Algorithms

30 pages, 2302 KiB

Open AccessReview

Early Detection of Alzheimer’s Disease Using Generative Models: A Review of GANs and Diffusion Models in Medical Imaging

by Md Minul Alam and Shahram Latifi

Algorithms 2025, 18(7), 434; https://doi.org/10.3390/a18070434 (registering DOI) - 15 Jul 2025

Abstract

Alzheimer’s disease (AD) is a progressive, non-curable neurodegenerative disorder that poses persistent challenges for early diagnosis due to its gradual onset and the difficulty in distinguishing pathological changes from normal aging. Neuroimaging, particularly MRI and PET, plays a key role in detection; however, [...] Read more.

Alzheimer’s disease (AD) is a progressive, non-curable neurodegenerative disorder that poses persistent challenges for early diagnosis due to its gradual onset and the difficulty in distinguishing pathological changes from normal aging. Neuroimaging, particularly MRI and PET, plays a key role in detection; however, limitations in data availability and the complexity of early structural biomarkers constrain traditional diagnostic approaches. This review investigates the use of generative models, specifically Generative Adversarial Networks (GANs) and Diffusion Models, as emerging tools to address these challenges. These models are capable of generating high-fidelity synthetic brain images, augmenting datasets, and enhancing machine learning performance in classification tasks. The review synthesizes findings across multiple studies, revealing that GAN-based models achieved diagnostic accuracies up to 99.70%, with image quality metrics such as SSIM reaching 0.943 and PSNR up to 33.35 dB. Diffusion Models, though relatively new, demonstrated strong performance with up to 92.3% accuracy and FID scores as low as 11.43. Integrating generative models with convolutional neural networks (CNNs) and multimodal inputs further improved diagnostic reliability. Despite these advancements, challenges remain, including high computational demands, limited interpretability, and ethical concerns regarding synthetic data. This review offers a comprehensive perspective to inform future AI-driven research in early AD detection. Full article

(This article belongs to the Special Issue Advancements in Signal Processing and Machine Learning for Healthcare)

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16 pages, 6915 KiB

Open AccessArticle

A Lightweight and Efficient Plant Disease Detection Method Integrating Knowledge Distillation and Dual-Scale Weighted Convolutions

by Xiong Yang, Hao Wang, Qi Zhou, Lei Lu, Lijuan Zhang, Changming Sun and Guilu Wu

Algorithms 2025, 18(7), 433; https://doi.org/10.3390/a18070433 (registering DOI) - 15 Jul 2025

Abstract

Plant diseases significantly undermine agricultural productivity. This study introduces an improved YOLOv10n model named WD-YOLO (Weighted and Double-scale YOLO), an advanced architecture for efficient plant disease detection. The PlantDoc dataset was initially enhanced using data augmentation techniques. Subsequently, we developed the DSConv module—a [...] Read more.

Plant diseases significantly undermine agricultural productivity. This study introduces an improved YOLOv10n model named WD-YOLO (Weighted and Double-scale YOLO), an advanced architecture for efficient plant disease detection. The PlantDoc dataset was initially enhanced using data augmentation techniques. Subsequently, we developed the DSConv module—a novel convolutional structure employing double-scale weighted convolutions that dynamically adjust to different scale perceptions and optimize attention allocation. This module replaces the conventional Conv module in YOLOv10. Furthermore, the WTConcat module was introduced, dynamically merging weighted concatenation with a channel attention mechanism to replace the Concat module in YOLOv10. The training of WD-YOLO incorporated knowledge distillation techniques using YOLOv10l as a teacher model to refine and compress the architectural learning. Empirical results reveal that WD-YOLO achieved an mAP50 of 65.4%, outperforming YOLOv10n by 9.1% without data augmentation and YOLOv10l by 2.3%, despite having significantly fewer parameters (9.3 times less than YOLOv10l), demonstrating substantial gains in detection efficiency and model compactness. Full article

(This article belongs to the Special Issue Algorithms for Feature Selection (3rd Edition))

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15 pages, 2117 KiB

Open AccessArticle

Clinical Validation of a Computed Tomography Image-Based Machine Learning Model for Segmentation and Quantification of Shoulder Muscles

by Hamidreza Rajabzadeh-Oghaz, Josie Elwell, Bradley Schoch, William Aibinder, Bruno Gobbato, Daniel Wessell, Vikas Kumar and Christopher P. Roche

Algorithms 2025, 18(7), 432; https://doi.org/10.3390/a18070432 (registering DOI) - 14 Jul 2025

Viewed by 46

Abstract

Introduction: We developed a computed tomography (CT)-based tool designed for automated segmentation of deltoid muscles, enabling quantification of radiomic features and muscle fatty infiltration. Prior to use in a clinical setting, this machine learning (ML)-based segmentation algorithm requires rigorous validation. The aim [...] Read more.

Introduction: We developed a computed tomography (CT)-based tool designed for automated segmentation of deltoid muscles, enabling quantification of radiomic features and muscle fatty infiltration. Prior to use in a clinical setting, this machine learning (ML)-based segmentation algorithm requires rigorous validation. The aim of this study is to conduct shoulder expert validation of a novel deltoid ML auto-segmentation and quantification tool. Materials and Methods: A SwinUnetR-based ML model trained on labeled CT scans is validated by three expert shoulder surgeons for 32 unique patients. The validation evaluates the quality of the auto-segmented deltoid images. Specifically, each of the three surgeons reviewed the auto-segmented masks relative to CT images, rated masks for clinical acceptance, and performed a correction on the ML-generated deltoid mask if the ML mask did not completely contain the full deltoid muscle, or if the ML mask included any tissue other than the deltoid. Non-inferiority of the ML model was assessed by comparing ML-generated to surgeon-corrected deltoid masks versus the inter-surgeon variation in metrics, such as volume and fatty infiltration. Results: The results of our expert shoulder surgeon validation demonstrates that 97% of ML-generated deltoid masks were clinically acceptable. Only two of the ML-generated deltoid masks required major corrections and only one was deemed clinically unacceptable. These corrections had little impact on the deltoid measurements, as the median error in the volume and fatty infiltration measurements was <1% between the ML-generated deltoid masks and the surgeon-corrected deltoid masks. The non-inferiority analysis demonstrates no significant difference between the ML-generated to surgeon-corrected masks relative to inter-surgeon variations. Conclusions: Shoulder expert validation of this CT image analysis tool demonstrates clinically acceptable performance for deltoid auto-segmentation, with no significant differences observed between deltoid image-based measurements derived from the ML generated masks and those corrected by surgeons. These findings suggest that this CT image analysis tool has potential to reliably quantify deltoid muscle size, shape, and quality. Incorporating these CT image-based measurements into the pre-operative planning process may facilitate more personalized treatment decision making, and help orthopedic surgeons make more evidence-based clinical decisions. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))

23 pages, 5245 KiB

Open AccessArticle

Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean

by Dandan Li, Shaojun Zheng, Chenyu Zheng, Lingling Xie and Li Yan

Algorithms 2025, 18(7), 431; https://doi.org/10.3390/a18070431 - 14 Jul 2025

Viewed by 56

Abstract

The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity [...] Read more.

