Pub Date : 2021-01-01DOI: 10.14311/nnw.2021.31.021
Sadia Nazim, Syed Sajjad Hussai, M. Moinuddin, Muhammad Zubair, Jawwad Ahmad
The healthcare area is entirely different from other industries. It is of the highly significant area and people supposed to gain the utmost care and facilities irrespective of the cost. Reliable image detection and classification is considered a significant capability in medical image investigation problems. The key challenge is that the whole image has to be searched for a particular event and then classified accordingly but it is necessary to ensure that any important piece of information or instance shouldn’t be skipped. With regards to image analysis by radiologists, it is quite restricted because of its partiality, the intricacy of the images, wide variations that happen amongst various analysts and weariness. However, the introduction of deep learning is a promising way to improve this situation by sorting out the issue according to human leaning mechanism consequently it brings high-tech changes in medical image classification problems. In this context, a new ensemble deep learning topology is being proposed in the direction of a more precise classification of musculoskeletal ailments. In this regard, a comparison has been accomplished based on different learning rates, drop-out rates, and optimizers. This comparative research proved to be a baseline to gauge the up-to-the-mark performance of the proposed ensemble deep learning architecture.
{"title":"A neoteric ensemble deep learning network for musculoskeletal disorder classification","authors":"Sadia Nazim, Syed Sajjad Hussai, M. Moinuddin, Muhammad Zubair, Jawwad Ahmad","doi":"10.14311/nnw.2021.31.021","DOIUrl":"https://doi.org/10.14311/nnw.2021.31.021","url":null,"abstract":"The healthcare area is entirely different from other industries. It is of the highly significant area and people supposed to gain the utmost care and facilities irrespective of the cost. Reliable image detection and classification is considered a significant capability in medical image investigation problems. The key challenge is that the whole image has to be searched for a particular event and then classified accordingly but it is necessary to ensure that any important piece of information or instance shouldn’t be skipped. With regards to image analysis by radiologists, it is quite restricted because of its partiality, the intricacy of the images, wide variations that happen amongst various analysts and weariness. However, the introduction of deep learning is a promising way to improve this situation by sorting out the issue according to human leaning mechanism consequently it brings high-tech changes in medical image classification problems. In this context, a new ensemble deep learning topology is being proposed in the direction of a more precise classification of musculoskeletal ailments. In this regard, a comparison has been accomplished based on different learning rates, drop-out rates, and optimizers. This comparative research proved to be a baseline to gauge the up-to-the-mark performance of the proposed ensemble deep learning architecture.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67125181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.14311/nnw.2021.31.014
David Švorc, Tomáš Tichý, M. Růžička
The main aim of this paper is to present a new possibility for detection and recognition of different categories of electric and conventional (equipped with combustion engine) vehicles. These possibilities are provided by use of thermal and visual video cameras and two methods of machine learning. The used methods are Haar cascade classifier and convolutional neural network (CNN). The thermal images, obtained through an infrared thermography camera, were used for the training database. The thermal cameras can complement or substitute visible spectrum of video cameras and other conventional sensors and provide detailed recognition and classification data needed for vehicle type recognition. The first listed method was used as an object detector and serves for the localization of the vehicle on the road without any further classification. The second method was trained for vehicle recognition on the thermal image database and classifies a localized object according to one of the defined categories. The results confirmed that it is possible to use infrared thermography for vehicle drive categorization according to the thermal features of vehicle exteriors together with methods of machine learning for vehicle type recognition.
