Two-Derivative Runge–Kutta methods have been proposed by Chan and Tsai in 2010 and order conditions up to the fifth order are given. In this work, for the first time, we derive order conditions for order six. Simplifying assumptions that reduce the number of order conditions are also given. The procedure for constructing sixth-order methods is presented. A specific method is derived in order to illustrate the procedure; this method is of the sixth algebraic order with a reduced phase-lag and amplification error. For numerical comparison, five well-known test problems have been solved using a seventh-order Two-Derivative Runge–Kutta method developed by Chan and Tsai and several Runge–Kutta methods of orders 6 and 8. Diagrams of the maximum absolute error vs. computation time show the efficiency of the new method.
{"title":"Construction of Two-Derivative Runge–Kutta Methods of Order Six","authors":"Z. Kalogiratou, T. Monovasilis","doi":"10.3390/a16120558","DOIUrl":"https://doi.org/10.3390/a16120558","url":null,"abstract":"Two-Derivative Runge–Kutta methods have been proposed by Chan and Tsai in 2010 and order conditions up to the fifth order are given. In this work, for the first time, we derive order conditions for order six. Simplifying assumptions that reduce the number of order conditions are also given. The procedure for constructing sixth-order methods is presented. A specific method is derived in order to illustrate the procedure; this method is of the sixth algebraic order with a reduced phase-lag and amplification error. For numerical comparison, five well-known test problems have been solved using a seventh-order Two-Derivative Runge–Kutta method developed by Chan and Tsai and several Runge–Kutta methods of orders 6 and 8. Diagrams of the maximum absolute error vs. computation time show the efficiency of the new method.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"13 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138596633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quadratic unconstrained binary optimization (QUBO) is a classic NP-hard problem with an enormous number of applications. Local search strategy (LSS) is one of the most fundamental algorithmic concepts and has been successfully applied to a wide range of hard combinatorial optimization problems. One LSS that has gained the attention of researchers is the r-flip (also known as r-Opt) strategy. Given a binary solution with n variables, the r-flip strategy “flips” r binary variables to obtain a new solution if the changes improve the objective function. The main purpose of this paper is to develop several results for the implementation of r-flip moves in QUBO, including a necessary and sufficient condition that when a 1-flip search reaches local optimality, the number of candidates for implementation of the r-flip moves can be reduced significantly. The results of the substantial computational experiments are reported to compare an r-flip strategy-embedded algorithm and a multiple start tabu search algorithm on a set of benchmark instances and three very-large-scale QUBO instances. The r-flip strategy implemented within the algorithm makes the algorithm very efficient, leading to very high-quality solutions within a short CPU time.
{"title":"An Efficient Closed-Form Formula for Evaluating r-Flip Moves in Quadratic Unconstrained Binary Optimization","authors":"B. Alidaee, Haibo Wang, L. Sua","doi":"10.3390/a16120557","DOIUrl":"https://doi.org/10.3390/a16120557","url":null,"abstract":"Quadratic unconstrained binary optimization (QUBO) is a classic NP-hard problem with an enormous number of applications. Local search strategy (LSS) is one of the most fundamental algorithmic concepts and has been successfully applied to a wide range of hard combinatorial optimization problems. One LSS that has gained the attention of researchers is the r-flip (also known as r-Opt) strategy. Given a binary solution with n variables, the r-flip strategy “flips” r binary variables to obtain a new solution if the changes improve the objective function. The main purpose of this paper is to develop several results for the implementation of r-flip moves in QUBO, including a necessary and sufficient condition that when a 1-flip search reaches local optimality, the number of candidates for implementation of the r-flip moves can be reduced significantly. The results of the substantial computational experiments are reported to compare an r-flip strategy-embedded algorithm and a multiple start tabu search algorithm on a set of benchmark instances and three very-large-scale QUBO instances. The r-flip strategy implemented within the algorithm makes the algorithm very efficient, leading to very high-quality solutions within a short CPU time.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"121 11","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. K. Alzahrani, A. Alsheikhy, T. Shawly, Ahmed Azzahrani, Y. Said
Blood cancer occurs due to changes in white blood cells (WBCs). These changes are known as leukemia. Leukemia occurs mostly in children and affects their tissues or plasma. However, it could occur in adults. This disease becomes fatal and causes death if it is discovered and diagnosed late. In addition, leukemia can occur from genetic mutations. Therefore, there is a need to detect it early to save a patient’s life. Recently, researchers have developed various methods to detect leukemia using different technologies. Deep learning approaches (DLAs) have been widely utilized because of their high accuracy. However, some of these methods are time-consuming and costly. Thus, a need for a practical solution with low cost and higher accuracy is required. This article proposes a novel segmentation and classification framework model to discover and categorize leukemia using a deep learning structure. The proposed system encompasses two main parts, which are a deep learning technology to perform segmentation and characteristic extraction and classification on the segmented section. A new UNET architecture is developed to provide the segmentation and feature extraction processes. Various experiments were performed on four datasets to evaluate the model using numerous performance factors, including precision, recall, F-score, and Dice Similarity Coefficient (DSC). It achieved an average 97.82% accuracy for segmentation and categorization. In addition, 98.64% was achieved for F-score. The obtained results indicate that the presented method is a powerful technique for discovering leukemia and categorizing it into suitable groups. Furthermore, the model outperforms some of the implemented methods. The proposed system can assist healthcare providers in their services.
