The deployments of deep learning models must be highly optimized by experts or hardware suppliers before being used in practice, and it has always been a long-term goal for the compiler community to enable compilers to automatically optimize code. However, there is no feasible solution in practice as running a program costs a considerable amount of optimization time to obtain a desired latency. Aiming at making up for the deficiency of long optimization time of TVM compiler, a novel optimized hybrid aging evolutionary algorithm is proposed to predict the running time of the code and accelerate automatic code optimization for Ansor, an auto-tuning framework for TVM. The algorithm alternately removes the worst and oldest individuals in the population during the evolution process. Unlike previous evolutionary algorithm, if an individual seeks to survive in the evolving population for a long time, it must have excellent scalability and flexibility, not just the individual's own adaptability. In this way, this algorithm not only ensures a strong search capability, but also improves the convergence speed and accuracy, significantly reducing the optimization time of tensor programs for deep learning inference. Experimental results show that the algorithm can accelerate convergence speed. For the same task, our algorithm provides 9% to 16% shorter optimization time on average while achieving similar or better optimization quality (i.e., inference time).
{"title":"An optimized hybrid evolutionary algorithm for accelerating automatic code optimization","authors":"Yasong Zhang, Yu'e Li, Xiaolin Wang","doi":"10.1117/12.2667392","DOIUrl":"https://doi.org/10.1117/12.2667392","url":null,"abstract":"The deployments of deep learning models must be highly optimized by experts or hardware suppliers before being used in practice, and it has always been a long-term goal for the compiler community to enable compilers to automatically optimize code. However, there is no feasible solution in practice as running a program costs a considerable amount of optimization time to obtain a desired latency. Aiming at making up for the deficiency of long optimization time of TVM compiler, a novel optimized hybrid aging evolutionary algorithm is proposed to predict the running time of the code and accelerate automatic code optimization for Ansor, an auto-tuning framework for TVM. The algorithm alternately removes the worst and oldest individuals in the population during the evolution process. Unlike previous evolutionary algorithm, if an individual seeks to survive in the evolving population for a long time, it must have excellent scalability and flexibility, not just the individual's own adaptability. In this way, this algorithm not only ensures a strong search capability, but also improves the convergence speed and accuracy, significantly reducing the optimization time of tensor programs for deep learning inference. Experimental results show that the algorithm can accelerate convergence speed. For the same task, our algorithm provides 9% to 16% shorter optimization time on average while achieving similar or better optimization quality (i.e., inference time).","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"60 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124316906","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}
A person's state is reflected in many aspects, such as emotions and body movements. Online teaching makes it difficult for teachers to accurately understand the learning status of students due to the separation of space between teachers and students. This paper extracts images from video cameras, from which identifies the learner's emotion, head posture and fatigue, and evaluates the learner's learning state by synthesizing the three-sided information. The seven emotions were divided into three categories: negative, positive and natural. Head posture is defined by Euler angles, and fatigue is determined by blinking frequency. Hierarchical decision-making method is used in the model for information fusion. The learning state assessment method proposed in this paper integrates the performance of both internal and external aspects of psychology and behavior, and has high reliability. Real-time understanding of students' learning status can help improve the effectiveness of teaching.
{"title":"Research on multimodal online learning status evaluation based on video camera","authors":"Hui Xu, Xu Zhao, Yifan Wu, Huirong Wang","doi":"10.1117/12.2667460","DOIUrl":"https://doi.org/10.1117/12.2667460","url":null,"abstract":"A person's state is reflected in many aspects, such as emotions and body movements. Online teaching makes it difficult for teachers to accurately understand the learning status of students due to the separation of space between teachers and students. This paper extracts images from video cameras, from which identifies the learner's emotion, head posture and fatigue, and evaluates the learner's learning state by synthesizing the three-sided information. The seven emotions were divided into three categories: negative, positive and natural. Head posture is defined by Euler angles, and fatigue is determined by blinking frequency. Hierarchical decision-making method is used in the model for information fusion. The learning state assessment method proposed in this paper integrates the performance of both internal and external aspects of psychology and behavior, and has high reliability. Real-time understanding of students' learning status can help improve the effectiveness of teaching.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"104 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120907562","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}
More and more complex and diverse network security problems bring great challenges to the analysis of abnormal network behavior. In order to detect the abnormal connection behavior of the network more accurately, this paper first uses the knowledge graph technology to extract the graph feature parameters that can reflect the node and the overall situation of the network, and then proposes a two-stage unsupervised anomaly analysis method for the abnormal changes of the feature parameters. In the first stage, the anomaly analysis of the whole network graph features is carried out based on clustering technology, so the rough positioning is carried out. In the second stage, the abnormal trend analysis is performed on the graph features of important nodes to determine the category of abnormal connection behavior. On this basis, the time series prediction method is used to predict the node graph features, so as to provide early warning for network security. The experimental results show that the method can effectively extract the network abnormal behavior and predict the development trend of the network in the future, and provide a good support for the understanding of network security situation.
