Pub Date : 2017-02-04DOI: 10.1109/ICACI.2017.7974491
Abdelkerim Souahlia, A. Belatreche, A. Benyettou, K. Curran
We propose a novel supervised technique for blood vessel segmentation in retinal images based on echo state networks. Retinal vessel segmentation is widely used for numerous clinical purposes such as the detection of various cardiovascular and ophthalmologic diseases. A large number of retinal vessel segmentation methods have been reported, yet achieving accurate and efficient vessel segmentation still remains a challenge. Recently, reservoir computing has drawn much attention as a new computing framework based on recurrent neural networks. The Echo State Network (ESN), which uses neural nodes as the computing elements of the recurrent network, represents one of the efficient learning models of reservoir computing. This paper investigates the viability of echo state networks for blood vessel segmentation in retinal images. Initial image features are projected onto the echo state network reservoir which maps them, through its internal nodes activations, into a new set of features to be classified into vessel or non-vessel by the echo state network readout which consists, in the proposed approach, of a multi-layer perceptron. Experimental results on the publicly available DRIVE dataset, commonly used in retinal vessel segmentation research, demonstrate the ability of the proposed method in achieving promising performance results in terms of both segmentation accuracy and efficiency.
{"title":"Blood vessel segmentation in retinal images using echo state networks","authors":"Abdelkerim Souahlia, A. Belatreche, A. Benyettou, K. Curran","doi":"10.1109/ICACI.2017.7974491","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974491","url":null,"abstract":"We propose a novel supervised technique for blood vessel segmentation in retinal images based on echo state networks. Retinal vessel segmentation is widely used for numerous clinical purposes such as the detection of various cardiovascular and ophthalmologic diseases. A large number of retinal vessel segmentation methods have been reported, yet achieving accurate and efficient vessel segmentation still remains a challenge. Recently, reservoir computing has drawn much attention as a new computing framework based on recurrent neural networks. The Echo State Network (ESN), which uses neural nodes as the computing elements of the recurrent network, represents one of the efficient learning models of reservoir computing. This paper investigates the viability of echo state networks for blood vessel segmentation in retinal images. Initial image features are projected onto the echo state network reservoir which maps them, through its internal nodes activations, into a new set of features to be classified into vessel or non-vessel by the echo state network readout which consists, in the proposed approach, of a multi-layer perceptron. Experimental results on the publicly available DRIVE dataset, commonly used in retinal vessel segmentation research, demonstrate the ability of the proposed method in achieving promising performance results in terms of both segmentation accuracy and efficiency.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133579546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974497
Han Liu, Ella Haig
Sentiment analysis, which is also known as opinion mining, aims to recognise the attitude or emotion of people through natural language processing, text analysis and computational linguistics. In recent years, many studies have focused on sentiment classification in the context of machine learning, e.g. to identify that a sentiment is positive or negative. In particular, the bag-of-words method has been popularly used to transform textual data into structured data, in order to enable the direct use of machine learning algorithms for sentiment classification. Through the bag-of-words method, each single term in a text document is turned into a single attribute to make up a structured data set, which results in high dimensionality of the data set and thus negative impact on the interpretability of computational models for sentiment analysis. This paper proposes the use of fuzzy rule based systems as computational models towards accurate and interpretable analysis of sentiments. The use of fuzzy logic is better aligned with the inherent uncertainty of language, while the “white box” characteristic of the rule based learning approaches leads to better interpretability of the results. The proposed approach is tested on four datasets containing movie reviews; the aim is to compare its performance in terms of accuracy with two other approaches for sentiment analysis that are known to perform very well. The results indicate that the fuzzy rule based approach performs marginally better than the well-known machine learning techniques, while reducing the computational complexity and increasing the interpretability.
