Algorithms

23 pages, 5381 KiB

Open AccessArticle

An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D

by Gilles Labonté

Algorithms 2024, 17(9), 420; https://doi.org/10.3390/a17090420 (registering DOI) - 20 Sep 2024

An algorithm is described to find the shortest route through a field of obstacles of arbitrary shapes and positions. It has the appreciable advantage of not having to find mathematical formulas to represent the obstacles: it works directly with a digital image of [...] Read more.

An algorithm is described to find the shortest route through a field of obstacles of arbitrary shapes and positions. It has the appreciable advantage of not having to find mathematical formulas to represent the obstacles: it works directly with a digital image of the terrain and is implemented solely with standard graphical functions. Key to this algorithm is the definition of digraphs, the edges of which are built with obstacle bitangents and border enveloping convex arcs that incorporate the fundamental features of shortest paths. These graphs have a remarkably lower cardinality than those that have been proposed before to solve this problem; their edges are a concatenation of sequences of what individual edges and nodes are in formerly defined graphs. Furthermore, a thorough analysis of the topology of the terrain yields a procedure to eliminate the edges that have no possibility of being part of the shortest path. The A* graph optimization algorithm is adapted to deal with this type of graph. A new quite general theorem is proved, which applies to all graphs in which the triangle inequality holds, which allows the discarding of one of the normal steps of the A* algorithm. The effectiveness of the algorithm is demonstrated by calculating the shortest path for real complex terrains of areas between 25 km² and 900 km². In all cases, the required calculation time is less than 0.6 s on a Core i7-10750H CPU @ 2.60 GHz laptop computer. Full article

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20 pages, 7057 KiB

Open AccessArticle

Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy

by Kristina I. Haljasmaa, Andrey M. Bramm, Pavel V. Matrenin and Stanislav A. Eroshenko

Algorithms 2024, 17(9), 419; https://doi.org/10.3390/a17090419 (registering DOI) - 20 Sep 2024

Abstract

Together with the growing interest towards renewable energy sources within the framework of different strategies of various countries, the number of solar power plants keeps growing. However, managing optimal power generation for solar power plants has its own challenges. First comes the problem [...] Read more.

Together with the growing interest towards renewable energy sources within the framework of different strategies of various countries, the number of solar power plants keeps growing. However, managing optimal power generation for solar power plants has its own challenges. First comes the problem of work interruption and reduction in power generation. As the system must be tolerant to the faults, the relevance and significance of short-term forecasting of solar power generation becomes crucial. Within the framework of this research, the applicability of different forecasting methods for short-time forecasting is explained. The main goal of the research is to show an approach regarding how to make the forecast more accurate and overcome the above-mentioned challenges using opensource data as features. The data clustering algorithm based on KMeans is proposed to train unique models for specific groups of data samples to improve the generation forecast accuracy. Based on practical calculations, machine learning models based on Random Forest algorithm are selected which have been proven to have higher efficiency in predicting the generation of solar power plants. The proposed algorithm was successfully tested in practice, with an achieved accuracy near to 90%. Full article

(This article belongs to the Special Issue Algorithms for Time Series Forecasting and Classification)

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40 pages, 11208 KiB

Open AccessArticle

Mapping the Frontier: A Bibliometric Analysis of Artificial Intelligence Applications in Local and Regional Studies

by Camelia Delcea, Ionuț Nica, Ștefan Ionescu, Bianca Cibu and Horațiu Țibrea

Algorithms 2024, 17(9), 418; https://doi.org/10.3390/a17090418 - 20 Sep 2024

Abstract

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from [...] Read more.

