and Capacity Determination Based on a Generalized Least-Cost Model of Traffic Distribution

by Mingzhao Ma, Feng Wang, Lirong Xiong, Yuhonghao Wang and Wenxin Li

Algorithms 2025, 18(8), 479; https://doi.org/10.3390/a18080479 - 4 Aug 2025

Abstract

With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due [...] Read more.

With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due to low traffic flow, resulting in a waste of resources. Areas with high traffic flow may have fewer charging stations, resulting in long queues and road congestion. The purpose of this study is to optimize the location of charging stations and the number of charging piles in the stations based on the distribution of traffic flow, and to construct a bi-level programming model by analyzing the distribution of traffic flow. The upper-level planning model is the user-balanced flow allocation model, which is solved to obtain the optimal traffic flow allocation of the road network, and the output of the upper-level planning model is used as the input of the lower-layer model. The lower-level planning model is a generalized minimum cost model with driving time, charging waiting time, charging time, and the cost of electricity consumed to reach the destination of the trip as objective functions. In this study, an empirical simulation is conducted on the road network of Hefei City, Anhui Province, utilizing three algorithms—GA, GWO, and PSO—for optimization and sensitivity analysis. The optimized results are compared with the existing charging station deployment scheme in the road network to demonstrate the effectiveness of the proposed methodology. Full article

(This article belongs to the Special Issue Advances in Algorithms Through Heuristics: Theory, Applications, and Innovations)

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27 pages, 1853 KiB

Open AccessArticle

Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation

by Juan Chen and Qiao Li

Algorithms 2025, 18(8), 478; https://doi.org/10.3390/a18080478 - 3 Aug 2025

Abstract

Next Point-of-Interest (POI) recommendation is aimed at predicting users’ future visits based on their current status and historical check-in records, providing convenience to users and potential profits to businesses. The Graph Neural Network (GNN) has become a common approach for this task due [...] Read more.

Next Point-of-Interest (POI) recommendation is aimed at predicting users’ future visits based on their current status and historical check-in records, providing convenience to users and potential profits to businesses. The Graph Neural Network (GNN) has become a common approach for this task due to the capabilities of modeling relations between nodes in a global perspective. However, most existing studies overlook the more prevalent heterogeneous relations in real-world scenarios, and manually constructed graphs may suffer from inaccuracies. To address these limitations, we propose a model called Heterogeneous Graph Structure Learning for Next POI Recommendation (HGSL-POI), which integrates three key components: heterogeneous graph contrastive learning, graph structure learning, and sequence modeling. The model first employs meta-path-based subgraphs and the user–POI interaction graph to obtain initial representations of users and POIs. Based on these representations, it reconstructs the subgraphs through graph structure learning. Finally, based on the embeddings from the reconstructed graphs, sequence modeling incorporating graph neural networks captures users’ sequential preferences to make recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model. Additional studies confirm its robustness and superior performance across diverse recommendation tasks. Full article

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24 pages, 1054 KiB

Open AccessArticle

Consensus-Based Automatic Group Decision-Making Method with Reliability and Subjectivity Measures Based on Sentiment Analysis

by Johnny Bajaña-Zajía, José Ramón Trillo, Francisco Javier Cabrerizo and Juan Antonio Morente-Molinera

Algorithms 2025, 18(8), 477; https://doi.org/10.3390/a18080477 - 3 Aug 2025

Abstract

The use of informal language on social media and the sheer volume of information make it difficult for a computer system to analyse it automatically. The aim of this work is to design a new group decision-making method that applies two new consensus [...] Read more.

The use of informal language on social media and the sheer volume of information make it difficult for a computer system to analyse it automatically. The aim of this work is to design a new group decision-making method that applies two new consensus methods based on sentiment analysis. This method is designed for application in the analysis of texts on social media. To test the method, we will use posts from the so called social network X. The proposed model differs from previous work in this field by defining a new degree of subjectivity and a new degree of reliability associated with user opinions. This work also presents two new consensus measures, one focused on measuring the number of words classified as positive and negative and the other on analysing the percentage of occurrence of those words. Our method allows us to automatically extract preferences from the transcription of the texts used in the debate, avoiding the need for users to explicitly indicate their preferences. The application to a real case of public investment demonstrates the effectiveness of the approach in collaborative contexts that used natural language. Full article

(This article belongs to the Special Issue Multi-Objective and Multi-Level Optimization: Algorithms and Applications (2nd Edition))

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23 pages, 3124 KiB

Open AccessArticle

Bee Swarm Metropolis–Hastings Sampling for Bayesian Inference in the Ginzburg–Landau Equation

by Shucan Xia and Lipu Zhang

Algorithms 2025, 18(8), 476; https://doi.org/10.3390/a18080476 - 2 Aug 2025

Abstract

To improve the sampling efficiency of Markov Chain Monte Carlo in complex parameter spaces, this paper proposes an adaptive sampling method that integrates a swarm intelligence mechanism called the BeeSwarm-MH algorithm. The method combines global exploration by scout bees with local exploitation by [...] Read more.