The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ’s seasonal variations using the XGBoost machine learning algorithm. The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ’s seasonal variations, achieving coefficients of determination (R²) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. The proposed multi-feature integrated modeling framework delivers a high spatiotemporal resolution analytical tool for tropical Indian Ocean circulation dynamics research, while simultaneously establishing critical data infrastructure to decode monsoon current coupling mechanisms, advancing early warning systems for extreme climatic events, and optimizing regional marine resource governance. Full article

(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)

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19 pages, 2636 KiB

Open AccessArticle

Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global Trends

by Shima Veysi, Mohammad Moshfeghi, Amir Sadrfaridpour and Peiman Emamy

Algorithms 2025, 18(7), 430; https://doi.org/10.3390/a18070430 - 14 Jul 2025

Viewed by 68

Abstract

This study presents a comprehensive forecasting approach to evaluate the future of electric vehicle (EV) adoption in the United Kingdom through 2035. Using three complementary models—SARIMAX, Prophet with regressors, and XGBoost—the analysis balances statistical robustness, policy sensitivity, and interpretability. Historical data from 2015 [...] Read more.

This study presents a comprehensive forecasting approach to evaluate the future of electric vehicle (EV) adoption in the United Kingdom through 2035. Using three complementary models—SARIMAX, Prophet with regressors, and XGBoost—the analysis balances statistical robustness, policy sensitivity, and interpretability. Historical data from 2015 to 2024 was used to train the models, incorporating key drivers such as battery prices, GDP growth, public charging infrastructure, and government policy targets. XGBoost demonstrated the highest historical accuracy, making it a strong explanatory tool, particularly for assessing variable importance. However, due to its limitations in extrapolation, it was not used for long-term forecasting. Instead, Prophet and SARIMAX were employed to project EV sales under baseline, optimistic, and pessimistic policy scenarios. The results suggest that the UK could achieve between 2,964,000 and 3,188,000 EV sales by 2035 under baseline assumptions. Scenario analysis revealed high sensitivity to infrastructure growth and policy enforcement, with potential shortfalls of up to 500,000 vehicles in pessimistic scenarios. These findings highlight the importance of sustained government commitment and investment in EV infrastructure and supply chains. By combining machine learning diagnostics with transparent forecasting models, the study offers actionable insights for policymakers, investors, and stakeholders navigating the UK’s zero-emission transition. Full article

(This article belongs to the Collection Feature Papers in Evolutionary Algorithms and Machine Learning)

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34 pages, 924 KiB

Open AccessSystematic Review

Smart Microgrid Management and Optimization: A Systematic Review Towards the Proposal of Smart Management Models

by Paul Arévalo, Dario Benavides, Danny Ochoa-Correa, Alberto Ríos, David Torres and Carlos W. Villanueva-Machado

Algorithms 2025, 18(7), 429; https://doi.org/10.3390/a18070429 - 11 Jul 2025

Viewed by 250

Abstract

The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids. This systematic review, following the PRISMA 2020 methodology, analyzed 66 studies focused on advanced energy storage systems, intelligent control strategies, [...] Read more.

The increasing integration of renewable energy sources (RES) in power systems presents challenges related to variability, stability, and efficiency, particularly in smart microgrids. This systematic review, following the PRISMA 2020 methodology, analyzed 66 studies focused on advanced energy storage systems, intelligent control strategies, and optimization techniques. Hybrid storage solutions combining battery systems, hydrogen technologies, and pumped hydro storage were identified as effective approaches to mitigate RES intermittency and balance short- and long-term energy demands. The transition from centralized to distributed control architectures, supported by predictive analytics, digital twins, and AI-based forecasting, has improved operational planning and system monitoring. However, challenges remain regarding interoperability, data privacy, cybersecurity, and the limited availability of high-quality data for AI model training. Economic analyses show that while initial investments are high, long-term operational savings and improved resilience justify the adoption of advanced microgrid solutions when supported by appropriate policies and financial mechanisms. Future research should address the standardization of communication protocols, development of explainable AI models, and creation of sustainable business models to enhance resilience, efficiency, and scalability. These efforts are necessary to accelerate the deployment of decentralized, low-carbon energy systems capable of meeting future energy demands under increasingly complex operational conditions. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))

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21 pages, 21492 KiB

Open AccessArticle

SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n

by Yue Fang, Chenbo Yang, Jie Li and Jingmin Tu

Algorithms 2025, 18(7), 428; https://doi.org/10.3390/a18070428 - 11 Jul 2025

Viewed by 232

Abstract

The flowering stage is a critical phase in the growth of rapeseed crops, and non-destructive, high-throughput quantitative analysis of rape flower clusters in field environments holds significant importance for rapeseed breeding. However, detecting and counting rape flower clusters remains challenging in complex field [...] Read more.

The flowering stage is a critical phase in the growth of rapeseed crops, and non-destructive, high-throughput quantitative analysis of rape flower clusters in field environments holds significant importance for rapeseed breeding. However, detecting and counting rape flower clusters remains challenging in complex field conditions due to their small size, severe overlapping and occlusion, and the large parameter sizes of existing models. To address these challenges, this study proposes a lightweight rape flower clusters detection model, SPL-YOLOv8. First, the model introduces StarNet as a lightweight backbone network for efficient feature extraction, significantly reducing computational complexity and parameter counts. Second, a feature fusion module (C2f-Star) is integrated into the backbone to enhance the feature representation capability of the neck through expanded spatial dimensions, mitigating the impact of occluded regions on detection performance. Additionally, a lightweight Partial Group Convolution Detection Head (PGCD) is proposed, which employs Partial Convolution combined with Group Normalization to enable multi-scale feature interaction. By incorporating additional learnable parameters, the PGCD enhances the detection and localization of small targets. Finally, channel pruning based on the Layer-Adaptive Magnitude-based Pruning (LAMP) score is applied to reduce model parameters and runtime memory. Experimental results on the Rapeseed Flower-Raceme Benchmark (RFRB) demonstrate that the SPL-YOLOv8n-prune model achieves a detection accuracy of 92.2% in Average Precision (AP₅₀), comparable to SOTA methods, while reducing the giga floating point operations per second (GFLOPs) and parameters by 86.4% and 95.4%, respectively. The model size is only 0.5 MB and the real-time frame rate is 171 fps. The proposed model effectively detects rape flower clusters with minimal computational overhead, offering technical support for yield prediction and elite cultivar selection in rapeseed breeding. Full article

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

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18 pages, 1871 KiB

Open AccessArticle

Interpretable Reinforcement Learning for Sequential Strategy Prediction in Language-Based Games

by Jun Zhao, Jintian Ji, Robail Yasrab, Shuxin Wang, Liang Yu and Lingzhen Zhao

Algorithms 2025, 18(7), 427; https://doi.org/10.3390/a18070427 - 11 Jul 2025

Viewed by 212

Abstract

Accurate and interpretable prediction plays a vital role in natural language processing (NLP) tasks, particularly for enhancing user trust and model transparency. However, existing models often struggle with poor adaptability and limited interpretability when applied to dynamic language prediction tasks such as Wordle [...] Read more.