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top contributing sources. From the perspective of prominent contributing affiliations, institutions like Duy Tan University, Ton Duc Thang University, and the Chinese Academy of Sciences emerge as leading contributors, with Vietnam, Portugal, and China being the countries with the highest citation counts. Furthermore, a word cloud analysis is able to highlight the recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, and “deep learning”. The co-occurrence network analysis reveals five clusters, amongst them being “artificial neural network”, “regional development”, “climate change”, “regional economy”, “management”, “technology”, “risk”, and “fuzzy inference system”. Our findings support the fact that AI is increasingly employed to address complex regional challenges, such as resource management and urban planning. AI applications, including machine learning algorithms and neural networks, have become essential for optimizing processes and decision-making at the local level. The study concludes with the fact that while AI holds vast potential for transforming local and regional research, ongoing international collaboration and the development of adaptable AI models are essential for maximizing the benefits of these technologies. Such efforts will ensure the effective implementation of AI in diverse contexts, thereby supporting sustainable regional development. Full article

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

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63 pages, 2957 KiB

Open AccessArticle

Hybrid Four Vector Intelligent Metaheuristic with Differential Evolution for Structural Single-Objective Engineering Optimization

by Hussam N. Fakhouri, Ahmad Sami Al-Shamayleh, Abdelraouf Ishtaiwi, Sharif Naser Makhadmeh, Sandi N. Fakhouri and Faten Hamad

Algorithms 2024, 17(9), 417; https://doi.org/10.3390/a17090417 - 20 Sep 2024

Abstract

Complex and nonlinear optimization challenges pose significant difficulties for traditional optimizers, which often struggle to consistently locate the global optimum within intricate problem spaces. To address these challenges, the development of hybrid methodologies is essential for solving complex, real-world, and engineering design problems. [...] Read more.

Complex and nonlinear optimization challenges pose significant difficulties for traditional optimizers, which often struggle to consistently locate the global optimum within intricate problem spaces. To address these challenges, the development of hybrid methodologies is essential for solving complex, real-world, and engineering design problems. This paper introduces FVIMDE, a novel hybrid optimization algorithm that synergizes the Four Vector Intelligent Metaheuristic (FVIM) with Differential Evolution (DE). The FVIMDE algorithm is rigorously tested and evaluated across two well-known benchmark suites (i.e., CEC2017, CEC2022) and an additional set of 50 challenging benchmark functions. Comprehensive statistical analyses, including mean, standard deviation, and the Wilcoxon rank-sum test, are conducted to assess its performance. Moreover, FVIMDE is benchmarked against state-of-the-art optimizers, revealing its superior adaptability and robustness. The algorithm is also applied to solve five structural engineering challenges. The results highlight FVIMDE’s ability to outperform existing techniques across a diverse range of optimization problems, confirming its potential as a powerful tool for complex optimization tasks. Full article

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47 pages, 2834 KiB

Open AccessReview

Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms

by Noor A. Rashed, Yossra H. Ali and Tarik A. Rashid

Algorithms 2024, 17(9), 416; https://doi.org/10.3390/a17090416 - 19 Sep 2024

Abstract

The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative [...] Read more.

The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative metaheuristic techniques. The methods used for analysis include bibliometric analysis, keyword analysis, and content analysis, focusing on studies from the period 2000–2023. Databases such as IEEE Xplore, SpringerLink, and ScienceDirect were extensively utilized. Our analysis reveals that while traditional algorithms like evolutionary optimization (EO) and particle swarm optimization (PSO) remain popular, newer methods like the fitness-dependent optimizer (FDO) and learner performance-based behavior (LPBB) are gaining attraction due to their adaptability and efficiency. The main conclusion emphasizes the importance of algorithmic diversity, benchmarking standards, and performance evaluation metrics, highlighting future research paths including the exploration of hybrid algorithms, use of domain-specific knowledge, and addressing scalability issues in multi-objective optimization. Full article

(This article belongs to the Special Issue Scheduling: Algorithms and Real-World Applications)

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26 pages, 7481 KiB

Open AccessArticle

Meshfree Variational-Physics-Informed Neural Networks (MF-VPINN): An Adaptive Training Strategy

by Stefano Berrone and Moreno Pintore

Algorithms 2024, 17(9), 415; https://doi.org/10.3390/a17090415 - 19 Sep 2024

Abstract

In this paper, we introduce a Meshfree Variational-Physics-Informed Neural Network. It is a Variational-Physics-Informed Neural Network that does not require the generation of the triangulation of the entire domain and that can be trained with an adaptive set of test functions. In order [...] Read more.