To improve the sampling efficiency of Markov Chain Monte Carlo in complex parameter spaces, this paper proposes an adaptive sampling method that integrates a swarm intelligence mechanism called the BeeSwarm-MH algorithm. The method combines global exploration by scout bees with local exploitation by worker bees. It employs multi-stage perturbation intensities and adaptive step-size tuning to enable efficient posterior sampling. Focusing on Bayesian inference for parameter estimation in the soliton solutions of the two-dimensional complex Ginzburg–Landau equation, we design a dedicated inference framework to systematically compare the performance of BeeSwarm-MH with the classical Metropolis–Hastings algorithm. Experimental results demonstrate that BeeSwarm-MH achieves comparable estimation accuracy while significantly reducing the required number of iterations and total computation time for convergence. Moreover, it exhibits superior global search capabilities and adaptive features, offering a practical approach for efficient Bayesian inference in complex physical models. Full article

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29 pages, 1132 KiB

Open AccessArticle

Generating Realistic Synthetic Patient Cohorts: Enforcing Statistical Distributions, Correlations, and Logical Constraints

by Ahmad Nader Fasseeh, Rasha Ashmawy, Rok Hren, Kareem ElFass, Attila Imre, Bertalan Németh, Dávid Nagy, Balázs Nagy and Zoltán Vokó

Algorithms 2025, 18(8), 475; https://doi.org/10.3390/a18080475 - 1 Aug 2025

Abstract

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This [...] Read more.

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article

(This article belongs to the Collection Feature Papers in Algorithms for Multidisciplinary Applications)

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24 pages, 623 KiB

Open AccessArticle

Evaluation of Competitiveness and Sustainable Development Prospects of French-Speaking African Countries Based on TOPSIS and Adaptive LASSO Algorithms

by Binglin Liu, Liwen Li, Hang Ren, Jianwan Qin and Weijiang Liu

Algorithms 2025, 18(8), 474; https://doi.org/10.3390/a18080474 - 30 Jul 2025

Abstract

This study evaluates the competitiveness and sustainable development prospects of French-speaking African countries by constructing a comprehensive framework integrating the TOPSIS method and adaptive LASSO algorithm. Using multivariate data from sources such as the World Bank, 30 indicators covering core, basic, and auxiliary [...] Read more.

This study evaluates the competitiveness and sustainable development prospects of French-speaking African countries by constructing a comprehensive framework integrating the TOPSIS method and adaptive LASSO algorithm. Using multivariate data from sources such as the World Bank, 30 indicators covering core, basic, and auxiliary competitiveness were selected to quantitatively analyze the competitiveness of 26 French-speaking African countries. Results show that their comprehensive competitiveness exhibits spatial patterns of “high in the north and south, low in the east and west” and “high in coastal areas, low in inland areas”. Algeria, Morocco, and six other countries demonstrate high competitiveness, while Central African countries generally show low competitiveness. The adaptive LASSO algorithm identifies three key influencing factors, including the proportion of R&D expenditure to GDP, high-tech exports, and total reserves, as well as five secondary key factors, including the number of patent applications and total number of domestic listed companies, revealing that scientific and technological investment, financial strength, and innovation transformation capabilities are core constraints. Based on these findings, sustainable development strategies are proposed, such as strengthening scientific and technological research and development and innovation transformation, optimizing financial reserves and capital markets, and promoting China–Africa collaborative cooperation, providing decision-making references for competitiveness improvement and regional cooperation of French-speaking African countries under the background of the “Belt and Road Initiative”. Full article

(This article belongs to the Special Issue Hybrid Intelligent Algorithms (2nd Edition))

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35 pages, 4940 KiB

Open AccessArticle

A Novel Lightweight Facial Expression Recognition Network Based on Deep Shallow Network Fusion and Attention Mechanism

by Qiaohe Yang, Yueshun He, Hongmao Chen, Youyong Wu and Zhihua Rao

Algorithms 2025, 18(8), 473; https://doi.org/10.3390/a18080473 - 30 Jul 2025

Abstract

Facial expression recognition (FER) is a critical research direction in artificial intelligence, which is widely used in intelligent interaction, medical diagnosis, security monitoring, and other domains. These applications highlight its considerable practical value and social significance. Face expression recognition models often need to [...] Read more.

