Tao Zheng, Guofeng Shao, Qingyun Zhou, Qinning Wang, Mengmeng Ye
The purpose of this study was to investigate the clinical value of CT angiography (CTA) images processed by the segmentation denoising technique based on deep convolution neural network algorithm in the diagnosis of abdominal aortic aneurysm (AAA) and the detection of disease changes. A total of 98 patients with ruptured AAA were retrospectively selected as the study subjects. Patients were grouped according to whether the CTA images were optimized, the images receiving artificial intelligence segmentation and denoising were set as the observation group, and the CTA images without optimization were set as the control group. The detection and diagnosis effects of CTA images before and after the treatment were compared. The surgical results were used as the standard to analyze the diagnostic effect, and the maximum diameter measurement results of AAA and the proportion results of intraluminal thrombus (ILT) were compared. Although the sensitivity and accuracy of diagnosis in the observation group (97.73% and 94.9%) were higher than those in the control group (95.45% and 92.86%), there was no significant statistical significance ( P > 0.05 ). When the diameter of AAA was no less than 5 cm, all results showed that the coverage percentage of intraluminal thrombus (ILT) was over 50%. When the diameter of AAA was less than 5 cm, only 55.56% of the results showed that the percentage of ILT coverage was over 50%, with considerable differences ( P > 0.05 ). According to the results of the study, it was found that there was a certain relationship between the thrombus coverage of the abdominal aortic wall and the growth rate of AAA. The deep convolution neural network algorithm had a certain effect on the treatment of CTA, but it is not obvious. However, CTA had a better clinical diagnostic effect on AAA.
{"title":"Abdominal Enhanced Computed Tomography Image by Artificial Intelligence Algorithm in the Diagnosis of Abdominal Aortic Aneurysm","authors":"Tao Zheng, Guofeng Shao, Qingyun Zhou, Qinning Wang, Mengmeng Ye","doi":"10.1155/2021/8721464","DOIUrl":"https://doi.org/10.1155/2021/8721464","url":null,"abstract":"The purpose of this study was to investigate the clinical value of CT angiography (CTA) images processed by the segmentation denoising technique based on deep convolution neural network algorithm in the diagnosis of abdominal aortic aneurysm (AAA) and the detection of disease changes. A total of 98 patients with ruptured AAA were retrospectively selected as the study subjects. Patients were grouped according to whether the CTA images were optimized, the images receiving artificial intelligence segmentation and denoising were set as the observation group, and the CTA images without optimization were set as the control group. The detection and diagnosis effects of CTA images before and after the treatment were compared. The surgical results were used as the standard to analyze the diagnostic effect, and the maximum diameter measurement results of AAA and the proportion results of intraluminal thrombus (ILT) were compared. Although the sensitivity and accuracy of diagnosis in the observation group (97.73% and 94.9%) were higher than those in the control group (95.45% and 92.86%), there was no significant statistical significance (\u0000 \u0000 P\u0000 >\u0000 0.05\u0000 \u0000 ). When the diameter of AAA was no less than 5 cm, all results showed that the coverage percentage of intraluminal thrombus (ILT) was over 50%. When the diameter of AAA was less than 5 cm, only 55.56% of the results showed that the percentage of ILT coverage was over 50%, with considerable differences (\u0000 \u0000 P\u0000 >\u0000 0.05\u0000 \u0000 ). According to the results of the study, it was found that there was a certain relationship between the thrombus coverage of the abdominal aortic wall and the growth rate of AAA. The deep convolution neural network algorithm had a certain effect on the treatment of CTA, but it is not obvious. However, CTA had a better clinical diagnostic effect on AAA.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"21 1","pages":"8721464:1-8721464:8"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90051402","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}
B. Gobinathan, M. A. Mukunthan, S. Surendran, K. Somasundaram, Syed Abdul Moeed, P. Niranjan, V. Gouthami, G. Ashmitha, Gouse Baig Mohammad, V. Shanmuganathan, Yuvaraj Natarajan, K. Srihari, Venkatesa Prabhu Sundramurthy
In recent times, the utility and privacy are trade-off factors with the performance of one factor tends to sacrifice the other. Therefore, the dataset cannot be published without privacy. It is henceforth crucial to maintain an equilibrium between the utility and privacy of data. In this paper, a novel technique on trade-off between the utility and privacy is developed, where the former is developed with a metaheuristic algorithm and the latter is developed using a cryptographic model. The utility is carried out with the process of clustering, and the privacy model encrypts and decrypts the model. At first, the input datasets are clustered, and after clustering, the privacy of data is maintained. The simulation is conducted on the manufacturing datasets over various existing models. The results show that the proposed model shows improved clustering accuracy and data privacy than the existing models. The evaluation with the proposed model shows a trade-off privacy preservation and utility clustering in smart manufacturing datasets.
