This paper studies the stationary bipartite consensus problem of a kind of multi-agent systems with second-order dynamics, where the impulsive control approach is utilized to design the control protocol. The impulsive control law is only considered by using position-based information, and the structure of control law is induced by a structurally balanced graph. Then, the stationary bipartite consensus problem has been converted to a convergence problem with respect to a finite product of stochastic matrices. By using the norm matrix and convex theory, this convergence problem is proven to be stability, which means that the stationary bipartite consensus problem is ensured. Subsequently, a numerical example is given to show the obtained result.
{"title":"Stationary bipartite consensus of second-order multi-agent systems: an impulsive approach","authors":"Zhuguo Li, Wenqing Wang, Yongqing Fan, Wenle Zhang","doi":"10.1109/SSCI44817.2019.9002903","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002903","url":null,"abstract":"This paper studies the stationary bipartite consensus problem of a kind of multi-agent systems with second-order dynamics, where the impulsive control approach is utilized to design the control protocol. The impulsive control law is only considered by using position-based information, and the structure of control law is induced by a structurally balanced graph. Then, the stationary bipartite consensus problem has been converted to a convergence problem with respect to a finite product of stochastic matrices. By using the norm matrix and convex theory, this convergence problem is proven to be stability, which means that the stationary bipartite consensus problem is ensured. Subsequently, a numerical example is given to show the obtained result.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"22 1","pages":"1249-1254"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88147724","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 : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002875
Katsuki Shimada, T. Migita, Norikazu Takahashi
In some applications of multiagent networks, it is desired that each agent can evaluate how well the network is connected. In this paper, we propose a novel discrete-time algorithm for each agent to compute the algebraic connectivity of the network in a pseudo-decentralized manner. The proposed algorithm requires less computational cost than the conventional algorithm. We also analyze the dynamical behavior of the proposed algorithm and prove under some assumptions on the parameter values and the initial state values of the agents that all agents can compute the algebraic connectivity.
{"title":"An Infinity Norm-Based Pseudo-Decentralized Discrete-Time Algorithm for Computing Algebraic Connectivity","authors":"Katsuki Shimada, T. Migita, Norikazu Takahashi","doi":"10.1109/SSCI44817.2019.9002875","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002875","url":null,"abstract":"In some applications of multiagent networks, it is desired that each agent can evaluate how well the network is connected. In this paper, we propose a novel discrete-time algorithm for each agent to compute the algebraic connectivity of the network in a pseudo-decentralized manner. The proposed algorithm requires less computational cost than the conventional algorithm. We also analyze the dynamical behavior of the proposed algorithm and prove under some assumptions on the parameter values and the initial state values of the agents that all agents can compute the algebraic connectivity.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"57 1","pages":"1292-1298"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87461992","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 : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002803
Narges Manouchehri, M. Rahmanpour, N. Bouguila, Wentao Fan
Finite mixture models are progressively employed in various fields of science due to their high potential as inference engines to model multimodal and complex data. To develop them, we face some crucial issues such as choosing proper distributions with enough flexibility to well-fit the data. To learn our model, two other significant challenges, namely, parameter estimation and defining model complexity have to be addressed. Some methods such as maximum likelihood and Bayesian inference have been widely considered to tackle the first problem and both have some drawbacks such as local maxima or high computational complexity. Simultaneously, the proper number of components was determined with some approaches such as minimum message length. In this work, multivariate Beta mixture models have been deployed thanks to their flexibility and we propose a novel variational inference via an entropy-based splitting method. The performance of this approach is evaluated on real-world applications, namely, breast tissue texture classification, cytological breast data analysis, cell image categorization and age estimation.
{"title":"Learning of Multivariate Beta Mixture Models via Entropy-based component splitting","authors":"Narges Manouchehri, M. Rahmanpour, N. Bouguila, Wentao Fan","doi":"10.1109/SSCI44817.2019.9002803","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002803","url":null,"abstract":"Finite mixture models are progressively employed in various fields of science due to their high potential as inference engines to model multimodal and complex data. To develop them, we face some crucial issues such as choosing proper distributions with enough flexibility to well-fit the data. To learn our model, two other significant challenges, namely, parameter estimation and defining model complexity have to be addressed. Some methods such as maximum likelihood and Bayesian inference have been widely considered to tackle the first problem and both have some drawbacks such as local maxima or high computational complexity. Simultaneously, the proper number of components was determined with some approaches such as minimum message length. In this work, multivariate Beta mixture models have been deployed thanks to their flexibility and we propose a novel variational inference via an entropy-based splitting method. The performance of this approach is evaluated on real-world applications, namely, breast tissue texture classification, cytological breast data analysis, cell image categorization and age estimation.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"15 1","pages":"2825-2832"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87522389","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 : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002874
Yanli Yao, Qiang Yu, Longbiao Wang, J. Dang
Different from traditional artificial neural networks (ANNs), spiking neural networks (SNNs) represent and transfer information in spikes, which are considered more like human brain. SNNs contain time information, which make them more suitable for addressing time-structured speech signals. However, it still remains challenging for spiking neural network (SNN) to implement classification tasks based on speech due to the lack of a proper encoding. In this paper, an integrated spiking neural network is proposed to perform the gender classification task. The whole system consists of three functional parts, including encoding, learning and readout. As convolutional restricted Boltzmann machine (CRBM) has shown outstanding capability for unsupervised learning of auditory features, we adopt it in this paper as a feature extractor, followed by a spike-latency encoding layer that converts the feature values into spike times. Then these spikes are processed by the spiking neural networks with the tempotron learning rule. We use the TIMIT database to evaluate the performance of our system. Our results show that the as-proposed system is robust for gender classification across a wide range of noise levels.