In this paper, we introduce a Meshfree Variational-Physics-Informed Neural Network. It is a Variational-Physics-Informed Neural Network that does not require the generation of the triangulation of the entire domain and that can be trained with an adaptive set of test functions. In order to generate the test space, we exploit an a posteriori error indicator and add test functions only where the error is higher. Four training strategies are proposed and compared. Numerical results show that the accuracy is higher than the one of a Variational-Physics-Informed Neural Network trained with the same number of test functions but defined on a quasi-uniform mesh. Full article

(This article belongs to the Special Issue Numerical Optimization and Algorithms: 2nd Edition)

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22 pages, 2570 KiB

Open AccessArticle

Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure

by Tongyu Wu, Minjie Li and Shuangjiao Lin

Algorithms 2024, 17(9), 414; https://doi.org/10.3390/a17090414 - 19 Sep 2024

Abstract

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare [...] Read more.

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs. Full article

(This article belongs to the Topic Complex Networks and Social Networks)

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16 pages, 2152 KiB

Open AccessArticle

Research on Applying Deep Learning to Visual–Motor Integration Assessment Systems in Pediatric Rehabilitation Medicine

by Yu-Ting Tsai, Jin-Shyan Lee and Chien-Yu Huang

Algorithms 2024, 17(9), 413; https://doi.org/10.3390/a17090413 - 18 Sep 2024

Abstract

In pediatric rehabilitation medicine, manual assessment methods for visual–motor integration result in inconsistent scoring standards. To address these issues, incorporating artificial intelligence (AI) technology is a feasible approach that can reduce time and improve accuracy. Existing research on visual–motor integration scoring has proposed [...] Read more.

In pediatric rehabilitation medicine, manual assessment methods for visual–motor integration result in inconsistent scoring standards. To address these issues, incorporating artificial intelligence (AI) technology is a feasible approach that can reduce time and improve accuracy. Existing research on visual–motor integration scoring has proposed a framework based on convolutional neural networks (CNNs) for the Beery–Buktenica developmental test of visual–motor integration. However, as the number of training questions increases, the accuracy of this framework significantly decreases. This paper proposes a new architecture to reduce the number of features, channels, and overall model complexity. The architectureoptimizes input features by concatenating question numbers with answer features and selecting appropriate channel ratios and optimizes the output vector by designing the task as a multi-class classification. This paper also proposes a model named improved DenseNet. After experimentation, DenseNet201 was identified as the most suitable pre-trained model for this task and was used as the backbone architecture for improved DenseNet. Additionally, new fully connected layers were added for feature extraction and classification, allowing for specialized feature learning. The architecture can provide reasons for unscored results based on prediction results and decoding rules, offering directions for children’s training. The final experimental results show that the proposed new architecture improves the accuracy of scoring 6 question graphics by 12.8% and 12 question graphics by 20.14% compared to the most relevant literature. The accuracy of the proposed new architecture surpasses the model frameworks of the most relevant literature, demonstrating the effectiveness of this approach in improving scoring accuracy and stability. Full article

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24 pages, 912 KiB

Open AccessArticle

Compatibility Model between Encapsulant Compounds and Antioxidants by the Implementation of Machine Learning

by Juliana Quintana-Rojas, Rafael Amaya-Gómez and Nicolas Ratkovich

Algorithms 2024, 17(9), 412; https://doi.org/10.3390/a17090412 - 17 Sep 2024

Abstract

The compatibility between antioxidant compounds (ACs) and wall materials (WMs) is one of the most crucial aspects of the encapsulation process, as the encapsulated compounds’ stability depends on the affinity between the compounds, which is influenced by their chemical properties. A compatibility model [...] Read more.