{"title":"Fuzzy rule based systems for interpretable sentiment analysis","authors":"Han Liu, Ella Haig","doi":"10.1109/ICACI.2017.7974497","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974497","url":null,"abstract":"Sentiment analysis, which is also known as opinion mining, aims to recognise the attitude or emotion of people through natural language processing, text analysis and computational linguistics. In recent years, many studies have focused on sentiment classification in the context of machine learning, e.g. to identify that a sentiment is positive or negative. In particular, the bag-of-words method has been popularly used to transform textual data into structured data, in order to enable the direct use of machine learning algorithms for sentiment classification. Through the bag-of-words method, each single term in a text document is turned into a single attribute to make up a structured data set, which results in high dimensionality of the data set and thus negative impact on the interpretability of computational models for sentiment analysis. This paper proposes the use of fuzzy rule based systems as computational models towards accurate and interpretable analysis of sentiments. The use of fuzzy logic is better aligned with the inherent uncertainty of language, while the “white box” characteristic of the rule based learning approaches leads to better interpretability of the results. The proposed approach is tested on four datasets containing movie reviews; the aim is to compare its performance in terms of accuracy with two other approaches for sentiment analysis that are known to perform very well. The results indicate that the fuzzy rule based approach performs marginally better than the well-known machine learning techniques, while reducing the computational complexity and increasing the interpretability.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122527579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974490
Yunsheng Fan, Yutong Sun, Guofeng Wang
Aimed at the trajectory tracking control problem and dynamic collision avoidance problem of the nonholonomic car-like mobile robots under the unknown circumstance, a dynamic collision avoidance method based on nonlinear trajectory tracking control is proposed. Firstly, we develop mathematical model of the car-like mobile robot and design a trajectory tracking controller based on backstepping method. Then, the dynamic collision avoidance controller is designed using the velocity resolution in the process of reference trajectory tracking. Finally, dynamic collision avoidance control during the trajectory tracking for car-like mobile robot is realized. The simulation results show that both the nonlinear algorithm of trajectory tracking control and the velocity resolution of dynamic collision avoidance is validity. It can provide the reference for dynamic collision avoidance design during the trajectory tracking of the car-like mobile robot.
{"title":"Dynamic collision avoidance for car-like mobile robot based on nonlinear trajectory tracking control","authors":"Yunsheng Fan, Yutong Sun, Guofeng Wang","doi":"10.1109/ICACI.2017.7974490","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974490","url":null,"abstract":"Aimed at the trajectory tracking control problem and dynamic collision avoidance problem of the nonholonomic car-like mobile robots under the unknown circumstance, a dynamic collision avoidance method based on nonlinear trajectory tracking control is proposed. Firstly, we develop mathematical model of the car-like mobile robot and design a trajectory tracking controller based on backstepping method. Then, the dynamic collision avoidance controller is designed using the velocity resolution in the process of reference trajectory tracking. Finally, dynamic collision avoidance control during the trajectory tracking for car-like mobile robot is realized. The simulation results show that both the nonlinear algorithm of trajectory tracking control and the velocity resolution of dynamic collision avoidance is validity. It can provide the reference for dynamic collision avoidance design during the trajectory tracking of the car-like mobile robot.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122152463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974498
A. Shatnawi, M. Fraiwan, Hadi S. Al-Qahtani
Academic institutions are moving toward automated management of the educational process. One aspect of this process is the exam scheduling. The large number of students, classes, professors, and venues renders the manual scheduling process tedious and useless. In this paper, we describe the efforts of the Arab East College for High Education in Saudi Arabia in scheduling exams in the least number of conflicts, among other constraints. We give the details for a two-stage solution approach; the first stage is a greedy algorithm and the second one is a genetic algorithm. The two algorithms work in tandem to generate the best exam timetable. Automation of this process has greatly reduced the number of conflicts, exam days, and the required venues.
{"title":"Exam scheduling: A case study","authors":"A. Shatnawi, M. Fraiwan, Hadi S. Al-Qahtani","doi":"10.1109/ICACI.2017.7974498","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974498","url":null,"abstract":"Academic institutions are moving toward automated management of the educational process. One aspect of this process is the exam scheduling. The large number of students, classes, professors, and venues renders the manual scheduling process tedious and useless. In this paper, we describe the efforts of the Arab East College for High Education in Saudi Arabia in scheduling exams in the least number of conflicts, among other constraints. We give the details for a two-stage solution approach; the first stage is a greedy algorithm and the second one is a genetic algorithm. The two algorithms work in tandem to generate the best exam timetable. Automation of this process has greatly reduced the number of conflicts, exam days, and the required venues.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"07 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129631176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974482
Zhenzhen Wu, Chuandong Li
In almost all of the previous publications about the impulsive systems, the impulses involved are fixed instants. However, in many actual applications, impulsive moments can not be prescribed ahead of schedule. Hence, in this manuscript, a generalized model of neural networks with delays and impulsive time window is formulated. And then, through the use of lyapunov stability theory method, several original and easy-to-prove sufficient conditions are derived to notarize that the model which concerning the impulsive time window is global exponential stable. Moreover a framework combining the exponential convergence rate with the various parameters of impulsive is constructed. Finally, the effectiveness of the theoretical results are demonstrated by the numerical simulations.