{"title":"A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis","authors":"A. K. Alzahrani, A. Alsheikhy, T. Shawly, Ahmed Azzahrani, Y. Said","doi":"10.3390/a16120556","DOIUrl":"https://doi.org/10.3390/a16120556","url":null,"abstract":"Blood cancer occurs due to changes in white blood cells (WBCs). These changes are known as leukemia. Leukemia occurs mostly in children and affects their tissues or plasma. However, it could occur in adults. This disease becomes fatal and causes death if it is discovered and diagnosed late. In addition, leukemia can occur from genetic mutations. Therefore, there is a need to detect it early to save a patient’s life. Recently, researchers have developed various methods to detect leukemia using different technologies. Deep learning approaches (DLAs) have been widely utilized because of their high accuracy. However, some of these methods are time-consuming and costly. Thus, a need for a practical solution with low cost and higher accuracy is required. This article proposes a novel segmentation and classification framework model to discover and categorize leukemia using a deep learning structure. The proposed system encompasses two main parts, which are a deep learning technology to perform segmentation and characteristic extraction and classification on the segmented section. A new UNET architecture is developed to provide the segmentation and feature extraction processes. Various experiments were performed on four datasets to evaluate the model using numerous performance factors, including precision, recall, F-score, and Dice Similarity Coefficient (DSC). It achieved an average 97.82% accuracy for segmentation and categorization. In addition, 98.64% was achieved for F-score. The obtained results indicate that the presented method is a powerful technique for discovering leukemia and categorizing it into suitable groups. Furthermore, the model outperforms some of the implemented methods. The proposed system can assist healthcare providers in their services.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"125 40","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
José Manuel Porras, J. Lara, Cristóbal Romero, Sebastián Ventura
Predicting student dropout is a crucial task in online education. Traditionally, each educational entity (institution, university, faculty, department, etc.) creates and uses its own prediction model starting from its own data. However, that approach is not always feasible or advisable and may depend on the availability of data, local infrastructure, and resources. In those cases, there are various machine learning approaches for sharing data and/or models between educational entities, using a classical centralized machine learning approach or other more advanced approaches such as transfer learning or federated learning. In this paper, we used data from three different LMS Moodle servers representing homogeneous different-sized educational entities. We tested the performance of the different machine learning approaches for the problem of predicting student dropout with multiple educational entities involved. We used a deep learning algorithm as a predictive classifier method. Our preliminary findings provide useful information on the benefits and drawbacks of each approach, as well as suggestions for enhancing performance when there are multiple institutions. In our case, repurposed transfer learning, stacked transfer learning, and centralized approaches produced similar or better results than the locally trained models for most of the entities.
