Algorithms

15 pages, 2638 KB

Open AccessArticle

Research on Energy Storage Configuration Optimization Method for Wind Farm Substations Based on Wind Power Fluctuation Prediction Integrating Chaotic Features and Bidirectional Gated Recurrent Units

by Fei Wang, Zikai Fan, Yifei Fan, Jiayi Ren, Yan Li, Leiming Suo and Jinrui Tang

Algorithms 2025, 18(11), 698; https://doi.org/10.3390/a18110698 - 3 Nov 2025

Abstract

To address wind power fluctuations causing curtailment and high costs, this study proposes an integrated method combining wind power forecasting with substation optimization. An enhanced Bidirectional Gated Recurrent Unit (BiGRU) model is developed by incorporating chaotic features (maximum Lyapunov exponent) and sliding-window statistical [...] Read more.

To address wind power fluctuations causing curtailment and high costs, this study proposes an integrated method combining wind power forecasting with substation optimization. An enhanced Bidirectional Gated Recurrent Unit (BiGRU) model is developed by incorporating chaotic features (maximum Lyapunov exponent) and sliding-window statistical features (mean, standard deviation), significantly improving short-term prediction accuracy. Based on these high-precision forecasts, a dynamic transformer switching optimization model is established to maximize the wind farm’s net profit. This model finely balances power generation revenue, wind curtailment penalties, and transformer losses (no-load and load) at a 15 min timescale. Experimental results from a wind farm in Xinjiang demonstrate that the proposed method effectively enhances the economic efficiency of wind farm operations. The study provides a valuable framework for optimizing energy storage configuration and improving profitability by leveraging accurate forecasting. Full article

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22 pages, 1196 KB

Open AccessArticle

Interpretable Machine Learning for Coronary Artery Disease Risk Stratification: A SHAP-Based Analysis

by Nurdaulet Tasmurzayev, Zhanel Baigarayeva, Bibars Amangeldy, Baglan Imanbek, Shugyla Kurmanbek, Gulmira Dikhanbayeva and Gulshat Amirkhanova

Algorithms 2025, 18(11), 697; https://doi.org/10.3390/a18110697 - 3 Nov 2025

Abstract

Coronary artery disease (CAD) is a leading cause of global mortality, demanding accurate and early risk assessment. While machine learning models offer strong predictive power, their clinical adoption is often hindered by a lack of transparency and reliability. This study aimed to develop [...] Read more.

Coronary artery disease (CAD) is a leading cause of global mortality, demanding accurate and early risk assessment. While machine learning models offer strong predictive power, their clinical adoption is often hindered by a lack of transparency and reliability. This study aimed to develop and rigorously evaluate a calibrated, interpretable machine learning framework for CAD prediction using 56 routinely collected clinical and demographic variables from the Z-Alizadeh Sani dataset (n = 303). A systematic protocol involving comprehensive preprocessing, class rebalancing using SMOTE, and grid-search hyperparameter tuning was applied to five distinct classifiers. The XGBoost model demonstrated the highest predictive performance, achieving an accuracy of 0.9011, an F1 score of 0.8163, and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.92. Post hoc interpretability analysis using SHAP (Shapley Additive Explanations) identified HTN, valvular heart disease (VHD), and diabetes mellitus (DM) as the most significant predictors of CAD. Furthermore, calibration analysis confirmed that the mode’s probability estimates are reliable for clinical risk stratification. This work presents a robust framework that combines high predictive accuracy with clinical interpretability, offering a promising tool for early CAD screening and decision support. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence for Disease Detection and Secure Monitoring Systems)

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31 pages, 1591 KB

Open AccessArticle

An Innovative Approach Regarding Efficient and Expedited Early Building Renovation Cost Estimation Utilizing ANNs and the TOPSIS Methodology

by Vasso E. Papadimitriou and Georgios N. Aretoulis

Algorithms 2025, 18(11), 696; https://doi.org/10.3390/a18110696 - 3 Nov 2025

Abstract

Early cost assessment is an essential part of building construction strategy; however, preliminary estimates are occasionally unreliable given incomplete data, which causes budgetary overruns. In general, traditional prediction techniques are imprecise and sluggish, particularly while the project scope is still unclear. By introducing [...] Read more.