The compatibility between antioxidant compounds (ACs) and wall materials (WMs) is one of the most crucial aspects of the encapsulation process, as the encapsulated compounds’ stability depends on the affinity between the compounds, which is influenced by their chemical properties. A compatibility model between the encapsulant and antioxidant chemicals was built using machine learning (ML) to discover optimal matches without costly and time-consuming trial-and-error experiments. The attributes of the required antioxidant and wall material components were recollected, and two datasets were constructed. As a result, a tying process was performed to connect both datasets and identify significant relationships between parameters of ACs and WMs to define the compatibility or incompatibility of the compounds, as this was necessary to enrich the dataset by incorporating decoys. As a result, a simple statistical analysis was conducted to examine the indicated correlations between variables, and a Principal Component Analysis (PCA) was performed to reduce the dimensionality of the dataset without sacrificing essential information. The K-nearest neighbor (KNN) algorithm was used and designed to handle the classification problems of the compatibility of the combinations to integrate ML in the model. In this way, the model accuracy was 0.92, with a sensitivity of 0.84 and a specificity of 1. These results indicate that the KNN model performs well, exhibiting high accuracy and correctly classifying positive and negative combinations as evidenced by the sensitivity and specificity scores. Full article

(This article belongs to the Special Issue Algorithm Engineering in Bioinformatics)

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17 pages, 655 KiB

Open AccessArticle

A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel Engines: A Case Study

by Francesco Maione, Paolo Lino, Guido Maione and Giuseppe Giannino

Algorithms 2024, 17(9), 411; https://doi.org/10.3390/a17090411 - 14 Sep 2024

Abstract

The development of artificial intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are changing, both from a theoretical and a practical perspective. Namely, conventional maintenance reacts to faults and breakdowns [...] Read more.

The development of artificial intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are changing, both from a theoretical and a practical perspective. Namely, conventional maintenance reacts to faults and breakdowns as they occur or schedules the necessary inspections of systems and their parts at fixed times by using statistics on component failures, but this can be improved by a predictive maintenance based on the real component’s health status, which is inspected by appropriate sensors. In this way, maintenance time and costs are saved. Improvements can be achieved even in the marine industry, in which complex ship propulsion systems are produced for operation in many different scenarios. In more detail, data-driven models, through machine learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements on the asset, for a diagnosis based on the difference between expectations and observations. The first step towards realization of predictive maintenance is choosing the ML algorithm. This selection is often not the consequence of an in-depth analysis of the different algorithms available in the literature. For that reason, here the authors propose a framework to support an initial implementation stage of predictive maintenance based on a benchmarking of the most suitable ML algorithms. The comparison is tested to predict failures of the oil circuit in a diesel marine engine as a case study. The algorithms are compared by considering not only the mean squared error between the algorithm predictions and the data, but also the response time, which is a crucial variable for maintenance. The results clearly indicate the framework well supports predictive maintenance and the prediction error and running time are appropriate variables to choose the most suitable ML algorithm for prediction. Moreover, the proposed framework can be used to test different algorithms, on the basis of more performance indexes, and to apply predictive maintenance to other engine components. Full article

(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition)

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17 pages, 397 KiB

Open AccessArticle

Algorithm for Option Number Selection in Stochastic Paired Comparison Models

by László Gyarmati, Csaba Mihálykó and Éva Orbán-Mihálykó

Algorithms 2024, 17(9), 410; https://doi.org/10.3390/a17090410 - 14 Sep 2024

Abstract

In this paper, paired comparison models with a stochastic background are investigated and compared from the perspective of the option numbers allowed. As two-option and three-option models are the ones most frequently used, we mainly focus on the relationships between two-option and four-option [...] Read more.