Accurate and interpretable prediction plays a vital role in natural language processing (NLP) tasks, particularly for enhancing user trust and model transparency. However, existing models often struggle with poor adaptability and limited interpretability when applied to dynamic language prediction tasks such as Wordle. To address these challenges, this study proposes an interpretable reinforcement learning framework based on an Enhanced Deep Deterministic Policy Gradient (Enhanced-DDPG) algorithm. By leveraging a custom simulation environment and integrating key linguistic features word frequency, letter frequency, and repeated letter patterns (rep) the model dynamically predicts the number of attempts needed to solve Wordle puzzles. Experimental results demonstrate that Enhanced-DDPG outperforms traditional methods such as Random Forest Regression (RFR), XGBoost, LightGBM, METRA, and SQIRL in terms of both prediction accuracy (MSE = 0.0134, R² = 0.8439) and robustness under noisy conditions. Furthermore, SHapley Additive exPlanations (SHAP) are employed to interpret the model’s decision process, revealing that repeated letter patterns significantly influence low-attempt predictions, while word and letter frequencies are more relevant for higher attempt scenarios. This research highlights the potential of combining interpretable artificial intelligence (I-AI) and reinforcement learning to develop robust, transparent, and high-performance NLP prediction systems for real-world applications. Full article

(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)

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26 pages, 2178 KiB

Open AccessArticle

Cross-Modal Fake News Detection Method Based on Multi-Level Fusion Without Evidence

by Ping He, Hanxue Zhang, Shufu Cao and Yali Wu

Algorithms 2025, 18(7), 426; https://doi.org/10.3390/a18070426 - 10 Jul 2025

Viewed by 187

Abstract

Although multimodal feature fusion technology in fake news detection can integrate complementary information from different modal data, the semantic inconsistency of multimodal features will lead to feature fusion difficulties. And there is the problem of information loss during one fusion process. In addition, [...] Read more.

Although multimodal feature fusion technology in fake news detection can integrate complementary information from different modal data, the semantic inconsistency of multimodal features will lead to feature fusion difficulties. And there is the problem of information loss during one fusion process. In addition, although it is possible to improve the detection effect by increasing the support of external evidence in fake news detection, there is a lag in obtaining external evidence and the reliability and completeness of the evidence source is difficult to guarantee. Additional noise may be introduced to interfere with the model judgment. Therefore, a cross-modal fake news detection method (CM-MLF) based on evidence-free multilevel fusion is proposed. The method solves the semantic inconsistency problem by utilizing cross-modal alignment processing. And it utilizes the attention mechanism to perform multilevel fusion of text and image features without the assistance of other evidential features to further enhance the expressive power of the features. Experiments show that the method achieves better detection results on multiple benchmark datasets, effectively improving the accuracy and robustness of cross-modal fake news detection. Full article

(This article belongs to the Special Issue Algorithms for Feature Selection (3rd Edition))

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25 pages, 1885 KiB

Open AccessArticle

Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines

by Robert Rosenthal, Nils Albersmann and Mohieddine Jelali

Algorithms 2025, 18(7), 425; https://doi.org/10.3390/a18070425 - 9 Jul 2025

Viewed by 140

Abstract

To ensure the product quality of a steel slab continuous casting machine, the mechanical alignment of the guide rolls must be monitored and corrected regularly. Misaligned guide rolls cause stress and strain in the partially solidified steel strand, leading to internal cracks and [...] Read more.

To ensure the product quality of a steel slab continuous casting machine, the mechanical alignment of the guide rolls must be monitored and corrected regularly. Misaligned guide rolls cause stress and strain in the partially solidified steel strand, leading to internal cracks and other quality issues. Current methods of alignment measurement are either not suited for regular maintenance or provide only indirect alignment information in the form of angle measurements. This paper presents three new algorithms that convert the available angle measurements into the absolute position of each guide roll, which is equivalent to the mechanical alignment. The algorithms are based on geometry and trigonometry or the gradient descent optimization algorithm. Under near ideal conditions, all algorithms yield very accurate position results. However, when tested and evaluated under various conditions, their susceptibility to real-world disturbances is revealed. Here, only the optimization-based algorithm reaches the desired accuracy. Under the influence of randomly distributed angle measurement errors with an amplitude of 0.01°, it is able to determine 90% of roll positions within

0.1

mm of their actual position. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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25 pages, 875 KiB

Open AccessArticle

Filter Learning-Based Partial Least Squares Regression and Its Application in Infrared Spectral Analysis

by Yi Mou, Long Zhou, Weizhen Chen, Jianguo Liu and Teng Li

Algorithms 2025, 18(7), 424; https://doi.org/10.3390/a18070424 - 9 Jul 2025

Viewed by 161

Abstract

Partial Least Squares (PLS) regression has been widely used to model the relationship between predictors and responses. However, PLS may be limited in its capacity to handle complex spectral data contaminated with significant noise and interferences. In this paper, we propose a novel [...] Read more.

