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Qingguo Zhou
Lanzhou University, Lanzhou, 730000, China

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Article
Published: 24 August 2021 in The Journal of Supercomputing
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Malware has seriously threatened the safety of computer systems for a long time. Due to the rapid development of anti-detection technology, traditional detection methods based on static analysis and dynamic analysis have limited effects. With its better predictive performance, AI-based malware detection has been increasingly used to deal with malware in recent years. However, due to the diversity of malware, it is difficult to extract feature from malware, which make malware detection not conductive to the application of AI technology. To solve the problem, a malware classifier based on graph convolutional network is designed to adapt to the difference of malware characteristics. The specific method is to firstly extract the API call sequence from the malware code and generate a directed cycle graph, then use the Markov chain and principal component analysis method to extract the feature map of the graph, and design a classifier based on graph convolutional network, and finally analyze and compare the performance of the method. The results show that the method has better performance in most detection, and the highest accuracy is \(98.32\%\), compared with existing methods, our model is superior to other methods in terms of FPR and accuracy. It is also stable to deal with the development and growth of malware.

ACS Style

Shanxi Li; Qingguo Zhou; Rui Zhou; Qingquan Lv. Intelligent malware detection based on graph convolutional network. The Journal of Supercomputing 2021, 1 -17.

AMA Style

Shanxi Li, Qingguo Zhou, Rui Zhou, Qingquan Lv. Intelligent malware detection based on graph convolutional network. The Journal of Supercomputing. 2021; ():1-17.

Chicago/Turabian Style

Shanxi Li; Qingguo Zhou; Rui Zhou; Qingquan Lv. 2021. "Intelligent malware detection based on graph convolutional network." The Journal of Supercomputing , no. : 1-17.

Journal article
Published: 03 August 2021 in IEEE Transactions on Intelligent Transportation Systems
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Different from the existing train delay studies that had strived to explore sophisticated algorithms, this paper focuses on finding the bound of improvements on predicting multi-scenario train delays with different machine learning methods. Motivated by the observation of deep learning methods failing to improve the prediction performance if the delay occurs rarely, we present a novel augmented machine learning approach to improve the overall prediction accuracy further. Our solution proposes a rule-driven automation (RDA) method, including a delay status labeling (DSL) algorithm, and the resilience of section (RSE) and resilience of station (RST) indicators to generate the forecast for train delays. The experiment results demonstrate that the Random Forest based implementation of our RDA method (RF-RDA) can significantly improve the generalization ability of multivariate multi-step forecast models for multi-scenario train delay prediction. The proposed solution surpasses state-of-art baselines based on real-world traffic datasets, which treat various real-time delays differently. Even when the predictability of conventional deep learning methods decreases, the performance of our method is still acceptable for practical use to provide accurate forecasts.

ACS Style

Jianqing Wu; Yihui Wang; Bo Du; Qiang Wu; Yanlong Zhai; Jun Shen; Luping Zhou; Chen Cai; Wei Wei; Qingguo Zhou. The Bounds of Improvements Toward Real-Time Forecast of Multi-Scenario Train Delays. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -12.

AMA Style

Jianqing Wu, Yihui Wang, Bo Du, Qiang Wu, Yanlong Zhai, Jun Shen, Luping Zhou, Chen Cai, Wei Wei, Qingguo Zhou. The Bounds of Improvements Toward Real-Time Forecast of Multi-Scenario Train Delays. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-12.

Chicago/Turabian Style

Jianqing Wu; Yihui Wang; Bo Du; Qiang Wu; Yanlong Zhai; Jun Shen; Luping Zhou; Chen Cai; Wei Wei; Qingguo Zhou. 2021. "The Bounds of Improvements Toward Real-Time Forecast of Multi-Scenario Train Delays." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-12.