Early cost assessment is an essential part of building construction strategy; however, preliminary estimates are occasionally unreliable given incomplete data, which causes budgetary overruns. In general, traditional prediction techniques are imprecise and sluggish, particularly while the project scope is still unclear. By introducing a hybrid framework that utilizes ANNs for renovation cost estimation and features enhancements by the TOPSIS method to guarantee contextual relevance and input accuracy, the present research overcomes these drawbacks. Utilizing data from projects that are structurally and contextually comparable enhances the model’s predicted reliability and robustness. The study builds, trains, and tests two ANN models using IBM SPSS Statistics software, which is based on a thorough literature review and actual renovation data from construction businesses. One model utilized 53 data points from prior building renovation projects, whereas the second model employed 11 data points from post-TOPSIS technique building renovation projects. The Radial Basis Function (RBF) procedure is the basis for models that include 14 input data such as total initial cost, estimated completion time, initial demolition drainage cost, initial cost of plumbing work, initial heating cost, initial cost of electrical work, initial cost of masonry coatings, initial cost of plasterboard construction, initial bathroom cost, initial flooring costs, initial frame cost, initial door cost, initial paint cost, and initial kitchen construction cost, and one output data, the total final cost. The models show excellent performance with near 0.5 relative error and up to 0.3 monetary units sum of squares error before applying the TOPSIS method and nearly 0.6 relative error and up to 0.8 monetary units sum of squares error after the TOPSIS implementation, proving the usefulness and demonstrating the speed of the ANN in estimating overall renovation costs in combination with the TOPSIS approach. By employing this hybridized approach, the entire contingent procedure is expedited and accomplished more rapidly. Full article

(This article belongs to the Special Issue AI-Driven Solutions for Smart Systems in Engineering, Computing, Education, and Society)

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21 pages, 1053 KB

Open AccessArticle

Machine Learning Systems Tuned by Bayesian Optimization to Forecast Electricity Demand and Production

by Zhen Wang, Salim Lahmiri and Stelios Bekiros

Algorithms 2025, 18(11), 695; https://doi.org/10.3390/a18110695 - 3 Nov 2025

Abstract

Given the critical importance of accurate energy demand and production forecasting in managing power grids and integrating renewable energy sources, this study explores the application of advanced machine learning techniques to forecast electricity load and wind generation data in Austria, Germany, and the [...] Read more.

Given the critical importance of accurate energy demand and production forecasting in managing power grids and integrating renewable energy sources, this study explores the application of advanced machine learning techniques to forecast electricity load and wind generation data in Austria, Germany, and the Netherlands at different sampling frequencies: 15 min and 60 min. Specifically, we assess the performance of the convolutional neural networks (CNNs), temporal CNN (TCNN), Long Short-Term Memory (LSTM), bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), bidirectional GRU (BiGRU), and the deep neural network (DNN). In addition, the standard machine learning models, namely the k-nearest neighbors (kNN) algorithm and decision trees (DTs), are adopted as baseline predictive models. Bayesian optimization is applied for hyperparameter tuning across multiple models. In total, 54 experimental tasks were performed. For the electricity load at 15 min intervals, the DT shows exceptional performance, while for the electricity load at 60 min intervals, DNN performs the best, in general. For wind generation at 15 min intervals, DT is the best performer, while for wind generation at 60 min intervals, both DT and TCNN provide good results, in general. The insights derived from this study not only advance the field of energy forecasting but also offer practical implications for energy policymakers and stakeholders in optimizing grid performance and renewable energy integration. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 3rd Edition)

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26 pages, 1157 KB

Open AccessArticle

Robust Control of Heat Diffusion Systems with Time Delay Using Fuzzy Logic and Model-Based Compensation

by Rui S. Mendes and Isabel S. Jesus

Algorithms 2025, 18(11), 694; https://doi.org/10.3390/a18110694 - 3 Nov 2025

Abstract

Traditional heat diffusion systems are typically regulated using Proportional–Integral–Derivative (PID) controllers. PID controllers still remain the backbone of numerous industrial control applications due to their simplicity, robustness, and efficiency. However, traditional tuning methods—such as Ziegler–Nichols or Cohen–Coon—often exhibit limitations when applied to systems [...] Read more.