{"title":"Analysis and prediction of network connection behavior anomaly based on knowledge graph features","authors":"Liqiong Deng, Xuesi Xu, Yuan Ren","doi":"10.1117/12.2667439","DOIUrl":"https://doi.org/10.1117/12.2667439","url":null,"abstract":"More and more complex and diverse network security problems bring great challenges to the analysis of abnormal network behavior. In order to detect the abnormal connection behavior of the network more accurately, this paper first uses the knowledge graph technology to extract the graph feature parameters that can reflect the node and the overall situation of the network, and then proposes a two-stage unsupervised anomaly analysis method for the abnormal changes of the feature parameters. In the first stage, the anomaly analysis of the whole network graph features is carried out based on clustering technology, so the rough positioning is carried out. In the second stage, the abnormal trend analysis is performed on the graph features of important nodes to determine the category of abnormal connection behavior. On this basis, the time series prediction method is used to predict the node graph features, so as to provide early warning for network security. The experimental results show that the method can effectively extract the network abnormal behavior and predict the development trend of the network in the future, and provide a good support for the understanding of network security situation.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125178828","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 adaptability of an electromagnetic radiation measurement system in the explosive field environment of energetic materials is studied, the installation method of the instrument is demonstrated, and the measurement point layout is studied. The effective measurement area of the explosion field is determined from the radiation source field area, the antenna measurement field area, the radius of the explosion fireball, the sensitivity of the data acquisition instrument, etc. The number of layouts at the same distance is determined and the dislocation arrangement is adopted to achieve omnidirectional coverage of the measurement range, finally a complete electromagnetic radiation measurement system has been established.
{"title":"Research on the layout of electromagnetic radiation measurement points for explosion of energetic materials","authors":"Yuanbo Cui, Hang Zhou","doi":"10.1117/12.2667241","DOIUrl":"https://doi.org/10.1117/12.2667241","url":null,"abstract":"The adaptability of an electromagnetic radiation measurement system in the explosive field environment of energetic materials is studied, the installation method of the instrument is demonstrated, and the measurement point layout is studied. The effective measurement area of the explosion field is determined from the radiation source field area, the antenna measurement field area, the radius of the explosion fireball, the sensitivity of the data acquisition instrument, etc. The number of layouts at the same distance is determined and the dislocation arrangement is adopted to achieve omnidirectional coverage of the measurement range, finally a complete electromagnetic radiation measurement system has been established.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"165 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113983424","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}
Reducing network power consumption plays an important role in the research of wireless sensor networks. This paper focuses on energy efficiency in environmental data collection scenarios. The data collected in this scenario are usually redundant due to spatial and temporal correlation. Consequently, selecting some nodes for dormancy can reduce power consumption. It is the key issue that how to select dormant nodes, and existing research mainly focuses on uniform clustering and optimal routing algorithms. However, the algorithms cannot guide the selection of dormant nodes because of their less consideration of attribute characteristics. Therefore, this paper proposes a node dormancy strategy for temporal-spatial correlated nodes in wireless sensor networks. The temporal-spatial correlation of the data is firstly verified; then the attributes combined with the location information are provided for FCM clustering; after, dormant node selection and head node selection are performed according to the clustering. Experiments on real temperature datasets demonstrate that using this paper's strategy, data accuracy can still be maintained at more than 95% of what no dormant node perform when 50% of nodes are dormant and around 90% when 80% of nodes are dormant. The improvement even reaches at most 80% against the traditional strategy with the same percentage of dormancy.
{"title":"Dormancy strategy for temporal-spatial correlated nodes in wireless sensor networks","authors":"Jiang Yu, Yu Meng, Xingchuan Liu, Yongjie Nie","doi":"10.1117/12.2667233","DOIUrl":"https://doi.org/10.1117/12.2667233","url":null,"abstract":"Reducing network power consumption plays an important role in the research of wireless sensor networks. This paper focuses on energy efficiency in environmental data collection scenarios. The data collected in this scenario are usually redundant due to spatial and temporal correlation. Consequently, selecting some nodes for dormancy can reduce power consumption. It is the key issue that how to select dormant nodes, and existing research mainly focuses on uniform clustering and optimal routing algorithms. However, the algorithms cannot guide the selection of dormant nodes because of their less consideration of attribute characteristics. Therefore, this paper proposes a node dormancy strategy for temporal-spatial correlated nodes in wireless sensor networks. The temporal-spatial correlation of the data is firstly verified; then the attributes combined with the location information are provided for FCM clustering; after, dormant node selection and head node selection are performed according to the clustering. Experiments on real temperature datasets demonstrate that using this paper's strategy, data accuracy can still be maintained at more than 95% of what no dormant node perform when 50% of nodes are dormant and around 90% when 80% of nodes are dormant. The improvement even reaches at most 80% against the traditional strategy with the same percentage of dormancy.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122858112","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 channel estimation of IR-UWB ultra wideband wireless communication system realized by using compressed sensing theory. Firstly, sparse signal, observation matrix and reconstruction algorithm of compressed sensing theory were discussed. Secondly, discussed the composition of IR-UWB wireless communication system. The IEEE802.15.SG3a channel model was adopted for UWB multipath channel. According to the matrix calculation method of cyclic convolution, the compressed sensing model for channel estimation of IR-UWB system was derived, and GOMP algorithm was used to reconstruct the channel parameters of IR-UWB system. With the help of Matlab software, the simulation results showed that GOMP algorithm can reconstruct the channel parameters of IR-UWB system well.