Journal article
Published: 24 July 2021 in Computer Physics Communications
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The simulation of beam energy transfer and medium dynamics is essential in nuclear studies and designs involving the beam loading on targets. Although the existing methods are relatively mature in simulating the thermohydrodynamics of a solid or liquid target coupled with an energetic beam, they may not be entirely applicable to a complex porous medium such as granular materials with massive discrete elements. In this study, a GPU (Graphics Processing Unit) based discrete energy deposition simulation method is proposed for the thermohydrodynamical simulation of granular flow targets with beam-grain coupling. The results of physical validations show that a significant improvement in accuracy was achieved by our method compared to the equivalent homogenization method which just simplified the granular medium to a whole block. This method provides a more accurate alternative solution to the problem of calculating energy deposition on grains. Although the method is computationally intensive, it can be synchronously executed with the granular dynamics simulation owing to the powerful parallel computing capability of GPUs. Thus, the studies on the dynamical and thermal behaviors of beam-grain coupling can be conducted in a relatively fast and precise way.

ACS Style

Yuan Tian; Ping Lin; Hanjie Cai; Yaling Zhang; Qiong Yang; Meiling Qi; Guanghui Yang; Xiaofei Gao; Xiaolong Chen; Lei Yang; Qingguo Zhou. A fast and accurate GPU based method on simulating energy deposition for beam-target coupling with granular materials. Computer Physics Communications 2021, 269, 108104 .

AMA Style

Yuan Tian, Ping Lin, Hanjie Cai, Yaling Zhang, Qiong Yang, Meiling Qi, Guanghui Yang, Xiaofei Gao, Xiaolong Chen, Lei Yang, Qingguo Zhou. A fast and accurate GPU based method on simulating energy deposition for beam-target coupling with granular materials. Computer Physics Communications. 2021; 269 ():108104.

Chicago/Turabian Style

Yuan Tian; Ping Lin; Hanjie Cai; Yaling Zhang; Qiong Yang; Meiling Qi; Guanghui Yang; Xiaofei Gao; Xiaolong Chen; Lei Yang; Qingguo Zhou. 2021. "A fast and accurate GPU based method on simulating energy deposition for beam-target coupling with granular materials." Computer Physics Communications 269, no. : 108104.

Methodologies and application
Published: 04 April 2021 in Soft Computing
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The problem of identifying the top-k influential node is still an open and deeply felt issue. The development of a stable and efficient algorithm to deal with such identification is still a challenging research hot spot. Although conventional centrality-based and greedy-based methods show high performance, they are not very efficient when dealing with large-scale social networks. Recently, algorithms based on swarm intelligence are applied to solve the problems mentioned above, and the existing researches show that such algorithms can obtain the optimal global solution. In particular, the discrete bat algorithm (DBA) has been proved to have excellent performance, but the evolution mechanism based on a random selection strategy leads to the optimal solution's instability. To solve this problem, in this paper, we propose a clique-DBA algorithm. The proposed algorithm is based on the clique partition of a network and enhances the initial DBA algorithm's stability. The experimental results show that the proposed clique-DBA algorithm converges to a determined local influence estimation (LIE) value in each run, eliminating the phenomenon of large fluctuation of LIE fitness value generated by the original DBA algorithm. Finally, the simulated results achieved under the independent cascade model show that the clique-DBA algorithm has a comparable performance of influence spreading compared with the algorithms proposed in the state of the art.

ACS Style

Lihong Han; Kuan-Ching Li; Arcangelo Castiglione; Jianxin Tang; Hengjun Huang; Qingguo Zhou. A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks. Soft Computing 2021, 1 -18.

AMA Style

Lihong Han, Kuan-Ching Li, Arcangelo Castiglione, Jianxin Tang, Hengjun Huang, Qingguo Zhou. A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks. Soft Computing. 2021; ():1-18.

Chicago/Turabian Style

Lihong Han; Kuan-Ching Li; Arcangelo Castiglione; Jianxin Tang; Hengjun Huang; Qingguo Zhou. 2021. "A clique-based discrete bat algorithm for influence maximization in identifying top-k influential nodes of social networks." Soft Computing , no. : 1-18.

Journal article
Published: 25 February 2021 in Computer Communications
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With the commercialization of 5G technology, various Mobile Edge Computing (MEC) services are being deployed widely. Generally, MEC services rely on MEC devices and servers deployed at the edge of the network. Whether it is a MEC device or an edge server, most of them lack computing resources, and it is difficult to implement powerful security capabilities. Moreover, there are a large number of MEC service providers, different standards, and different protocols, which extend the attack interface of MEC services. In response to this situation, this paper proposes MECGuard, an attack detection solution designed for the MEC environment based on deep learning technology. Based on its distributed architecture designed for the MEC environment, MECGuard implements a lightweight TCP-level protocol extractor based on Decision Tree, and an attack detection network based on Gated Recurrent Unit (GRU). Experiments prove that MECGuard could have a good performance of malicious traffic detection in the EMC environment.

