With the rapid development of network technology and information technology, the amount of information contained in images has increased significantly. How to effectively extract text information from complex images has become the focus of current research in this field. Firstly, the Canny algorithm in the edge detection algorithm is improved and applied to the edge detection of complex images. Then the K-means algorithm is optimized to achieve better clustering effect of pixels. Finally, the text information in the image is extracted from the two. The results show that under the influence of salt and pepper noise from 0% to 90%, the quality factor obtained by the improved Canny algorithm is at least 0.4, and the detection accuracy is higher; The minimum peak signal-to-noise ratio of the algorithm is 38, and the maximum mean square error is 30, which are both better than the LOG algorithm and the traditional Canny algorithm, and have better noise reduction effect and image fidelity. It is used together in the extraction process of image text information, and the text recognition accuracy rate of the combined algorithm reaches a maximum of 93%, and is stable at more than 90%, indicating that this method has a high text recognition accuracy rate and provides text extraction for complex images. A reference path is available.
{"title":"Edge detection algorithm in complex image text information extraction","authors":"Zhuguo Li","doi":"10.3233/jcm-226722","DOIUrl":"https://doi.org/10.3233/jcm-226722","url":null,"abstract":"With the rapid development of network technology and information technology, the amount of information contained in images has increased significantly. How to effectively extract text information from complex images has become the focus of current research in this field. Firstly, the Canny algorithm in the edge detection algorithm is improved and applied to the edge detection of complex images. Then the K-means algorithm is optimized to achieve better clustering effect of pixels. Finally, the text information in the image is extracted from the two. The results show that under the influence of salt and pepper noise from 0% to 90%, the quality factor obtained by the improved Canny algorithm is at least 0.4, and the detection accuracy is higher; The minimum peak signal-to-noise ratio of the algorithm is 38, and the maximum mean square error is 30, which are both better than the LOG algorithm and the traditional Canny algorithm, and have better noise reduction effect and image fidelity. It is used together in the extraction process of image text information, and the text recognition accuracy rate of the combined algorithm reaches a maximum of 93%, and is stable at more than 90%, indicating that this method has a high text recognition accuracy rate and provides text extraction for complex images. A reference path is available.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"90 10 1","pages":"1381-1393"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84923296","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 rapid growth of Internet video image information, there is a large amount of redundancy in image data. Use less data stream information to transfer the image or the amount of information contained in the image. Its purpose is to reduce the redundancy of images, so as to store them at low bit rate and reduce the data storage space. In the general image compression method, the hybrid coding framework is adopted. Each algorithm adopts a fixed algorithm mode through a specific design algorithm, without global optimization. Image compression is mainly divided into prediction, transformation, quantization, digital entropy coding and other steps. At present, there are many researches on super-resolution network based on deep learning technology. The main function is to reconstruct high-resolution image replace image magnification low-resolution images such as linear interpolation, which has a great performance improvement image resolution, noise reduction, deblurring and so on, but there is no effective way to use super-resolution network applications to improve quality of compression reconstructed image quality. This paper involves a new method that using image super-resolution residual learning network to improve quality of compression image, our method, the reduced image is encoded into a content stream and a transmission corresponding parameter is encoded into a model stream. Firstly, the original image is scaled down 1/2 size of source image, then encode the small image into content stream with the existing codec. Secondly, the residual learning super-resolution (SR) network is used for image filtering to scale up reconstructed image with decode image resizing method and boost the quality of edge feature extraction of image. Our results show that there is significant performance improvement of h265 in low resolution reconstructed image (bits-per-pixel less than 0.1).