{"title":"Application of the compressed sensing theory in the IR-UWB system channel estimation","authors":"Xuan Dong, Lihui Pan, Rui Pan, Wei Shan","doi":"10.1117/12.2667442","DOIUrl":"https://doi.org/10.1117/12.2667442","url":null,"abstract":"The channel estimation of IR-UWB ultra wideband wireless communication system realized by using compressed sensing theory. Firstly, sparse signal, observation matrix and reconstruction algorithm of compressed sensing theory were discussed. Secondly, discussed the composition of IR-UWB wireless communication system. The IEEE802.15.SG3a channel model was adopted for UWB multipath channel. According to the matrix calculation method of cyclic convolution, the compressed sensing model for channel estimation of IR-UWB system was derived, and GOMP algorithm was used to reconstruct the channel parameters of IR-UWB system. With the help of Matlab software, the simulation results showed that GOMP algorithm can reconstruct the channel parameters of IR-UWB system well.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127715617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problems of traditional Rapidly Exploring Random Tree (RRT) algorithm in route planning, such as slow speed, poor route quality and low flightability, a route planning algorithm based on integrated improvement of RRT was proposed. Firstly, in the selection of nodes to be expanded, the minimum sum of the distance between nodes and the target and the random sampling point is taken as the selection basis instead of the original method of determining nodes only according to random sampling points, so as to increase the probability of nodes near the target in the random tree being selected as nodes to be expanded. Secondly, in the process of node expansion, the reachable region of the next waypoint was determined according to the UAV dynamic constraints, and then multiple alternative nodes were randomly generated in this region. Then the route cost function is designed and the comprehensive generation value of the route formed by the alternative nodes is taken as the judgment criterion for node addition. Finally, B-spline curve smoothing is carried out to further improve the route quality. The simulation results show that the improved algorithm has obvious advantages in improving the planning speed and air route quality, and the obtained air route satisfies the UAV dynamic constraints and has high flightability.
{"title":"UAV path planning algorithm based on improved RRT","authors":"Yu Liu, Zi-lv Gu, Cheng Li, Bao-guo Wang, Henglin Wu, Wen-jing Liu","doi":"10.1117/12.2667637","DOIUrl":"https://doi.org/10.1117/12.2667637","url":null,"abstract":"Aiming at the problems of traditional Rapidly Exploring Random Tree (RRT) algorithm in route planning, such as slow speed, poor route quality and low flightability, a route planning algorithm based on integrated improvement of RRT was proposed. Firstly, in the selection of nodes to be expanded, the minimum sum of the distance between nodes and the target and the random sampling point is taken as the selection basis instead of the original method of determining nodes only according to random sampling points, so as to increase the probability of nodes near the target in the random tree being selected as nodes to be expanded. Secondly, in the process of node expansion, the reachable region of the next waypoint was determined according to the UAV dynamic constraints, and then multiple alternative nodes were randomly generated in this region. Then the route cost function is designed and the comprehensive generation value of the route formed by the alternative nodes is taken as the judgment criterion for node addition. Finally, B-spline curve smoothing is carried out to further improve the route quality. The simulation results show that the improved algorithm has obvious advantages in improving the planning speed and air route quality, and the obtained air route satisfies the UAV dynamic constraints and has high flightability.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132117467","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 changes of people’s consuming attitudes and the popularization of mobile payment, credit card seems increasingly indispensable in life. However, as the number of issued credit cards and credit lines is increasing, there emerges more and more fraud cases involving credit cards. Due to the rapid development of the Internet industry, the channels for capital flow have become unprecedentedly smooth, making it very difficult to prevent credit card fraud cases. If that continues, the development of banks and other financial institutions in the credit card field would be restricted, which might affect people's daily consumption and even the normal running of the society. The Bayesian Deep Learning method is used to quantify the uncertainty of credit card fraud prediction in this essay. Through experimental analysis, the accuracy of the model is over 99%. Compared with conventional classification models, this model has superior performance.
