In this paper, an algorithm is presented that calculates the module of generalized invariants. Generalized invariants are useful in the study of modular invariant theory, where the characteristic of the base field divides the order of the group. The structure theorems of generalized invariants were given by the author in 2016, and computational aspects of these results are studied in this note. Implementation codes are also provided for MAGMA, computational algebra system.
{"title":"The Computation of Generalized Invariants","authors":"Uğur Madran","doi":"10.1155/2023/2175379","DOIUrl":"https://doi.org/10.1155/2023/2175379","url":null,"abstract":"In this paper, an algorithm is presented that calculates the module of generalized invariants. Generalized invariants are useful in the study of modular invariant theory, where the characteristic of the base field divides the order of the group. The structure theorems of generalized invariants were given by the author in 2016, and computational aspects of these results are studied in this note. Implementation codes are also provided for MAGMA, computational algebra system.","PeriodicalId":43667,"journal":{"name":"Muenster Journal of Mathematics","volume":"51 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88615827","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}
Convolutional neural networks (CNNs) are often used in tasks involving vision processing, and unclear images can hinder the performance of convolutional neural networks and increase its computational time. Furthermore, artificial intelligence (AI) and machine learning (ML) are related technologies, which are considered a branch of computer science, which are used to simulate and enhance human intelligence. In e-healthcare, AI and ML can be used to optimize the workflow, automatically process large amounts of medical data, and provide effective medical decision support. In this paper, the authors take several mainstream artificial intelligence models currently open on the market for reference. In this paper, the optimized model (AL-CNN) is tested for noise image recognition, and the AL-CNN model is established by using activation functions, matrix operations, and feature recognition methods, and the noisy images are processed after custom configuration. Not only does this model require no prior preparation when processing images, but it also improves the accuracy of dealing with noise in convolutional neural networks. In the AL-CNN in this paper, the architecture of the convolutional neural network includes a noise layer and a layer that can be automatically resized. After the comparison of the recognition experiments, the accuracy rate of AL-CNN is 20% higher than that of MatConvNet-moderate, and the accuracy rate is 40% higher than that of MatConvNet-chronic. In the second set of experiments, the accuracy exceeds MXNet and TensorFlow by 50% and 70%, respectively. In addition, the authors optimized the convolutional layer, pooling layer, and loss function of AL-CNN in different parameters, which improved the stability of noise processing, respectively. After customizing the two configuration optimizations, the authors found that the second optimized AL-CNN has higher recognition accuracy, and after the optimization test, the error rate can be continuously decreased as the number of recognition increases in a very short number of times.
{"title":"Establishment and Test Effect of Artificial Intelligence Optimization Model Based on Convolutional Neural Network","authors":"Chunrong Zhou, Zhenghong Jiang","doi":"10.1155/2023/4216012","DOIUrl":"https://doi.org/10.1155/2023/4216012","url":null,"abstract":"Convolutional neural networks (CNNs) are often used in tasks involving vision processing, and unclear images can hinder the performance of convolutional neural networks and increase its computational time. Furthermore, artificial intelligence (AI) and machine learning (ML) are related technologies, which are considered a branch of computer science, which are used to simulate and enhance human intelligence. In e-healthcare, AI and ML can be used to optimize the workflow, automatically process large amounts of medical data, and provide effective medical decision support. In this paper, the authors take several mainstream artificial intelligence models currently open on the market for reference. In this paper, the optimized model (AL-CNN) is tested for noise image recognition, and the AL-CNN model is established by using activation functions, matrix operations, and feature recognition methods, and the noisy images are processed after custom configuration. Not only does this model require no prior preparation when processing images, but it also improves the accuracy of dealing with noise in convolutional neural networks. In the AL-CNN in this paper, the architecture of the convolutional neural network includes a noise layer and a layer that can be automatically resized. After the comparison of the recognition experiments, the accuracy rate of AL-CNN is 20% higher than that of MatConvNet-moderate, and the accuracy rate is 40% higher than that of MatConvNet-chronic. In the second set of experiments, the accuracy exceeds MXNet and TensorFlow by 50% and 70%, respectively. In addition, the authors optimized the convolutional layer, pooling layer, and loss function of AL-CNN in different parameters, which improved the stability of noise processing, respectively. After customizing the two configuration optimizations, the authors found that the second optimized AL-CNN has higher recognition accuracy, and after the optimization test, the error rate can be continuously decreased as the number of recognition increases in a very short number of times.","PeriodicalId":43667,"journal":{"name":"Muenster Journal of Mathematics","volume":"5 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88910787","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 purpose of research, invention, and production is to effectively supply demand, which requires not only financial support but also time to complete. First, the differential dynamic model among funds, demand, and research (including invention and production) with time delay is constructed by using the predator-prey theory. Second, the sufficiency of local asymptotic stability of the positive equilibrium point of the model is obtained by using the Hopf bifurcation theory and stability theory. Then, the formulas for determining the direction of Hopf bifurcation and the stability of the periodic solution are calculated by using the central manifold theory and normative theory. Finally, the evolution process of the differential dynamic model with controlled time delay is numerically simulated so as to verify the correctness of the relevant analytical conclusions.
{"title":"Dynamic Analysis of the Project Investment Model with Time Delay and Density Constraints","authors":"Debao Gao","doi":"10.1155/2023/3791676","DOIUrl":"https://doi.org/10.1155/2023/3791676","url":null,"abstract":"The purpose of research, invention, and production is to effectively supply demand, which requires not only financial support but also time to complete. First, the differential dynamic model among funds, demand, and research (including invention and production) with time delay is constructed by using the predator-prey theory. Second, the sufficiency of local asymptotic stability of the positive equilibrium point of the model is obtained by using the Hopf bifurcation theory and stability theory. Then, the formulas for determining the direction of Hopf bifurcation and the stability of the periodic solution are calculated by using the central manifold theory and normative theory. Finally, the evolution process of the differential dynamic model with controlled time delay is numerically simulated so as to verify the correctness of the relevant analytical conclusions.","PeriodicalId":43667,"journal":{"name":"Muenster Journal of Mathematics","volume":"16 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82010618","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}