Based on the development of the 3D vascular printer, the forming process of ink from the nozzle to the rotating rod was studied. In this study, to online detect the ink flow from the nozzle during 3D bioprinting of tubular tissue, we established a geometric model according to the region of interest (ROI) of the ink flow picture of 3D printing of tubular tissue, selected description features of the ink contour, and studied how to select mathematical expressions of the features. Principal component analysis (PCA) was used to simplify the image features into 15 features. We used a back propagation (BP) neural network to predict the printing ink flow. The results show that the error between the actual ink flow rate and the flow rate based on the BP neural network is within 5%. The BP neural network can be used to monitor the quality status of the printing target in real time, evaluate the 3D bioprinting quality online, and predict the printing ink flow for the subsequent improvement of the 3D bioprinting accuracy of tubular tissue.
在开发三维血管打印机的基础上，研究了墨水从喷嘴到旋转杆的成型过程。在本研究中，为了在线检测管状组织三维生物打印过程中从喷嘴流出的墨水，我们根据管状组织三维打印墨水流动图片的感兴趣区（ROI）建立了一个几何模型，选择了墨水轮廓的描述特征，并研究了如何选择特征的数学表达式。我们使用主成分分析法（PCA）将图像特征简化为 15 个特征。我们使用反向传播（BP）神经网络预测印刷油墨流量。结果表明，实际油墨流量与基于 BP 神经网络的流量之间的误差在 5%以内。BP 神经网络可用于实时监控打印目标的质量状态，在线评估三维生物打印质量，并预测打印墨水流量，从而提高管状组织的三维生物打印精度。
{"title":"Prediction of ink flow for 3D bioprinting of tubular tissue based on a back propagation neural network","authors":"Xiaoyan Wu, Shu Wang","doi":"10.3233/jcm-226991","DOIUrl":"https://doi.org/10.3233/jcm-226991","url":null,"abstract":"Based on the development of the 3D vascular printer, the forming process of ink from the nozzle to the rotating rod was studied. In this study, to online detect the ink flow from the nozzle during 3D bioprinting of tubular tissue, we established a geometric model according to the region of interest (ROI) of the ink flow picture of 3D printing of tubular tissue, selected description features of the ink contour, and studied how to select mathematical expressions of the features. Principal component analysis (PCA) was used to simplify the image features into 15 features. We used a back propagation (BP) neural network to predict the printing ink flow. The results show that the error between the actual ink flow rate and the flow rate based on the BP neural network is within 5%. The BP neural network can be used to monitor the quality status of the printing target in real time, evaluate the 3D bioprinting quality online, and predict the printing ink flow for the subsequent improvement of the 3D bioprinting accuracy of tubular tissue.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"78 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999652","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}
Jiantao Cui, Zhongzhou Du, Kejian Liu, Junpeng Cao
With the upgrading of the traditional home industry, the deep integration of home design and Internet of Things technology is the development direction of modern smart home design, and it creates a comfortable, energy-saving and safe working and living environment for people. This paper designs an application scenario of the Internet of Things in smart home design, integrates smart home, Internet, Internet of Things and other technologies, designs the overall architecture, topology and IP address allocation scheme of the system, and realizes the intelligent door control and temperature control system of smart home based on the simulation platform. The whole network is connected to ISP Internet through home gateway and mobile data. After testing, the system runs stably as a whole, thus providing a solution for the application of the Internet of Things in smart home.
{"title":"The simulation design of smart home system based on Internet of Things","authors":"Jiantao Cui, Zhongzhou Du, Kejian Liu, Junpeng Cao","doi":"10.3233/jcm-226995","DOIUrl":"https://doi.org/10.3233/jcm-226995","url":null,"abstract":"With the upgrading of the traditional home industry, the deep integration of home design and Internet of Things technology is the development direction of modern smart home design, and it creates a comfortable, energy-saving and safe working and living environment for people. This paper designs an application scenario of the Internet of Things in smart home design, integrates smart home, Internet, Internet of Things and other technologies, designs the overall architecture, topology and IP address allocation scheme of the system, and realizes the intelligent door control and temperature control system of smart home based on the simulation platform. The whole network is connected to ISP Internet through home gateway and mobile data. After testing, the system runs stably as a whole, thus providing a solution for the application of the Internet of Things in smart home.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"84 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998651","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 a result of alkali ASP flooding in oil and gas fields, strata and pipelines become seriously scaled, which poses a threat to the normal operation of crude oil production. We propose an intelligent knowledge reasoning model for dynamic scaling prediction in order to address the problems of high directivity, poor generalization ability, and poor application effect of existing scaling prediction methods. The model framework includes the knowledge acquisition layer which mainly relates to the manual acquisition of scaling prediction knowledge and the intelligent training of the knowledge base, and it includes the knowledge modeling layer that provides a set of standard domain common ontology and knowledge organization system using the ontology modeling technology, it also includes the knowledge inference layer which is the application layer of the model. The three layers collaborate and finally complete the scaling prediction through inference and expression. A total of 238 wells were selected for experimentation in the northern development area of the Xingshugang Oilfield. Experimental results indicate that the model has the highest accuracy of 91.87%. Additionally, the time series prediction trend for the six ions matches the trend of change in ion concentration in the scaling state, verifying the accuracy of the model’s predictions.