Traditional heat diffusion systems are typically regulated using Proportional–Integral–Derivative (PID) controllers. PID controllers still remain the backbone of numerous industrial control applications due to their simplicity, robustness, and efficiency. However, traditional tuning methods—such as Ziegler–Nichols or Cohen–Coon—often exhibit limitations when applied to systems with nonlinear dynamics, time-varying behaviors, or parametric uncertainties. To address these challenges, Fuzzy Logic Controllers (FLC) have emerged as a promising hybrid strategy, by translating quantitative and imprecise linguistic inputs into quantitative control actions, thereby enabling more adaptive and precise regulation. This is achieved through the integration of fuzzy inference mechanisms that dynamically adjust PID gains in response to changing system conditions. This study proposes a fuzzy logic control strategy for a heat diffusion system and conducts a comparative analysis against conventional PID control. The methodology encompasses system modeling, design of the fuzzy inference system, and simulation studies. To improve transient response and address time delays, additional features such as Anti-Windup compensation and a Smith Predictor are integrated into the control scheme. The final validation step involves the introduction of simulated environmental disturbances, including abrupt temperature drops, to evaluate the controller’s robustness. Simulation results demonstrate that the proposed FLC provides superior dynamic performance compared to the conventional PID controller, achieving approximately 5–7% faster rise time and 8–10% lower settling time. The incorporation of an anti-windup mechanism did not yield significant benefits in this application. In contrast, the integration of a Smith Predictor further reduced oscillatory behavior and substantially improved disturbance rejection, tracking accuracy, and adaptability under simulated thermal variations. These results underscore the effectiveness of the FLC in handling systems with time delays and nonlinearities, reinforcing its role as a robust and adaptable control strategy for thermal processes with complex dynamics. Full article

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20 pages, 1224 KB

Open AccessArticle

Explainable AI for Coronary Artery Disease Stratification Using Routine Clinical Data

by Nurdaulet Tasmurzayev, Baglan Imanbek, Assiya Boltaboyeva, Gulmira Dikhanbayeva, Sarsenbek Zhussupbekov, Qarlygash Saparbayeva and Gulshat Amirkhanova

Algorithms 2025, 18(11), 693; https://doi.org/10.3390/a18110693 - 3 Nov 2025

Abstract

Background: Coronary artery disease (CAD) remains a leading cause of morbidity and mortality. Early diagnosis reduces adverse outcomes and alleviates the burden on healthcare, yet conventional approaches are often invasive, costly, and not always available. In this context, machine learning offers promising solutions. [...] Read more.

Background: Coronary artery disease (CAD) remains a leading cause of morbidity and mortality. Early diagnosis reduces adverse outcomes and alleviates the burden on healthcare, yet conventional approaches are often invasive, costly, and not always available. In this context, machine learning offers promising solutions. Objective: The objective of this study is to evaluate the feasibility of reliably predicting both the presence and the severity of CAD. The analysis is based on a harmonized, multi-center UCI dataset that includes cohorts from Cleveland, Hungary, Switzerland, and Long Beach. The work aims to assess the accuracy and practical utility of models built exclusively on routine tabular clinical and demographic data, without relying on imaging. These models are designed to improve risk stratification and guide patient routing. Methods and Results: The study is based on a uniform and standardized data processing pipeline. This pipeline includes handling missing values, feature encoding, scaling, an 80/20 train–test split and applying the SMOTE method exclusively to the training set to prevent information leakage. Within this pipeline, a standardized comparison of a wide range of models (including gradient boosting, tree-based ensembles, support vector methods, etc.) was conducted with hyperparameter tuning via GridSearchCV. The best results were demonstrated by the CatBoost model: accuracy—0.8278, recall—0.8407, and F1-score—0.8436. Conclusions: A key distinction of this work is the comprehensive evaluation of the models’ practical suitability. Beyond standard metrics, the analysis of calibration curves confirmed the reliability of the probabilistic predictions. Patient-level interpretability using SHAP showed that the model relies on clinically significant predictors, including ST-segment depression. Calibrated and explainable models based on readily available data are positioned as a practical tool for scalable risk stratification and decision support, especially in resource-constrained settings. Full article

(This article belongs to the Special Issue Explainable AI: Advances in Interpretability Algorithms and Applications)

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22 pages, 2341 KB

Open AccessArticle

A Multi-Expert Evolutionary Boosting Method for Proactive Control in Unstable Environments

by Alexander Musaev and Dmitry Grigoriev

Algorithms 2025, 18(11), 692; https://doi.org/10.3390/a18110692 - 2 Nov 2025

Abstract

Unstable technological processes, such as turbulent gas and hydrodynamic flows, generate time series that deviate sharply from the assumptions of classical statistical forecasting. These signals are shaped by stochastic chaos, characterized by weak inertia, abrupt trend reversals, and pronounced low-frequency contamination. Traditional extrapolators, [...] Read more.