{"title":"Exponential stability analysis of delayed neural networks with impulsive time window","authors":"Zhenzhen Wu, Chuandong Li","doi":"10.1109/ICACI.2017.7974482","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974482","url":null,"abstract":"In almost all of the previous publications about the impulsive systems, the impulses involved are fixed instants. However, in many actual applications, impulsive moments can not be prescribed ahead of schedule. Hence, in this manuscript, a generalized model of neural networks with delays and impulsive time window is formulated. And then, through the use of lyapunov stability theory method, several original and easy-to-prove sufficient conditions are derived to notarize that the model which concerning the impulsive time window is global exponential stable. Moreover a framework combining the exponential convergence rate with the various parameters of impulsive is constructed. Finally, the effectiveness of the theoretical results are demonstrated by the numerical simulations.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129697721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974489
Ting Yin, Boshan Chen, J. Zhong
The paper concerns with the global O(t−α) synchronization for a type of FDNNs (fractional-order neural networks) with time-varying delays. Two types of output feedback controller are designed to synchronize a class of FDNNs. And we give some sufficient conditions in the form of algebraic inequalities. Finally, to demonstrate the reliability of the derived results, we give two illustrative numerical examples.
{"title":"Synchronization control of fractional-order neural networks with time-varying delays","authors":"Ting Yin, Boshan Chen, J. Zhong","doi":"10.1109/ICACI.2017.7974489","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974489","url":null,"abstract":"The paper concerns with the global O(t−α) synchronization for a type of FDNNs (fractional-order neural networks) with time-varying delays. Two types of output feedback controller are designed to synchronize a class of FDNNs. And we give some sufficient conditions in the form of algebraic inequalities. Finally, to demonstrate the reliability of the derived results, we give two illustrative numerical examples.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123678921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974508
Tianzheng Wang, K. Wang, Jie Li, Hua Yu, Wang Shuai, Jiang Bian, Xiaoguang Zhao
Recognition of human climbing fences in trans-former substations is very essential in a power substation. This paper proposed an innovative and practical human climbing fences detection method based on image processing. At first, the Gaussian Mixture Model background modelling algorithm is exploited to detect motion objects under a view of fix surveillant camera in a power substation. After obtaining the motion regions of interest, the Histogram of Oriented Gradient (HOG) feature is extracted to describe inner human. And then, based on the result of HOG feature extraction, the Support Vector Machine (SVM) is trained to classify pedestrians. Next, an improved Hough Transform is implemented to detect fences. Finally a Sparse Optical Flow method is applied to track the motion of human. Compelling experimental results demonstrated the correctness and effectiveness of our proposed method.
在变电所中，对人爬围栏的识别是十分必要的。本文提出了一种创新实用的基于图像处理的人体爬栅栏检测方法。首先，将高斯混合模型背景建模算法应用于变电站固定监控摄像机视野下的运动目标检测。在获得感兴趣的运动区域后，提取定向梯度直方图(Histogram of Oriented Gradient, HOG)特征来描述人体内部。然后，基于HOG特征提取结果，训练支持向量机(SVM)对行人进行分类。接下来，实现了改进的霍夫变换来检测栅栏。最后应用稀疏光流方法对人体运动进行跟踪。实验结果证明了该方法的正确性和有效性。
{"title":"Fast recognition of human climbing fences in transformer substations","authors":"Tianzheng Wang, K. Wang, Jie Li, Hua Yu, Wang Shuai, Jiang Bian, Xiaoguang Zhao","doi":"10.1109/ICACI.2017.7974508","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974508","url":null,"abstract":"Recognition of human climbing fences in trans-former substations is very essential in a power substation. This paper proposed an innovative and practical human climbing fences detection method based on image processing. At first, the Gaussian Mixture Model background modelling algorithm is exploited to detect motion objects under a view of fix surveillant camera in a power substation. After obtaining the motion regions of interest, the Histogram of Oriented Gradient (HOG) feature is extracted to describe inner human. And then, based on the result of HOG feature extraction, the Support Vector Machine (SVM) is trained to classify pedestrians. Next, an improved Hough Transform is implemented to detect fences. Finally a Sparse Optical Flow method is applied to track the motion of human. Compelling experimental results demonstrated the correctness and effectiveness of our proposed method.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116489428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974511
Mohamed O. M. Khelifa, M. Belkasmi, A. Yousfi, Y. Elhadj
The majority of successful automatic speech recognition (ASR) systems utilize a probabilistic modeling of the speech signal via hidden Markov models (HMMs). In a standard HMM model, state duration probabilities decrease exponentially with time, which fails to satisfactorily describe the temporal structure of speech. Incorporating explicit state durational probability distribution functions (pdf) into the HMM is a famous solution to overcome this feebleness. This way is well-known as a hidden semi-Markov model (HSMM). Previous papers have confirmed that using HSMM models instead of the standard HMMs have enhanced the recognition accuracy in many targeted languages. This paper addresses an important stage of our on-going work which aims to construct an accurate Arabic recognizer for teaching and learning purposes. It presents an implementation of an HSMM model whose principal goal is improving the classical HMM's durational behavior. In this implementation, the Gaussian distribution is used for modeling state durations. Experiments have been carried out on a particular Arabic speech corpus collected from recitations of the Holy Quran. Results show an increase in recognition accuracy by around 1% We confirmed via these results that such a system outperforms the baseline HTK when the Gaussian distribution is integrated into the HTK's recognizer back-end.