{"title":"Efficient image compression method using image super-resolution residual learning network","authors":"Jianhua Hu, Bo Wang, Xiaolin Liu, Shuzhao Zheng, Zongren Chen, Weimei Wu, Jianding Guo, Woqing Huang","doi":"10.3233/jcm-226653","DOIUrl":"https://doi.org/10.3233/jcm-226653","url":null,"abstract":"With the rapid growth of Internet video image information, there is a large amount of redundancy in image data. Use less data stream information to transfer the image or the amount of information contained in the image. Its purpose is to reduce the redundancy of images, so as to store them at low bit rate and reduce the data storage space. In the general image compression method, the hybrid coding framework is adopted. Each algorithm adopts a fixed algorithm mode through a specific design algorithm, without global optimization. Image compression is mainly divided into prediction, transformation, quantization, digital entropy coding and other steps. At present, there are many researches on super-resolution network based on deep learning technology. The main function is to reconstruct high-resolution image replace image magnification low-resolution images such as linear interpolation, which has a great performance improvement image resolution, noise reduction, deblurring and so on, but there is no effective way to use super-resolution network applications to improve quality of compression reconstructed image quality. This paper involves a new method that using image super-resolution residual learning network to improve quality of compression image, our method, the reduced image is encoded into a content stream and a transmission corresponding parameter is encoded into a model stream. Firstly, the original image is scaled down 1/2 size of source image, then encode the small image into content stream with the existing codec. Secondly, the residual learning super-resolution (SR) network is used for image filtering to scale up reconstructed image with decode image resizing method and boost the quality of edge feature extraction of image. Our results show that there is significant performance improvement of h265 in low resolution reconstructed image (bits-per-pixel less than 0.1).","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"64 1","pages":"1561-1571"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75382691","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 China’s tourism industry is on the right track, the country has gradually paid more attention to the ecological protection of tourism areas. Under the concept of sustainable development, the research on environmental adaptability of tourist attractions has become a hotspot. This study took Huanglongxi Ancient Town in Shuangliu District, Chengdu City, Sichuan Province as the research object, and determined seven ecological protection spaces of Huanglongxi Ancient Town by MSPA method, and then used the landscape connectivity method to identify the priority of ecological sources. The high green space and water are the “source”, and finally the path network is constructed using space syntax, and the relationship between the flow of people and the path resistance disturbance is calculated. After analysis, it is concluded that Huanglongxi Ancient Town has 2 green spaces with higher priority and 1 water area with higher priority. The route layout can meet the current annual reception volume and will not cause obvious congestion during the daily peak. Huanglongxi Ancient Town has 6 enterprises above designated size and 20,000 square kilometers of arable land. The average dLLC of the green space in Huanglongxi Ancient Town is 19.10, the average dPC is 20.92, the maximum time resistance is 0.951 + 1.703*10-7*V151.3, and the maximum time resistance disturbance is 0.999. Huanglongxi Ancient Town can pass between paths 7–8. Add new paths to improve the path situation.
{"title":"The new path of tourism planning development based on MSPA-connectivity-space syntax","authors":"Yimin Cao","doi":"10.3233/jcm-226707","DOIUrl":"https://doi.org/10.3233/jcm-226707","url":null,"abstract":"As China’s tourism industry is on the right track, the country has gradually paid more attention to the ecological protection of tourism areas. Under the concept of sustainable development, the research on environmental adaptability of tourist attractions has become a hotspot. This study took Huanglongxi Ancient Town in Shuangliu District, Chengdu City, Sichuan Province as the research object, and determined seven ecological protection spaces of Huanglongxi Ancient Town by MSPA method, and then used the landscape connectivity method to identify the priority of ecological sources. The high green space and water are the “source”, and finally the path network is constructed using space syntax, and the relationship between the flow of people and the path resistance disturbance is calculated. After analysis, it is concluded that Huanglongxi Ancient Town has 2 green spaces with higher priority and 1 water area with higher priority. The route layout can meet the current annual reception volume and will not cause obvious congestion during the daily peak. Huanglongxi Ancient Town has 6 enterprises above designated size and 20,000 square kilometers of arable land. The average dLLC of the green space in Huanglongxi Ancient Town is 19.10, the average dPC is 20.92, the maximum time resistance is 0.951 + 1.703*10-7*V151.3, and the maximum time resistance disturbance is 0.999. Huanglongxi Ancient Town can pass between paths 7–8. Add new paths to improve the path situation.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"43 1","pages":"1321-1333"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87170842","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 layout and design of ecological landscaping is an important part of the construction and development of modern cities. In the 3D reconstruction of the spatial pattern of the light and shadow interlaced zone of the ecological landscape, the complexity and particularity of the ecological landscape structure make it difficult for the three-dimensional reconstruction stereo matching set to meet the accuracy requirements, and the quality 3D image construction cannot meet the requirements of landscape planning. Based on the principle of binocular stereo vision, a regional feature stereo matching algorithm (rsurf) is used to improve the accuracy of feature matching. Considering that the algorithm is easy to filter out the detailed features of the image, the improved RANSAC algorithm is used to filter the matching results. The experimental results show that in the matching cost test of the optimal matching window, the 15 × window neighborhood has the lowest matching cost, and the generated value in the 100 × 100 source window is 0.824. In the test after matching and fusion, the rsurf algorithm is superior to the surf algorithm in both RMS and PMS error performance, and can better meet the requirements of 3D reconstruction of the binocular vision system. The research content has an important reference for the application of landscape visualization 3D technology, and improves the overall layout effect of landscape landscape.