Facial expression recognition (FER) is a critical research direction in artificial intelligence, which is widely used in intelligent interaction, medical diagnosis, security monitoring, and other domains. These applications highlight its considerable practical value and social significance. Face expression recognition models often need to run efficiently on mobile devices or edge devices, so the research on lightweight face expression recognition is particularly important. However, feature extraction and classification methods of lightweight convolutional neural network expression recognition algorithms mostly used at present are not specifically and fully optimized for the characteristics of facial expression images, yet fail to make full use of the feature information in face expression images. To address the lack of facial expression recognition models that are both lightweight and effectively optimized for expression-specific feature extraction, this study proposes a novel network design tailored to the characteristics of facial expressions. In this paper, we refer to the backbone architecture of MobileNet V2 network, and redesign LightExNet, a lightweight convolutional neural network based on the fusion of deep and shallow layers, attention mechanism, and joint loss function, according to the characteristics of the facial expression features. In the network architecture of LightExNet, firstly, deep and shallow features are fused in order to fully extract the shallow features in the original image, reduce the loss of information, alleviate the problem of gradient disappearance when the number of convolutional layers increases, and achieve the effect of multi-scale feature fusion. The MobileNet V2 architecture has also been streamlined to seamlessly integrate deep and shallow networks. Secondly, by combining the own characteristics of face expression features, a new channel and spatial attention mechanism is proposed to obtain the feature information of different expression regions as much as possible for encoding. Thus improve the accuracy of expression recognition effectively. Finally, the improved center loss function is superimposed to further improve the accuracy of face expression classification results, and corresponding measures are taken to significantly reduce the computational volume of the joint loss function. In this paper, LightExNet is tested on the three mainstream face expression datasets: Fer2013, CK+ and RAF-DB, respectively, and the experimental results show that LightExNet has 3.27 M Parameters and 298.27 M Flops, and the accuracy on the three datasets is 69.17%, 97.37%, and 85.97%, respectively. The comprehensive performance of LightExNet is better than the current mainstream lightweight expression recognition algorithms such as MobileNet V2, IE-DBN, Self-Cure Net, Improved MobileViT, MFN, Ada-CM, Parallel CNN(Convolutional Neural Network), etc. Experimental results confirm that LightExNet effectively improves recognition accuracy and computational efficiency while reducing energy consumption and enhancing deployment flexibility. These advantages underscore its strong potential for real-world applications in lightweight facial expression recognition. Full article

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14 pages, 1771 KiB

Open AccessArticle

An Adaptive Overcurrent Protection Method for Distribution Networks Based on Dynamic Multi-Objective Optimization Algorithm

by Biao Xu, Fan Ouyang, Yangyang Li, Kun Yu, Fei Ao, Hui Li and Liming Tan

Algorithms 2025, 18(8), 472; https://doi.org/10.3390/a18080472 - 28 Jul 2025

Abstract

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This [...] Read more.

With the large-scale integration of renewable energy into distribution networks, traditional fixed-setting overcurrent protection strategies struggle to adapt to rapid fluctuations in renewable energy (e.g., wind and photovoltaic) output. Optimizing current settings is crucial for enhancing the stability of modern distribution networks. This paper proposes an adaptive overcurrent protection method based on an improved NSGA-II algorithm. By dynamically detecting renewable power fluctuations and generating adaptive solutions, the method enables the online optimization of protection parameters, effectively reducing misoperation rates, shortening operation times, and significantly improving the reliability and resilience of distribution networks. Using the rate of renewable power variation as the core criterion, renewable power changes are categorized into abrupt and gradual scenarios. Depending on the scenario, either a random solution injection strategy (DNSGA-II-A) or a Gaussian mutation strategy (DNSGA-II-B) is dynamically applied to adjust overcurrent protection settings and time delays, ensuring real-time alignment with grid conditions. Hard constraints such as sensitivity, selectivity, and misoperation rate are embedded to guarantee compliance with relay protection standards. Additionally, the convergence of the Pareto front change rate serves as the termination condition, reducing computational redundancy and avoiding local optima. Simulation tests on a 10 kV distribution network integrated with a wind farm validate the effectiveness of the proposed method. Full article