{"title":"A Bayesian deep learning method for credit card fraud detection with uncertainty quantification","authors":"Qiming Yu, Qizhi Zhang, Xihan Cao, Tianlin Zhang, Jiawei He, Ruimin Wang, Zhengyi Ma","doi":"10.1117/12.2667363","DOIUrl":"https://doi.org/10.1117/12.2667363","url":null,"abstract":"With the changes of people’s consuming attitudes and the popularization of mobile payment, credit card seems increasingly indispensable in life. However, as the number of issued credit cards and credit lines is increasing, there emerges more and more fraud cases involving credit cards. Due to the rapid development of the Internet industry, the channels for capital flow have become unprecedentedly smooth, making it very difficult to prevent credit card fraud cases. If that continues, the development of banks and other financial institutions in the credit card field would be restricted, which might affect people's daily consumption and even the normal running of the society. The Bayesian Deep Learning method is used to quantify the uncertainty of credit card fraud prediction in this essay. Through experimental analysis, the accuracy of the model is over 99%. Compared with conventional classification models, this model has superior performance.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"19 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134447108","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 mainly discusses processing the video stream obtained from the network camera by using OpenCV image processing function to detect the face. Face recognition classifier uses OpenCV comes with the face classifier that trains by HAAR characteristics , which draw out of the face part with a blue box. Secondly, we identify the meaning of gesture. The recognition of gesture uses the classifier which uses Haar characteristics to extract the target feature to make the classifier. And also the use of pattern matching method become the second choice. Finally we use JNA to call the Windows API, to make the Windows platform browser switch to the web page. Also we use JavaFX to create the GUI, to take pictures, set the value of HSV, and display the meaning of gestures. Key words-OpenCV; Gesture Recognition; Pattern Matching; Face Recognition
{"title":"Face and gesture recognition system based on OpenCV","authors":"Yiping Liu, Wei Wang, W. Zeng","doi":"10.1117/12.2667539","DOIUrl":"https://doi.org/10.1117/12.2667539","url":null,"abstract":"This paper mainly discusses processing the video stream obtained from the network camera by using OpenCV image processing function to detect the face. Face recognition classifier uses OpenCV comes with the face classifier that trains by HAAR characteristics , which draw out of the face part with a blue box. Secondly, we identify the meaning of gesture. The recognition of gesture uses the classifier which uses Haar characteristics to extract the target feature to make the classifier. And also the use of pattern matching method become the second choice. Finally we use JNA to call the Windows API, to make the Windows platform browser switch to the web page. Also we use JavaFX to create the GUI, to take pictures, set the value of HSV, and display the meaning of gestures. Key words-OpenCV; Gesture Recognition; Pattern Matching; Face Recognition","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115631032","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 a security defense technology to protect the network from attack, network intrusion detection system plays a very important role in the field of computer system and network security. Aiming at the multi classification problem of unbalanced data in network intrusion detection, machine learning has been widely used in intrusion detection, which is more intelligent and accurate than traditional methods. The existing multi classification methods of network intrusion detection are improved, and an intrusion detection model using smote and ensemble learning is proposed. The model is mainly divided into two parts: smote oversampling and stacking classifier. The NSL-KDD dataset is used to test the Stacked Ensemble model in this paper. Compared with the other five basic learner models, the Stacked Ensemble has a higher detection rate. Stacked Ensemble has significant advantages in solving the multi classification problem of unbalanced network intrusion detection data. It is a practical and feasible intrusion detection method.
{"title":"An intrusion detection model using smote and ensemble learning","authors":"Lingfeng Qiu, Yafei Song","doi":"10.1117/12.2667349","DOIUrl":"https://doi.org/10.1117/12.2667349","url":null,"abstract":"As a security defense technology to protect the network from attack, network intrusion detection system plays a very important role in the field of computer system and network security. Aiming at the multi classification problem of unbalanced data in network intrusion detection, machine learning has been widely used in intrusion detection, which is more intelligent and accurate than traditional methods. The existing multi classification methods of network intrusion detection are improved, and an intrusion detection model using smote and ensemble learning is proposed. The model is mainly divided into two parts: smote oversampling and stacking classifier. The NSL-KDD dataset is used to test the Stacked Ensemble model in this paper. Compared with the other five basic learner models, the Stacked Ensemble has a higher detection rate. Stacked Ensemble has significant advantages in solving the multi classification problem of unbalanced network intrusion detection data. It is a practical and feasible intrusion detection method.","PeriodicalId":128051,"journal":{"name":"Third International Seminar on Artificial Intelligence, Networking, and Information Technology","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124953387","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：