{"title":"Research on prediction model of scaling in ASP flooding based on data mining","authors":"Yanan Hu, Mingyang Lv","doi":"10.3233/jcm227003","DOIUrl":"https://doi.org/10.3233/jcm227003","url":null,"abstract":"As a result of alkali ASP flooding in oil and gas fields, strata and pipelines become seriously scaled, which poses a threat to the normal operation of crude oil production. We propose an intelligent knowledge reasoning model for dynamic scaling prediction in order to address the problems of high directivity, poor generalization ability, and poor application effect of existing scaling prediction methods. The model framework includes the knowledge acquisition layer which mainly relates to the manual acquisition of scaling prediction knowledge and the intelligent training of the knowledge base, and it includes the knowledge modeling layer that provides a set of standard domain common ontology and knowledge organization system using the ontology modeling technology, it also includes the knowledge inference layer which is the application layer of the model. The three layers collaborate and finally complete the scaling prediction through inference and expression. A total of 238 wells were selected for experimentation in the northern development area of the Xingshugang Oilfield. Experimental results indicate that the model has the highest accuracy of 91.87%. Additionally, the time series prediction trend for the six ions matches the trend of change in ion concentration in the scaling state, verifying the accuracy of the model’s predictions.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"45 19","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138995734","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}
Randomness of grain boundaries makes it difficult to reach a broad consensus about mechanical properties of polycrystalline graphene (PG). In the present paper, based on principle of Voronoi diagram, the models of PG with different grain sizes were established, and the fracture mechanism and mechanical properties were investigated by molecular dynamics (MD). The results showed that the crack initiation point of PG always located at the multiple junction of grain boundaries, and the crack propagation and fracture mode of PG was mainly dependent on not only the relative size but also the relative location of the indenter and grain boundaries. Additionally, the effects of grain size, indentation speed, temperature and indenter diameter on the mechanical properties were studied, which showed some interesting and different phenomena from the tensile case, e.g., the grain size seems no regular effect on mechanical properties. Furthermore, the ultimate indentation force, indentation depth and fracture showed an increase trend with the increase of indenter diameter and indentation speed, while they decreased with the increase of temperature. But when it came to the elastic modulus, it showed a decreasing trend with the increase of indenter diameter and indentation speed, while it first increased and then decreased with the increase of temperature.
{"title":"Research on fracture mechanism and mechanical properties of polycrystalline graphene by nanoindentation: A molecular dynamics study","authors":"Yingsheng Wang, Yongkun Liu, Sha Ding","doi":"10.3233/jcm-226966","DOIUrl":"https://doi.org/10.3233/jcm-226966","url":null,"abstract":"Randomness of grain boundaries makes it difficult to reach a broad consensus about mechanical properties of polycrystalline graphene (PG). In the present paper, based on principle of Voronoi diagram, the models of PG with different grain sizes were established, and the fracture mechanism and mechanical properties were investigated by molecular dynamics (MD). The results showed that the crack initiation point of PG always located at the multiple junction of grain boundaries, and the crack propagation and fracture mode of PG was mainly dependent on not only the relative size but also the relative location of the indenter and grain boundaries. Additionally, the effects of grain size, indentation speed, temperature and indenter diameter on the mechanical properties were studied, which showed some interesting and different phenomena from the tensile case, e.g., the grain size seems no regular effect on mechanical properties. Furthermore, the ultimate indentation force, indentation depth and fracture showed an increase trend with the increase of indenter diameter and indentation speed, while they decreased with the increase of temperature. But when it came to the elastic modulus, it showed a decreasing trend with the increase of indenter diameter and indentation speed, while it first increased and then decreased with the increase of temperature.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"125 15","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999564","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}
This study aims to explore the application status of Internet of Things and multimedia technology in English online teaching and its optimization measures. The Internet of Things is a technology that connects objects together and realizes information exchange through the network, while multimedia technology refers to the integration and processing of different forms of information including text, images, sound, video and so on. Firstly, the development status of Internet of Things and multimedia technology at home and abroad, as well as the development trend and demand of online English teaching, are analyzed. Secondly, through a questionnaire survey, data on the teaching methods, content, student feedback, and teachers’ teaching methods and effects in English online teaching were collected. Through in-depth data analysis and empirical research, this paper discusses how to integrate the Internet of Things and multimedia technology to build a more efficient online English teaching model. On this basis, the problems existing in the Internet of Things and multimedia technology in English online teaching are summarized, such as uneven application of technology, lack of targeted teaching design, and insufficient interactivity. In response to these problems, a series of optimization measures are proposed, including balancing the application of technology, personalized teaching design, improving interactivity, cultivating independent learning ability and solving technical problems. Finally, the future development of English online teaching in the application of Internet of Things and multimedia technology is prospected, focusing on technological innovation and application, personalized and intelligent teaching, teaching mode and method innovation, teacher role change and evaluation system construction.