{"title":"An accurate HSMM-based system for Arabic phonemes recognition","authors":"Mohamed O. M. Khelifa, M. Belkasmi, A. Yousfi, Y. Elhadj","doi":"10.1109/ICACI.2017.7974511","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974511","url":null,"abstract":"The majority of successful automatic speech recognition (ASR) systems utilize a probabilistic modeling of the speech signal via hidden Markov models (HMMs). In a standard HMM model, state duration probabilities decrease exponentially with time, which fails to satisfactorily describe the temporal structure of speech. Incorporating explicit state durational probability distribution functions (pdf) into the HMM is a famous solution to overcome this feebleness. This way is well-known as a hidden semi-Markov model (HSMM). Previous papers have confirmed that using HSMM models instead of the standard HMMs have enhanced the recognition accuracy in many targeted languages. This paper addresses an important stage of our on-going work which aims to construct an accurate Arabic recognizer for teaching and learning purposes. It presents an implementation of an HSMM model whose principal goal is improving the classical HMM's durational behavior. In this implementation, the Gaussian distribution is used for modeling state durations. Experiments have been carried out on a particular Arabic speech corpus collected from recitations of the Holy Quran. Results show an increase in recognition accuracy by around 1% We confirmed via these results that such a system outperforms the baseline HTK when the Gaussian distribution is integrated into the HTK's recognizer back-end.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123432546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974493
Hui Yan, Lifeng Xu
In this paper we investigate the stability in distribution for some stochastic systems. By the applications of coupling method and flatness technique some new criteria on the stationary behavior are obtained.
{"title":"Some new criteria on stability in distribution for stochastic systems","authors":"Hui Yan, Lifeng Xu","doi":"10.1109/ICACI.2017.7974493","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974493","url":null,"abstract":"In this paper we investigate the stability in distribution for some stochastic systems. By the applications of coupling method and flatness technique some new criteria on the stationary behavior are obtained.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127138609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-02-01DOI: 10.1109/ICACI.2017.7974495
Yu Zeng
The research of the cascading failure and the spread of the disease belong to two separate fields. However, in practice, there are many mutual effects between two processes each other. This paper first introduces some basic concepts of complex networks, and analyzes the application of cascading failure model and several typical virus transmission models. Then, based on a spreading model, the interaction of the models between the virus propagation and the cascading failure is established. Furthermore, the dynamic process of the node state and their interaction is analyzed for the interaction model. According to the dynamic process, the qualitative analysis and quantitative analysis are both carried out. Finally, the proportion on the different states of the nodes in the network is simulated and analyzed based on the equilibrium of the virus spreading when the cascading failure is over. The effectiveness and practicability of the model is verified by comparing the simulation value with the theoretical value. The work provides a guideline for designing and improving the security of the network system in real life.
{"title":"Research on cascading failure of complex system and its virus propation","authors":"Yu Zeng","doi":"10.1109/ICACI.2017.7974495","DOIUrl":"https://doi.org/10.1109/ICACI.2017.7974495","url":null,"abstract":"The research of the cascading failure and the spread of the disease belong to two separate fields. However, in practice, there are many mutual effects between two processes each other. This paper first introduces some basic concepts of complex networks, and analyzes the application of cascading failure model and several typical virus transmission models. Then, based on a spreading model, the interaction of the models between the virus propagation and the cascading failure is established. Furthermore, the dynamic process of the node state and their interaction is analyzed for the interaction model. According to the dynamic process, the qualitative analysis and quantitative analysis are both carried out. Finally, the proportion on the different states of the nodes in the network is simulated and analyzed based on the equilibrium of the virus spreading when the cascading failure is over. The effectiveness and practicability of the model is verified by comparing the simulation value with the theoretical value. The work provides a guideline for designing and improving the security of the network system in real life.","PeriodicalId":260701,"journal":{"name":"2017 Ninth International Conference on Advanced Computational Intelligence (ICACI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126850739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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