In this paper, paired comparison models with a stochastic background are investigated and compared from the perspective of the option numbers allowed. As two-option and three-option models are the ones most frequently used, we mainly focus on the relationships between two-option and four-option models and three-option and five-option models, and then we turn to the general s- and

(s + 2)

-option models. We compare them from both theoretical and practical perspectives; the latter are based on computer simulations. We examine, when it is possible, mandatory, or advisable how to convert four-, five-, and

(s + 2)

-option models into two-, three-, and s-option models, respectively. The problem also exists in reverse: when is it advisable to use four-, five-, and

(s + 2)

-option models instead of two-, three-, and s-option models? As a result of these investigations, we set up an algorithm to perform the decision process. Full article

(This article belongs to the Special Issue Numerical Optimization and Algorithms: 2nd Edition)

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17 pages, 3327 KiB

Open AccessArticle

Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides

by Sayed Mehedi Azim, Aravind Balasubramanyam, Sheikh Rabiul Islam, Jinglin Fu and Iman Dehzangi

Algorithms 2024, 17(9), 409; https://doi.org/10.3390/a17090409 - 12 Sep 2024

Abstract

Enzymes play key roles in the biological functions of living organisms, which serve as catalysts to and regulate biochemical reaction pathways. Recent studies suggest that peptides are promising molecules for modulating enzyme function due to their advantages in large chemical diversity and well-established [...] Read more.

Enzymes play key roles in the biological functions of living organisms, which serve as catalysts to and regulate biochemical reaction pathways. Recent studies suggest that peptides are promising molecules for modulating enzyme function due to their advantages in large chemical diversity and well-established methods for library synthesis. Experimental approaches to identify protein-binding peptides are time-consuming and costly. Hence, there is a demand to develop a fast and accurate computational approach to tackle this problem. Another challenge in developing a computational approach is the lack of a large and reliable dataset. In this study, we develop a new machine learning approach called PepBind-SVM to predict protein-binding peptides. To build this model, we extract different sequential and physicochemical features from peptides and use a Support Vector Machine (SVM) as the classification technique. We train this model on the dataset that we also introduce in this study. PepBind-SVM achieves 92.1% prediction accuracy, outperforming other classifiers at predicting protein-binding peptides. Full article

(This article belongs to the Special Issue Machine Learning for Pattern Recognition (2nd Edition))

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18 pages, 302 KiB

Open AccessReview

Using Wearable Technology to Detect, Monitor, and Predict Major Depressive Disorder—A Scoping Review and Introductory Text for Clinical Professionals

by Quinty Walschots, Milan Zarchev, Maurits Unkel and Astrid Kamperman

Algorithms 2024, 17(9), 408; https://doi.org/10.3390/a17090408 - 12 Sep 2024

Abstract

The rising popularity of wearable devices allows for extensive and unobtrusive collection of personal health data for extended periods of time. Recent studies have used machine learning to create predictive algorithms to assess symptoms of major depressive disorder (MDD) based on these data. [...] Read more.

The rising popularity of wearable devices allows for extensive and unobtrusive collection of personal health data for extended periods of time. Recent studies have used machine learning to create predictive algorithms to assess symptoms of major depressive disorder (MDD) based on these data. This review evaluates the clinical relevance of these models. Studies were selected to represent the range of methodologies and applications of wearables for MDD algorithms, with a focus on wrist-worn devices. The reviewed studies demonstrated that wearable-based algorithms were able to predict symptoms of MDD with considerable accuracy. These models may be used in the clinic to complement the monitoring of treatments or to facilitate early intervention in high-risk populations. In a preventative context, they could prompt users to seek help for earlier intervention and better clinical outcomes. However, the lack of standardized methodologies and variation in which performance metrics are reported complicates direct comparisons between studies. Issues with reproducibility, overfitting, small sample sizes, and limited population demographics also limit the generalizability of findings. As such, wearable-based algorithms show considerable promise for predicting and monitoring MDD, but there is significant room for improvement before this promise can be fulfilled. Full article

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

20 pages, 4893 KiB

Open AccessArticle

Interactive 3D Vase Design Based on Gradient Boosting Decision Trees

by Dongming Wang, Xing Xu, Xuewen Xia and Heming Jia

Algorithms 2024, 17(9), 407; https://doi.org/10.3390/a17090407 - 11 Sep 2024

Abstract

Traditionally, ceramic design began with sketches on rough paper and later evolved into using CAD software for more complex designs and simulations. With technological advancements, optimization algorithms have gradually been introduced into ceramic design to enhance design efficiency and creative diversity. The use [...] Read more.