{"title":"An infrared video detection and categorization system based on machine learning","authors":"David Švorc, Tomáš Tichý, M. Růžička","doi":"10.14311/nnw.2021.31.014","DOIUrl":"https://doi.org/10.14311/nnw.2021.31.014","url":null,"abstract":"The main aim of this paper is to present a new possibility for detection and recognition of different categories of electric and conventional (equipped with combustion engine) vehicles. These possibilities are provided by use of thermal and visual video cameras and two methods of machine learning. The used methods are Haar cascade classifier and convolutional neural network (CNN). The thermal images, obtained through an infrared thermography camera, were used for the training database. The thermal cameras can complement or substitute visible spectrum of video cameras and other conventional sensors and provide detailed recognition and classification data needed for vehicle type recognition. The first listed method was used as an object detector and serves for the localization of the vehicle on the road without any further classification. The second method was trained for vehicle recognition on the thermal image database and classifies a localized object according to one of the defined categories. The results confirmed that it is possible to use infrared thermography for vehicle drive categorization according to the thermal features of vehicle exteriors together with methods of machine learning for vehicle type recognition.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67124450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.14311/nnw.2020.30.001
L. Gitto, G. Massini, F. Mennini, C. Mento, P. Buscema
Chronic headache is a major liability in the individuals’ quality of life. Identifying in advance the main features common to patients with headache may allow planning a preventive strategy of intervention. An artificial neural network model (Auto Contractive Maps – AutoCM), aimed at analyzing the correlations between patients’ characteristics, affective symptoms and posture indicators has been developed in this paper. Patients suffering from chronic headache were observed at a neurological centre in Sicily (Italy). Headache and affective states were measured using the Profile of Mood States (POMS), the Beck Depression Inventory (BDI), the Toronto Alexithymia Scale (TAS-20) and the Repression Scale. Postural evaluations were carried through a stabilometric platform. The method of analysis selected allowed to reconstruct some records that were missing, through a Recirculation AutoAssociative Neural Network, and to obtain sound results. The results showed how some items from TAS-20, Repression and POMS were closely linked. The postural abnormalities were correlated primarily with repression features. The highest scores of the POMS were correlated with the items of the BDI. The results obtained lead to interesting remarks about the common traits to patients with headache. The main conclusion lies in the potentialities offered by the new methodology applied, that may contribute, overall, to a better understanding of the complexity of chronic diseases, where many factors concur to define patients’ health conditions.
{"title":"Affective symptoms and postural abnormalities as predictors of headache: an application of artificial neural networks","authors":"L. Gitto, G. Massini, F. Mennini, C. Mento, P. Buscema","doi":"10.14311/nnw.2020.30.001","DOIUrl":"https://doi.org/10.14311/nnw.2020.30.001","url":null,"abstract":"Chronic headache is a major liability in the individuals’ quality of life. Identifying in advance the main features common to patients with headache may allow planning a preventive strategy of intervention. An artificial neural network model (Auto Contractive Maps – AutoCM), aimed at analyzing the correlations between patients’ characteristics, affective symptoms and posture indicators has been developed in this paper. Patients suffering from chronic headache were observed at a neurological centre in Sicily (Italy). Headache and affective states were measured using the Profile of Mood States (POMS), the Beck Depression Inventory (BDI), the Toronto Alexithymia Scale (TAS-20) and the Repression Scale. Postural evaluations were carried through a stabilometric platform. The method of analysis selected allowed to reconstruct some records that were missing, through a Recirculation AutoAssociative Neural Network, and to obtain sound results. The results showed how some items from TAS-20, Repression and POMS were closely linked. The postural abnormalities were correlated primarily with repression features. The highest scores of the POMS were correlated with the items of the BDI. The results obtained lead to interesting remarks about the common traits to patients with headache. The main conclusion lies in the potentialities offered by the new methodology applied, that may contribute, overall, to a better understanding of the complexity of chronic diseases, where many factors concur to define patients’ health conditions.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"40 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.14311/NNW.2020.30.021
M. Markosová, B. Rudolf, P. Nather, L. Benusková
The purpose of this study was to investigate degree distributions of functional brain networks. Particular functional brain networks were constructed from the fMRI measurements of three groups of participants namely, young healthy participants, elderly healthy participants and elderly participants with Alzheimer disease. Functional brain networks were constructed for three different correlation thresholds of voxel activity correlated over time. We have noticed that the character of degree distribution changes when the value of correlation threshold decreases. In order to explain the degree distribution changes with the changes of value of correlation threshold, we created two different, yet related network models. The crucial factor both models contain is an increasing noise as the voxel activity correlation threshold is lowered, which in our models corresponds to an increase of the number of random correlations between the voxels – nodes of the functional network. The models account for how initially scale-free character of the degree distribution changes as the correlation threshold is lowered based on the processes of network growth and edge addition. The two models differ in the manner of preferential and random edge addition while the second model is a refinement of the first one. On average, the second model leads to a better quantitative match with the data. To our knowledge, such functional brain network models, which take into account the correlation threshold as an independent variable have not been introduced before.