Unstable technological processes, such as turbulent gas and hydrodynamic flows, generate time series that deviate sharply from the assumptions of classical statistical forecasting. These signals are shaped by stochastic chaos, characterized by weak inertia, abrupt trend reversals, and pronounced low-frequency contamination. Traditional extrapolators, including linear and polynomial models, therefore act only as weak forecasters, introducing systematic phase lag and rapidly losing directional reliability. To address these challenges, this study introduces an evolutionary boosting framework within a multi-expert system (MES) architecture. Each expert is defined by a compact genome encoding training-window length and polynomial order, and experts evolve across generations through variation, mutation, and selection. Unlike conventional boosting, which adapts only weights, evolutionary boosting adapts both the weights and the structure of the expert pool, allowing the system to escape local optima and remain responsive to rapid environmental shifts. Numerical experiments on real monitoring data demonstrate consistent error reduction, highlighting the advantage of short windows and moderate polynomial orders in balancing responsiveness with robustness. The results show that evolutionary boosting transforms weak extrapolators into a strong short-horizon forecaster, offering a lightweight and interpretable tool for proactive control in environments dominated by chaotic dynamics. Full article

(This article belongs to the Special Issue Evolutionary and Swarm Computing for Emerging Applications)

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24 pages, 2569 KB

Open AccessArticle

Attribution-Driven Teaching Interventions: Linking I-AHP Weighted Assessment to Explainable Student Clustering

by Yanzheng Liu, Xuan Yang, Ying Zhu, Jin Wang, Mi Zuo, Lei Yang and Lingtong Sun

Algorithms 2025, 18(11), 691; https://doi.org/10.3390/a18110691 - 1 Nov 2025

Abstract

Student course performance evaluation serves as a critical pedagogical tool for diagnosing learning gaps and enhancing educational outcomes, yet conventional assessments often suffer from rigid single-metric scoring and ambiguous causality. This study proposes an integrated analytic framework addressing these limitations by synergizing pedagogical [...] Read more.

Student course performance evaluation serves as a critical pedagogical tool for diagnosing learning gaps and enhancing educational outcomes, yet conventional assessments often suffer from rigid single-metric scoring and ambiguous causality. This study proposes an integrated analytic framework addressing these limitations by synergizing pedagogical expertise with data-driven diagnostics through four key measure: (1) Interval Analytic Hierarchy Process (I-AHP) to derive criterion weights reflecting instructional priorities via expert judgment; (2) K-means clustering to objectively stratify students into performance cohorts based on multidimensional metrics; (3) Random Forest classification and SHAP value analysis to quantitatively identify key discriminators of cluster membership and interpret decision boundaries; and (4) attribution-guided interventions targeting cohort-specific deficiencies. Leveraging a dual-channel ecosystem across pre-class, in-class, and post-class phases, we established a hierarchical evaluation system where I-AHP weighted pedagogical sub-criteria to generate comprehensive student scores. Full article

18 pages, 2271 KB

Open AccessArticle

DAFF-Net: A Dual-Branch Attention-Guided Feature Fusion Network for Vehicle Re-Identification

by Yi Guo, Guowu Yuan, Wen Li and Hao Li

Algorithms 2025, 18(11), 690; https://doi.org/10.3390/a18110690 - 29 Oct 2025

Abstract

Vehicle re-identification (Re-ID) is a critical task in the fields of intelligent transportation and urban surveillance. This task faces numerous challenges, such as significant changes in shooting angles, strong similarities in appearance between different vehicles of the same model, and difficulties in modeling [...] Read more.

Vehicle re-identification (Re-ID) is a critical task in the fields of intelligent transportation and urban surveillance. This task faces numerous challenges, such as significant changes in shooting angles, strong similarities in appearance between different vehicles of the same model, and difficulties in modeling fine-grained differences. To overcome the shortcomings of existing methods in local feature extraction and multi-scale fusion, this paper proposes an attention-guided dual-branch feature fusion network (DAFF-Net). The network uses ResNet50-ibn as its backbone and designs two complementary feature extraction branches. One branch fuses cross-layer attention between shallow and deep features, introducing a Temperature-Calibration Attention Fusion Module (TCAF) to improve the accuracy of cross-layer feature fusion effectively. The other branch enhances multi-scale attention for mid-layer features, constructing a Multi-Scale Gated Attention Module (MSGA) to extract local details and directional structural information. Finally, the discriminative ability of the enhanced features is improved by concatenating the two branch features and jointly optimizing the network using triplet loss, cross-entropy loss, and center loss. Experimental results on the VeRi-776 and VehicleID public datasets indicate that the proposed DAFF-Net outperforms existing mainstream methods in multiple key metrics. On the VeRi-776 dataset, mAP and CMC@1 increased to 82.2% and 97.5%, respectively. In the three test subsets of the VehicleID dataset, the CMC@1 metric achieved 90.7%, 84.6%, and 82.1%, respectively, demonstrating the effectiveness of the proposed network in vehicle re-identification tasks. Full article

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28 pages, 1976 KB

Open AccessArticle

ECG Signal Analysis and Abnormality Detection Application

by Ales Jandera, Yuliia Petryk, Martin Muzelak and Tomas Skovranek

Algorithms 2025, 18(11), 689; https://doi.org/10.3390/a18110689 - 29 Oct 2025

Abstract

The electrocardiogram (ECG) signal carries information crucial for health assessment, but its analysis can be challenging due to noise and signal variability; therefore, automated processing focused on noise removal and detection of key features is necessary. This paper introduces an ECG signal analysis [...] Read more.