Traditionally, ceramic design began with sketches on rough paper and later evolved into using CAD software for more complex designs and simulations. With technological advancements, optimization algorithms have gradually been introduced into ceramic design to enhance design efficiency and creative diversity. The use of Interactive Genetic Algorithms (IGAs) for ceramic design is a new approach, but an IGA requires a significant amount of user evaluation, which can result in user fatigue. To overcome this problem, this paper introduces the LightGBM algorithm and the CatBoost algorithm to improve the IGA because they have excellent predictive capabilities that can assist users in evaluations. The algorithms are also applied to a vase design platform for validation. First, bicubic Bézier surfaces are used for modeling, and the genetic encoding of the vase is designed with appropriate evolutionary operators selected. Second, user data from the online platform are collected to train and optimize the LightGBM and CatBoost algorithms. Finally, LightGBM and CatBoost are combined with an IGA and applied to the vase design platform to verify their effectiveness. Comparing the improved algorithm to traditional IGAs, KD trees, Random Forest, and XGBoost, it is found that IGAs improve with LightGBM, and CatBoost performs better overall, requiring fewer evaluations and less time. Its R² is higher than other proxy models, achieving 0.816 and 0.839, respectively. The improved method proposed in this paper can effectively alleviate user fatigue and enhance the user experience in product design participation. Full article

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20 pages, 1836 KiB

Open AccessArticle

Advanced Detection of Abnormal ECG Patterns Using an Optimized LADTree Model with Enhanced Predictive Feature: Potential Application in CKD

by Muhammad Binsawad and Bilal Khan

Algorithms 2024, 17(9), 406; https://doi.org/10.3390/a17090406 - 11 Sep 2024

Abstract

Detecting abnormal ECG patterns is a crucial area of study aimed at enhancing diagnostic accuracy and enabling early identification of Chronic Kidney Disease (CKD)-related abnormalities. This study compares a unique strategy for abnormal ECG patterns using the LADTree model to standard machine learning [...] Read more.

Detecting abnormal ECG patterns is a crucial area of study aimed at enhancing diagnostic accuracy and enabling early identification of Chronic Kidney Disease (CKD)-related abnormalities. This study compares a unique strategy for abnormal ECG patterns using the LADTree model to standard machine learning (ML) models. The study design includes data collection from the MIT-BIH Arrhythmia dataset, preprocessing to address missing values, and feature selection using the CfsSubsetEval method using Best First Search, Harmony Search, and Particle Swarm Optimization Search approaches. The performance assessment consists of two scenarios: percentage splitting and K-fold cross-validation, with several evaluation measures such as Kappa statistic (KS), Best First Search, recall, precision-recall curve (PRC) area, receiver operating characteristic (ROC) area, and accuracy. In scenario 1, LADTree outperforms other ML models in terms of mean absolute error (MAE), KS, recall, ROC area, and PRC. Notably, the Naïve Bayes (NB) model has the lowest MAE, but the Support Vector Machine (SVM) performs badly. In scenario 2, NB has the lowest MAE but the highest KS, recall, ROC area, and PRC area, closely followed by LADTree. Overall, the findings indicate that the LADTree model, when optimized for ECG signal data, delivers promising results in detecting abnormal ECG patterns potentially related with CKD. This study advances predictive modeling tools for identifying abnormal ECG patterns, which could enhance early detection and management of CKD, potentially leading to improved patient outcomes and healthcare practices. Full article