{"title":"Network models for changing degree distributions of functional brain networks","authors":"M. Markosová, B. Rudolf, P. Nather, L. Benusková","doi":"10.14311/NNW.2020.30.021","DOIUrl":"https://doi.org/10.14311/NNW.2020.30.021","url":null,"abstract":"The purpose of this study was to investigate degree distributions of functional brain networks. Particular functional brain networks were constructed from the fMRI measurements of three groups of participants namely, young healthy participants, elderly healthy participants and elderly participants with Alzheimer disease. Functional brain networks were constructed for three different correlation thresholds of voxel activity correlated over time. We have noticed that the character of degree distribution changes when the value of correlation threshold decreases. In order to explain the degree distribution changes with the changes of value of correlation threshold, we created two different, yet related network models. The crucial factor both models contain is an increasing noise as the voxel activity correlation threshold is lowered, which in our models corresponds to an increase of the number of random correlations between the voxels – nodes of the functional network. The models account for how initially scale-free character of the degree distribution changes as the correlation threshold is lowered based on the processes of network growth and edge addition. The two models differ in the manner of preferential and random edge addition while the second model is a refinement of the first one. On average, the second model leads to a better quantitative match with the data. To our knowledge, such functional brain network models, which take into account the correlation threshold as an independent variable have not been introduced before.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"30 1","pages":"309-332"},"PeriodicalIF":0.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.14311/nnw.2020.30.016
Wen Zhou, Yiwen Liang, Zhe Ming, Hongbin Dong
Earthquake prediction is an extraordinarily stochastic process. Determining the occurrence time, location of epicenter and magnitude of a coming earthquake in the following month is an extremely difficult task. Nowadays, some geophysical, statistical and machine learning methods are adopted to predict earthquakes, however, for the insufficient medium-large seismic data, their results are not satisfactory. Due to there is no obvious empirical relationship between seismicity features, magnitude and location of a coming earthquake in a particular time window, an earthquake prediction approach based on danger theory is proposed in this paper. It extracts eight indicators calculated from earthquake data for recent years in Sichuan and surroundings by Gutenberg-Richter(GR) inverse power-law, and predicts quakes with magnitude lager than 4.5 during the following month by numerical differential based Dendritic Cell Algorithm (ndDCA). We compare this approach with six state-of-art earthquake prediction algorithms. Overall our algorithm yields the encouraging results in all the qualified parameters assessed, and it provides technical support for the application of earthquake prediction.