{"title":"A Novel Method to Solve Real Time Security Issues in Software Industry Using Advanced Cryptographic Techniques","authors":"B. Gobinathan, M. A. Mukunthan, S. Surendran, K. Somasundaram, Syed Abdul Moeed, P. Niranjan, V. Gouthami, G. Ashmitha, Gouse Baig Mohammad, V. Shanmuganathan, Yuvaraj Natarajan, K. Srihari, Venkatesa Prabhu Sundramurthy","doi":"10.1155/2021/3611182","DOIUrl":"https://doi.org/10.1155/2021/3611182","url":null,"abstract":"In recent times, the utility and privacy are trade-off factors with the performance of one factor tends to sacrifice the other. Therefore, the dataset cannot be published without privacy. It is henceforth crucial to maintain an equilibrium between the utility and privacy of data. In this paper, a novel technique on trade-off between the utility and privacy is developed, where the former is developed with a metaheuristic algorithm and the latter is developed using a cryptographic model. The utility is carried out with the process of clustering, and the privacy model encrypts and decrypts the model. At first, the input datasets are clustered, and after clustering, the privacy of data is maintained. The simulation is conducted on the manufacturing datasets over various existing models. The results show that the proposed model shows improved clustering accuracy and data privacy than the existing models. The evaluation with the proposed model shows a trade-off privacy preservation and utility clustering in smart manufacturing datasets.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"1 1","pages":"3611182:1-3611182:9"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89954255","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}
In the educational sector, an evaluation index is required to draw up planning. The establishment of an evaluation index is useful to properly predict the employment quality of graduates. Such valuable indices help educational administrative departments to formulate talent training standards. Multicriteria decision making is a decision-making tool that can be used in the formulation of the evaluation index. This research work proposes an effective evaluation model to assess the employment quality of graduate students. The model uses 10 evaluation indicators which are considered to be the standard employment quality. The proposed evaluation method utilizes the entropy method and fuzzy comprehensive evaluation. Correlation between the employment quality evaluation index and employment quality is computed. The analytic hierarchy model is used to solve the weight of each employment quality evaluation index to the employment quality evaluation coefficient. According to the value characteristics of the 14 employment indicators, the expert method is used to assign scores to the sample data on each indicator. Thus, the indicator scores of the sample corresponding to the item are obtained. Through the evaluation of the employment quality of a certain university, the evaluation results are consistent with the actual employment quality of graduates. The employment quality evaluation model of college graduates established in this paper provides effective means and applications.
{"title":"University Employment Quality Evaluation System Based on Multicriteria Decision and Data Analysis","authors":"Long-long Song","doi":"10.1155/2021/3838140","DOIUrl":"https://doi.org/10.1155/2021/3838140","url":null,"abstract":"In the educational sector, an evaluation index is required to draw up planning. The establishment of an evaluation index is useful to properly predict the employment quality of graduates. Such valuable indices help educational administrative departments to formulate talent training standards. Multicriteria decision making is a decision-making tool that can be used in the formulation of the evaluation index. This research work proposes an effective evaluation model to assess the employment quality of graduate students. The model uses 10 evaluation indicators which are considered to be the standard employment quality. The proposed evaluation method utilizes the entropy method and fuzzy comprehensive evaluation. Correlation between the employment quality evaluation index and employment quality is computed. The analytic hierarchy model is used to solve the weight of each employment quality evaluation index to the employment quality evaluation coefficient. According to the value characteristics of the 14 employment indicators, the expert method is used to assign scores to the sample data on each indicator. Thus, the indicator scores of the sample corresponding to the item are obtained. Through the evaluation of the employment quality of a certain university, the evaluation results are consistent with the actual employment quality of graduates. The employment quality evaluation model of college graduates established in this paper provides effective means and applications.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"18 1","pages":"3838140:1-3838140:7"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81836747","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 traditional image object detection algorithm applied in power inspection cannot effectively position power components, and the accuracy of recognition is low in scenes with some interference. In this research, we proposed a data-driven power detection method based on the improved YOLOv4-tiny model, which combined the ResNet-D module and the adjusted Res-CBAM to the backbone network of the existing YOLOv4-tiny module. We replaced the CSPOSANet module in the YOLOv4-tiny backbone network with the ResNet-D module to reduce the FLOPS required by the model. At the same time, the adjusted Res-CBAM whose feature fusion ways were replaced with stacking in the channels was combined as an auxiliary classifier. Finally, the features of five different receptive scales were used for prediction, and the display of the results was optimized by merging the prediction boxes. In the experiment, 57134 images collected on the power inspection line were processed and labeled, and the default anchor boxes were re-clustered, and the speed and accuracy of the model were evaluated by video and validation set of 3459 images. Processing multiple pictures and videos collected from the power inspection projects, we re-clustered the default anchor box and tested the speed and accuracy of the model. The results show that compared with the original YOLOv4-tiny model, the accuracy of our method that can position objects under occlusion and complex lighting conditions is guaranteed while the detection speed is about 13% faster.