{"title":"Application of Internet of Things and multimedia technology in English online teaching","authors":"Jing Yan, Aiping Chen, Jinjin Chao","doi":"10.3233/jcm-226928","DOIUrl":"https://doi.org/10.3233/jcm-226928","url":null,"abstract":"This study aims to explore the application status of Internet of Things and multimedia technology in English online teaching and its optimization measures. The Internet of Things is a technology that connects objects together and realizes information exchange through the network, while multimedia technology refers to the integration and processing of different forms of information including text, images, sound, video and so on. Firstly, the development status of Internet of Things and multimedia technology at home and abroad, as well as the development trend and demand of online English teaching, are analyzed. Secondly, through a questionnaire survey, data on the teaching methods, content, student feedback, and teachers’ teaching methods and effects in English online teaching were collected. Through in-depth data analysis and empirical research, this paper discusses how to integrate the Internet of Things and multimedia technology to build a more efficient online English teaching model. On this basis, the problems existing in the Internet of Things and multimedia technology in English online teaching are summarized, such as uneven application of technology, lack of targeted teaching design, and insufficient interactivity. In response to these problems, a series of optimization measures are proposed, including balancing the application of technology, personalized teaching design, improving interactivity, cultivating independent learning ability and solving technical problems. Finally, the future development of English online teaching in the application of Internet of Things and multimedia technology is prospected, focusing on technological innovation and application, personalized and intelligent teaching, teaching mode and method innovation, teacher role change and evaluation system construction.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"39 12","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138995691","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}
This paper aims to improve the efficacy assessment of County-level Integrated Health Organizations (CIHOs) in China. By analyzing CIHOs in Fujian Province, an empirical study was done to confirm the efficacy of the combined weighted-grey synthetic evaluation approach. The combined weights of evaluation indicators are calculated using the suggested method, which combines the analytical network process method and the coefficient of variation method. The grey center points of the CIHOs to be evaluated are determined, the whitenization weight functions is constructed, a comprehensive evaluation matrix is established, and then the composite score values are calculated and ranked. A more thorough evaluation of CIHOs can be accomplished scientifically using this comprehensive approach.
{"title":"Evaluation of county-level integrated health organizations: Combination of the weighting-grey synthetic evaluation method","authors":"Qianqian Wu, Kejia Chen","doi":"10.3233/jcm-237009","DOIUrl":"https://doi.org/10.3233/jcm-237009","url":null,"abstract":"This paper aims to improve the efficacy assessment of County-level Integrated Health Organizations (CIHOs) in China. By analyzing CIHOs in Fujian Province, an empirical study was done to confirm the efficacy of the combined weighted-grey synthetic evaluation approach. The combined weights of evaluation indicators are calculated using the suggested method, which combines the analytical network process method and the coefficient of variation method. The grey center points of the CIHOs to be evaluated are determined, the whitenization weight functions is constructed, a comprehensive evaluation matrix is established, and then the composite score values are calculated and ranked. A more thorough evaluation of CIHOs can be accomplished scientifically using this comprehensive approach.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"37 3","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997602","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 key to the reform of the power system is to design a fair bidding and trading system. Analyzing the transaction process of electricity price competition, suppressing market power and other unfavorable factors, and finding a perfect bidding system are the research goals of this paper. In order to study the competition in the power spot market and power contract market, this paper employs the game model of complete information and the game theory as a tool. The power spot market adopts the Market Clearing Price (MCP) settlement method, in which the power grid determines the maximal real-time price of the generator node as the MCP. The price is based on the three bidding strategy curves of the power plant. As a result, a Nash equilibrium of power plant revenue is formed. According to the Cournot model and Stackelberg model that analyze the power contract market, the long-term equilibrium price of Stackelberg model in the power contract market is higher than that of the perfectly competitive market and less than or equal to the output of perfect monopoly market. The long-term equilibrium price and output in the power contract market are both certain and stable. This paper has analyzed the static game of complete information in the power market and carried out practical application. The results show that the bidding strategies of power plants have a Nash equilibrium and they have an incentive to collude. The MCP mechanism cannot solve the problem of market power influence. The conclusion of the research provides a basis for the design of the power hybrid auction system.