{"title":"Three-dimensional system modeling and design of ecological garden landscape based on the interlaced spatial pattern of light and shadow","authors":"Cheng Chen","doi":"10.3233/jcm-226712","DOIUrl":"https://doi.org/10.3233/jcm-226712","url":null,"abstract":"The layout and design of ecological landscaping is an important part of the construction and development of modern cities. In the 3D reconstruction of the spatial pattern of the light and shadow interlaced zone of the ecological landscape, the complexity and particularity of the ecological landscape structure make it difficult for the three-dimensional reconstruction stereo matching set to meet the accuracy requirements, and the quality 3D image construction cannot meet the requirements of landscape planning. Based on the principle of binocular stereo vision, a regional feature stereo matching algorithm (rsurf) is used to improve the accuracy of feature matching. Considering that the algorithm is easy to filter out the detailed features of the image, the improved RANSAC algorithm is used to filter the matching results. The experimental results show that in the matching cost test of the optimal matching window, the 15 × window neighborhood has the lowest matching cost, and the generated value in the 100 × 100 source window is 0.824. In the test after matching and fusion, the rsurf algorithm is superior to the surf algorithm in both RMS and PMS error performance, and can better meet the requirements of 3D reconstruction of the binocular vision system. The research content has an important reference for the application of landscape visualization 3D technology, and improves the overall layout effect of landscape landscape.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"145 1","pages":"1503-1516"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75856975","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 solve the problem that the presence of foreign matters in cosmetics will affect the safety and health of consumers and is not conducive to the development of the cosmetics industry, an intelligent identification system for foreign matters in cosmetics is established using the improved BP algorithm. Scan cosmetic samples to identify foreign matters and extract foreign matter features, so as to achieve non-destructive detection of foreign matters in cosmetics. Comparing the traditional BP algorithm, Faster R-CNN algorithm and the improved BP algorithm, the results show that the convergence time of the improved BP algorithm is 60 s and 30 s earlier than that of the traditional BP algorithm and Faster R-CNN algorithm respectively; Whether there is noise or not, the recognition rate of the improved BP algorithm is always higher than that of the traditional BP algorithm and Faster R-CNN algorithm. The accuracy rate of the improved BP algorithm is between 0.88 and 0.96, the accuracy rate of the traditional BP algorithm is between 0.57 and 0.75, and the accuracy rate of the Faster R-CNN algorithm is between 0.76 and 0.81. This shows that the improved BP algorithm can realize the nondestructive detection of foreign matters in cosmetics, ensure a high accuracy and fast speed, and provide consumers with a sense of safe use of cosmetics, it can also improve consumers’ satisfaction with the use of cosmetic products.