{"title":"A Case-Study Comparison of Machine Learning Approaches for Predicting Student’s Dropout from Multiple Online Educational Entities","authors":"José Manuel Porras, J. Lara, Cristóbal Romero, Sebastián Ventura","doi":"10.3390/a16120554","DOIUrl":"https://doi.org/10.3390/a16120554","url":null,"abstract":"Predicting student dropout is a crucial task in online education. Traditionally, each educational entity (institution, university, faculty, department, etc.) creates and uses its own prediction model starting from its own data. However, that approach is not always feasible or advisable and may depend on the availability of data, local infrastructure, and resources. In those cases, there are various machine learning approaches for sharing data and/or models between educational entities, using a classical centralized machine learning approach or other more advanced approaches such as transfer learning or federated learning. In this paper, we used data from three different LMS Moodle servers representing homogeneous different-sized educational entities. We tested the performance of the different machine learning approaches for the problem of predicting student dropout with multiple educational entities involved. We used a deep learning algorithm as a predictive classifier method. Our preliminary findings provide useful information on the benefits and drawbacks of each approach, as well as suggestions for enhancing performance when there are multiple institutions. In our case, repurposed transfer learning, stacked transfer learning, and centralized approaches produced similar or better results than the locally trained models for most of the entities.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"45 19","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138605469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recognizing human actions can help in numerous ways, such as health monitoring, intelligent surveillance, virtual reality and human–computer interaction. A quick and accurate detection algorithm is required for daily real-time detection. This paper first proposes to generate a lightweight graph neural network by self-distillation for human action recognition tasks. The lightweight graph neural network was evaluated on the NTU-RGB+D dataset. The results demonstrate that, with competitive accuracy, the heavyweight graph neural network can be compressed by up to 80%. Furthermore, the learned representations have denser clusters, estimated by the Davies–Bouldin index, the Dunn index and silhouette coefficients. The ideal input data and algorithm capacity are also discussed.
{"title":"A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation","authors":"Miao Feng, Jean Meunier","doi":"10.3390/a16120552","DOIUrl":"https://doi.org/10.3390/a16120552","url":null,"abstract":"Recognizing human actions can help in numerous ways, such as health monitoring, intelligent surveillance, virtual reality and human–computer interaction. A quick and accurate detection algorithm is required for daily real-time detection. This paper first proposes to generate a lightweight graph neural network by self-distillation for human action recognition tasks. The lightweight graph neural network was evaluated on the NTU-RGB+D dataset. The results demonstrate that, with competitive accuracy, the heavyweight graph neural network can be compressed by up to 80%. Furthermore, the learned representations have denser clusters, estimated by the Davies–Bouldin index, the Dunn index and silhouette coefficients. The ideal input data and algorithm capacity are also discussed.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"7 5","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juan Cisneros, Alain Lalande, Binnaz Yalcin, Fabrice Meriaudeau, Stephan Collins
Using a high-throughput neuroanatomical screen of histological brain sections developed in collaboration with the International Mouse Phenotyping Consortium, we previously reported a list of 198 genes whose inactivation leads to neuroanatomical phenotypes. To achieve this milestone, tens of thousands of hours of manual image segmentation were necessary. The present work involved developing a full pipeline to automate the application of deep learning methods for the automated segmentation of 24 anatomical regions used in the aforementioned screen. The dataset includes 2000 annotated parasagittal slides (24,000 × 14,000 pixels). Our approach consists of three main parts: the conversion of images (.ROI to .PNG), the training of the deep learning approach on the compressed images (512 × 256 and 2048 × 1024 pixels of the deep learning approach) to extract the regions of interest using either the U-Net or Attention U-Net architectures, and finally the transformation of the identified regions (.PNG to .ROI), enabling visualization and editing within the Fiji/ImageJ 1.54 software environment. With an image resolution of 2048 × 1024, the Attention U-Net provided the best results with an overall Dice Similarity Coefficient (DSC) of 0.90 ± 0.01 for all 24 regions. Using one command line, the end-user is now able to pre-analyze images automatically, then runs the existing analytical pipeline made of ImageJ macros to validate the automatically generated regions of interest resulting. Even for regions with low DSC, expert neuroanatomists rarely correct the results. We estimate a time savings of 6 to 10 times.