{"title":"A Decision Support System for Power Components Based on Improved YOLOv4-Tiny","authors":"Yangyang Tian, Wandeng Mao, Shaoguang Yuan, Diming Wan, Yuan-Wei Chen","doi":"10.1155/2021/4447271","DOIUrl":"https://doi.org/10.1155/2021/4447271","url":null,"abstract":"The traditional image object detection algorithm applied in power inspection cannot effectively position power components, and the accuracy of recognition is low in scenes with some interference. In this research, we proposed a data-driven power detection method based on the improved YOLOv4-tiny model, which combined the ResNet-D module and the adjusted Res-CBAM to the backbone network of the existing YOLOv4-tiny module. We replaced the CSPOSANet module in the YOLOv4-tiny backbone network with the ResNet-D module to reduce the FLOPS required by the model. At the same time, the adjusted Res-CBAM whose feature fusion ways were replaced with stacking in the channels was combined as an auxiliary classifier. Finally, the features of five different receptive scales were used for prediction, and the display of the results was optimized by merging the prediction boxes. In the experiment, 57134 images collected on the power inspection line were processed and labeled, and the default anchor boxes were re-clustered, and the speed and accuracy of the model were evaluated by video and validation set of 3459 images. Processing multiple pictures and videos collected from the power inspection projects, we re-clustered the default anchor box and tested the speed and accuracy of the model. The results show that compared with the original YOLOv4-tiny model, the accuracy of our method that can position objects under occlusion and complex lighting conditions is guaranteed while the detection speed is about 13% faster.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"60 1","pages":"4447271:1-4447271:11"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83959913","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}
Aiming at the shortcomings of the existing community emergency service platform, such as single function, poor scalability, and strong subjectivity, an intelligent community emergency service platform based on convolutional neural network was constructed. Firstly, the requirements analysis of the emergency service platform was carried out, and the functional demand of the emergency service platform was analyzed from the aspects of community environment, safety, infrastructure, health management, emergency response, and so on. Secondly, through logistics network, big data, cloud computing, artificial intelligence, and all kinds of applications, the intelligent community emergency service platform was designed. Finally, a semantic matching emergency question answering system based on convolutional neural network was developed to provide key technical support for the emergency preparation stage of intelligent community. The results show that the intelligent community emergency service platform plays an important role in preventing community emergency events and taking active and effective measures to ensure the health and safety of community residents.