{"title":"Formation mechanism of on-grid power tariff using game model of complete information","authors":"Jiaojiao Li, Linfeng Zhao, Lihao Dong","doi":"10.3233/jcm-226926","DOIUrl":"https://doi.org/10.3233/jcm-226926","url":null,"abstract":"The key to the reform of the power system is to design a fair bidding and trading system. Analyzing the transaction process of electricity price competition, suppressing market power and other unfavorable factors, and finding a perfect bidding system are the research goals of this paper. In order to study the competition in the power spot market and power contract market, this paper employs the game model of complete information and the game theory as a tool. The power spot market adopts the Market Clearing Price (MCP) settlement method, in which the power grid determines the maximal real-time price of the generator node as the MCP. The price is based on the three bidding strategy curves of the power plant. As a result, a Nash equilibrium of power plant revenue is formed. According to the Cournot model and Stackelberg model that analyze the power contract market, the long-term equilibrium price of Stackelberg model in the power contract market is higher than that of the perfectly competitive market and less than or equal to the output of perfect monopoly market. The long-term equilibrium price and output in the power contract market are both certain and stable. This paper has analyzed the static game of complete information in the power market and carried out practical application. The results show that the bidding strategies of power plants have a Nash equilibrium and they have an incentive to collude. The MCP mechanism cannot solve the problem of market power influence. The conclusion of the research provides a basis for the design of the power hybrid auction system.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"2 34","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139001141","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}
During the long-term operation of the photovoltaic (PV) system, occlusion will reduce the solar radiation energy received by the PV module, as well as the photoelectric conversion efficiency and economy. However, the occlusion detection of the PV power station has the defects of low efficiency, poor accuracy, and untimely detection, which will cause unknown system losses. Based on the deep learning algorithm, this paper conducts research on PV module occlusion detection. In order to accurately obtain the occlusion area and position information of the PV panel, a PV module occlusion detection model based on the Segment-You Only Look Once (Seg-YOLO) algorithm is established. Based on the YOLOv5 algorithm, the loss function is modified, the Segment Head detection module is introduced, and the convolutional block attention module (CBAM) attention mechanism is added to achieve the accurate detection of small targets by the algorithm model and the fast detection of the PV module occlusion area identify. The model performance research is carried out on three types of occlusion datasets: leaf, bird dropping, and shadow. According to the experimental results, the proposed model has better recognition accuracy and speed than SSD, Faster-Rcnn, YOLOv4, and U-Net. The precision rate, recall rate, and recognition speed can reach 90.52%, 92.41%, and 92.3 FPS, respectively. This model can lay a theoretical foundation for the intelligent operation and maintenance of PV systems.
{"title":"A novel object recognition method for photovoltaic (PV) panel occlusion based on deep learning","authors":"Jing Yu, Rongqiang Guan, Cungui Zhang, Fang Shao","doi":"10.3233/jcm-237108","DOIUrl":"https://doi.org/10.3233/jcm-237108","url":null,"abstract":"During the long-term operation of the photovoltaic (PV) system, occlusion will reduce the solar radiation energy received by the PV module, as well as the photoelectric conversion efficiency and economy. However, the occlusion detection of the PV power station has the defects of low efficiency, poor accuracy, and untimely detection, which will cause unknown system losses. Based on the deep learning algorithm, this paper conducts research on PV module occlusion detection. In order to accurately obtain the occlusion area and position information of the PV panel, a PV module occlusion detection model based on the Segment-You Only Look Once (Seg-YOLO) algorithm is established. Based on the YOLOv5 algorithm, the loss function is modified, the Segment Head detection module is introduced, and the convolutional block attention module (CBAM) attention mechanism is added to achieve the accurate detection of small targets by the algorithm model and the fast detection of the PV module occlusion area identify. The model performance research is carried out on three types of occlusion datasets: leaf, bird dropping, and shadow. According to the experimental results, the proposed model has better recognition accuracy and speed than SSD, Faster-Rcnn, YOLOv4, and U-Net. The precision rate, recall rate, and recognition speed can reach 90.52%, 92.41%, and 92.3 FPS, respectively. This model can lay a theoretical foundation for the intelligent operation and maintenance of PV systems.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"28 4","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138998733","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}
Predicting students’ course grades is an essential element in teaching. This paper used decision tree generation rules to study the prediction of students’ ideological and political course grades. Firstly, ID3 and C4.5 algorithms were briefly introduced; then, an improved C4.5 algorithm with higher computational efficiency was put forward. The formula of the C4.5 algorithm was optimized using theories such as the Taylor series. Finally, experiments were performed on the UCI dataset and students’ ideological and political course datasets. The results suggested that the average classification accuracy and computation time of the improved C4.5 algorithm was 79.37% and 74.1 ms, respectively, on the UCI dataset, which was better than the traditional C4.5 algorithm. Then, the experiment predicting students’ course grades demonstrated that the average quiz grade and the number of video views had the greatest impact on the final grades. The prediction accuracy of the improved C4.5 algorithm reached 93.46%, and the average computation time was 54.8 ms, which was 19.17% less than the C4.5 algorithm. The experimental results verify the effectiveness of the generation rule of the improved C4.5 algorithm in predicting students’ ideological and political course grades. This algorithm can be applied in the actual grade prediction.
