Pub Date : 2020-01-01DOI: 10.32604/jnm.2020.010062
Xiaoyan Chen, Jiahuan Chen, Zhongcheng Sha
Aiming at the problem that the detection effect of traditional edge detection algorithm is not good, and the problem that the existing edge detection algorithm based on convolution network cannot solve the thick edge problem from the model itself, this paper proposes a new edge detection method based on the generative adversarial network. The confrontation network consists of generator network and discriminator network, generator network is composed of U-net network and discriminator network is composed of five-layer convolution network. In this paper, we use BSDS500 training data set to train the model. Finally, several images are randomly selected from BSDS500 test set to compare with the results of traditional edge detection algorithm and HED algorithm. The results of BSDS500 benchmark test show that the ODS and OIS indices of the proposed method are 0.779 and 0.782 respectively, which are much higher than those of traditional edge detection algorithms, and the indices of HED algorithm using non-maximum suppression are similar.
{"title":"Edge Detection Based on Generative Adversarial Networks","authors":"Xiaoyan Chen, Jiahuan Chen, Zhongcheng Sha","doi":"10.32604/jnm.2020.010062","DOIUrl":"https://doi.org/10.32604/jnm.2020.010062","url":null,"abstract":"Aiming at the problem that the detection effect of traditional edge detection algorithm is not good, and the problem that the existing edge detection algorithm based on convolution network cannot solve the thick edge problem from the model itself, this paper proposes a new edge detection method based on the generative adversarial network. The confrontation network consists of generator network and discriminator network, generator network is composed of U-net network and discriminator network is composed of five-layer convolution network. In this paper, we use BSDS500 training data set to train the model. Finally, several images are randomly selected from BSDS500 test set to compare with the results of traditional edge detection algorithm and HED algorithm. The results of BSDS500 benchmark test show that the ODS and OIS indices of the proposed method are 0.779 and 0.782 respectively, which are much higher than those of traditional edge detection algorithms, and the indices of HED algorithm using non-maximum suppression are similar.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69794840","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}
Pub Date : 2020-01-01DOI: 10.32604/jnm.2020.010125
Leyao Chen, Lei Hong, Jiaying Liu
: This paper aims to analyze the microblog data published by the official account in a certain province of China, and finds out the rule of Weibo that is easier to be forwarded in the new police media perspective. In this paper, a new topic-based model is proposed. Firstly, the LDA topic clustering algorithm is used to extract the topic categories with forwarding heat from the microblogs with high forwarding numbers, then the Naive Bayesian algorithm is used to topic categories. The sample data is processed to predict the type of microblog forwarding. In order to evaluate this method, a large number of microblog online data is used to analysis. The experimental results show that the proposed method can accurately predict the forwarding of Weibo. on this, we propose an experimental method to predict the forwarding behavior of Weibo. The method is based on the LDA model and is modeled using the Naïve Bayes algorithm for prediction. Experiments show that there are two popular forwarding themes in public security police microblog: social hotspot case notification and life safety. From the final recall and precision of the model, this experimental method has certain accurate prediction ability. Through the predictions of the model, the life warning class (preventing fraud, etc.) is the most popular type of microblog tweets that can be forwarded by users. It can be seen from the displayed topic category keywords that the user forwards relevant content before and after the college entrance examination.