Partial Least Squares (PLS) regression has been widely used to model the relationship between predictors and responses. However, PLS may be limited in its capacity to handle complex spectral data contaminated with significant noise and interferences. In this paper, we propose a novel filter learning-based PLS (FPLS) model that integrates an adaptive filter into the PLS framework. The FPLS model is designed to maximize the covariance between the filtered spectral data and the response. This modification enables FPLS to dynamically adapt to the characteristics of the data, thereby enhancing its feature extraction and noise suppression capabilities. We have developed an efficient algorithm to solve the FPLS optimization problem and provided theoretical analyses regarding the convergence of the model, the prediction variance, and the relationships among the objective functions of FPLS, PLS, and the filter length. Furthermore, we have derived bounds for the Root Mean Squared Error of Prediction (RMSEP) and the Cosine Similarity (CS) to evaluate model performance. Experimental results using spectral datasets from Corn, Octane, Mango, and Soil Nitrogen show that the FPLS model outperforms PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso in terms of prediction accuracy. The theoretical analyses align with the experimental results, emphasizing the effectiveness and robustness of the FPLS model in managing complex spectral data. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity, Second Edition)

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20 pages, 1353 KiB

Open AccessArticle

Dynamic Modeling and Validation of Peak Ability of Biomass Units

by Dawei Xia, Guizhou Cao, Jiayao Pan, Xinghai Wang, Kai Meng, Yuancheng Sun and Zhenlong Wu

Algorithms 2025, 18(7), 423; https://doi.org/10.3390/a18070423 - 9 Jul 2025

Viewed by 154

Abstract

Biomass units can play a certain role in peak summer and winter due to their advantages in terms of their environmental and short-term peak ability. To analyze the peak ability of biomass units, this paper focuses on the dynamic modeling of biomass unit [...] Read more.

Biomass units can play a certain role in peak summer and winter due to their advantages in terms of their environmental and short-term peak ability. To analyze the peak ability of biomass units, this paper focuses on the dynamic modeling of biomass unit peak ability. Firstly, the process of biomass feeding amount to power output is divided into a feed–heat module, heat–main steam pressure module and main steam pressure–power module. A two-input and two-output dynamic model is established where the feeding amount and turbine valve opening serve as inputs, and the main steam pressure and power serve as outputs. Then the effectiveness of the established model is validated by actual operation data of a 30 MW biomass unit. This dynamic model can provide a mechanistic model for analyzing the impact of fuel calorific value on the power output, and provide support for fuel management and scheduling strategies during the peak period of biomass units. Full article

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

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18 pages, 580 KiB

Open AccessArticle

Feature Transformation-Based Few-Shot Class-Incremental Learning

by Xubo Zhang and Yang Luo

Algorithms 2025, 18(7), 422; https://doi.org/10.3390/a18070422 - 9 Jul 2025

Viewed by 246

Abstract

In the process of few-shot class-incremental learning, the limited number of samples for newly introduced classes makes it difficult to adequately adapt model parameters, resulting in poor feature representations for these classes. To address this issue, this paper proposes a feature transformation method [...] Read more.

In the process of few-shot class-incremental learning, the limited number of samples for newly introduced classes makes it difficult to adequately adapt model parameters, resulting in poor feature representations for these classes. To address this issue, this paper proposes a feature transformation method that mitigates feature degradation in few-shot incremental learning. The transformed features better align with the ideal feature distribution required by an optimal classifier, thereby alleviating performance decline during incremental updates. Before classification, the method learns a well-conditioned linear mapping from the available base classes. After classification, both class prototypes and query samples are projected into the transformed feature space to improve the overall feature distribution. Experimental results on three benchmark datasets demonstrate that the proposed method achieves strong performance: it reduces performance degradation to 24.85 percentage points on miniImageNet, 24.45 on CIFAR100, and 24.14 on CUB, consistently outperforming traditional methods such as iCaRL (44.13–50.71 points degradation) and recent techniques like FeTrIL and PL-FSCIL. Further analysis shows that the transformed features bring class prototypes significantly closer to the theoretically optimal equiangular configuration described by neural collapse, highlighting the effectiveness of the proposed approach. Full article

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17 pages, 1101 KiB

Open AccessArticle

Ship Scheduling Algorithm Based on Markov-Modulated Fluid Priority Queues

by Jianzhi Deng, Shuilian Lv, Yun Li, Liping Luo, Yishan Su, Xiaolin Wang and Xinzhi Liu

Algorithms 2025, 18(7), 421; https://doi.org/10.3390/a18070421 - 8 Jul 2025

Viewed by 134

Abstract

As a key node in port logistics systems, ship anchorage is often faced with congestion caused by ship flow fluctuations, multi-priority scheduling imbalances and the poor adaptability of scheduling models to complex environments. To solve the above problems, this paper constructs a ship [...] Read more.

As a key node in port logistics systems, ship anchorage is often faced with congestion caused by ship flow fluctuations, multi-priority scheduling imbalances and the poor adaptability of scheduling models to complex environments. To solve the above problems, this paper constructs a ship scheduling algorithm based on a Markov-modulated fluid priority queue, which describes the stochastic evolution of the anchorage operation state via a continuous-time Markov chain and abstracts the arrival and service processes of ships into a continuous fluid input and output mechanism modulated by the state. The algorithm introduces a multi-priority service strategy to achieve the differentiated scheduling of different types of ships and improves the computational efficiency and scalability based on a matrix analysis method. Simulation results show that the proposed model reduces the average waiting time of ships by more than 90% compared with the M/G/1/1 and RL strategies and improves the utilization of anchorage resources by about 20% through dynamic service rate adjustment, showing significant advantages over traditional scheduling methods in multi-priority scenarios. Full article

(This article belongs to the Topic Optimization Control and Fault Diagnosis of Intelligent Transportation Systems)

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21 pages, 561 KiB

Open AccessArticle

Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example

by Yahya Masri, Zifu Wang, Anusha Srirenganathan Malarvizhi, Samir Ahmed, Tayven Stover, David W. S. Wong, Yongyao Jiang, Yun Li, Qian Liu, Mathieu Bere, Daniel Rothbart, Dieter Pfoser and Chaowei Yang

Algorithms 2025, 18(7), 420; https://doi.org/10.3390/a18070420 - 8 Jul 2025

Viewed by 296

Abstract

To obtain actionable information for humanitarian and other emergency responses, an accurate classification of news or events is critical. Daily news and social media are hard to classify based on conveyed information, especially when multiple categories of information are embedded. This research used [...] Read more.