{"title":"Non-destructive testing technology for intelligent identification of foreign objects in cosmetics based on BP algorithm","authors":"Jingjing Xu","doi":"10.3233/jcm-226696","DOIUrl":"https://doi.org/10.3233/jcm-226696","url":null,"abstract":"To solve the problem that the presence of foreign matters in cosmetics will affect the safety and health of consumers and is not conducive to the development of the cosmetics industry, an intelligent identification system for foreign matters in cosmetics is established using the improved BP algorithm. Scan cosmetic samples to identify foreign matters and extract foreign matter features, so as to achieve non-destructive detection of foreign matters in cosmetics. Comparing the traditional BP algorithm, Faster R-CNN algorithm and the improved BP algorithm, the results show that the convergence time of the improved BP algorithm is 60 s and 30 s earlier than that of the traditional BP algorithm and Faster R-CNN algorithm respectively; Whether there is noise or not, the recognition rate of the improved BP algorithm is always higher than that of the traditional BP algorithm and Faster R-CNN algorithm. The accuracy rate of the improved BP algorithm is between 0.88 and 0.96, the accuracy rate of the traditional BP algorithm is between 0.57 and 0.75, and the accuracy rate of the Faster R-CNN algorithm is between 0.76 and 0.81. This shows that the improved BP algorithm can realize the nondestructive detection of foreign matters in cosmetics, ensure a high accuracy and fast speed, and provide consumers with a sense of safe use of cosmetics, it can also improve consumers’ satisfaction with the use of cosmetic products.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"102 4 1","pages":"1395-1407"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78005962","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 intelligent age, computers are required to help people complete simple daily work. Among them, computer voice databases and systems occupy a very important position in the field due to their wide application. In order to optimize the system design method, the application of IGA algorithm is proposed, and the performance of the model under the algorithm is compared and tested. The algorithm experiment shows that when the IGA objective function value is 34.4, there is no change, and the number of iterations is 100; Compared with the traditional genetic algorithm, the value of the optimal solution is always the minimum. Then the error of the optimal solution under different algorithms is compared and analyzed. It is found that the error of the optimal solution under IGA operation has the minimum value of 0.0079; The experiment of speech recognition efficiency shows that the speech recognition rate under the intervention of IGA algorithm has increased by 8%, and the overall efficiency is higher than 95%. It can be seen from the above results that IGA is helpful to the acquisition of voice database data, and improves the recognition efficiency. The feasibility of the method is high, which is of great significance to the development of China’s intelligent system industry. But at present, the overall progress of the voice system is still limited, so expanding research methods to apply to the field of voice system is still the next research direction that can be explored.
{"title":"Speech data system and computer database design based on improved genetic algorithm","authors":"Weiwei Zhang","doi":"10.3233/jcm-226698","DOIUrl":"https://doi.org/10.3233/jcm-226698","url":null,"abstract":"In the intelligent age, computers are required to help people complete simple daily work. Among them, computer voice databases and systems occupy a very important position in the field due to their wide application. In order to optimize the system design method, the application of IGA algorithm is proposed, and the performance of the model under the algorithm is compared and tested. The algorithm experiment shows that when the IGA objective function value is 34.4, there is no change, and the number of iterations is 100; Compared with the traditional genetic algorithm, the value of the optimal solution is always the minimum. Then the error of the optimal solution under different algorithms is compared and analyzed. It is found that the error of the optimal solution under IGA operation has the minimum value of 0.0079; The experiment of speech recognition efficiency shows that the speech recognition rate under the intervention of IGA algorithm has increased by 8%, and the overall efficiency is higher than 95%. It can be seen from the above results that IGA is helpful to the acquisition of voice database data, and improves the recognition efficiency. The feasibility of the method is high, which is of great significance to the development of China’s intelligent system industry. But at present, the overall progress of the voice system is still limited, so expanding research methods to apply to the field of voice system is still the next research direction that can be explored.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"16 1","pages":"1691-1703"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84603408","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 improvement of the national living standard, the buyers have higher and higher requirements for the rationality and aesthetics of the spatial planning and layout of the residential area. The traditional residential space planning method is purely manual design, which is inefficient, and the design effect will be greatly affected by the designer’s work experience and personal aesthetics. Therefore, this research attempts to combine Pareto solution set and piecewise prediction idea into genetic algorithm, propose an algorithm for solving multi-objective optimization problems, and build an intelligent housing environment planning system based on this. The statistical results of simulation experiments show that the system can output more design schemes with better overall quality than the comparison system and manual planning results, and the stability of multiple operations is higher. When the number of iterations reaches 200, the average value of Pareto optimal solution number and optimal solution quality index QPS of the former is 44 and 0.41 respectively. The expert group analyzed the design results of this method and manual method for an actual case, and found that the results designed by this method met the requirements and the calculation efficiency was much faster than manual processing. From the simulation test data and the actual case analysis, it can be seen that the intelligent housing environment planning system designed in this study is helpful to improve the efficiency of residential space design and the stability of residential space scheme style.
