摘要 :
Traditional public opinion information identification methods have poor performance, eitherlow accuracy, or rely on hand-designed features. This paper converts public opinion information identification to text classification probl...
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Traditional public opinion information identification methods have poor performance, eitherlow accuracy, or rely on hand-designed features. This paper converts public opinion information identification to text classification problem, and proposes a public opinion information identification method based on Word2Vec and graph convolutional networks. First, Word2Vec is used to train word vector and word-article graphs are constructed; then, the graphs are trained and classified by graph convolutional neural network; finally, network public opinion information recognition is completed according to the classification results. The experimental results on the constructed Central Asian country data set show that the proposed method has achieved better performance,where the average identification accuracy of “Belt and Road” network public opinion information reached 85.58%.Furthermore, the performance on other data sets is also comparable to current mainstream text classification methods.
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摘要 :
Traditional public opinion information identification methods have poor performance, eitherlow accuracy, or rely on hand-designed features. This paper converts public opinion information identification to text classification probl...
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Traditional public opinion information identification methods have poor performance, eitherlow accuracy, or rely on hand-designed features. This paper converts public opinion information identification to text classification problem, and proposes a public opinion information identification method based on Word2Vec and graph convolutional networks. First, Word2Vec is used to train word vector and word-article graphs are constructed; then, the graphs are trained and classified by graph convolutional neural network; finally, network public opinion information recognition is completed according to the classification results. The experimental results on the constructed Central Asian country data set show that the proposed method has achieved better performance,where the average identification accuracy of “Belt and Road” network public opinion information reached 85.58%.Furthermore, the performance on other data sets is also comparable to current mainstream text classification methods.
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When several Web services with simple functions need to be combined to provide more complex functions, how to choose from a large number of Web services with the same functions but different quality of service is a QoS-based servi...
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When several Web services with simple functions need to be combined to provide more complex functions, how to choose from a large number of Web services with the same functions but different quality of service is a QoS-based service composition problem. Currently, there are many classical methods and reinforcement learning methods applied to the QoS-based service composition problem. However, these methods require long computation time. We address three challenges in building an end-to-end supervised learning framework. 1) The number of Web services composing different composite services varies. 2) The topological relationships among Web services are difficult to express and difficult to integrate into neural networks. 3) The number of Web services providing each sub-function in composite services varies. Finally, we propose DeepQSC, a deep supervised learning framework based on graph convolutional networks and attention mechanisms. The framework can form high QoS composite services with limited computation time. We conducted experiments on a real-world dataset. The experiments show that DeepQSC has a significant advantage over six current state-of-the-art algorithms.
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摘要 :
When several Web services with simple functions need to be combined to provide more complex functions, how to choose from a large number of Web services with the same functions but different quality of service is a QoS-based servi...
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When several Web services with simple functions need to be combined to provide more complex functions, how to choose from a large number of Web services with the same functions but different quality of service is a QoS-based service composition problem. Currently, there are many classical methods and reinforcement learning methods applied to the QoS-based service composition problem. However, these methods require long computation time. We address three challenges in building an end-to-end supervised learning framework. 1) The number of Web services composing different composite services varies. 2) The topological relationships among Web services are difficult to express and difficult to integrate into neural networks. 3) The number of Web services providing each sub-function in composite services varies. Finally, we propose DeepQSC, a deep supervised learning framework based on graph convolutional networks and attention mechanisms. The framework can form high QoS composite services with limited computation time. We conducted experiments on a real-world dataset. The experiments show that DeepQSC has a significant advantage over six current state-of-the-art algorithms.
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Among all the respiratory cancers, the incidence of laryngeal carcinoma is second only to lung cancer, accounting for about 1% of all body tumors. At present, the most common detection method for laryngeal carcinoma is still the m...