{"title":"Automatic Segmentation of Histological Images of Mouse Brains","authors":"Juan Cisneros, Alain Lalande, Binnaz Yalcin, Fabrice Meriaudeau, Stephan Collins","doi":"10.3390/a16120553","DOIUrl":"https://doi.org/10.3390/a16120553","url":null,"abstract":"Using a high-throughput neuroanatomical screen of histological brain sections developed in collaboration with the International Mouse Phenotyping Consortium, we previously reported a list of 198 genes whose inactivation leads to neuroanatomical phenotypes. To achieve this milestone, tens of thousands of hours of manual image segmentation were necessary. The present work involved developing a full pipeline to automate the application of deep learning methods for the automated segmentation of 24 anatomical regions used in the aforementioned screen. The dataset includes 2000 annotated parasagittal slides (24,000 × 14,000 pixels). Our approach consists of three main parts: the conversion of images (.ROI to .PNG), the training of the deep learning approach on the compressed images (512 × 256 and 2048 × 1024 pixels of the deep learning approach) to extract the regions of interest using either the U-Net or Attention U-Net architectures, and finally the transformation of the identified regions (.PNG to .ROI), enabling visualization and editing within the Fiji/ImageJ 1.54 software environment. With an image resolution of 2048 × 1024, the Attention U-Net provided the best results with an overall Dice Similarity Coefficient (DSC) of 0.90 ± 0.01 for all 24 regions. Using one command line, the end-user is now able to pre-analyze images automatically, then runs the existing analytical pipeline made of ImageJ macros to validate the automatically generated regions of interest resulting. Even for regions with low DSC, expert neuroanatomists rarely correct the results. We estimate a time savings of 6 to 10 times.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"5 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138627079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As computer networks become increasingly important in various domains, the need for secure and reliable networks becomes more pressing, particularly in the context of blockchain-enabled supply chain networks. One way to ensure network security is by using intrusion detection systems (IDSs), which are specialised devices that detect anomalies and attacks in the network. However, these systems are vulnerable to data poisoning attacks, such as label and distance-based flipping, which can undermine their effectiveness within blockchain-enabled supply chain networks. In this research paper, we investigate the effect of these attacks on a network intrusion detection system using several machine learning models, including logistic regression, random forest, SVC, and XGB Classifier, and evaluate each model via their F1 Score, confusion matrix, and accuracy. We run each model three times: once without any attack, once with random label flipping with a randomness of 20%, and once with distance-based label flipping attacks with a distance threshold of 0.5. Additionally, this research tests an eight-layer neural network using accuracy metrics and a classification report library. The primary goal of this research is to provide insights into the effect of data poisoning attacks on machine learning models within the context of blockchain-enabled supply chain networks. By doing so, we aim to contribute to developing more robust intrusion detection systems tailored to the specific challenges of securing blockchain-based supply chain networks.
随着计算机网络在各个领域变得越来越重要，对安全可靠网络的需求也变得更加迫切，特别是在区块链支持的供应链网络中。确保网络安全的一种方法是使用入侵检测系统（IDS），这是一种检测网络异常和攻击的专用设备。然而，这些系统很容易受到数据中毒攻击，如基于标签和距离的翻转，这会削弱它们在区块链供应链网络中的有效性。在本研究论文中，我们使用几种机器学习模型（包括逻辑回归、随机森林、SVC 和 XGB 分类器）研究了这些攻击对网络入侵检测系统的影响，并通过 F1 分数、混淆矩阵和准确率对每个模型进行了评估。我们将每个模型运行三次：一次不带任何攻击，一次是随机标签翻转（随机性为 20%），一次是基于距离的标签翻转攻击（距离阈值为 0.5）。此外，本研究还使用准确度指标和分类报告库测试了八层神经网络。本研究的主要目标是深入了解在区块链支持的供应链网络中，数据中毒攻击对机器学习模型的影响。通过这样做，我们旨在为开发更强大的入侵检测系统做出贡献，该系统是针对确保基于区块链的供应链网络安全所面临的具体挑战而量身定制的。
{"title":"Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks","authors":"Usman Javed Butt, Osama Hussien, Krison Hasanaj, Khaled Shaalan, Bilal Hassan, Haider al-Khateeb","doi":"10.3390/a16120549","DOIUrl":"https://doi.org/10.3390/a16120549","url":null,"abstract":"As computer networks become increasingly important in various domains, the need for secure and reliable networks becomes more pressing, particularly in the context of blockchain-enabled supply chain networks. One way to ensure network security is by using intrusion detection systems (IDSs), which are specialised devices that detect anomalies and attacks in the network. However, these systems are vulnerable to data poisoning attacks, such as label and distance-based flipping, which can undermine their effectiveness within blockchain-enabled supply chain networks. In this research paper, we investigate the effect of these attacks on a network intrusion detection system using several machine learning models, including logistic regression, random forest, SVC, and XGB Classifier, and evaluate each model via their F1 Score, confusion matrix, and accuracy. We run each model three times: once without any attack, once with random label flipping with a randomness of 20%, and once with distance-based label flipping attacks with a distance threshold of 0.5. Additionally, this research tests an eight-layer neural network using accuracy metrics and a classification report library. The primary goal of this research is to provide insights into the effect of data poisoning attacks on machine learning models within the context of blockchain-enabled supply chain networks. By doing so, we aim to contribute to developing more robust intrusion detection systems tailored to the specific challenges of securing blockchain-based supply chain networks.