{"title":"Research on the Construction of Intelligent Community Emergency Service Platform Based on Convolutional Neural Network","authors":"Yu Chen, Zhong Tang","doi":"10.1155/2021/5089236","DOIUrl":"https://doi.org/10.1155/2021/5089236","url":null,"abstract":"Aiming at the shortcomings of the existing community emergency service platform, such as single function, poor scalability, and strong subjectivity, an intelligent community emergency service platform based on convolutional neural network was constructed. Firstly, the requirements analysis of the emergency service platform was carried out, and the functional demand of the emergency service platform was analyzed from the aspects of community environment, safety, infrastructure, health management, emergency response, and so on. Secondly, through logistics network, big data, cloud computing, artificial intelligence, and all kinds of applications, the intelligent community emergency service platform was designed. Finally, a semantic matching emergency question answering system based on convolutional neural network was developed to provide key technical support for the emergency preparation stage of intelligent community. The results show that the intelligent community emergency service platform plays an important role in preventing community emergency events and taking active and effective measures to ensure the health and safety of community residents.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"14 1","pages":"5089236:1-5089236:14"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88010509","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}
Construction of the entrepreneurial ability evaluation system based on the Communist Youth League’s second class is presented in this paper. Drawing on the advanced experience of foreign countries and in accordance with the requirements of UNESCO, the objectives of innovation and entrepreneurship education should be integrated into school education and teaching objectives, and the content, curriculum, and atmosphere of social entrepreneurship education should be highlighted, with the effectiveness of entrepreneurship education as the focus of practice. Combining the characteristics and advantages of all disciplines and disciplines, we will create an innovative and pioneering education system that integrates interyear, interdisciplinary, interdisciplinary, and distinctive features and infiltrates the entire process of cultivating outstanding professionals in various fields. Through entrepreneurship education, general education courses to guide students to focus more on professional courses pay more attention to the latest developments in professional fields and innovation thus optimizing their knowledge structure and cultivating their innovative thinking, entrepreneurial awareness, and professional competence
{"title":"Optimizing the Construction of Multidimensional System of Entrepreneurship Education from the Perspective of the Second Classroom","authors":"Mengjiao Zhu, In-Jae Kim, Z. An","doi":"10.1155/2021/2344527","DOIUrl":"https://doi.org/10.1155/2021/2344527","url":null,"abstract":"Construction of the entrepreneurial ability evaluation system based on the Communist Youth League’s second class is presented in this paper. Drawing on the advanced experience of foreign countries and in accordance with the requirements of UNESCO, the objectives of innovation and entrepreneurship education should be integrated into school education and teaching objectives, and the content, curriculum, and atmosphere of social entrepreneurship education should be highlighted, with the effectiveness of entrepreneurship education as the focus of practice. Combining the characteristics and advantages of all disciplines and disciplines, we will create an innovative and pioneering education system that integrates interyear, interdisciplinary, interdisciplinary, and distinctive features and infiltrates the entire process of cultivating outstanding professionals in various fields. Through entrepreneurship education, general education courses to guide students to focus more on professional courses pay more attention to the latest developments in professional fields and innovation thus optimizing their knowledge structure and cultivating their innovative thinking, entrepreneurial awareness, and professional competence","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"8 1","pages":"2344527:1-2344527:7"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84153099","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}
In recent years, due to the rapid development of rural tourism, rural tourism has lost its unique rurality, which has led to a certain impact on the sustainable development of rural tourism. Primarily, based on the rural characteristics, the social environment development, population development, and economic development are taken as the research indexes, and the evaluation index system of rural tourism destination is constructed. Afterward, an empirical study on the spatial pattern of rural tourism is carried out with examples, and the model is simulated and analyzed by MATLAB software. Finally, the spatial autocorrelation method is used to analyze the evolution characteristics of the rural tourism spatial pattern. The results show that through the analysis of the evaluation error curve of the Back Propagation Neural Network (BPNN), the evaluation error and the actual error range are within 0.08%, which proves that the BPNN algorithm has good calculation accuracy. The BPNN rural tourism destination rurality evaluation model established here can make an effective evaluation of rural tourism space. The results show that the proportion of employees in the primary industry and the penetration rate of mobile phones are the decisive factors in the adjustment of industrial structure and social environmental factors, respectively. Rural per capita tourism income and the proportion of primary industry output value will also have a certain impact on rural evolution. Certain guiding significance is provided for the sustainable development of rural tourism.