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12 pages, 1066 KiB

Open AccessArticle

Prediction of the Maximum and Minimum Prices of Stocks in the Stock Market Using a Hybrid Model Based on Stacking

by Sebastian Tuesta, Nahum Flores and David Mauricio

Algorithms 2025, 18(8), 471; https://doi.org/10.3390/a18080471 - 28 Jul 2025

Abstract

Predicting stock prices on stock markets is challenging due to the nonlinear and nonstationary nature of financial markets. This study presents a hybrid model based on integrated machine learning (ML) techniques—neural networks, support vector regression (SVR), and decision trees—that uses the stacking method [...] Read more.

Predicting stock prices on stock markets is challenging due to the nonlinear and nonstationary nature of financial markets. This study presents a hybrid model based on integrated machine learning (ML) techniques—neural networks, support vector regression (SVR), and decision trees—that uses the stacking method to estimate the next day’s maximum and minimum stock prices. The model’s performance was evaluated using three data sets: Brazil’s São Paulo Stock Exchange (iBovespa)—Companhia Energética do Rio Grande do Norte (CSRN) and CPFL Energia (CPFE)—and one from the New York Stock Exchange (NYSE), the Dow Jones Industrial Average (DJI). The datasets covered the following time periods: CSRN and CPFE from 1 January 2008 to 30 September 2013, and DJI from 3 December 2018 to 31 August 2024. For the CSRN ensemble, the hybrid model achieved a mean absolute percentage error (MAPE) of 0.197% for maximum price and 0.224% for minimum price, outperforming results from the literature. For the CPFE set, the model showed a MAPE of 0.834% for the maximum price and 0.937% for the minimum price, demonstrating comparable accuracy. The model obtained a MAPE of 0.439% for the DJI set for maximum price and 0.474% for minimum price, evidencing its applicability across different market contexts. These results suggest that the proposed hybrid approach offers a robust alternative for stock price prediction by overcoming the limitations of using a single ML technique. Full article

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18 pages, 2100 KiB

Open AccessArticle

Hybrid ARIMA-ANN for Crime Risk Forecasting: Enhancing Interpretability and Predictive Accuracy Through Socioeconomic and Environmental Indicators

by Paul Iacobescu and Ioan Susnea

Algorithms 2025, 18(8), 470; https://doi.org/10.3390/a18080470 - 27 Jul 2025

Abstract

As the demand for more accurate crime prediction and risk assessment grows, researchers have been developing smarter models that blend statistical methods with machine learning. This study compares a hybrid ARIMA-ANN model with traditional classification techniques to see which best forecast monthly crime [...] Read more.

As the demand for more accurate crime prediction and risk assessment grows, researchers have been developing smarter models that blend statistical methods with machine learning. This study compares a hybrid ARIMA-ANN model with traditional classification techniques to see which best forecast monthly crime risk levels in Galați County, Romania. The analysis is based on a newly compiled dataset of 132 monthly observations from January 2014 to December 2024, which combines a broad array of social, economic, and environmental data points. The main variable, ‘Crime risk’, is based on normalized counts of offenses per capita and divided into five balanced levels: very low, low, moderate, high, and very high. The hybrid ARIMA-ANN model merges the strengths of statistical time series analysis with the flexible learning ability of artificial neural networks. Performance is evaluated against multinomial logistic regression, decision trees, random forests, and support vector machines. Overall, the results show that an ARIMA-ANN model consistently outperforms traditional methods, especially in recognizing patterns over time, seasonal trends, and complex nonlinear relationships in crime data. This study not only sets a new benchmark for crime analytics in Romania but also offers a flexible, scalable framework for classifying crime risk levels across different regions. Full article

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17 pages, 1545 KiB

Open AccessArticle

Delayed Star Subgradient Methods for Constrained Nondifferentiable Quasi-Convex Optimization

by Ontima Pankoon and Nimit Nimana

Algorithms 2025, 18(8), 469; https://doi.org/10.3390/a18080469 - 26 Jul 2025

Abstract

In this work, we consider the problem of minimizing a quasi-convex function over a nonempty closed convex constrained set. In order to approximate a solution of the considered problem, we propose delayed star subgradient methods. The main feature of the proposed methods is [...] Read more.