{"title":"Analysis and Prediction of New Media Information Dissemination of Police Microblog","authors":"Leyao Chen, Lei Hong, Jiaying Liu","doi":"10.32604/jnm.2020.010125","DOIUrl":"https://doi.org/10.32604/jnm.2020.010125","url":null,"abstract":": This paper aims to analyze the microblog data published by the official account in a certain province of China, and finds out the rule of Weibo that is easier to be forwarded in the new police media perspective. In this paper, a new topic-based model is proposed. Firstly, the LDA topic clustering algorithm is used to extract the topic categories with forwarding heat from the microblogs with high forwarding numbers, then the Naive Bayesian algorithm is used to topic categories. The sample data is processed to predict the type of microblog forwarding. In order to evaluate this method, a large number of microblog online data is used to analysis. The experimental results show that the proposed method can accurately predict the forwarding of Weibo. on this, we propose an experimental method to predict the forwarding behavior of Weibo. The method is based on the LDA model and is modeled using the Naïve Bayes algorithm for prediction. Experiments show that there are two popular forwarding themes in public security police microblog: social hotspot case notification and life safety. From the final recall and precision of the model, this experimental method has certain accurate prediction ability. Through the predictions of the model, the life warning class (preventing fraud, etc.) is the most popular type of microblog tweets that can be forwarded by users. It can be seen from the displayed topic category keywords that the user forwards relevant content before and after the college entrance examination.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69795403","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}
Pub Date : 2020-01-01DOI: 10.32604/jnm.2020.010135
P. Yang, Gang Liu, Xiaoyu Li, Li Qin, Xiaoxia Liu
In order to solve the problems of low efficiency and heavy workload of tumor coding in hospitals, we proposed a Drools-based intelligent tumors coding method. At present, most tumor hospitals use manual coding, the trained coders follow the main diagnosis selection rules to select the main diagnosis from the discharge diagnosis of the tumor patients, and then code all the discharge diagnoses according to the coding rules. Owing to different coders have different familiarity with the main diagnosis selection rules and ICD-10 disease coding, it will reduce the efficiency of the artificial coding results and affect the quality of the whole medical record. We first analyze the ICD library information, doctor's diagnostic information, radiotherapy information or chemotherapy information, surgery information, hospitalization information and other related information, and then generated Drools rule files based on the main diagnostic selection principles and coding principles, we also combined the text similarity analysis algorithm to construct an intelligent diagnostic information coding method. Practice shows that the coding method can be used to make the work efficiently and at the same time obtain the coding results which meet the standard and have high accuracy, so that the coders can be free from the repeated work and pay more attention to coding quality control and the coding logic adjustment.
{"title":"An Intelligent Tumors Coding Method Based on Drools","authors":"P. Yang, Gang Liu, Xiaoyu Li, Li Qin, Xiaoxia Liu","doi":"10.32604/jnm.2020.010135","DOIUrl":"https://doi.org/10.32604/jnm.2020.010135","url":null,"abstract":"In order to solve the problems of low efficiency and heavy workload of tumor coding in hospitals, we proposed a Drools-based intelligent tumors coding method. At present, most tumor hospitals use manual coding, the trained coders follow the main diagnosis selection rules to select the main diagnosis from the discharge diagnosis of the tumor patients, and then code all the discharge diagnoses according to the coding rules. Owing to different coders have different familiarity with the main diagnosis selection rules and ICD-10 disease coding, it will reduce the efficiency of the artificial coding results and affect the quality of the whole medical record. We first analyze the ICD library information, doctor's diagnostic information, radiotherapy information or chemotherapy information, surgery information, hospitalization information and other related information, and then generated Drools rule files based on the main diagnostic selection principles and coding principles, we also combined the text similarity analysis algorithm to construct an intelligent diagnostic information coding method. Practice shows that the coding method can be used to make the work efficiently and at the same time obtain the coding results which meet the standard and have high accuracy, so that the coders can be free from the repeated work and pay more attention to coding quality control and the coding logic adjustment.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69795489","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}
Ning Chen, Naernaer Xialihaer, Weiliang Kong, Jiping Ren
: This paper analyzes the energy consumption situation in Beijing, based on the comparison of common energy consumption prediction methods. Here we use multiple linear regression analysis, grey prediction, BP neural net-work prediction, grey BP neural network prediction combined method, LSTM long-term and short-term memory network model prediction method. Firstly, before constructing the model, the whole model is explained theoretically. The advantages and disadvantages of each model are analyzed before the modeling, and the corresponding advantages and disadvantages of these models are pointed out. Finally, these models are used to construct the Beijing energy forecasting model, and some years are selected as test samples to test the prediction accuracy. Finally, all models were used to predict the development trend of Beijing's total energy consumption from 2018 to 2019, and the relevant energy-saving opinions were given.