The electrocardiogram (ECG) signal carries information crucial for health assessment, but its analysis can be challenging due to noise and signal variability; therefore, automated processing focused on noise removal and detection of key features is necessary. This paper introduces an ECG signal analysis and abnormality detection application developed to process single-lead ECG signals. In this study, the Lobachevsky University database (LUDB) was used as the source of ECG signals, as it includes annotated recordings using a multi-class, multi-label taxonomy that covers several diagnostic categories, each with specific diagnoses that reflect clinical ECG interpretation practices. The main aim of the paper is to provide a tool that efficiently filters noisy ECG data, accurately detects the QRS complex, PQ and QT intervals, calculates heart rate, and compares these values with normal ranges based on age and gender. Additionally, a multi-class, multi-label SVM-based model was developed and integrated into the application for heart abnormality diagnostics, i.e., assigning one or several diagnoses from various diagnostic categories. The MATLAB-based application is capable of processing raw ECG signals, allowing the use of ECG records not only from LUDB but also from other databases. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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25 pages, 1608 KB

Open AccessArticle

Online Imputation of Corrupted Glucose Sensor Data Using Deep Neural Networks and Physiological Inputs

by Oscar D. Sanchez, Eduardo Mendez-Palos, Daniel A. Pascoe, Hannia M. Hernandez, Jesus G. Alvarez and Alma Y. Alanis

Algorithms 2025, 18(11), 688; https://doi.org/10.3390/a18110688 - 29 Oct 2025

Abstract

One of the main challenges when working with time series captured online using sensors is the appearance of noise or null values, generally caused by sensor failures or temporary disconnections. These errors compromise data reliability and can lead to incorrect decisions. Particularly in [...] Read more.

One of the main challenges when working with time series captured online using sensors is the appearance of noise or null values, generally caused by sensor failures or temporary disconnections. These errors compromise data reliability and can lead to incorrect decisions. Particularly in the treatment of diabetes mellitus, where medical decisions depend on continuous glucose monitoring (CGM) systems provided by modern sensors, the presence of corrupted data can pose a significant risk to patient health. This work presents an approach that encompasses online detection and imputation of anomalous data using physiological inputs (insulin and carbohydrate intake), which enables decision-making in automatic glucose monitoring systems or for glucose control purposes. Four deep neural network architectures are proposed: CNN-LSTM, GRU, 1D-CNN, and Transformer-LSTM, under a controlled fault injection protocol and compared with the ARIMA model and the Temporal Convolutional Network (TCN). The obtained performance is compared using regression (MAE, RMSE, MARD) and classification (accuracy, precision, recall, F1-score, AUC) metrics. Results show that the CNN-LSTM network is the most effective for fault detection, achieving an F1-score of 0.876 and an accuracy of 0.979. Regarding data imputation, the 1D-CNN network obtained the best performance, with an MAE of 2.96 mg/dL and an RMSE of 3.75 mg/dL. Then, validation on the OhioT1DM dataset, containing real CGM data with natural sensor disconnections, showed that the CNN–LSTM model accurately detected anomalies and reliably imputed missing glucose segments under real-world conditions. Full article

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34 pages, 62676 KB

Open AccessArticle

Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos

by Parnian Azizian, Mohammadmahdi Honarmand, Aditi Jaiswal, Aaron Kline, Kaitlyn Dunlap, Peter Washington and Dennis P. Wall

Algorithms 2025, 18(11), 687; https://doi.org/10.3390/a18110687 - 29 Oct 2025

Abstract

Autism diagnosis remains a critical healthcare challenge, with current assessments contributing to average diagnostic ages of 5 and extending to 8 in underserved populations. With the FDA approval of CanvasDx in 2021, the paradigm of human-in-the-loop AI diagnostics entered the pediatric market as [...] Read more.