{"title":"Evaluation of Rural Tourism Spatial Pattern Based on Multifactor-Weighted Neural Network Algorithm Model in Big Data Era","authors":"Qiang Xu","doi":"10.1155/2021/8108287","DOIUrl":"https://doi.org/10.1155/2021/8108287","url":null,"abstract":"In recent years, due to the rapid development of rural tourism, rural tourism has lost its unique rurality, which has led to a certain impact on the sustainable development of rural tourism. Primarily, based on the rural characteristics, the social environment development, population development, and economic development are taken as the research indexes, and the evaluation index system of rural tourism destination is constructed. Afterward, an empirical study on the spatial pattern of rural tourism is carried out with examples, and the model is simulated and analyzed by MATLAB software. Finally, the spatial autocorrelation method is used to analyze the evolution characteristics of the rural tourism spatial pattern. The results show that through the analysis of the evaluation error curve of the Back Propagation Neural Network (BPNN), the evaluation error and the actual error range are within 0.08%, which proves that the BPNN algorithm has good calculation accuracy. The BPNN rural tourism destination rurality evaluation model established here can make an effective evaluation of rural tourism space. The results show that the proportion of employees in the primary industry and the penetration rate of mobile phones are the decisive factors in the adjustment of industrial structure and social environmental factors, respectively. Rural per capita tourism income and the proportion of primary industry output value will also have a certain impact on rural evolution. Certain guiding significance is provided for the sustainable development of rural tourism.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"41 1","pages":"8108287:1-8108287:11"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80966497","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}
To retain valuable information to the maximum extent and enhance the ability to mine the crude oil trade purchase price demand, this paper proposes a crude oil trade purchase model based on the DEA-Malmquist algorithm. The intranet of the management and control platform shall share the same database, and the intranet shall only allow managers to access and manage the system and only allow all registered users to access and realize data exchange between the intranet and the intranet through two-dimensional code scanning; moreover, due to the resource sharing between the intranet and the intranet for crude oil trade procurement, suppliers and other registered users can immediately grasp the procurement trends of enterprises. Under the DEA-Malmquist algorithm, the uncertainty of procurement management is analyzed by fuzzy theory, and the refined procurement decision model with fuzzy parameters is established. The optimal order time and purchase quantity are determined through the symbol distance and the method of the center of gravity. Experimental results show that the method can effectively retain valuable information in the initial sequence and has better practical application value of material procurement demand intelligent mining. The proposed model obtained the highest accuracy of 98.62%.
{"title":"Research on Crude Oil Trade Procurement Model Based on DEA-Malmquist Algorithm","authors":"Liu Yan","doi":"10.1155/2021/6360439","DOIUrl":"https://doi.org/10.1155/2021/6360439","url":null,"abstract":"To retain valuable information to the maximum extent and enhance the ability to mine the crude oil trade purchase price demand, this paper proposes a crude oil trade purchase model based on the DEA-Malmquist algorithm. The intranet of the management and control platform shall share the same database, and the intranet shall only allow managers to access and manage the system and only allow all registered users to access and realize data exchange between the intranet and the intranet through two-dimensional code scanning; moreover, due to the resource sharing between the intranet and the intranet for crude oil trade procurement, suppliers and other registered users can immediately grasp the procurement trends of enterprises. Under the DEA-Malmquist algorithm, the uncertainty of procurement management is analyzed by fuzzy theory, and the refined procurement decision model with fuzzy parameters is established. The optimal order time and purchase quantity are determined through the symbol distance and the method of the center of gravity. Experimental results show that the method can effectively retain valuable information in the initial sequence and has better practical application value of material procurement demand intelligent mining. The proposed model obtained the highest accuracy of 98.62%.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"8 1","pages":"6360439:1-6360439:10"},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85580427","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}
With the advancement of technology represented by artificial intelligence, art creation is becoming increasingly rich, and content expression is intelligent, interactive, and data-driven, making the relationship between technology, art, and people increasingly close and bringing opportunities for the development of emerging interaction. Artificial intelligence technologies aim to perfectly replicate the human mind by enabling natural responses based on the surrounding environment, decoding emotions, and recognizing human traits within the energy range. Driven by AI technology, interactive art no longer focuses on a single audiovisual sensory experience but rather on integrated artistic expressions that are highly interactive, kinetic, and emotional, based on the study of natural human behavior and integrated senses, combined with intelligence. In this paper, we first sort out the intersection of AI technology development and interactive art expression streams on the timeline based on historical development and analyze the deconstructive relationship between the two from the macroperspective of the historical development of technology and art. First, based on the conceptual connotation, development history, technical application, and singularity outlook of AI, we identify the current characteristics and development trends of interactive art; second, based on exploring the advantages of AI technology, we propose the impact of AI on the creative thinking, creative mode, and artistic experience of interactive art and establish the paradigm of interactive art creation in the context of AI. It solves the problem that experts are unable to quickly locate the category of painters when facing different styles of unsigned digital Chinese painting images in the authenticity identification task.