In this work, we consider the problem of minimizing a quasi-convex function over a nonempty closed convex constrained set. In order to approximate a solution of the considered problem, we propose delayed star subgradient methods. The main feature of the proposed methods is that it allows us to use the stale star subgradients when updating the next iteration rather than computing the new star subgradient in every iteration. We subsequently investigate the convergence results of sequences generated by the proposed methods. Finally, we present some numerical experiments on the Cobb–Douglas production efficiency problem to illustrate the effectiveness of the proposed method. Full article

(This article belongs to the Section Analysis of Algorithms and Complexity Theory)

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16 pages, 5703 KiB

Open AccessArticle

Document Image Shadow Removal Based on Illumination Correction Method

by Depeng Gao, Wenjie Liu, Shuxi Chen, Jianlin Qiu, Xiangxiang Mei and Bingshu Wang

Algorithms 2025, 18(8), 468; https://doi.org/10.3390/a18080468 - 26 Jul 2025

Abstract

Due to diverse lighting conditions and photo environments, shadows are almost ubiquitous in images, especially document images captured with mobile devices. Shadows not only seriously affect the visual quality and readability of a document but also significantly hinder image processing. Although shadow removal [...] Read more.

Due to diverse lighting conditions and photo environments, shadows are almost ubiquitous in images, especially document images captured with mobile devices. Shadows not only seriously affect the visual quality and readability of a document but also significantly hinder image processing. Although shadow removal research has achieved good results in natural scenes, specific studies on document images are lacking. To effectively remove shadows in document images, the dark illumination correction network is proposed, which mainly consists of two modules: shadow detection and illumination correction. First, a simplified shadow-corrected attention block is designed to combine spatial and channel attention, which is used to extract the features, detect the shadow mask, and correct the illumination. Then, the shadow detection block detects shadow intensity and outputs a soft shadow mask to determine the probability of each pixel belonging to shadow. Lastly, the illumination correction block corrects dark illumination with a soft shadow mask and outputs a shadow-free document image. Our experiments on five datasets show that the proposed method achieved state-of-the-art results, proving the effectiveness of illumination correction. Full article

(This article belongs to the Section Combinatorial Optimization, Graph, and Network Algorithms)

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34 pages, 2083 KiB

Open AccessArticle

EvoDevo: Bioinspired Generative Design via Evolutionary Graph-Based Development

by Farajollah Tahernezhad-Javazm, Andrew Colligan, Imelda Friel, Simon J. Hickinbotham, Paul Goodall, Edgar Buchanan, Mark Price, Trevor Robinson and Andy M. Tyrrell

Algorithms 2025, 18(8), 467; https://doi.org/10.3390/a18080467 - 26 Jul 2025

Abstract

Automated generative design is increasingly used across engineering disciplines to accelerate innovation and reduce costs. Generative design offers the prospect of simplifying manual design tasks by exploring the efficacy of solutions automatically. However, existing generative design frameworks rely heavily on expensive optimisation procedures [...] Read more.

Automated generative design is increasingly used across engineering disciplines to accelerate innovation and reduce costs. Generative design offers the prospect of simplifying manual design tasks by exploring the efficacy of solutions automatically. However, existing generative design frameworks rely heavily on expensive optimisation procedures and often produce customised solutions, lacking reusable generative rules that transfer across different problems. This work presents a bioinspired generative design algorithm utilising the concept of evolutionary development (EvoDevo). This evolves a set of developmental rules that can be applied to different engineering problems to rapidly develop designs without the need to run full optimisation procedures. In this approach, an initial design is decomposed into simple entities called cells, which independently control their local growth over a development cycle. In biology, the growth of cells is governed by a gene regulatory network (GRN), but there is no single widely accepted model for this in artificial systems. The GRN responds to the state of the cell induced by external stimuli in its environment, which, in this application, is the loading regime on a bridge truss structure (but can be generalised to any engineering structure). Two GRN models are investigated: graph neural network (GNN) and graph-based Cartesian genetic programming (CGP) models. Both GRN models are evolved using a novel genetic search algorithm for parameter search, which can be re-used for other design problems. It is revealed that the CGP-based method produces results similar to those obtained using the GNN-based methods while offering more interpretability. In this work, it is shown that this EvoDevo approach is able to produce near-optimal truss structures via growth mechanisms such as moving vertices or changing edge features. The technique can be set up to provide design automation for a range of engineering design tasks. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 2nd Edition)

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