ACS Style

Xin Liu; Wenqiang Zhang; Xiaokang Zhou; Qingguo Zhou. MECGuard: GRU enhanced attack detection in Mobile Edge Computing environment. Computer Communications 2021, 172, 1 -9.

AMA Style

Xin Liu, Wenqiang Zhang, Xiaokang Zhou, Qingguo Zhou. MECGuard: GRU enhanced attack detection in Mobile Edge Computing environment. Computer Communications. 2021; 172 ():1-9.

Chicago/Turabian Style

Xin Liu; Wenqiang Zhang; Xiaokang Zhou; Qingguo Zhou. 2021. "MECGuard: GRU enhanced attack detection in Mobile Edge Computing environment." Computer Communications 172, no. : 1-9.

Journal article
Published: 01 February 2021 in IEEE Access
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The top-k influential individuals in a social network under a specific topic play an important role in reality. Identifying top-k influential nodes of a social network is still an open and deeply-felt problem. In recent years, some researchers adopt the swarm intelligence algorithm to solve such problems and obtain competitive results. There are two main algorithm models for swarm intelligence, namely Ant Colony System (ACS) and Particle Swarm Optimization (PSO). The discretized basic Particle Swarm Algorithm (DPSO) shows comparable performance in identifying top-k influential nodes of a social network. However, the performance of the DPSO algorithm is directly related to the choice of its local search strategy. The local search strategy based on the greedy mechanism of the initial DPSO can easily lead to the global suboptimal solution due to the premature convergence of the algorithm. In this paper, we adopt the degree centrality based on different neighbourhoods to enhance its local search ability. Through experiments, we find that local search strategies based on different neighbourhoods have significant differences in the improvement of the algorithm’s global exploration capabilities, and the enhancement of the DPSO algorithm based on the degree centrality of different neighbourhoods has a saturation effect. Finally, based on the degree centrality of the best neighbourhood with improved local search ability, we propose the DPSO_NDC algorithm. Experimental results in six real-world social networks show that the proposed algorithm outperforms the initial DPSO algorithm and other state-of-the-art algorithms in identifying the top-k influence nodes.

ACS Style

Lihong Han; Qingguo Zhou; Jianxin Tang; Xuhui Yang; Hengjun Huang. Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality. IEEE Access 2021, 9, 21345 -21356.

AMA Style

Lihong Han, Qingguo Zhou, Jianxin Tang, Xuhui Yang, Hengjun Huang. Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality. IEEE Access. 2021; 9 ():21345-21356.

Chicago/Turabian Style

Lihong Han; Qingguo Zhou; Jianxin Tang; Xuhui Yang; Hengjun Huang. 2021. "Identifying Top-k Influential Nodes Based on Discrete Particle Swarm Optimization With Local Neighborhood Degree Centrality." IEEE Access 9, no. : 21345-21356.

Journal article
Published: 29 January 2021 in IEEE Transactions on Dependable and Secure Computing
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Edge computing (EC) is an emerging paradigm that extends cloud computing by pushing computing resources onto edge servers that are attached to base stations or access points at the edge of the cloud in close proximity with end-users. Due to edge servers' geographic distribution, the EC paradigm is challenged by many new security threats, including the notorious distributed Denial-of-Service (DDoS) attack. In the EC environment, edge servers usually have constrained processing capacities due to their limited sizes. Thus, they are particularly vulnerable to DDoS attacks. DDoS attacks in the EC environment render existing DDoS mitigation approaches obsolete with its new characteristics. In this paper, we make the first attempt to tackle the edge DDoS mitigation (EDM) problem. We model it as a constraint optimization problem and prove its NP-hardness. To solve this problem, we propose an optimal approach named EDMOpti and a novel game-theoretical approach named EDMGame for mitigating edge DDoS attacks. EDMGame formulates the EDM problem as a potential EDM Game that admits a Nash equilibrium and employs a decentralized algorithm to find the Nash equilibrium as the solution. Through theoretical analysis and experimental evaluation, we demonstrate that our approaches can solve the EDM problem effectively and efficiently.