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"52 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139209509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid and accurate detection of orthopedic medical devices is pivotal in enhancing health care delivery, particularly by improving workflow efficiency. Despite advancements in medical imaging technology, current detection models often fail to meet the unique requirements of orthopedic device detection. To address this gap, we introduce OrthoDETR, a Transformer-based object detection model specifically designed and optimized for orthopedic medical devices. OrthoDETR is an evolution of the DETR (Detection Transformer) model, with several key modifications to better serve orthopedic applications. We replace the ResNet backbone with the MLP-Mixer, improve the multi-head self-attention mechanism, and refine the loss function for more accurate detections. In our comparative study, OrthoDETR outperformed other models, achieving an AP50 score of 0.897, an AP50:95 score of 0.864, an AR50:95 score of 0.895, and a frame per second (FPS) rate of 26. This represents a significant improvement over the DETR model, which achieved an AP50 score of 0.852, an AP50:95 score of 0.842, an AR50:95 score of 0.862, and an FPS rate of 20. OrthoDETR not only accelerates the detection process but also maintains an acceptable performance trade-off. The real-world impact of this model is substantial. By facilitating the precise and quick detection of orthopedic devices, OrthoDETR can potentially revolutionize the management of orthopedic workflows, improving patient care, and enhancing the efficiency of healthcare systems. This paper underlines the significance of specialized object detection models in orthopedics and sets the stage for further research in this direction.
{"title":"OrthoDETR: A Streamlined Transformer-Based Approach for Precision Detection of Orthopedic Medical Devices","authors":"Xiaobo Zhang, Huashun Li, Jingzhao Li, Xuehai Zhou","doi":"10.3390/a16120550","DOIUrl":"https://doi.org/10.3390/a16120550","url":null,"abstract":"The rapid and accurate detection of orthopedic medical devices is pivotal in enhancing health care delivery, particularly by improving workflow efficiency. Despite advancements in medical imaging technology, current detection models often fail to meet the unique requirements of orthopedic device detection. To address this gap, we introduce OrthoDETR, a Transformer-based object detection model specifically designed and optimized for orthopedic medical devices. OrthoDETR is an evolution of the DETR (Detection Transformer) model, with several key modifications to better serve orthopedic applications. We replace the ResNet backbone with the MLP-Mixer, improve the multi-head self-attention mechanism, and refine the loss function for more accurate detections. In our comparative study, OrthoDETR outperformed other models, achieving an AP50 score of 0.897, an AP50:95 score of 0.864, an AR50:95 score of 0.895, and a frame per second (FPS) rate of 26. This represents a significant improvement over the DETR model, which achieved an AP50 score of 0.852, an AP50:95 score of 0.842, an AR50:95 score of 0.862, and an FPS rate of 20. OrthoDETR not only accelerates the detection process but also maintains an acceptable performance trade-off. The real-world impact of this model is substantial. By facilitating the precise and quick detection of orthopedic devices, OrthoDETR can potentially revolutionize the management of orthopedic workflows, improving patient care, and enhancing the efficiency of healthcare systems. This paper underlines the significance of specialized object detection models in orthopedics and sets the stage for further research in this direction.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139213500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial neural networks have changed many fields by giving scientists a strong way to model complex phenomena. They are also becoming increasingly useful for solving various difficult scientific problems. Still, people keep trying to find faster and more accurate ways to simulate dynamic systems. This research explores the transformative capabilities of physics-informed neural networks, a specialized subset of artificial neural networks, in modeling complex dynamical systems with enhanced speed and accuracy. These networks incorporate known physical laws into the learning process, ensuring predictions remain consistent with fundamental principles, which is crucial when dealing with scientific phenomena. This study focuses on optimizing the application of