{"title":"The Influence of Artificial Intelligence on Art Design in the Digital Age","authors":"Yan Shen, Fangzheng Yu","doi":"10.1155/2021/4838957","DOIUrl":"https://doi.org/10.1155/2021/4838957","url":null,"abstract":"With the advancement of technology represented by artificial intelligence, art creation is becoming increasingly rich, and content expression is intelligent, interactive, and data-driven, making the relationship between technology, art, and people increasingly close and bringing opportunities for the development of emerging interaction. Artificial intelligence technologies aim to perfectly replicate the human mind by enabling natural responses based on the surrounding environment, decoding emotions, and recognizing human traits within the energy range. Driven by AI technology, interactive art no longer focuses on a single audiovisual sensory experience but rather on integrated artistic expressions that are highly interactive, kinetic, and emotional, based on the study of natural human behavior and integrated senses, combined with intelligence. In this paper, we first sort out the intersection of AI technology development and interactive art expression streams on the timeline based on historical development and analyze the deconstructive relationship between the two from the macroperspective of the historical development of technology and art. First, based on the conceptual connotation, development history, technical application, and singularity outlook of AI, we identify the current characteristics and development trends of interactive art; second, based on exploring the advantages of AI technology, we propose the impact of AI on the creative thinking, creative mode, and artistic experience of interactive art and establish the paradigm of interactive art creation in the context of AI. It solves the problem that experts are unable to quickly locate the category of painters when facing different styles of unsigned digital Chinese painting images in the authenticity identification task.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"25 1","pages":"4838957:1-4838957:10"},"PeriodicalIF":0.0,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90159447","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 study aims to improve the economic income of pig breeding industry under environmental regulation and control the environmental pollution caused by pig breeding. Long short-term memory (LSTM) neural network combined with environmental regulation is proposed to forecast the price of live pigs, to reduce the cost of environmental pollution control and improve the production efficiency of pig breeding. Primarily, analyses are made on the industrial structure and pollution of pigs in China, and studies are carried out on the inevitability of large-scale and intensive pig breeding. Then, pig breeding and environmental pollution are coordinated under the environmental regulation. From the perspective of green total factor productivity, calculation is made on the profit of pig breeding and the cost of environmental pollution control. Next, the LSTM neural network is used to predict the price of live pigs, thus effectively controlling the scale of pig breeding and making timely decisions that conform to market rules. The results show that with the increase of feed and land prices, the advantages of large-scale pig breeding gradually become prominent, which leads to the small- and medium-sized scale farmers withdrawing from the market. Compared with other similar models, the designed model can better simulate the future trend of hog price, of which the prediction accuracy is over 80%. When combined with environmental regulations, the prediction accuracy of the model for different data sets reaches 83%, so the designed model can better predict the changing trend of the price of live pigs, thus improving the production efficiency of large-scale pig farmers.
{"title":"Production Efficiency Prediction of Pig Breeding Industry by Optimized LSTM Computer Algorithm under Environmental Regulation","authors":"Yunfei Jia, Zhaohui Zhang, Zejun He, Panpan Zhu, Yibei Zhang, Tianhua Sun","doi":"10.1155/2021/3074167","DOIUrl":"https://doi.org/10.1155/2021/3074167","url":null,"abstract":"The study aims to improve the economic income of pig breeding industry under environmental regulation and control the environmental pollution caused by pig breeding. Long short-term memory (LSTM) neural network combined with environmental regulation is proposed to forecast the price of live pigs, to reduce the cost of environmental pollution control and improve the production efficiency of pig breeding. Primarily, analyses are made on the industrial structure and pollution of pigs in China, and studies are carried out on the inevitability of large-scale and intensive pig breeding. Then, pig breeding and environmental pollution are coordinated under the environmental regulation. From the perspective of green total factor productivity, calculation is made on the profit of pig breeding and the cost of environmental pollution control. Next, the LSTM neural network is used to predict the price of live pigs, thus effectively controlling the scale of pig breeding and making timely decisions that conform to market rules. The results show that with the increase of feed and land prices, the advantages of large-scale pig breeding gradually become prominent, which leads to the small- and medium-sized scale farmers withdrawing from the market. Compared with other similar models, the designed model can better simulate the future trend of hog price, of which the prediction accuracy is over 80%. When combined with environmental regulations, the prediction accuracy of the model for different data sets reaches 83%, so the designed model can better predict the changing trend of the price of live pigs, thus improving the production efficiency of large-scale pig farmers.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"80 1","pages":"3074167:1-3074167:12"},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80422009","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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