To obtain actionable information for humanitarian and other emergency responses, an accurate classification of news or events is critical. Daily news and social media are hard to classify based on conveyed information, especially when multiple categories of information are embedded. This research used large language models (LLMs) and traditional transformer-based models, such as BERT, to classify news and social media events using the example of the Sudan Conflict. A systematic evaluation framework was introduced to test GPT models using Zero-Shot prompting, Retrieval-Augmented Generation (RAG), and RAG with In-Context Learning (ICL) against standard and hyperparameter-tuned bert-based and bert-large models. BERT outperformed GPT in F1-score and accuracy for multi-label classification (MLC) while GPT outperformed BERT in accuracy for Single-Label classification from Multi-Label Ground Truth (SL-MLG). The results illustrate that a larger model size improves classification accuracy for both BERT and GPT, while BERT benefits from hyperparameter tuning and GPT benefits from its enhanced contextual comprehension capabilities. By addressing challenges such as overlapping semantic categories, task-specific adaptation, and a limited dataset, this study provides a deeper understanding of LLMs’ applicability in constrained, real-world scenarios, particularly in highlighting the potential for integrating NLP with other applications such as GIS in future conflict analyses. Full article

(This article belongs to the Special Issue Evolution of Algorithms in the Era of Generative AI)

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25 pages, 1579 KiB

Open AccessSystematic Review

Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review

by Nikoletta-Anna Kapogianni, Angeliki Sideraki and Christos-Nikolaos Anagnostopoulos

Algorithms 2025, 18(7), 419; https://doi.org/10.3390/a18070419 - 8 Jul 2025

Viewed by 403

Abstract

This systematic review explores the role of smartwatches in stress management, mental health monitoring, and overall well-being. Drawing from 61 peer-reviewed studies published between 2016 and 2025, this review synthesizes empirical findings across diverse methodologies, including biometric data collection, machine learning algorithms, and [...] Read more.

This systematic review explores the role of smartwatches in stress management, mental health monitoring, and overall well-being. Drawing from 61 peer-reviewed studies published between 2016 and 2025, this review synthesizes empirical findings across diverse methodologies, including biometric data collection, machine learning algorithms, and user-centered design evaluations. Smartwatches, equipped with sensors for physiological signals such as heart rate, heart rate variability, electrodermal activity, and skin temperature, have demonstrated promise in detecting and predicting stress and mood fluctuations in both clinical and everyday contexts. This review emphasizes the need for interdisciplinary collaboration to advance technological precision, ethical data handling, and user experience design. Moreover, it highlights how different algorithms—such as Support Vector Machines (SVMs), Random Forests, Deep Neural Networks, and Boosting methods—perform across various physiological signals (e.g., HRV, EDA, skin temperature). Furthermore, it identifies performance trends and challenges across lab-based vs. real-world deployments, emphasizing the trade-off between generalizability and personalization in model design. Full article

(This article belongs to the Special Issue Algorithms for Smart Cities (2nd Edition))

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30 pages, 3108 KiB

Open AccessArticle

Research on the Integrated Scheduling of Imaging and Data Transmission for Earth Observation Satellites

by Guanfei Yu and Kunlun Zhang

Algorithms 2025, 18(7), 418; https://doi.org/10.3390/a18070418 - 8 Jul 2025

Viewed by 187

Abstract

This study focuses on the integrated scheduling issues of imaging and data transmission for Earth observation satellites, where each target needs to be imaged and transmitted within a feasible time window. The scheduling process also takes into account the constraints of satellite energy [...] Read more.

This study focuses on the integrated scheduling issues of imaging and data transmission for Earth observation satellites, where each target needs to be imaged and transmitted within a feasible time window. The scheduling process also takes into account the constraints of satellite energy and storage capacity. In this paper, a mixed-integer linear programming (MILP) model for the integrated scheduling of imaging data transmission has been proposed. The MILP model was validated through numerical experiments based on simulation data from SuperView-1 series satellites. Additionally, some neighborhood mechanisms are designed based on the characteristics of the problem. Based on the neighborhood mechanisms, the rule-based large neighborhood search algorithm (RLNS) was designed, which constructs initial solutions through various scheduling rules and iteratively optimizes the solutions using multiple destroying and repairing operators. To address the shortcomings of the overly regular mechanism of the destruction and repair operator for large neighborhood search, we design a genetic algorithms (GA) for tuning the heuristic scheduling rules. The calculation results demonstrate the effectiveness of RLNS and GA, highlighting their advantages over CPLEX in solving large-scale problems. Full article

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

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10 pages, 757 KiB

Open AccessArticle

Environmental Sensitivity in AI Tree Bark Detection: Identifying Key Factors for Improving Classification Accuracy

by Charles Warner, Fanyou Wu, Rado Gazo, Bedrich Benes and Songlin Fei

Algorithms 2025, 18(7), 417; https://doi.org/10.3390/a18070417 - 8 Jul 2025

Viewed by 191

Abstract

Accurate tree species identification through bark characteristics is essential for effective forest management, but traditionally requires extensive expertise. This study leverages artificial intelligence (AI), specifically the EfficientNet-B3 convolutional neural network, to enhance AI-based tree bark identification, focusing on northern red oak (Quercus [...] Read more.

Accurate tree species identification through bark characteristics is essential for effective forest management, but traditionally requires extensive expertise. This study leverages artificial intelligence (AI), specifically the EfficientNet-B3 convolutional neural network, to enhance AI-based tree bark identification, focusing on northern red oak (Quercus rubra), hackberry (Celtis occidentalis), and bitternut hickory (Carya cordiformis) using the CentralBark dataset. We investigated three environmental variables—time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation—to identify sources of classification inaccuracies. Results revealed that bark moisture significantly reduced accuracy by 8.19% in wet conditions (89.32% dry vs. 81.13% wet). In comparison, the time of day had a significant impact on hackberry (95.56% evening) and northern red oak (80.80% afternoon), with notable chi-squared associations (p < 0.05). Cardinal direction had minimal effect (4.72% variation). Bitternut hickory detection consistently underperformed (26.76%), highlighting morphological challenges. These findings underscore the need for targeted dataset augmentation with wet and afternoon images, alongside preprocessing techniques like illumination normalization, to improve model robustness. Enhanced AI tools will streamline forest inventories, support biodiversity monitoring, and bolster conservation in dynamic forest ecosystems. Full article

(This article belongs to the Special Issue Machine Learning Models and Algorithms for Image Processing)

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23 pages, 3338 KiB

Open AccessArticle

European Efficiency Schemes for Domestic Gas Boilers: Estimation of Savings in Heating of Settlements

by Dejan Brkić

Algorithms 2025, 18(7), 416; https://doi.org/10.3390/a18070416 - 6 Jul 2025

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Abstract

This article aims to evaluate the seasonal efficiency of natural gas boilers used in European households, highlighting the cost effectiveness, environmental benefits, and user comfort associated with higher-efficiency models, particularly those based on condensing technology. The study applies a standardized algorithm used in [...] Read more.