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Among all the respiratory cancers, the incidence of laryngeal carcinoma is second only to lung cancer, accounting for about 1% of all body tumors. At present, the most common detection method for laryngeal carcinoma is still the method of endoscopic imaging, including white-light endoscopy and endoscopic systems with narrow band imaging (NBI). As a new optical imaging technology developed in recent years, the probe-based confocal laser endomicroscopy (pCLE) can clearly display the changes in the tissue structure at the cellular levelˈwhich can be the mainstay for judging the cell condition. It has been used in some advanced hospitals and research institutes to detect laryngeal carcinoma, and its superiority and accuracy has been confirmed. However, there is no objective evaluation system about pCLE-based image diagnosis in the current medical community. The diagnostic results and diagnostic basis solely depend on professional physicians' personal capabilities and experience. At the same time, the professional doctors studying the PCLE image are very rare, and it costs a lot to cultivate such a professional doctor. As artificial intelligence develops and integrates into various fields rapidly, the research of computer-aided diagnosis based on "AI+medical imaging" is in full swing. Hence, there is an incredible need for developing computeraided diagnosis of pCLE laryngeal carcinoma imaging. Accordingly, this paper proposes and develops an intelligent diagnostic system based on the Transformer network, which is independent of surgeons and imaging experts. This system can perform computer-aided diagnosis of laryngeal carcinoma based on pCLE images automatically and quickly with high accuracy, and determine the different phases of the pathology with accurate diagnostic basis, thereby achieving the effect of computer graphics auxiliary diagnosis. The experimental result shows that the intelligent diagnostic system proposed in this paper performs even better than pCLE professional imaging physicians with more than 5-10 years of experience in the diagnosis of different pathology of laryngeal carcinoma. And this system is also superior to the traditional Deep Convolutional neural network in various performance indicators, and its diagnostic basis has also been recognized by professional image physicians. Based on this system, an objective standard system of laryngeal carcinoma pCLE imaging diagnosis can be established, which will effectively reduce the risk of subjective diagnosis of doctors, improve diagnostic efficiency, and relieve the burden to cultivate pCLE image physicians to a certain extent.
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摘要 :
Among all the respiratory cancers, the incidence of laryngeal carcinoma is second only to lung cancer, accounting for about 1% of all body tumors. At present, the most common detection method for laryngeal carcinoma is still the m...
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Among all the respiratory cancers, the incidence of laryngeal carcinoma is second only to lung cancer, accounting for about 1% of all body tumors. At present, the most common detection method for laryngeal carcinoma is still the method of endoscopic imaging, including white-light endoscopy and endoscopic systems with narrow band imaging (NBI). As a new optical imaging technology developed in recent years, the probe-based confocal laser endomicroscopy (pCLE) can clearly display the changes in the tissue structure at the cellular level?which can be the mainstay for judging the cell condition. It has been used in some advanced hospitals and research institutes to detect laryngeal carcinoma, and its superiority and accuracy has been confirmed. However, there is no objective evaluation system about pCLE-based image diagnosis in the current medical community. The diagnostic results and diagnostic basis solely depend on professional physicians' personal capabilities and experience. At the same time, the professional doctors studying the PCLE image are very rare, and it costs a lot to cultivate such a professional doctor. As artificial intelligence develops and integrates into various fields rapidly, the research of computer-aided diagnosis based on "AI+medical imaging" is in full swing. Hence, there is an incredible need for developing computeraided diagnosis of pCLE laryngeal carcinoma imaging. Accordingly, this paper proposes and develops an intelligent diagnostic system based on the Transformer network, which is independent of surgeons and imaging experts. This system can perform computer-aided diagnosis of laryngeal carcinoma based on pCLE images automatically and quickly with high accuracy, and determine the different phases of the pathology with accurate diagnostic basis, thereby achieving the effect of computer graphics auxiliary diagnosis. The experimental result shows that the intelligent diagnostic system proposed in this paper performs even better than pCLE professional imaging physicians with more than 5-10 years of experience in the diagnosis of different pathology of laryngeal carcinoma. And this system is also superior to the traditional Deep Convolutional neural network in various performance indicators, and its diagnostic basis has also been recognized by professional image physicians. Based on this system, an objective standard system of laryngeal carcinoma pCLE imaging diagnosis can be established, which will effectively reduce the risk of subjective diagnosis of doctors, improve diagnostic efficiency, and relieve the burden to cultivate pCLE image physicians to a certain extent.