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28 pages, 6717 KiB

Open AccessArticle

A Segmentation-Based Automated Corneal Ulcer Grading System for Ocular Staining Images Using Deep Learning and Hough Circle Transform

by Dulyawat Manawongsakul and Karn Patanukhom

Algorithms 2024, 17(9), 405; https://doi.org/10.3390/a17090405 - 10 Sep 2024

Abstract

Corneal ulcer is a prevalent ocular condition that requires ophthalmologists to diagnose, assess, and monitor symptoms. During examination, ophthalmologists must identify the corneal ulcer area and evaluate its severity by manually comparing ocular staining images with severity indices. However, manual assessment is time-consuming [...] Read more.

Corneal ulcer is a prevalent ocular condition that requires ophthalmologists to diagnose, assess, and monitor symptoms. During examination, ophthalmologists must identify the corneal ulcer area and evaluate its severity by manually comparing ocular staining images with severity indices. However, manual assessment is time-consuming and may provide inconsistent results. Variations can occur with repeated evaluations of the same images or with grading among different evaluators. To address this problem, we propose an automated corneal ulcer grading system for ocular staining images based on deep learning techniques and the Hough Circle Transform. The algorithm is structured into two components for cornea segmentation and corneal ulcer segmentation. Initially, we apply a deep learning method combined with the Hough Circle Transform to segment cornea areas. Subsequently, we develop the corneal ulcer segmentation model using deep learning methods. In this phase, the predicted cornea areas are utilized as masks for training the corneal ulcer segmentation models during the learning phase. Finally, this algorithm uses the results from these two components to determine two outputs: (1) the percentage of the ulcerated area on the cornea, and (2) the severity degree of the corneal ulcer based on the Type–Grade (TG) grading standard. These methodologies aim to enhance diagnostic efficiency across two key aspects: (1) ensuring consistency by delivering uniform and dependable results, and (2) enhancing robustness by effectively handling variations in eye size. In this research, our proposed method is evaluated using the SUSTech-SYSU public dataset, achieving an Intersection over Union of 89.23% for cornea segmentation and 82.94% for corneal ulcer segmentation, along with a Mean Absolute Error of 2.51% for determining the percentage of the ulcerated area on the cornea and an Accuracy of 86.15% for severity grading. Full article

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25 pages, 9129 KiB

Open AccessArticle

A Comparative Study of Maze Generation Algorithms in a Game-Based Mobile Learning Application for Learning Basic Programming Concepts

by Mia Čarapina, Ognjen Staničić, Ivica Dodig and Davor Cafuta

Algorithms 2024, 17(9), 404; https://doi.org/10.3390/a17090404 - 10 Sep 2024

Abstract

This study evaluates several maze generation algorithms applied to generate mazes in a game-based Android mobile application designed to support children in learning basic programming concepts and computational thinking. Each algorithm is assessed for its ability to generate solvable and educationally effective mazes, [...] Read more.

This study evaluates several maze generation algorithms applied to generate mazes in a game-based Android mobile application designed to support children in learning basic programming concepts and computational thinking. Each algorithm is assessed for its ability to generate solvable and educationally effective mazes, varying in complexity and size. Key findings indicate that Wilson’s and Aldous–Broder algorithms were identified as the most time inefficient. In comparison, Sidewinder and Binary Tree algorithms perform best for smaller mazes due to their straightforward traversal methods. The Hunt-and-Kill and Recursive backtracker algorithms maintain higher ratios of longest paths, making them suitable for the more complex maze generation required for advanced game levels. Additionally, the study explores various maze-solving algorithms, highlighting the efficiency of the recursive algorithm for simpler mazes and the reliability of Dijkstra’s algorithm across diverse maze structures. This research underscores the importance of selecting appropriate maze generation and solving algorithms to balance generation speed, path complexity, and navigational characteristics. While the study demonstrates the practical applicability of these algorithms in a mobile educational application, it also identifies limitations and suggests directions for future research. Full article