ACS Style

Qiang He; Cheng Wang; Guangming Cui; Bo Li; Rui Zhou; Qingguo Zhou; Yang Xiang; Hai Jin; Yun Yang. A Game-Theoretical Approach for MitigatingEdge DDoS Attack. IEEE Transactions on Dependable and Secure Computing 2021, PP, 1 -1.

AMA Style

Qiang He, Cheng Wang, Guangming Cui, Bo Li, Rui Zhou, Qingguo Zhou, Yang Xiang, Hai Jin, Yun Yang. A Game-Theoretical Approach for MitigatingEdge DDoS Attack. IEEE Transactions on Dependable and Secure Computing. 2021; PP (99):1-1.

Chicago/Turabian Style

Qiang He; Cheng Wang; Guangming Cui; Bo Li; Rui Zhou; Qingguo Zhou; Yang Xiang; Hai Jin; Yun Yang. 2021. "A Game-Theoretical Approach for MitigatingEdge DDoS Attack." IEEE Transactions on Dependable and Secure Computing PP, no. 99: 1-1.

Conference paper
Published: 07 December 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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In this paper, we propose an efficient semantic segmentation network named Tiny Feature Map Network (TFMNet). This network significantly improves the running speed while achieves good accuracy. Our scheme uses a lightweight backbone network to extract primary features from input images of particular sizes. The hybrid dilated convolution framework and the DenseASPP module are used to alleviate the gridding problem. We evaluate the proposed network on the Cityscapes and CamVid datasets, and obtain performance comparable with the existing state-of-the-art real-time semantic segmentation methods. Specifically, it achieves \(72.9\%\) mIoU on the Cityscapes test dataset with only 2.4M parameters and a speed of 113 FPS on NVIDIA GTX 1080 Ti without pre-training on the ImageNet dataset.

ACS Style

Hang Huang; Peng Zhi; Haoran Zhou; Yujin Zhang; Qiang Wu; Binbin Yong; Weijun Tan; Qingguo Zhou. An Efficient Tiny Feature Map Network for Real-Time Semantic Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 332 -343.

AMA Style

Hang Huang, Peng Zhi, Haoran Zhou, Yujin Zhang, Qiang Wu, Binbin Yong, Weijun Tan, Qingguo Zhou. An Efficient Tiny Feature Map Network for Real-Time Semantic Segmentation. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():332-343.

Chicago/Turabian Style

Hang Huang; Peng Zhi; Haoran Zhou; Yujin Zhang; Qiang Wu; Binbin Yong; Weijun Tan; Qingguo Zhou. 2020. "An Efficient Tiny Feature Map Network for Real-Time Semantic Segmentation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 332-343.

Articles
Published: 29 September 2020 in Connection Science
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Clothing is one of the symbols of human civilisation. Clothing design is an art form that combines practicality and artistry. The Dunhuang clothes culture has a long history which represents ancient Chinese aesthetics. Artificial intelligence (AI) technology has been recently applied to multiple areas, which is also drawing increasing attention in fashion. However, little research has been done on the usage of AI for the creation of clothing, especially in traditional culture. It is challenging that the exploration of computer science and Dunhuang clothing design, which is a cross-history interaction between AI and Chinese classical culture. In this paper, we propose ClothGAN, which is an innovative framework for “designing” new patterns and styles of clothes based on generative adversarial network (GAN) and style transfer algorithm. Besides, we built the Dunhuang clothes dataset and conducted experiments to generate new patterns and styles of clothes with Dunhuang elements. We evaluated these clothing works generated from different models by computing inception score (IS), human prefer score (HPS) and generated score (IS and HPS). The results show that our framework outperformed others in these designing works.

ACS Style

Qiang Wu; Baixue Zhu; Binbin Yong; Yongqiang Wei; Xuetao Jiang; Rui Zhou; Qingguo Zhou. ClothGAN: generation of fashionable Dunhuang clothes using generative adversarial networks. Connection Science 2020, 33, 341 -358.