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The research on molecular devices, fluorescent labels and fluorescent probes based on the interaction between biomolecular DNA and fluorescent dyes has been paid more attention at home and abroad. In this paper, the luminescence p...
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The research on molecular devices, fluorescent labels and fluorescent probes based on the interaction between biomolecular DNA and fluorescent dyes has been paid more attention at home and abroad. In this paper, the luminescence properties of the [Ru(bpy)_3]Cl_2 complex itself were investigated, and the luminescence properties of the [Ru(bpy)_3]Cl_2 complex under the interaction of the solution and the film were observed by association of the DNA complex with the [Ru(bpy)_3]Cl_2 complex. The results showed that [Ru(bpy)_3]Cl_2 emitted red light with its main emission peak wavelength was 610nm, and its fluorescence intensity was the highest when the concentration of solution substance was 10mmol/L. When doped with DNA solution in [Ru(bpy)_3]Cl_2 complex, a small amount of fluorescent dye [Ru(bpy)_3]Cl_2 can be used to achieve a higher luminous intensity At the same time, the fluorescent dye [Ru(bpy)_3]Cl_2 doped with DNA solution reached a higher luminous intensity in the thin-film state. This experiment provides an important experimental basis for the application of fluorescent substance [Ru(bpy)_3]Cl_2 in luminescent thin films.
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Objective: Construct an index system of evaluating emergency preparedness of urban community. Methods: Through two rounds of consultation with experts, the evaluation index system of emergency preparedness capacity of urban commun...
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Objective: Construct an index system of evaluating emergency preparedness of urban community. Methods: Through two rounds of consultation with experts, the evaluation index system of emergency preparedness capacity of urban community was constructed, and the weight of each index was calculated by Analytic Hierarchy Process and comprehensive evaluation was made by Fuzzy Comprehensive Process. Results: The evaluation index system for emergency preparedness of urban community composed of 7 first-level indexes, 20 second-level indexes and 51 third-level indexes was established. Conclusion: The index system was used to evaluate the emergency preparedness capability of X community in Xi'an. The advantages and disadvantages of emergency preparedness capability construction in this community were found and corresponding countermeasures were put forward.
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In recent years, microdroplet arrays have been widely used in biology, chemistry, materials science, and engineering, and droplet generation methods are becoming more effective and controllable. At the same time, droplet arrays ha...
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In recent years, microdroplet arrays have been widely used in biology, chemistry, materials science, and engineering, and droplet generation methods are becoming more effective and controllable. At the same time, droplet arrays have been widely studied for various applications due to their novel optical and electronic properties. In this mini review, Firstly, the principles of wetting and evaporation during the preparation of the droplet array are introduced. Secondly the hydrophilic and hydrophobic treatment methods for the preparation of the droplet array are discussed. Finally, the application of droplet arrays in different fields is summarized.
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摘要 :
In recent years, microdroplet arrays have been widely used in biology, chemistry, materials science, and engineering, and droplet generation methods are becoming more effective and controllable. At the same time, droplet arrays ha...
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In recent years, microdroplet arrays have been widely used in biology, chemistry, materials science, and engineering, and droplet generation methods are becoming more effective and controllable. At the same time, droplet arrays have been widely studied for various applications due to their novel optical and electronic properties. In this mini review, Firstly, the principles of wetting and evaporation during the preparation of the droplet array are introduced. Secondly the hydrophilic and hydrophobic treatment methods for the preparation of the droplet array are discussed. Finally, the application of droplet arrays in different fields is summarized.
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