{"title":"Multi-objective intelligent algorithm model design for housing environment optimization","authors":"Yuanyuan Xu","doi":"10.3233/jcm-226740","DOIUrl":"https://doi.org/10.3233/jcm-226740","url":null,"abstract":"With the improvement of the national living standard, the buyers have higher and higher requirements for the rationality and aesthetics of the spatial planning and layout of the residential area. The traditional residential space planning method is purely manual design, which is inefficient, and the design effect will be greatly affected by the designer’s work experience and personal aesthetics. Therefore, this research attempts to combine Pareto solution set and piecewise prediction idea into genetic algorithm, propose an algorithm for solving multi-objective optimization problems, and build an intelligent housing environment planning system based on this. The statistical results of simulation experiments show that the system can output more design schemes with better overall quality than the comparison system and manual planning results, and the stability of multiple operations is higher. When the number of iterations reaches 200, the average value of Pareto optimal solution number and optimal solution quality index QPS of the former is 44 and 0.41 respectively. The expert group analyzed the design results of this method and manual method for an actual case, and found that the results designed by this method met the requirements and the calculation efficiency was much faster than manual processing. From the simulation test data and the actual case analysis, it can be seen that the intelligent housing environment planning system designed in this study is helpful to improve the efficiency of residential space design and the stability of residential space scheme style.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"36 1","pages":"537-555"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84640665","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}
Human posture detection is easily affected by the external environment, resulting in blurred results of limb feature extraction. In order to improve the accuracy and speed of human motion detection, this paper proposes a deep learning-based motion detection method in competitive sports training. The double parallel convolution network algorithm in the depth learning algorithm is used to process the collected action information, extract the body action features, and greatly reduce the operation scale; With the help of the theory of motion mechanics, the mechanical parameters in the motion process are calculated to eliminate outliers and reduce feature dimensions; With the help of motion inertial sensors and joint degrees of freedom, the limb motion detection results are obtained. The experimental results show that the average recognition rate of the method for different motion actions is 99.5%, and the average detection time is 148 ms, with good application performance.
{"title":"Detection method of limb movement in competitive sports training based on deep learning","authors":"Yichen Wang, Pei Zhang, Yi Wang","doi":"10.3233/jcm-226688","DOIUrl":"https://doi.org/10.3233/jcm-226688","url":null,"abstract":"Human posture detection is easily affected by the external environment, resulting in blurred results of limb feature extraction. In order to improve the accuracy and speed of human motion detection, this paper proposes a deep learning-based motion detection method in competitive sports training. The double parallel convolution network algorithm in the depth learning algorithm is used to process the collected action information, extract the body action features, and greatly reduce the operation scale; With the help of the theory of motion mechanics, the mechanical parameters in the motion process are calculated to eliminate outliers and reduce feature dimensions; With the help of motion inertial sensors and joint degrees of freedom, the limb motion detection results are obtained. The experimental results show that the average recognition rate of the method for different motion actions is 99.5%, and the average detection time is 148 ms, with good application performance.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"34 1","pages":"1667-1678"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88084658","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 conducts an empirical investigation into the synergistic effects of fiscal spending and digital inclusive finance on poverty reduction. These two elements are noted as crucial linkages in the struggle against poverty. This paper employs the DEA-Malmquist index model and Tobit model analysis to assess the effectiveness of fiscal expenditure and digital inclusive finance synergy under relative poverty and the influencing factors in the central and western provinces of China using provincial panel data from 2014 to 2020. The study found that: first, the integrated effectiveness of fiscal spending and digital financial inclusion to reduce poverty is firstly higher than it is for fiscal spending alone; second, additional fiscal spending and technology for digital financial inclusion should be allocated to the central and western areas in particular; and third, for poverty reduction in central and western China, the level of financial development, financial payment capability, and industrial structure are the most crucial factors. The following recommendations are made based on the findings of the aforementioned research: Overcoming the geographical restrictions; we will improve the transfer payment system’s top-level architecture for places with extreme poverty; the design of transfer payments to communities with extreme poverty will be improved; increasing access to digital financial services; we’ll boost technical and scientific innovation in underdeveloped areas; compensate for the lack of knowledge in underdeveloped areas; we will advance digital financial inclusion’s science, technology, and accuracy to lessen poverty; the combination of fiscal and financial policies should be put into practice in accordance with the level of poverty and the state of poverty in the places that are affected by it.