AMA Style

Qiang Wu, Baixue Zhu, Binbin Yong, Yongqiang Wei, Xuetao Jiang, Rui Zhou, Qingguo Zhou. ClothGAN: generation of fashionable Dunhuang clothes using generative adversarial networks. Connection Science. 2020; 33 (2):341-358.

Chicago/Turabian Style

Qiang Wu; Baixue Zhu; Binbin Yong; Yongqiang Wei; Xuetao Jiang; Rui Zhou; Qingguo Zhou. 2020. "ClothGAN: generation of fashionable Dunhuang clothes using generative adversarial networks." Connection Science 33, no. 2: 341-358.

Special issue article
Published: 18 August 2020 in Transactions on Emerging Telecommunications Technologies
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The Internet of things (IoT), made up of a massive number of sensor devices interconnected, can be used for data exchange, intelligent identification, and management of interconnected “things.” IoT devices are proliferating and playing a crucial role in improving the living quality and living standard of the people. However, the real IoT is more vulnerable to attack by countless cyberattacks from the Internet, which may cause privacy data leakage, data tampering and also cause significant harm to society and individuals. Network security is essential in the IoT system, and Web injection is one of the most severe security problems, especially the webshell. To develop a safe IoT system, in this article, we apply essential machine learning models to detect webshell to build secure solutions for IoT network. Future, ensemble methods including random forest (RF), extremely randomized trees (ET), and Voting are used to improve the performances of these machine learning models. We also discuss webshell detection in lightweight and heavyweight computing scenarios for different IoT environments. Extensive experiments have been conducted on these models to verify the validity of webshell intrusion. Simulation results show that RF and ET are suitable for lightweight IoT scenarios, and Voting method is effective for heavyweight IoT scenarios.

ACS Style

Binbin Yong; Wei Wei; Kuan‐Ching Li; Jun Shen; Qingguo Zhou; Marcin Wozniak; Dawid Połap; Robertas Damaševičius. Ensemble machine learning approaches for webshell detection in Internet of things environments. Transactions on Emerging Telecommunications Technologies 2020, 1 .

AMA Style

Binbin Yong, Wei Wei, Kuan‐Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Połap, Robertas Damaševičius. Ensemble machine learning approaches for webshell detection in Internet of things environments. Transactions on Emerging Telecommunications Technologies. 2020; ():1.

Chicago/Turabian Style

Binbin Yong; Wei Wei; Kuan‐Ching Li; Jun Shen; Qingguo Zhou; Marcin Wozniak; Dawid Połap; Robertas Damaševičius. 2020. "Ensemble machine learning approaches for webshell detection in Internet of things environments." Transactions on Emerging Telecommunications Technologies , no. : 1.

Journal article
Published: 31 July 2020 in Sensors
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With smart city infrastructures growing, the Internet of Things (IoT) has been widely used in the intelligent transportation systems (ITS). The traditional adaptive traffic signal control method based on reinforcement learning (RL) has expanded from one intersection to multiple intersections. In this paper, we propose a multi-agent auto communication (MAAC) algorithm, which is an innovative adaptive global traffic light control method based on multi-agent reinforcement learning (MARL) and an auto communication protocol in edge computing architecture. The MAAC algorithm combines multi-agent auto communication protocol with MARL, allowing an agent to communicate the learned strategies with others for achieving global optimization in traffic signal control. In addition, we present a practicable edge computing architecture for industrial deployment on IoT, considering the limitations of the capabilities of network transmission bandwidth. We demonstrate that our algorithm outperforms other methods over 17% in experiments in a real traffic simulation environment.

ACS Style

Qiang Wu; Jianqing Wu; Jun Shen; Binbin Yong; Qingguo Zhou. An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control. Sensors 2020, 20, 4291 .

AMA Style

Qiang Wu, Jianqing Wu, Jun Shen, Binbin Yong, Qingguo Zhou. An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control. Sensors. 2020; 20 (15):4291.

Chicago/Turabian Style

Qiang Wu; Jianqing Wu; Jun Shen; Binbin Yong; Qingguo Zhou. 2020. "An Edge Based Multi-Agent Auto Communication Method for Traffic Light Control." Sensors 20, no. 15: 4291.