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

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16 pages, 3080 KiB

Open AccessArticle

Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System

by Kuansheng Zou, Yue Wang, Baowei Liu and Zhaojun Zhang

Algorithms 2024, 17(9), 403; https://doi.org/10.3390/a17090403 - 9 Sep 2024

Abstract

The widespread adoption of the power grid has led to increased attention to load frequency control (LFC) in power systems. The LFC strategy of multi-source hybrid power systems, including hydroelectric generators, Wind Turbine Generators (WTGs), and Photovoltaic Generators (PVGs), with thermal generators is [...] Read more.

The widespread adoption of the power grid has led to increased attention to load frequency control (LFC) in power systems. The LFC strategy of multi-source hybrid power systems, including hydroelectric generators, Wind Turbine Generators (WTGs), and Photovoltaic Generators (PVGs), with thermal generators is more challenging. Existing methods for LFC tasks pose challenges in achieving satisfactory outcomes in hybrid power systems. In this paper, a novel method for the multi-source hybrid power system LFC task by using an optimal active disturbance rejection control (ADRC) strategy is proposed, which is based on the combination of the improved linear quadratic regulator (LQR) and the ADRC controller. Firstly, an established model of a hybrid power system is presented, which incorporates multiple regions and multiple sources. Secondly, utilizing the state space representation, a novel control strategy is developed by integrating improved LQR and ARDC. Finally, a series of comparative simulation experiments has been conducted using the Simulink model. Compared with the LQR with ESO, the maximum relative error of the maximum peaks of frequency deviation and tie-line exchanged power of the hybrid power system is reduced by 96% and 83%, respectively, by using the proposed strategy. The experimental results demonstrate that the strategy proposed in this paper exhibits a substantial enhancement in control performance. Full article

(This article belongs to the Topic Recent Trends in Nonlinear, Chaotic and Complex Systems)

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28 pages, 6310 KiB

Open AccessArticle

Integrating Eye Movement, Finger Pressure, and Foot Pressure Information to Build an Intelligent Driving Fatigue Detection System

by Jong-Chen Chen and Yin-Zhen Chen

Algorithms 2024, 17(9), 402; https://doi.org/10.3390/a17090402 - 8 Sep 2024

Abstract

Fatigued driving is a problem that every driver will face, and traffic accidents caused by drowsy driving often occur involuntarily. If there is a fatigue detection and warning system, it is generally believed that the occurrence of some incidents can be reduced. However, [...] Read more.

Fatigued driving is a problem that every driver will face, and traffic accidents caused by drowsy driving often occur involuntarily. If there is a fatigue detection and warning system, it is generally believed that the occurrence of some incidents can be reduced. However, everyone’s driving habits and methods may differ, so it is not easy to establish a suitable general detection system. If a customized intelligent fatigue detection system can be established, it may reduce unfortunate accidents. With its potential to mitigate unfortunate accidents, this study offers hope for a safer driving environment. Thus, on the one hand, this research hopes to integrate the information obtained from three different sensing devices (eye movement, finger pressure, and plantar pressure), which are chosen for their ability to provide comprehensive and reliable data on a driver’s physical and mental state. On the other hand, it uses an autonomous learning architecture to integrate these three data types to build a customized fatigued driving detection system. This study used a system that simulated a car driving environment and then invited subjects to conduct tests on fixed driving routes. First, we demonstrated that the system established in this study could be used to learn and classify different driving clips. Then, we showed that it was possible to judge whether the driver was fatigued through a series of driving behaviors, such as lane drifting, sudden braking, and irregular acceleration, rather than a single momentary behavior. Finally, we tested the hypothesized situation in which drivers were experiencing three cases of different distractions. The results show that the entire system can establish a personal driving system through autonomous learning behavior and further detect whether fatigued driving abnormalities occur. Full article

(This article belongs to the Special Issue Algorithms for Feature Selection (2nd Edition))

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