Autism diagnosis remains a critical healthcare challenge, with current assessments contributing to average diagnostic ages of 5 and extending to 8 in underserved populations. With the FDA approval of CanvasDx in 2021, the paradigm of human-in-the-loop AI diagnostics entered the pediatric market as the first medical device for clinically precise autism diagnosis at scale, while fully automated deep learning approaches have remained underdeveloped. However, the importance of early autism detection, ideally before 3 years of age, underscores the value of developing even more automated AI approaches, due to their potentials for scale, reach, and privacy. We present the first systematic evaluation of multimodal LLMs as direct replacements for human annotation in AI-based autism detection. Evaluating seven Gemini model variants (1.5–2.5 series) on 50 YouTube videos shows clear generational progression: version 1.5 models achieve 72–80% accuracy, version 2.0 models reach 80%, and version 2.5 models attain 85–90%, with the best model (2.5 Pro) achieving 89.6% classification accuracy using validated autism detection AI models (LR5)—comparable to the 88% clinical baseline and approaching crowdworker performance of 92–98%. The 24% improvement across two generations suggests the gap is closing. LLMs demonstrate high within-model consistency versus moderate human agreement, with distinct assessment strategies: LLMs focus on language/behavioral markers, crowdworkers prioritize social-emotional engagement, clinicians balance both. While LLMs have yet to match the highest-performing subset of human annotators in their ability to extract behavioral features that are useful for human-in-the-loop AI diagnosis, their rapid improvement and advantages in consistency, scalability, cost, and privacy position them as potentially viable alternatives for aiding diagnostic processes in the future. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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35 pages, 2828 KB

Open AccessSystematic Review

A Systematic Review of Bio-Inspired Metaheuristic Optimization Algorithms: The Untapped Potential of Plant-Based Approaches

by Hossein Jamali, Sergiu M. Dascalu and Frederick C. Harris, Jr.

Algorithms 2025, 18(11), 686; https://doi.org/10.3390/a18110686 - 29 Oct 2025

Abstract

Nature has evolved sophisticated optimization strategies over billions of years, yet computational algorithms inspired by plants remain remarkably underexplored. We present a comprehensive systematic review following PRISMA 2020 guidelines, analyzing 175 studies (2000–2025) of plant-inspired metaheuristic optimization algorithms and their predominantly animal-inspired counterparts. [...] Read more.

Nature has evolved sophisticated optimization strategies over billions of years, yet computational algorithms inspired by plants remain remarkably underexplored. We present a comprehensive systematic review following PRISMA 2020 guidelines, analyzing 175 studies (2000–2025) of plant-inspired metaheuristic optimization algorithms and their predominantly animal-inspired counterparts. Despite constituting only 9.7% of bio-inspired optimization literature, plant-inspired algorithms demonstrate competitive and often superior performance compared to animal-inspired approaches. Through a meta-analysis of empirical studies, we document that algorithms like Phototropic Growth and Binary Plant Rhizome Growth achieve 97% superiority on CEC2017 benchmarks and 81% accuracy on high-dimensional feature-selection tasks—significantly exceeding established animal-inspired methods like Particle Swarm Optimization and Genetic Algorithms (p < 0.05). However, our review reveals a critical gap: the majority of these algorithms lack the formal theoretical foundations of their counterparts. This paper systematically documents these theoretical deficiencies and positions them as a key area for future research. Our framework maps botanical processes to computational operators, providing structured guidance for future algorithm development. Plant-inspired approaches excel particularly in distributed optimization, resource allocation, and multi-objective problems by leveraging unique mechanisms evolved for survival in sessile, resource-limited environments. These findings establish plant-inspired approaches as a promising yet severely underexplored frontier in optimization theory, with immediate applications in sustainable computing, resilient network design, and resource-constrained artificial intelligence. Full article

(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)

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28 pages, 3097 KB

Open AccessArticle

Cover Edge-Based Novel Triangle Counting

by David A. Bader, Fuhuan Li, Zhihui Du, Palina Pauliuchenka, Oliver Alvarado Rodriguez, Anant Gupta, Sai Sri Vastav Minnal, Valmik Nahata, Anya Ganeshan, Ahmet Cemal Gundogdu and Jason Lew

Algorithms 2025, 18(11), 685; https://doi.org/10.3390/a18110685 - 28 Oct 2025

Abstract

Counting and listing triangles in graphs is a fundamental task in network analysis, supporting applications such as community detection, clustering coefficient computation, k-truss decomposition, and triangle centrality. We introduce the cover-edge set, a novel concept that eliminates unnecessary edges during triangle enumeration, thereby [...] Read more.