{"title":"A study on predicting students’ grades for ideological and political courses with decision tree generation rules","authors":"Jianwei Zhao, Wenjing Li","doi":"10.3233/jcm-226953","DOIUrl":"https://doi.org/10.3233/jcm-226953","url":null,"abstract":"Predicting students’ course grades is an essential element in teaching. This paper used decision tree generation rules to study the prediction of students’ ideological and political course grades. Firstly, ID3 and C4.5 algorithms were briefly introduced; then, an improved C4.5 algorithm with higher computational efficiency was put forward. The formula of the C4.5 algorithm was optimized using theories such as the Taylor series. Finally, experiments were performed on the UCI dataset and students’ ideological and political course datasets. The results suggested that the average classification accuracy and computation time of the improved C4.5 algorithm was 79.37% and 74.1 ms, respectively, on the UCI dataset, which was better than the traditional C4.5 algorithm. Then, the experiment predicting students’ course grades demonstrated that the average quiz grade and the number of video views had the greatest impact on the final grades. The prediction accuracy of the improved C4.5 algorithm reached 93.46%, and the average computation time was 54.8 ms, which was 19.17% less than the C4.5 algorithm. The experimental results verify the effectiveness of the generation rule of the improved C4.5 algorithm in predicting students’ ideological and political course grades. This algorithm can be applied in the actual grade prediction.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"123 11","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999663","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}
There are many kinds of Marine organisms and their biological forms differ greatly, so it is difficult to guarantee the accuracy of artificial species identification, which brings great challenges to the work of Marine species identification. In this paper, we propose a recognition method of Marine biological image classification using residual neural network, redefining convolution layer and using batch regularization to avoid gradient parameter disorder. The bottleneck layer is realized by the residual connection in the neural network, and the residual network ResNet50 is constructed by the transfer learning method. The classification training was conducted on 19 common Marine animal data sets, and the experimental results showed that the recognition accuracy of ResNet50 reached about 90%. Compared with the traditional convolutional neural network VGG19, the results showed that the recognition efficiency of ResNet50 was better, thus verifying the effectiveness of the Marine animal classification and recognition model proposed in this paper.
{"title":"Research on the construction of marine creatures classification and identification model based on ResNet50","authors":"Hongsuo Tang, Yuchen Zhou, Pengfei Hou, Libao Xing, Yanyan Chen, Hui Li","doi":"10.3233/jcm-226974","DOIUrl":"https://doi.org/10.3233/jcm-226974","url":null,"abstract":"There are many kinds of Marine organisms and their biological forms differ greatly, so it is difficult to guarantee the accuracy of artificial species identification, which brings great challenges to the work of Marine species identification. In this paper, we propose a recognition method of Marine biological image classification using residual neural network, redefining convolution layer and using batch regularization to avoid gradient parameter disorder. The bottleneck layer is realized by the residual connection in the neural network, and the residual network ResNet50 is constructed by the transfer learning method. The classification training was conducted on 19 common Marine animal data sets, and the experimental results showed that the recognition accuracy of ResNet50 reached about 90%. Compared with the traditional convolutional neural network VGG19, the results showed that the recognition efficiency of ResNet50 was better, thus verifying the effectiveness of the Marine animal classification and recognition model proposed in this paper.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":"3 6","pages":""},"PeriodicalIF":0.5,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139001293","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}
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