This article aims to evaluate the seasonal efficiency of natural gas boilers used in European households, highlighting the cost effectiveness, environmental benefits, and user comfort associated with higher-efficiency models, particularly those based on condensing technology. The study applies a standardized algorithm used in European energy labeling schemes to calculate the seasonal efficiency of household gas boilers. It further includes a comparative analysis of selected boiler models available on the Serbian market and outlines a step-by-step method for estimating gas savings when replacing older, less efficient boilers with modern units. Condensing boilers demonstrate significantly higher seasonal efficiency than standard models by recovering additional heat from exhaust gases. These improved boilers produce lower greenhouse gas emissions and offer annual fuel savings of approximately 10% to 30%, depending on the boiler’s age, system design, and usage patterns. The results also confirm the direct correlation between seasonal efficiency and annual fuel consumption, validating the use of efficiency-based cost comparisons. The analysis focuses on residential gas boilers available in the Serbian market, although the models examined are commonly distributed across Europe. The findings highlight the important role of energy efficiency labels—based on a standardized algorithm—in guiding boiler selection, helping consumers and policymakers make informed decisions that promote energy savings and reduce environmental impact. This article contributes to the theoretical and practical understanding of gas boiler efficiency by integrating algorithm-based evaluation with market data and user-centered considerations. It offers actionable insights for consumers, energy advisors, and policymakers in the context of Europe’s energy transition. Verifying the efficiency calculations of gas boilers requires a careful combination of theoretical methods, measured data, and adherence to standards. Full article

(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour (3rd Edition))

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21 pages, 5436 KiB

Open AccessArticle

Engine Optimization Model for Accurate Prediction of Friction Model in Marine Dual-Fuel Engine

by Mina Tadros

Algorithms 2025, 18(7), 415; https://doi.org/10.3390/a18070415 - 4 Jul 2025

Viewed by 279

Abstract

This paper presents an innovative engine optimization model integrated with a friction fitting tool to enhance the accuracy of computed performance for a marine dual-fuel engine. The focus is on determining the terms of the Chen–Flynn correlation—an empirical engine friction model—to improve the [...] Read more.

This paper presents an innovative engine optimization model integrated with a friction fitting tool to enhance the accuracy of computed performance for a marine dual-fuel engine. The focus is on determining the terms of the Chen–Flynn correlation—an empirical engine friction model—to improve the precision of friction and performance predictions. The developed model employs WAVE, a 1D engine simulation software, coupled with a nonlinear optimizer to identify the optimal configuration of key parameters, including the turbocharger, injection system, combustion behavior, and friction model. The optimization procedure maximizes the air–fuel ratio (AFR) within the engine while adhering to various predefined constraints. The model is applied to four operational points along the propeller curve, with the optimized results subsequently integrated into a friction fitting tool. This tool predicts the terms of the Chen–Flynn correlation through an updated procedure, achieving highly accurate results with a coefficient of determination (R²) value of 99.88%, eliminating the need for experimental testing. The optimized friction model provides a reliable foundation for future studies and applications, enabling precise friction predictions across various engine types and fuel compositions. 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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21 pages, 1097 KiB

Open AccessArticle

An Industry Application of Secure Augmentation and Gen-AI for Transforming Engineering Design and Manufacturing

by Dulana Rupanetti, Corissa Uberecken, Adam King, Hassan Salamy, Cheol-Hong Min and Samantha Schmidgall

Algorithms 2025, 18(7), 414; https://doi.org/10.3390/a18070414 - 4 Jul 2025

Viewed by 320

Abstract

This paper explores the integration of Large Language Models (LLMs) and secure Gen-AI technologies within engineering design and manufacturing, with a focus on improving inventory management, component selection, and recommendation workflows. The system is intended for deployment and evaluation in a real-world industrial [...] Read more.

This paper explores the integration of Large Language Models (LLMs) and secure Gen-AI technologies within engineering design and manufacturing, with a focus on improving inventory management, component selection, and recommendation workflows. The system is intended for deployment and evaluation in a real-world industrial environment. It utilizes vector embeddings, vector databases, and Approximate Nearest Neighbor (ANN) search algorithms to implement Retrieval-Augmented Generation (RAG), enabling context-aware searches for inventory items and addressing the limitations of traditional text-based methods. Built on an LLM framework enhanced by RAG, the system performs similarity-based retrieval and part recommendations while preserving data privacy through selective obfuscation using the ROT13 algorithm. In collaboration with an industry sponsor, real-world testing demonstrated strong results: 88.4% for Answer Relevance, 92.1% for Faithfulness, 80.2% for Context Recall, and 83.1% for Context Precision. These results demonstrate the system’s ability to deliver accurate and relevant responses while retrieving meaningful context and minimizing irrelevant information. Overall, the approach presents a practical and privacy-aware solution for manufacturing, bridging the gap between traditional inventory tools and modern AI capabilities and enabling more intelligent workflows in design and production processes. Full article

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23 pages, 6016 KiB

Open AccessArticle

Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network

by Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang and Jinjuan Feng

Algorithms 2025, 18(7), 413; https://doi.org/10.3390/a18070413 - 4 Jul 2025

Viewed by 401

Abstract

The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer [...] Read more.

The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was evaluated on a SARS-CoV-2 CT scan dataset, demonstrating superior performance across all metrics compared to baseline models. The model achieved an accuracy of 95.98%, precision of 96.07%, recall of 96.01%, F1-score of 95.98%, and AUC of 98.69%, outperforming existing approaches. These results indicate that ViTGNN is an effective diagnostic tool that can be applied beyond COVID-19 detection to other medical imaging tasks. Full article

(This article belongs to the Special Issue Algorithms and Applications of Machine Learning Techniques for Healthcare)

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17 pages, 299 KiB

Open AccessArticle

Analysis of Reliability and Efficiency of Information Extraction Using AI-Based Chatbot: The More-for-Less Paradox

by Eugene Levner and Boris Kriheli

Algorithms 2025, 18(7), 412; https://doi.org/10.3390/a18070412 - 3 Jul 2025

Viewed by 187

Abstract

This paper addresses the problem of information extraction using an AI-powered chatbot. The problem concerns searching and extracting relevant information from large databases in response to a human user’s query. Expanding the traditional discrete search problem well known in operations research, this problem [...] Read more.