{"title":"Research on Prediction Methods of Energy Consumption Data","authors":"Ning Chen, Naernaer Xialihaer, Weiliang Kong, Jiping Ren","doi":"10.32604/jnm.2020.09889","DOIUrl":"https://doi.org/10.32604/jnm.2020.09889","url":null,"abstract":": This paper analyzes the energy consumption situation in Beijing, based on the comparison of common energy consumption prediction methods. Here we use multiple linear regression analysis, grey prediction, BP neural net-work prediction, grey BP neural network prediction combined method, LSTM long-term and short-term memory network model prediction method. Firstly, before constructing the model, the whole model is explained theoretically. The advantages and disadvantages of each model are analyzed before the modeling, and the corresponding advantages and disadvantages of these models are pointed out. Finally, these models are used to construct the Beijing energy forecasting model, and some years are selected as test samples to test the prediction accuracy. Finally, all models were used to predict the development trend of Beijing's total energy consumption from 2018 to 2019, and the relevant energy-saving opinions were given.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69796525","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}
Jiahui He, Chaozhi Wang, Hongyu Wu, Leiming Yan, Christian Lu
Multi-label text categorization refers to the problem of categorizing text through a multi-label learning algorithm. Text classification for Asian languages such as Chinese is different from work for other languages such as English which use spaces to separate words. Before classifying text, it is necessary to perform a word segmentation operation to convert a continuous language into a list of separate words and then convert it into a vector of a certain dimension. Generally, multi-label learning algorithms can be divided into two categories, problem transformation methods and adapted algorithms. This work will use customer's comments about some hotels as a training data set, which contains labels for all aspects of the hotel evaluation, aiming to analyze and compare the performance of various multi-label learning algorithms on Chinese text classification. The experiment involves three basic methods of problem transformation methods: Support Vector Machine, Random Forest, k-Nearest-Neighbor; and one adapted algorithm of Convolutional Neural Network. The experimental results show that the Support Vector Machine has better performance.
{"title":"Multi-Label Chinese Comments Categorization: Comparison of Multi-Label Learning Algorithms","authors":"Jiahui He, Chaozhi Wang, Hongyu Wu, Leiming Yan, Christian Lu","doi":"10.32604/jnm.2019.06238","DOIUrl":"https://doi.org/10.32604/jnm.2019.06238","url":null,"abstract":"Multi-label text categorization refers to the problem of categorizing text through a multi-label learning algorithm. Text classification for Asian languages such as Chinese is different from work for other languages such as English which use spaces to separate words. Before classifying text, it is necessary to perform a word segmentation operation to convert a continuous language into a list of separate words and then convert it into a vector of a certain dimension. Generally, multi-label learning algorithms can be divided into two categories, problem transformation methods and adapted algorithms. This work will use customer's comments about some hotels as a training data set, which contains labels for all aspects of the hotel evaluation, aiming to analyze and compare the performance of various multi-label learning algorithms on Chinese text classification. The experiment involves three basic methods of problem transformation methods: Support Vector Machine, Random Forest, k-Nearest-Neighbor; and one adapted algorithm of Convolutional Neural Network. The experimental results show that the Support Vector Machine has better performance.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69794966","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}
Zhizheng Zhang, Jing Feng, Jun Yan, Xiaolei Wang, Xiaocun Shu
Clouds play an important role in modulating radiation processes and climate changes in the Earth's atmosphere. Currently, measurement of meteorological elements such as temperature, air pressure, humidity, and wind has been automated. However, the cloud's automatic identification technology is still not perfect. Thus, this paper presents an approach that extracts dense scale-invariant feature transform (Dense_SIFT) as the local features of four typical cloud images. The extracted cloud features are then clustered by K-means algorithm, and the bag-of-words (BoW) model is used to describe each ground-based cloud image. Finally, support vector machine (SVM) is used for classification and recognition. Based on this design, a nephogram recognition intelligent application is implemented. Experiments show that, compared with other classifiers, our approach has better performance and achieved a recognition rate of 88.1%.
{"title":"Ground-Based Cloud Recognition Based on Dense_SIFT Features","authors":"Zhizheng Zhang, Jing Feng, Jun Yan, Xiaolei Wang, Xiaocun Shu","doi":"10.32604/JNM.2019.05937","DOIUrl":"https://doi.org/10.32604/JNM.2019.05937","url":null,"abstract":"Clouds play an important role in modulating radiation processes and climate changes in the Earth's atmosphere. Currently, measurement of meteorological elements such as temperature, air pressure, humidity, and wind has been automated. However, the cloud's automatic identification technology is still not perfect. Thus, this paper presents an approach that extracts dense scale-invariant feature transform (Dense_SIFT) as the local features of four typical cloud images. The extracted cloud features are then clustered by K-means algorithm, and the bag-of-words (BoW) model is used to describe each ground-based cloud image. Finally, support vector machine (SVM) is used for classification and recognition. Based on this design, a nephogram recognition intelligent application is implemented. Experiments show that, compared with other classifiers, our approach has better performance and achieved a recognition rate of 88.1%.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69794761","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}
: Video object tracking is an important research topic of computer vision, which finds a wide range of applications in video surveillance, robotics, human-computer interaction and so on. Although many moving object tracking algorithms have been proposed, there are still many difficulties in the actual tracking process, such as illumination change, occlusion, motion blurring, scale change, self-change and so on. Therefore, the development of object tracking technology is still challenging. The emergence of deep learning theory and method provides a new opportunity for the research of object tracking, and it is also the main theoretical framework for the research of moving object tracking algorithm in this paper. In this paper, the existing deep tracking-based target tracking algorithms are classified and sorted out. Based on the previous knowledge and my own understanding, several solutions are proposed for the existing methods. In addition, the existing deep learning target tracking method is still difficult to meet the requirements of real-time, how to design the network and tracking process to achieve speed and effect improvement, there is still a lot of research space.