this specialized network for simultaneous system dynamics simulations and learning time-varying parameters, particularly when the number of unknowns in the system matches the number of undetermined parameters. Additionally, we explore scenarios with a mismatch between parameters and equations, optimizing network architecture to enhance convergence speed, computational efficiency, and accuracy in learning the time-varying parameter. Our approach enhances the algorithm’s performance and accuracy, ensuring optimal use of computational resources and yielding more precise results. Extensive experiments are conducted on four different dynamical systems: first-order irreversible chain reactions, biomass transfer, the Brusselsator model, and the Lotka-Volterra model, using synthetically generated data to validate our approach. Additionally, we apply our method to the susceptible-infected-recovered model, utilizing real-world COVID-19 data to learn the time-varying parameters of the pandemic’s spread. A comprehensive comparison between the performance of our approach and fully connected deep neural networks is presented, evaluating both accuracy and computational efficiency in parameter identification and system dynamics capture. The results demonstrate that the physics-informed neural networks outperform fully connected deep neural networks in performance, especially with increased network depth, making them ideal for real-time complex system modeling. This underscores the physics-informed neural network’s effectiveness in scientific modeling in scenarios with balanced unknowns and parameters. Furthermore, it provides a fast, accurate, and efficient alternative for analyzing dynamic systems.
{"title":"Optimizing Physics-Informed Neural Network in Dynamic System Simulation and Learning of Parameters","authors":"Ebenezer O. Oluwasakin, Abdul Q. M. Khaliq","doi":"10.3390/a16120547","DOIUrl":"https://doi.org/10.3390/a16120547","url":null,"abstract":"Artificial neural networks have changed many fields by giving scientists a strong way to model complex phenomena. They are also becoming increasingly useful for solving various difficult scientific problems. Still, people keep trying to find faster and more accurate ways to simulate dynamic systems. This research explores the transformative capabilities of physics-informed neural networks, a specialized subset of artificial neural networks, in modeling complex dynamical systems with enhanced speed and accuracy. These networks incorporate known physical laws into the learning process, ensuring predictions remain consistent with fundamental principles, which is crucial when dealing with scientific phenomena. This study focuses on optimizing the application of this specialized network for simultaneous system dynamics simulations and learning time-varying parameters, particularly when the number of unknowns in the system matches the number of undetermined parameters. Additionally, we explore scenarios with a mismatch between parameters and equations, optimizing network architecture to enhance convergence speed, computational efficiency, and accuracy in learning the time-varying parameter. Our approach enhances the algorithm’s performance and accuracy, ensuring optimal use of computational resources and yielding more precise results. Extensive experiments are conducted on four different dynamical systems: first-order irreversible chain reactions, biomass transfer, the Brusselsator model, and the Lotka-Volterra model, using synthetically generated data to validate our approach. Additionally, we apply our method to the susceptible-infected-recovered model, utilizing real-world COVID-19 data to learn the time-varying parameters of the pandemic’s spread. A comprehensive comparison between the performance of our approach and fully connected deep neural networks is presented, evaluating both accuracy and computational efficiency in parameter identification and system dynamics capture. The results demonstrate that the physics-informed neural networks outperform fully connected deep neural networks in performance, especially with increased network depth, making them ideal for real-time complex system modeling. This underscores the physics-informed neural network’s effectiveness in scientific modeling in scenarios with balanced unknowns and parameters. Furthermore, it provides a fast, accurate, and efficient alternative for analyzing dynamic systems.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"93 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139216705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Biomedical imaging is a broad field concerning image capture for diagnostic and therapeutic purposes [...]
生物医学成像是一个广泛的领域，涉及用于诊断和治疗目的的图像捕捉 [...]
{"title":"Special Issue on “Algorithms for Biomedical Image Analysis and Processing”","authors":"L. Antonelli, Lucia Maddalena","doi":"10.3390/a16120544","DOIUrl":"https://doi.org/10.3390/a16120544","url":null,"abstract":"Biomedical imaging is a broad field concerning image capture for diagnostic and therapeutic purposes [...]","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"23 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139227007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：