Research article
Published: 11 June 2020 in Applied Soft Computing
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Forecasting models have been widely used in wind-speed time series forecasting that are often nonlinear, irregular, and non-stationary. Current forecasting models based on artificial neural network can adapt to various wind-speed time series. However, they cannot simultaneously and effectively forecast the entire wind-speed time series of a wind farm. In this paper, a novel combined forecasting system is developed for a wind farm that includes that SSAWD secondary de-noising algorithm is used to pre-process original wind speed data, and then the sub-model selection strategy is used to select five optimal sub models for the combined model. Meanwhile, a modified multi-objective optimization algorithm optimizes weight of the combined model, and the experimental results show that this forecasting system outperforms other traditional systems and can be effectively used to forecast wind-speed time series of a large wind farm.

ACS Style

Qingguo Zhou; Chen Wang; Gaofeng Zhang. A combined forecasting system based on modified multi-objective optimization and sub-model selection strategy for short-term wind speed. Applied Soft Computing 2020, 94, 106463 .

AMA Style

Qingguo Zhou, Chen Wang, Gaofeng Zhang. A combined forecasting system based on modified multi-objective optimization and sub-model selection strategy for short-term wind speed. Applied Soft Computing. 2020; 94 ():106463.

Chicago/Turabian Style

Qingguo Zhou; Chen Wang; Gaofeng Zhang. 2020. "A combined forecasting system based on modified multi-objective optimization and sub-model selection strategy for short-term wind speed." Applied Soft Computing 94, no. : 106463.

Journal article
Published: 13 April 2020 in IEEE Access
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Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and BALO algorithms to tackle the imbalanced classification problem, delivering different cost weights to different classes of samples. In our method, the BALO algorithm determines the cost weights and extract more significant features to improve the classification performance. Experiments conducted on eleven imbalanced data sets have shown that the CFGVM algorithm significantly improves the classification performance of minority class samples. By comparing with similar algorithms and state-of-the-art algorithms, the proposed algorithm significantly outperforms in performance and produces better classification results.

ACS Style

Fang Feng; Kuan-Ching Li; Jun Shen; Qingguo Zhou; Xuhui Yang. Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification. IEEE Access 2020, 8, 69979 -69996.

AMA Style

Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang. Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification. IEEE Access. 2020; 8 (99):69979-69996.

Chicago/Turabian Style

Fang Feng; Kuan-Ching Li; Jun Shen; Qingguo Zhou; Xuhui Yang. 2020. "Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification." IEEE Access 8, no. 99: 69979-69996.

Journal article
Published: 02 March 2020 in IEEE Access
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Cancer is still one of the most life threatening disease and by far it is still difficult to prevent, prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed. In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization algorithm are also used to select optimized features from mass spectrometry data. The learned features are further evaluated on three cancer datasets. The experimental results demonstrate that the cancer detection accuracy by learned features is as high as 100%. As our main contribution, the deep autoencoder method used in this study is a feasible and powerful instrument for mass spectrometry feature learning and also cancer diagnosis.

ACS Style

Qingguo Zhou; Binbin Yong; Qingquan Lv; Jun Shen; Xin Wang. Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection. IEEE Access 2020, 8, 45156 -45166.

AMA Style

Qingguo Zhou, Binbin Yong, Qingquan Lv, Jun Shen, Xin Wang. Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection. IEEE Access. 2020; 8 (99):45156-45166.

Chicago/Turabian Style

Qingguo Zhou; Binbin Yong; Qingquan Lv; Jun Shen; Xin Wang. 2020. "Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection." IEEE Access 8, no. 99: 45156-45166.

Conference paper
Published: 26 February 2020 in Lecture Notes in Electrical Engineering
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General Vector Machine (GVM) is a newly proposed machine learning model, which is applicable to small samples forecast scenarios. In this paper, the GVM is applied into electricity load forecast based on single point modeling method. Meanwhile, traditional time series forecast models, including back propagation neural network (BPNN), Support Vector Machine (SVM) and Autoregressive Integrated Moving Average Model (ARIMA), are also experimented for single point electricity load forecast. Further, the combined model based on GVM, BPNN, SVM and ARIMA are proposed and verified. Results show that GVM performs better than these traditional models, and the combined model outperforms any other single models for single point electricity load forecast.