This paper addresses the problem of information extraction using an AI-powered chatbot. The problem concerns searching and extracting relevant information from large databases in response to a human user’s query. Expanding the traditional discrete search problem well known in operations research, this problem introduces two players; the first player—an AI chatbot such as ChatGPT 4.0—sequentially scans available datasets to find an appropriate answer to a given query, while the second—a human user—conducts a dialogue with the chatbot and evaluates its answers in each round of the dialogue. The goal of an AI-powered chatbot is to provide maximally useful and accurate information. During a natural language conversation between a human user and an AI, the human user can modify and refine queries until s/he is satisfied with the chatbot’s output. We analyze two key characteristics of human–AI interaction: search reliability and efficiency. Search reliability is defined as the ability of a robot to understand user queries and provide correct answers; it is measured by the frequency (probability) of correct answers. Search efficiency of a chatbot indicates how accurate and relevant the information returned by the chatbot is; it is measured by the satisfaction level a human user receives for a correct answer. An AI chatbot must perform a sequence of scans over the given databases and continue searching until the human user declares, in some round, that the target has been found. Assuming that the chatbot is not completely reliable, each database may have to be scanned infinitely often; in this case, the objective of the problem is to determine a search policy for finding the optimal sequence of chatbot scans that maximizes the expected user satisfaction over an infinite time horizon. Along with these results, we found a counterintuitive relationship between AI chatbot reliability and search performance: under sufficiently general conditions, a less reliable AI chatbot may have higher expected search efficiency; this phenomenon aligns with other well-known “more-for-less” paradoxes. Finally, we discussed the underlying mechanism of this paradox. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity)

23 pages, 1678 KiB

Open AccessArticle

Development of Digital Training Twins in the Aircraft Maintenance Ecosystem

by Igor Kabashkin

Algorithms 2025, 18(7), 411; https://doi.org/10.3390/a18070411 - 3 Jul 2025

Viewed by 248

Abstract

This paper presents an integrated digital training twin framework for adaptive aircraft maintenance education, combining real-time competence modeling, algorithmic orchestration, and cloud–edge deployment architectures. The proposed system dynamically evaluates learner skill gaps and assigns individualized training resources through a multi-objective optimization function that [...] Read more.

This paper presents an integrated digital training twin framework for adaptive aircraft maintenance education, combining real-time competence modeling, algorithmic orchestration, and cloud–edge deployment architectures. The proposed system dynamically evaluates learner skill gaps and assigns individualized training resources through a multi-objective optimization function that balances skill alignment, Bloom’s cognitive level, fidelity tier, and time efficiency. A modular orchestration engine incorporates reinforcement learning agents for policy refinement, federated learning for privacy-preserving skill analytics, and knowledge graph-based curriculum models for dependency management. Simulation results were conducted on the Pneumatic Systems training module. The system’s validation matrix provides full-cycle traceability of instructional decisions, supporting regulatory audit-readiness and institutional reporting. The digital training twin ecosystem offers a scalable, regulation-compliant, and data-driven solution for next-generation aviation maintenance training, with demonstrated operational efficiency, instructional precision, and extensibility for future expansion. Full article

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23 pages, 787 KiB

Open AccessArticle

Integrating Machine Learning Techniques and the Theory of Planned Behavior to Assess the Drivers of and Barriers to the Use of Generative Artificial Intelligence: Evidence in Spain

by Antonio Pérez-Portabella, Jorge de Andrés-Sánchez, Mario Arias-Oliva and Mar Souto-Romero

Algorithms 2025, 18(7), 410; https://doi.org/10.3390/a18070410 - 3 Jul 2025

Viewed by 199

Abstract

Generative artificial intelligence (GAI) is emerging as a disruptive force, both economically and socially, with its use spanning from the provision of goods and services to everyday activities such as healthcare and household management. This study analyzes the enabling and inhibiting factors of [...] Read more.

Generative artificial intelligence (GAI) is emerging as a disruptive force, both economically and socially, with its use spanning from the provision of goods and services to everyday activities such as healthcare and household management. This study analyzes the enabling and inhibiting factors of GAI use in Spain based on a large-scale survey conducted by the Spanish Center for Sociological Research on the use and perception of artificial intelligence. The proposed model is based on the Theory of Planned Behavior and is fitted using machine learning techniques, specifically decision trees, Random Forest extensions, and extreme gradient boosting. While decision trees allow for detailed visualization of how variables interact to explain usage, Random Forest provides an excellent model fit (R² close to 95%) and predictive performance. The use of Shapley Additive Explanations reveals that knowledge about artificial intelligence, followed by innovation orientation, is the main explanatory variable of GAI use. Among sociodemographic variables, Generation X and Z stood out as the most relevant. It is also noteworthy that the perceived privacy risk does not show a clear inhibitory influence on usage. Factors representing the positive consequences of GAI, such as performance expectancy and social utility, exert a stronger influence than the negative impact of hindering factors such as perceived privacy or social risks. Full article

(This article belongs to the Special Issue Evolution of Algorithms in the Era of Generative AI)

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22 pages, 2789 KiB

Open AccessArticle

Longitudinal Tire Force Estimation Method for 4WIDEV Based on Data-Driven Modified Recursive Subspace Identification Algorithm

by Xiaoyu Wang, Te Chen and Jiankang Lu

Algorithms 2025, 18(7), 409; https://doi.org/10.3390/a18070409 - 3 Jul 2025

Viewed by 253

Abstract

For the longitudinal tire force estimation problem of four-wheel independent drive electric vehicles (4WIDEVs), traditional model-based observers have limitations such as high modeling complexity and strong parameter sensitivity, while pure data-driven methods are susceptible to noise interference and have insufficient generalization ability. Therefore, [...] Read more.

For the longitudinal tire force estimation problem of four-wheel independent drive electric vehicles (4WIDEVs), traditional model-based observers have limitations such as high modeling complexity and strong parameter sensitivity, while pure data-driven methods are susceptible to noise interference and have insufficient generalization ability. Therefore, this study proposes a joint estimation framework that integrates data-driven and modified recursive subspace identification algorithms. Firstly, based on the electromechanical coupling mechanism, an electric drive wheel dynamics model (EDWM) is constructed, and multidimensional driving data is collected through a chassis dynamometer experimental platform. Secondly, an improved proportional integral observer (PIO) is designed to decouple the longitudinal force from the system input into a state variable, and a subspace identification recursive algorithm based on correction term with forgetting factor (CFF-SIR) is introduced to suppress the residual influence of historical data and enhance the ability to track time-varying parameters. The simulation and experimental results show that under complex working conditions without noise and interference, with noise influence (5% white noise), and with interference (5% irregular signal), the mean and mean square error of longitudinal force estimation under the CFF-SIR algorithm are significantly reduced compared to the correction-based subspace identification recursive (C-SIR) algorithm, and the comprehensive estimation accuracy is improved by 8.37%. It can provide a high-precision and highly adaptive longitudinal force estimation solution for vehicle dynamics control and intelligent driving systems. Full article

(This article belongs to the Special Issue Dynamic System Modelling from Data: Emerging Algorithms and Applications: 2nd Edition)

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19 pages, 2917 KiB

Open AccessArticle

An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization

by Chenhao Li, Jiyin Zhang, Weilin Chen and Xiaogang Ma

Algorithms 2025, 18(7), 408; https://doi.org/10.3390/a18070408 - 3 Jul 2025

Viewed by 203

Abstract

The rapid growth of scientific publications, coupled with rising retraction rates, has intensified the challenge of identifying trustworthy academic articles. To address this issue, we propose a three-layer ranking system that integrates natural language processing and machine learning techniques for relevance and trust [...] Read more.