{"title":"EARTHQUAKE PREDICTION MODEL BASED ON DANGER THEORY IN ARTIFICIAL IMMUNITY","authors":"Wen Zhou, Yiwen Liang, Zhe Ming, Hongbin Dong","doi":"10.14311/nnw.2020.30.016","DOIUrl":"https://doi.org/10.14311/nnw.2020.30.016","url":null,"abstract":"Earthquake prediction is an extraordinarily stochastic process. Determining the occurrence time, location of epicenter and magnitude of a coming earthquake in the following month is an extremely difficult task. Nowadays, some geophysical, statistical and machine learning methods are adopted to predict earthquakes, however, for the insufficient medium-large seismic data, their results are not satisfactory. Due to there is no obvious empirical relationship between seismicity features, magnitude and location of a coming earthquake in a particular time window, an earthquake prediction approach based on danger theory is proposed in this paper. It extracts eight indicators calculated from earthquake data for recent years in Sichuan and surroundings by Gutenberg-Richter(GR) inverse power-law, and predicts quakes with magnitude lager than 4.5 during the following month by numerical differential based Dendritic Cell Algorithm (ndDCA). We compare this approach with six state-of-art earthquake prediction algorithms. Overall our algorithm yields the encouraging results in all the qualified parameters assessed, and it provides technical support for the application of earthquake prediction.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"38 1","pages":"231-247"},"PeriodicalIF":0.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.14311/nnw.2020.30.002
Jianpei Hu, S. Cao, Chenchen Xu, Jianyong Yao, Zhiwei Xie
{"title":"High-accuracy motion control of a motor servo system with dead-zone based on a single hidden layer neural network","authors":"Jianpei Hu, S. Cao, Chenchen Xu, Jianyong Yao, Zhiwei Xie","doi":"10.14311/nnw.2020.30.002","DOIUrl":"https://doi.org/10.14311/nnw.2020.30.002","url":null,"abstract":"","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"30 1","pages":"27-44"},"PeriodicalIF":0.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.14311/nnw.2020.30.009
Zhang Yiyi, Jin Shangzhong, Wu Yufeng, Zhao Tianqi, Yan Yongqiang, Li Zenan, Li Yalan
With the rapid development of artificial intelligence in recent years, the application of intelligent security has become increasingly widespread. This paper presents a new intelligent system that uses Convolutional Neural Network (CNN) combined with a high-resolution camera to identify the theft behavior of customers. The CNN extracts relevant information from the theft and non-theft behavior of customers in supermarkets to establish a recognition model. Our results show that, by updating the data sets, the recognition model can be continuously optimized, and the average recognition accuracy finally reaches 83 %. The proposed system can independently identify the theft and non-theft behavior in video surveillance and sound alarm on the theft behavior in time. The advantages of the system are its low cost and high precision, which show excellent commercial value and application prospects.
{"title":"A new intelligent supermarket security system","authors":"Zhang Yiyi, Jin Shangzhong, Wu Yufeng, Zhao Tianqi, Yan Yongqiang, Li Zenan, Li Yalan","doi":"10.14311/nnw.2020.30.009","DOIUrl":"https://doi.org/10.14311/nnw.2020.30.009","url":null,"abstract":"With the rapid development of artificial intelligence in recent years, the application of intelligent security has become increasingly widespread. This paper presents a new intelligent system that uses Convolutional Neural Network (CNN) combined with a high-resolution camera to identify the theft behavior of customers. The CNN extracts relevant information from the theft and non-theft behavior of customers in supermarkets to establish a recognition model. Our results show that, by updating the data sets, the recognition model can be continuously optimized, and the average recognition accuracy finally reaches 83 %. The proposed system can independently identify the theft and non-theft behavior in video surveillance and sound alarm on the theft behavior in time. The advantages of the system are its low cost and high precision, which show excellent commercial value and application prospects.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"30 1","pages":"113-131"},"PeriodicalIF":0.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.14311/nnw.2020.30.012
Zhenghua Zhou, Yanqing Fu, Jianwei Zhao
Radial basis function (RBF) has been extensively applied for surface reconstruction from scattered 3D point data due to its strong ability of approximation. However, additional information, such as off-surface points, are usually required to be appended into constraints for determining the parameters, which apparently increases the computation cost and data unreliability. To avoid adding additional off surface point constraints, a novel surface reconstruction approach based on local coordinate system transform and partition of unity is proposed in this paper. Firstly, the explicit RBF functions are constructed to approximate the local surface patches, and then it is transformed into an equivalent implicit surface reconstruction form by local system coordinate transformation. Compared with the local implicit surface approximation, the proposed local explicit surface approximation method is capable of avoiding trivial solution occurred in RBF approximating, and does not increase the scale of data solution. A number of comparison experiments of the proposed method with the traditional RBF-based method and the multi-level partition of unity (MPU) method are carried out on some kinds of large dataset, non-uniformity dataset, noisy dataset. The experimental results illustrate that the proposed method is robust and effective in dealing with large-scale point clouds surface reconstruction.