Counting and listing triangles in graphs is a fundamental task in network analysis, supporting applications such as community detection, clustering coefficient computation, k-truss decomposition, and triangle centrality. We introduce the cover-edge set, a novel concept that eliminates unnecessary edges during triangle enumeration, thereby improving efficiency. This compact cover-edge set is rapidly constructed using a breadth-first search (BFS) strategy. Using this concept, we develop both sequential and parallel triangle-counting algorithms and conduct comprehensive comparisons with state-of-the-art methods. We also design a benchmarking framework to evaluate our sequential and parallel algorithms in a systematic and reproducible manner. Extensive experiments on the latest Intel Xeon 8480+ processor reveal clear performance differences among algorithms, demonstrate the benefits of various optimization strategies, and show how graph characteristics, such as diameter and degree distribution, affect algorithm performance. Our source code is available on GitHub. Full article

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

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21 pages, 9130 KB

Open AccessArticle

Feature-Differentiated Perception with Dynamic Mixed Convolution and Spatial Orthogonal Attention for Faster Aerial Object Detection

by Yiming Ma, Noridayu Manshor and Fatimah binti Khalid

Algorithms 2025, 18(11), 684; https://doi.org/10.3390/a18110684 - 28 Oct 2025

Abstract

In the field of remote sensing (RS) object detection, efficient and accurate target recognition is crucial for applications such as national defense and maritime monitoring. However, existing detection methods either have high computational complexity, making them unsuitable for real-time applications, or suffer from [...] Read more.

In the field of remote sensing (RS) object detection, efficient and accurate target recognition is crucial for applications such as national defense and maritime monitoring. However, existing detection methods either have high computational complexity, making them unsuitable for real-time applications, or suffer from feature redundancy issues that affect detection accuracy. To address these challenges, this paper proposes a Feature-Differentiated Perception (FDP) lightweight remote sensing object detection method, which optimizes computational efficiency while maintaining high detection accuracy. The proposed method introduces two critical innovations: (1) Dynamic mixed convolution (DM-Conv), which uses linear mapping to efficiently generate redundant feature maps, reducing convolutional computation. It combines features from different intermediate layers through weighted fusion, effectively reducing the number of channels and improving feature utilization. Channel refers to a single feature map in the multi-dimensional feature representation, where each channel corresponds to a specific feature pattern (e.g., edges, textures, or semantic information) learned by the network. (2) The Spatial Orthogonal Attention (SOA) mechanism, which enhances the ability to model long-range dependencies between distant pixels, thereby improving feature representation capability. Experiments on public remote sensing object detection datasets, including DOTA, HRSC2016, and UCMerced-LandUse, demonstrate that the proposed model achieves a significant reduction in computational complexity while maintaining nearly lossless detection accuracy. On the DOTA dataset, the proposed method achieves an mAP (mean Average Precision) of 79.37%, outperforming existing lightweight models in terms of both speed and accuracy. This study provides new insights and practical solutions for efficient remote sensing object detection in embedded and edge computing environments. Full article

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

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23 pages, 376 KB

Open AccessArticle

Differential Evolution with Secondary Mutation Strategies for Long-Term Search

by Jianyi Peng and Gang Chen

Algorithms 2025, 18(11), 683; https://doi.org/10.3390/a18110683 - 27 Oct 2025

Abstract

For numerous years, researchers have extensively explored real parameter single-objective optimization by evolutionary computation. Among the various types of evolutionary algorithms, Differential Evolution (DE) performs outstandingly. Recently, the academic community has began concerning itself with long-term search. IMODE is a good DE algorithm [...] Read more.

For numerous years, researchers have extensively explored real parameter single-objective optimization by evolutionary computation. Among the various types of evolutionary algorithms, Differential Evolution (DE) performs outstandingly. Recently, the academic community has began concerning itself with long-term search. IMODE is a good DE algorithm for long-term search. The algorithm is based on two primary mutation strategies and one secondary. Within the population, the control ratio of each mutation strategy is determined by their respective performance outcomes. Sequential Quadratic Programming (SQP), an iterative method for continuous optimization, is employed on the best individual in the final stage of IMODE at a dynamic probability as a local search method. Based on the DE algorithm, we propose Differential Evolution with Secondary Mutation Strategies (SMSDE). In the proposed algorithm, more secondary mutation strategies are added, in addition to the original one used in IMODE. In each generation, just one of the secondary mutation strategies is activated based on history performance to cooperate with the two primary mutation strategies. In addition, at a dynamic probability, SQP is now called not only for the best individual in the final stage, but also for the worst individual among old ones in each generation. The experimental results demonstrate that SMSDE performs better than a number of state-of-the-art algorithms, including IMODE. Full article

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31 pages, 1134 KB

Open AccessArticle

Classifying Factor Velocity with Swarm Intelligence: Market Pricing of Fast- and Slow-Moving Factors

by Ren-Raw Chen, Mengjie Huang and Yi Tang

Algorithms 2025, 18(11), 682; https://doi.org/10.3390/a18110682 - 25 Oct 2025

Abstract

Utilizing a dataset of 190 risk factors spanning over three decades, we apply a swarm-based classification model to estimate factor velocity and analyze its implications for asset pricing. Our results show that slower-moving factors generate higher abnormal returns than their faster-moving counterparts, underscoring [...] Read more.