ACS Style

Binbin Yong; Yongqiang Wei; Jun Shen; Fucun Li; Xuetao Jiang; Qingguo Zhou. Combined General Vector Machine for Single Point Electricity Load Forecast. Lecture Notes in Electrical Engineering 2020, 283 -291.

AMA Style

Binbin Yong, Yongqiang Wei, Jun Shen, Fucun Li, Xuetao Jiang, Qingguo Zhou. Combined General Vector Machine for Single Point Electricity Load Forecast. Lecture Notes in Electrical Engineering. 2020; ():283-291.

Chicago/Turabian Style

Binbin Yong; Yongqiang Wei; Jun Shen; Fucun Li; Xuetao Jiang; Qingguo Zhou. 2020. "Combined General Vector Machine for Single Point Electricity Load Forecast." Lecture Notes in Electrical Engineering , no. : 283-291.

Conference paper
Published: 01 February 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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In recent years, the problem of unbalanced demand and supply in electricity power industry has seriously affected the development of smart grid, especially in the capacity planning, power dispatching and electric power system control. Electricity demand forecasting, as a key solution to the problem, has been widely studied. However, electricity demand is influenced by many factors and nonlinear dependencies, which makes it difficult to forecast accurately. On the other hand, deep neural network technologies are developing rapidly and have been tried in time series forecasting problems. Hence, this paper proposes a novel deep learning model, which is based on the multiple Long Short-Term Memory (LSTM) neural networks to solve the problem of short-term electricity demand forecasting. Compared with autoregressive integrated moving average model (ARIMA) and back propagation neural network (BPNN), our model demonstrates competitive forecast accuracy, which proves that our model is promising for electricity demand forecasting.

ACS Style

Binbin Yong; Zebang Shen; Yongqiang Wei; Jun Shen; Qingguo Zhou. Short-Term Electricity Demand Forecasting Based on Multiple LSTMs. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 192 -200.

AMA Style

Binbin Yong, Zebang Shen, Yongqiang Wei, Jun Shen, Qingguo Zhou. Short-Term Electricity Demand Forecasting Based on Multiple LSTMs. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():192-200.

Chicago/Turabian Style

Binbin Yong; Zebang Shen; Yongqiang Wei; Jun Shen; Qingguo Zhou. 2020. "Short-Term Electricity Demand Forecasting Based on Multiple LSTMs." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 192-200.

Correspondence
Published: 17 January 2020 in Engineering
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ACS Style

Fashen Li; Lian Li; Jianping Yin; Yong Zhang; Qingguo Zhou; Kun Kuang. How to Interpret Machine Knowledge. Engineering 2020, 6, 218 -220.

AMA Style

Fashen Li, Lian Li, Jianping Yin, Yong Zhang, Qingguo Zhou, Kun Kuang. How to Interpret Machine Knowledge. Engineering. 2020; 6 (3):218-220.

Chicago/Turabian Style

Fashen Li; Lian Li; Jianping Yin; Yong Zhang; Qingguo Zhou; Kun Kuang. 2020. "How to Interpret Machine Knowledge." Engineering 6, no. 3: 218-220.

Journal article
Published: 01 November 2019 in International Journal of Information Management
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Immunization is an indispensable mechanism for preventing infectious diseases in modern society, and vaccine safety is closely related to public health and national security. However, issues such as vaccine expiration and vaccine record fraud are still widespread in vaccine supply chains. Therefore, an effective management system for the supervision of vaccine supply chains is urgently required. As the next generation of core technology after the Internet, blockchain is designed to build trust mechanisms that can change current information management methods. Meanwhile, the development of machine learning technologies provides additional ways to analyze the data in information management systems. The main objective of this study is to develop a “vaccine blockchain” system based on blockchain and machine learning technologies. This vaccine blockchain system is designed to support vaccine traceability and smart contract functions, and can be used to address the problems of vaccine expiration and vaccine record fraud. Additionally, the use of machine learning models can provide valuable recommendations to immunization practitioners and recipients, allowing them to choose better immunization methods and vaccines.