{"title":"An efficient method for surface reconstruction based on local coordinate system transform and partition of unity","authors":"Zhenghua Zhou, Yanqing Fu, Jianwei Zhao","doi":"10.14311/nnw.2020.30.012","DOIUrl":"https://doi.org/10.14311/nnw.2020.30.012","url":null,"abstract":"Radial basis function (RBF) has been extensively applied for surface reconstruction from scattered 3D point data due to its strong ability of approximation. However, additional information, such as off-surface points, are usually required to be appended into constraints for determining the parameters, which apparently increases the computation cost and data unreliability. To avoid adding additional off surface point constraints, a novel surface reconstruction approach based on local coordinate system transform and partition of unity is proposed in this paper. Firstly, the explicit RBF functions are constructed to approximate the local surface patches, and then it is transformed into an equivalent implicit surface reconstruction form by local system coordinate transformation. Compared with the local implicit surface approximation, the proposed local explicit surface approximation method is capable of avoiding trivial solution occurred in RBF approximating, and does not increase the scale of data solution. A number of comparison experiments of the proposed method with the traditional RBF-based method and the multi-level partition of unity (MPU) method are carried out on some kinds of large dataset, non-uniformity dataset, noisy dataset. The experimental results illustrate that the proposed method is robust and effective in dealing with large-scale point clouds surface reconstruction.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1184 1","pages":"161-176"},"PeriodicalIF":0.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.14311/nnw.2020.30.014
P. Provinský
The topic of this article is a floppy logic, a new multi-valued logic. Floppy logic is related to fuzzy logic and the theory of probability, but it also has interesting links to probability logic and standard Boolean logic. It provides a consistent and simple theory that is easy to apply in practice. This article examines the isomorphism theorem, which plays an important role in floppy logic. The theorem is described and proved. The most important consequences of the isomorphism theorem are: 1) All statements which are equivalent in standard Boolean logic are also equivalent in floppy logic. 2) Floppy logic has all the properties of standard Boolean logic which can be formulated as an equivalence. These include, for example, distributivity, the contradiction law, the law of excluded middle, and others. The article mainly examines floppy implication. We show that floppy implication does not satisfy Adam’s Thesis and that floppy logic is not limited by Lewis’ triviality result. We also present a range of inference rules which are generalizations of modus ponens and modus tollens. These rules hold in floppy logic, and of course, also apply to standard Boolean logic. All these results lead us to the notion that floppy logic is a many-valued generalization of standard Boolean logic.
{"title":"Floppy logic as a generalization of standard Boolean logic","authors":"P. Provinský","doi":"10.14311/nnw.2020.30.014","DOIUrl":"https://doi.org/10.14311/nnw.2020.30.014","url":null,"abstract":"The topic of this article is a floppy logic, a new multi-valued logic. Floppy logic is related to fuzzy logic and the theory of probability, but it also has interesting links to probability logic and standard Boolean logic. It provides a consistent and simple theory that is easy to apply in practice. This article examines the isomorphism theorem, which plays an important role in floppy logic. The theorem is described and proved. The most important consequences of the isomorphism theorem are: 1) All statements which are equivalent in standard Boolean logic are also equivalent in floppy logic. 2) Floppy logic has all the properties of standard Boolean logic which can be formulated as an equivalence. These include, for example, distributivity, the contradiction law, the law of excluded middle, and others. The article mainly examines floppy implication. We show that floppy implication does not satisfy Adam’s Thesis and that floppy logic is not limited by Lewis’ triviality result. We also present a range of inference rules which are generalizations of modus ponens and modus tollens. These rules hold in floppy logic, and of course, also apply to standard Boolean logic. All these results lead us to the notion that floppy logic is a many-valued generalization of standard Boolean logic.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"30 1","pages":"193-209"},"PeriodicalIF":0.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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