{"title":"An analysis of the synergistic poverty reduction effectiveness of fiscal spending and digital inclusive finance from the standpoint of relative poverty was conducted","authors":"Zhi-dong Jing, Yunyun Li","doi":"10.3233/jcm-226734","DOIUrl":"https://doi.org/10.3233/jcm-226734","url":null,"abstract":"This paper conducts an empirical investigation into the synergistic effects of fiscal spending and digital inclusive finance on poverty reduction. These two elements are noted as crucial linkages in the struggle against poverty. This paper employs the DEA-Malmquist index model and Tobit model analysis to assess the effectiveness of fiscal expenditure and digital inclusive finance synergy under relative poverty and the influencing factors in the central and western provinces of China using provincial panel data from 2014 to 2020. The study found that: first, the integrated effectiveness of fiscal spending and digital financial inclusion to reduce poverty is firstly higher than it is for fiscal spending alone; second, additional fiscal spending and technology for digital financial inclusion should be allocated to the central and western areas in particular; and third, for poverty reduction in central and western China, the level of financial development, financial payment capability, and industrial structure are the most crucial factors. The following recommendations are made based on the findings of the aforementioned research: Overcoming the geographical restrictions; we will improve the transfer payment system’s top-level architecture for places with extreme poverty; the design of transfer payments to communities with extreme poverty will be improved; increasing access to digital financial services; we’ll boost technical and scientific innovation in underdeveloped areas; compensate for the lack of knowledge in underdeveloped areas; we will advance digital financial inclusion’s science, technology, and accuracy to lessen poverty; the combination of fiscal and financial policies should be put into practice in accordance with the level of poverty and the state of poverty in the places that are affected by it.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"9 1","pages":"513-526"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84021760","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 rapid development of artificial intelligence and the continuous improvement of machine learning technology, speech recognition technology is also developing rapidly and the recognition accuracy is improving to meet the higher requirements of people for smart home devices, and combining smart home with voice recognition technology is an inevitable trend for future development. This study aims to propose a speech fuzzy enhancement algorithm based on neural network for smart home interactive speech recognition technology, so the study proposes a combination of fuzzy neural network algorithm (FNN) and stacked self-encoder (SAE) to form SAE-FNN algorithm, which has better non-linear characteristics and can better achieve feature learning, thus improving the performance of the whole system. The results show that with the SAE-FNN algorithm, the maximum relative error absolute value, average relative error and root mean square error are 0.355, 0.063 and 0.978, which are significantly higher than the other two individual algorithms, and the noise of the sound signal has little effect on the SAE-FNN algorithm. Therefore, it can be seen that the proposed SAE-FNN algorithm has excellent noise immunity performance. In summary, it can be seen that this neural network-based speech fuzzy enhancement algorithm for smart home interaction is extremely feasible.
{"title":"Neural network-based speech fuzzy enhancement algorithm for smart home interaction","authors":"Yongjian Dong, Qinrong Ye","doi":"10.3233/jcm-226702","DOIUrl":"https://doi.org/10.3233/jcm-226702","url":null,"abstract":"With the rapid development of artificial intelligence and the continuous improvement of machine learning technology, speech recognition technology is also developing rapidly and the recognition accuracy is improving to meet the higher requirements of people for smart home devices, and combining smart home with voice recognition technology is an inevitable trend for future development. This study aims to propose a speech fuzzy enhancement algorithm based on neural network for smart home interactive speech recognition technology, so the study proposes a combination of fuzzy neural network algorithm (FNN) and stacked self-encoder (SAE) to form SAE-FNN algorithm, which has better non-linear characteristics and can better achieve feature learning, thus improving the performance of the whole system. The results show that with the SAE-FNN algorithm, the maximum relative error absolute value, average relative error and root mean square error are 0.355, 0.063 and 0.978, which are significantly higher than the other two individual algorithms, and the noise of the sound signal has little effect on the SAE-FNN algorithm. Therefore, it can be seen that the proposed SAE-FNN algorithm has excellent noise immunity performance. In summary, it can be seen that this neural network-based speech fuzzy enhancement algorithm for smart home interaction is extremely feasible.","PeriodicalId":14668,"journal":{"name":"J. Comput. Methods Sci. Eng.","volume":"42 1","pages":"1225-1236"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85522102","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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