Utilizing a dataset of 190 risk factors spanning over three decades, we apply a swarm-based classification model to estimate factor velocity and analyze its implications for asset pricing. Our results show that slower-moving factors generate higher abnormal returns than their faster-moving counterparts, underscoring the critical role of price adjustment speed in market dynamics. Furthermore, our results suggest that trading frictions impede the rapid assimilation of information, contributing to the observed return patterns. This research offers new insights into return predictability and demonstrates the potential of swarm intelligence as a powerful tool for financial modeling. Full article

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

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31 pages, 9036 KB

Open AccessArticle

Algorithmic Investigation of Complex Dynamics Arising from High-Order Nonlinearities in Parametrically Forced Systems

by Barka Infal, Adil Jhangeer and Muhammad Muddassar

Algorithms 2025, 18(11), 681; https://doi.org/10.3390/a18110681 - 25 Oct 2025

Abstract

The geometric content of chaos in nonlinear systems with multiple stabilities of high order is a challenge to computation. We introduce a single algorithmic framework to overcome this difficulty in the present study, where a parametrically forced oscillator with cubic–quintic nonlinearities is considered [...] Read more.

The geometric content of chaos in nonlinear systems with multiple stabilities of high order is a challenge to computation. We introduce a single algorithmic framework to overcome this difficulty in the present study, where a parametrically forced oscillator with cubic–quintic nonlinearities is considered as an example. The framework starts with the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, which is a self-learned algorithm that extracts an interpretable and correct model by simply analyzing time-series data. The resulting parsimonious model is well-validated, and besides being highly predictive, it also offers a solid base on which one can conduct further investigations. Based on this tested paradigm, we propose a unified diagnostic pathway that includes bifurcation analysis, computation of the Lyapunov exponent, power spectral analysis, and recurrence mapping to formally describe the dynamical features of the system. The main characteristic of the framework is an effective algorithm of computational basin analysis, which is able to display attractor basins and expose the fine scale riddled structures and fractal structures that are the indicators of extreme sensitivity to initial conditions. The primary contribution of this work is a comprehensive dynamical analysis of the DM-CQDO, revealing the intricate structure of its stability landscape and multi-stability. This integrated workflow identifies the period-doubling cascade as the primary route to chaos and quantifies the stabilizing effects of key system parameters. This study demonstrates a systematic methodology for applying a combination of data-driven discovery and classical analysis to investigate the complex dynamics of parametrically forced, high-order nonlinear systems. Full article

(This article belongs to the Special Issue Algorithms for Dynamical Systems and Differential Equations: Theory, Computation Innovations and Applications)

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22 pages, 4258 KB

Open AccessArticle

Visible Image-Based Machine Learning for Identifying Abiotic Stress in Sugar Beet Crops

by Seyed Reza Haddadi, Masoumeh Hashemi, Richard C. Peralta and Masoud Soltani

Algorithms 2025, 18(11), 680; https://doi.org/10.3390/a18110680 - 24 Oct 2025

Abstract

Previous researches have proved that the synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable the rapid and cost-effective identification of sugar beet stresses caused [...] Read more.

Previous researches have proved that the synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable the rapid and cost-effective identification of sugar beet stresses caused by water and/or nitrogen deficiencies. RGB images representing stressed and non-stressed crops were used in the analysis. To improve robustness, data augmentation was applied, generating six variations on each image and expanding the dataset from 150 to 900 images for training and testing. Each MLIM was trained and tested using 54 combinations derived from nine canopy and RGB-based input features and six ML algorithms. The most accurate MLIM used RGB bands as inputs to a Multilayer Perceptron, achieving 96.67% accuracy for overall stress detection, and 95.93% and 94.44% for water and nitrogen stress identification, respectively. A Random Forest model, using only the green band, achieved 92.22% accuracy for stress detection while requiring only one-fourth the computation time. For specific stresses, a Random Forest (RF) model using a Scale-Invariant Feature Transform descriptor (SIFT) achieved 93.33% for water stress, while RF with RGB bands and canopy cover reached 85.56% for nitrogen stress. To address the trade-off between accuracy and computational cost, a bargaining theory-based framework was applied. This approach identified optimal MLIMs that balance performance and execution efficiency. 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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