ACS Style

Binbin Yong; Jun Shen; Xin Liu; Fucun Li; Huaming Chen; Qingguo Zhou. An intelligent blockchain-based system for safe vaccine supply and supervision. International Journal of Information Management 2019, 52, 102024 .

AMA Style

Binbin Yong, Jun Shen, Xin Liu, Fucun Li, Huaming Chen, Qingguo Zhou. An intelligent blockchain-based system for safe vaccine supply and supervision. International Journal of Information Management. 2019; 52 ():102024.

Chicago/Turabian Style

Binbin Yong; Jun Shen; Xin Liu; Fucun Li; Huaming Chen; Qingguo Zhou. 2019. "An intelligent blockchain-based system for safe vaccine supply and supervision." International Journal of Information Management 52, no. : 102024.

Journal article
Published: 01 July 2019 in Annals of Nuclear Energy
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In the beam debugging of China ADS Injector II, the mathematical matrix operation, software simulation or physical derivation methods are mainly used. These methods do not take into account noise conditions, so it is difficult to accurately control the deflection position of the beam. For this reason, this paper takes MEBT as an example to establish a function model of quadrupole magnet control parameter and beam position, and based on the BPNN to fit the function model to reduce the influence of noise on the control process, and then realizes control prediction model in beam debugging. The experiments results show that the prediction accuracy of the quadrupole magnet control voltages Q2 and Q3 are 0.9072 and 0.9352, respectively, and the MSE is 0.0064. This paper is the first attempt of deep neural network in China ADS injector II, which provides a new method for beam debugging of the system.

ACS Style

Xuhui Yang; Qingguo Zhou; Jinqiang Wang; Lihong Han; Rui Zhou; Yuan He; Kuan-Ching Li. Predictive control modeling of ADS’s MEBT using BPNN to reduce the impact of noise on the control system. Annals of Nuclear Energy 2019, 132, 576 -583.

AMA Style

Xuhui Yang, Qingguo Zhou, Jinqiang Wang, Lihong Han, Rui Zhou, Yuan He, Kuan-Ching Li. Predictive control modeling of ADS’s MEBT using BPNN to reduce the impact of noise on the control system. Annals of Nuclear Energy. 2019; 132 ():576-583.

Chicago/Turabian Style

Xuhui Yang; Qingguo Zhou; Jinqiang Wang; Lihong Han; Rui Zhou; Yuan He; Kuan-Ching Li. 2019. "Predictive control modeling of ADS’s MEBT using BPNN to reduce the impact of noise on the control system." Annals of Nuclear Energy 132, no. : 576-583.

Journal article
Published: 18 June 2019 in Computers & Electrical Engineering
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The Internet of Things (IoT) is gradually becoming an infrastructure, providing a wide range of applications, from health monitoring to industrial control and many other social domains. Unfortunately, for open connectivity, it is always built on Hypertext Transfer Protocol (HTTP), which inherently brings in new challenging security threats. Parameter injection, as a common and powerful attack, is often exploited by attackers to break into the HTTP servers of IoT by injecting malicious codes into the parameters of the HTTP requests. In this work we present a Hidden Markov Model (HMM) based detection system, which is designed as a novel bidirectory scoring architecture utilizing both benign and malicious Web traffic, to defend against parameter injection attacks in IoT systems. We evaluate the proposed system in terms of Web traffic data in real IoT environments. Results show improvements over baselines.

ACS Style

Binbin Yong; Xin Liu; Qingchen Yu; Liang Huang; Qingguo Zhou. Malicious Web traffic detection for Internet of Things environments. Computers & Electrical Engineering 2019, 77, 260 -272.

AMA Style

Binbin Yong, Xin Liu, Qingchen Yu, Liang Huang, Qingguo Zhou. Malicious Web traffic detection for Internet of Things environments. Computers & Electrical Engineering. 2019; 77 ():260-272.

Chicago/Turabian Style

Binbin Yong; Xin Liu; Qingchen Yu; Liang Huang; Qingguo Zhou. 2019. "Malicious Web traffic detection for Internet of Things environments." Computers & Electrical Engineering 77, no. : 260-272.