The rapid growth of scientific publications, coupled with rising retraction rates, has intensified the challenge of identifying trustworthy academic articles. To address this issue, we propose a three-layer ranking system that integrates natural language processing and machine learning techniques for relevance and trust assessment. First, we apply BERT-based embeddings to semantically match user queries with article content. Second, a Random Forest classifier is used to eliminate potentially problematic articles, leveraging features such as citation count, Altmetric score, and journal impact factor. Third, a custom ranking function combines relevance and trust indicators to score and sort the remaining articles. Evaluation using 16,052 articles from Retraction Watch and Web of Science datasets shows that our classifier achieves 90% accuracy and 97% recall for retracted articles. Citations emerged as the most influential trust signal (53.26%), followed by Altmetric and impact factors. This multi-layered approach offers a transparent and efficient alternative to conventional ranking algorithms, which can help researchers discover not only relevant but also reliable literature. Our system is adaptable to various domains and represents a promising tool for improving literature search and evaluation in the open science environment. 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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21 pages, 2212 KiB

Open AccessArticle

The Whale Optimization Algorithm and Markov Chain Monte Carlo-Based Approach for Optimizing Teacher Professional Development in Creative Learning Design with Technology

by Kalliopi Rigopouli, Dimitrios Kotsifakos and Yannis Psaromiligkos

Algorithms 2025, 18(7), 407; https://doi.org/10.3390/a18070407 - 2 Jul 2025

Viewed by 280

Abstract

In this article, we present a hybrid optimization methodology using the whale optimization algorithm and Markov Chain Monte Carlo sampling technique in a teachers’ training development program regarding creativity in technology-enhanced learning design. Finding the best possible training for creativity in learning design [...] Read more.

In this article, we present a hybrid optimization methodology using the whale optimization algorithm and Markov Chain Monte Carlo sampling technique in a teachers’ training development program regarding creativity in technology-enhanced learning design. Finding the best possible training for creativity in learning design with technology is a complex task, as many dynamic and multi-model variables need to be taken into consideration. When designing the best possible training, the whale optimization algorithm helped us in determining the right methods, resources, content, and assessment. A further Markov Chain Monte Carlo-based approach helped us in deciding with accuracy that these were the correct parameters of our training. In this article, we show that metaheuristic algorithms like the whale optimization algorithm, validated by a Markov chain technique like Markov Chain Monte Carlo, can help not only in areas like machine learning but also in fields without structured data, like creativity in technology-enhanced learning design. The best possible training for a teacher’s professional development in creative learning design is collaborative, hands-on, and utilizes creativity definitions for the product along with technology integration learning design models. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms and Generative AI in Education)

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26 pages, 5350 KiB

Open AccessArticle

Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography

by Suhad Naji Alrekaby, Maisa’a Abid Ali Khodher, Layth Kamil Adday and Reem Aljuaidi

Algorithms 2025, 18(7), 406; https://doi.org/10.3390/a18070406 - 1 Jul 2025

Viewed by 330

Abstract

The swift advancement of information and communication technology has made it increasingly difficult to guarantee the security of transmitted data. Traditional encryption techniques, particularly in multimedia applications, frequently fail to defend against sophisticated attacks, such as chosen-plaintext, differential, and statistical analysis attacks. More [...] Read more.

The swift advancement of information and communication technology has made it increasingly difficult to guarantee the security of transmitted data. Traditional encryption techniques, particularly in multimedia applications, frequently fail to defend against sophisticated attacks, such as chosen-plaintext, differential, and statistical analysis attacks. More often than not, traditional cryptographic methods lack proper diffusion and sufficient randomness, which is why they are vulnerable to these types of attacks. By combining multi-level chaotic maps with Least Significant Bit (LSB) steganography and Advanced Encryption Standard (AES) encryption, this study proposes an improved security approach for picture transmission. A hybrid chaotic system dynamically creates the encryption keys, guaranteeing high unpredictability and resistance to brute-force attacks. Next, it incorporates the encrypted images into video frames, making it challenging to find the secret data. The suggested method demonstrates its resilience to statistical attacks by achieving entropy values over 7.99 and number of pixels change rate (NPCR) values above 99.63% in contrast to traditional encryption techniques, showing how resilient it is to statistical attacks. Our hybrid approach improves data secrecy and resistance to various cryptographic attacks. Experimental results confirm the efficiency of the suggested technique by achieving entropy values around 7.99, number of pixels change rate (NPCR) values above 99.63%, and unified average changing intensity (UACI) values over 31.98%, ensuring the secure transmission of sensitive images while maintaining video imperceptibility. Full article

(This article belongs to the Section Parallel and Distributed Algorithms)

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24 pages, 842 KiB

Open AccessArticle

Predicting the Magnitude of Earthquakes Using Grammatical Evolution

by Constantina Kopitsa, Ioannis G. Tsoulos and Vasileios Charilogis

Algorithms 2025, 18(7), 405; https://doi.org/10.3390/a18070405 - 1 Jul 2025

Viewed by 275

Abstract

Throughout history, human societies have sought to explain natural phenomena through the lens of mythology. Earthquakes, as sudden and often devastating events, have inspired a range of symbolic and mythological interpretations across different civilizations. It was not until the 18th and 19th centuries [...] Read more.

Throughout history, human societies have sought to explain natural phenomena through the lens of mythology. Earthquakes, as sudden and often devastating events, have inspired a range of symbolic and mythological interpretations across different civilizations. It was not until the 18th and 19th centuries that a more positivist and scientific approach began to emerge regarding the explanation of earthquakes, recognizing their origin as stemming from processes occurring beneath the Earth’s surface. A pivotal moment in the emergence of modern seismology was the Lisbon earthquake of 1755, which marked a significant shift towards scientific inquiry. This means that the question of how earthquakes occur has been resolved; thanks to advancements in scientific, geological, and geophysical research, it is now well understood that seismic events result from the collision and movement of lithospheric or tectonic plates. The contemporary challenge that emerges, however, lies in whether such seismic phenomena can be accurately predicted. In this paper, a systematic attempt is made to use techniques based on Grammatical Evolution to determine the magnitude of earthquakes. These techniques use freely available data in which the history of large earthquakes is introduced before the application of the proposed techniques. From the execution of the experiments, it has become clear that the use of these techniques can allow for more effective estimation of the magnitude of earthquakes compared to other machine learning techniques from the relevant literature. Full article

(This article belongs to the Special Issue Algorithms in Data Classification (3rd Edition))

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