{"title":"Review on Video Object Tracking Based on Deep Learning","authors":"Fangming Bi, Xin Ma, Wei Chen, Weidong Fang, Huayi Chen, Jingru Li, Biruk Assefa","doi":"10.32604/jnm.2019.06253","DOIUrl":"https://doi.org/10.32604/jnm.2019.06253","url":null,"abstract":": Video object tracking is an important research topic of computer vision, which finds a wide range of applications in video surveillance, robotics, human-computer interaction and so on. Although many moving object tracking algorithms have been proposed, there are still many difficulties in the actual tracking process, such as illumination change, occlusion, motion blurring, scale change, self-change and so on. Therefore, the development of object tracking technology is still challenging. The emergence of deep learning theory and method provides a new opportunity for the research of object tracking, and it is also the main theoretical framework for the research of moving object tracking algorithm in this paper. In this paper, the existing deep tracking-based target tracking algorithms are classified and sorted out. Based on the previous knowledge and my own understanding, several solutions are proposed for the existing methods. In addition, the existing deep learning target tracking method is still difficult to meet the requirements of real-time, how to design the network and tracking process to achieve speed and effect improvement, there is still a lot of research space.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69795060","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 wide application of information technology in urban infrastructure, urban construction has entered the stage of smart city, forming a large number of network electronic records. These electronic records play a vital role in the maintenance of urban infrastructure. However, how to preserve the network electronic records in the field of urban construction is still lack of a comprehensive and serious study. Aiming at this problem, the paper proposes to use the technology of broadcast-storage network to preserve the network electronic records for a long time and gives the concrete preservation process.
{"title":"Preservation Mechanism of Network Electronic Records Based on Broadcast-Storage Network in Urban Construction","authors":"Fujian Zhu, Yongjun Ren, Qirun Wang, Jinyue Xia","doi":"10.32604/jnm.2019.05920","DOIUrl":"https://doi.org/10.32604/jnm.2019.05920","url":null,"abstract":"With the wide application of information technology in urban infrastructure, urban construction has entered the stage of smart city, forming a large number of network electronic records. These electronic records play a vital role in the maintenance of urban infrastructure. However, how to preserve the network electronic records in the field of urban construction is still lack of a comprehensive and serious study. Aiming at this problem, the paper proposes to use the technology of broadcast-storage network to preserve the network electronic records for a long time and gives the concrete preservation process.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69795148","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 an effective error correction technology, the Low Density Parity Check Code (LDPC) has been researched and applied by many scholars. Meanwhile, LDPC codes have some prominent performances, which involves close to the Shannon limit, achieving a higher bit rate and a fast decoding. However, whether these excellent characteristics are suitable for the resource-constrained Wireless Sensor Network (WSN), it seems to be seldom concerned. In this article, we review the LDPC code’s structure brief.ly, and them classify and summarize the LDPC codes’ construction and decoding algorithms, finally, analyze the applications of LDPC code for WSN. We believe that our contributions will be able to facilitate the application of LDPC code in WSN.
{"title":"LDPC Code’s Decoding Algorithms for Wireless Sensor Network: a Brief Review","authors":"Weidong Fang, Wuxiong Zhang, Lianhai Shan, Biruk Assefa, Wei Chen","doi":"10.32604/JNM.2019.05786","DOIUrl":"https://doi.org/10.32604/JNM.2019.05786","url":null,"abstract":"As an effective error correction technology, the Low Density Parity Check Code (LDPC) has been researched and applied by many scholars. Meanwhile, LDPC codes have some prominent performances, which involves close to the Shannon limit, achieving a higher bit rate and a fast decoding. However, whether these excellent characteristics are suitable for the resource-constrained Wireless Sensor Network (WSN), it seems to be seldom concerned. In this article, we review the LDPC code’s structure brief.ly, and them classify and summarize the LDPC codes’ construction and decoding algorithms, finally, analyze the applications of LDPC code for WSN. We believe that our contributions will be able to facilitate the application of LDPC code in WSN.","PeriodicalId":69198,"journal":{"name":"新媒体杂志(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69794939","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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