{"title":"An integrated system for robust gender classification with convolutional restricted Boltzmann machine and spiking neural network","authors":"Yanli Yao, Qiang Yu, Longbiao Wang, J. Dang","doi":"10.1109/SSCI44817.2019.9002874","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002874","url":null,"abstract":"Different from traditional artificial neural networks (ANNs), spiking neural networks (SNNs) represent and transfer information in spikes, which are considered more like human brain. SNNs contain time information, which make them more suitable for addressing time-structured speech signals. However, it still remains challenging for spiking neural network (SNN) to implement classification tasks based on speech due to the lack of a proper encoding. In this paper, an integrated spiking neural network is proposed to perform the gender classification task. The whole system consists of three functional parts, including encoding, learning and readout. As convolutional restricted Boltzmann machine (CRBM) has shown outstanding capability for unsupervised learning of auditory features, we adopt it in this paper as a feature extractor, followed by a spike-latency encoding layer that converts the feature values into spike times. Then these spikes are processed by the spiking neural networks with the tempotron learning rule. We use the TIMIT database to evaluate the performance of our system. Our results show that the as-proposed system is robust for gender classification across a wide range of noise levels.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"21 1","pages":"2348-2353"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88540205","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 : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002713
Lin Geng, Min Xu, Zeqiang Wei, Xiuzhuang Zhou
This paper focuses on the study of engagement recognition of online courses from students’ appearance and behavioral information using deep learning methods. Automatic engagement recognition can be applied to developing effective online instructional and assessment strategies for promoting learning. In this paper, we make two contributions. First, we propose a Convolutional 3D (C3D) neural networks-based approach to automatic engagement recognition, which models both the appearance and motion information in videos and recognize student engagement automatically. Second, we introduce the Focal Loss to address the class-imbalanced data distribution problem in engagement recognition by adaptively decreasing the weight of high engagement samples while increasing the weight of low engagement samples in deep spatiotemporal feature learning. Experiments on the DAiSEE dataset show the effectiveness of our method in comparison with the state-of-the-art automatic engagement recognition methods.
{"title":"Learning Deep Spatiotemporal Feature for Engagement Recognition of Online Courses","authors":"Lin Geng, Min Xu, Zeqiang Wei, Xiuzhuang Zhou","doi":"10.1109/SSCI44817.2019.9002713","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002713","url":null,"abstract":"This paper focuses on the study of engagement recognition of online courses from students’ appearance and behavioral information using deep learning methods. Automatic engagement recognition can be applied to developing effective online instructional and assessment strategies for promoting learning. In this paper, we make two contributions. First, we propose a Convolutional 3D (C3D) neural networks-based approach to automatic engagement recognition, which models both the appearance and motion information in videos and recognize student engagement automatically. Second, we introduce the Focal Loss to address the class-imbalanced data distribution problem in engagement recognition by adaptively decreasing the weight of high engagement samples while increasing the weight of low engagement samples in deep spatiotemporal feature learning. Experiments on the DAiSEE dataset show the effectiveness of our method in comparison with the state-of-the-art automatic engagement recognition methods.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"76 1","pages":"442-447"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79201293","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 : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002722
Ziyuan Zhou, Yuanpeng Zhang, Yizhang Jiang
In many practical applications, the fuzzy systems have been used due to the promising approximation accuracy and the high interpretability. Here, we proposed a novel multiview Takagi-Sugeno-Kang (TSK) fuzzy system in which a deep structure associating with a view-reduction mechanism are involved. The deep structure of each view is constructed by many basic components, i.e., the classic one-order TSK fuzzy systems which are linked in a layer by layer way using the stacked generalization principle. The view-reduction mechanism contains two parts: 1) A user-free parameter which is fixed according to the feature distribution is introduced to guild the view weight learning; 2) Views with noisy weights are automatically filtered by a reduction principle which is generated according to the training data. The proposed multi-view fuzzy system is finally applied for epileptic EEG signals detection.
{"title":"Deep View-Reduction TSK Fuzzy System: A Case Study on Epileptic EEG Signals Detection","authors":"Ziyuan Zhou, Yuanpeng Zhang, Yizhang Jiang","doi":"10.1109/SSCI44817.2019.9002722","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002722","url":null,"abstract":"In many practical applications, the fuzzy systems have been used due to the promising approximation accuracy and the high interpretability. Here, we proposed a novel multiview Takagi-Sugeno-Kang (TSK) fuzzy system in which a deep structure associating with a view-reduction mechanism are involved. The deep structure of each view is constructed by many basic components, i.e., the classic one-order TSK fuzzy systems which are linked in a layer by layer way using the stacked generalization principle. The view-reduction mechanism contains two parts: 1) A user-free parameter which is fixed according to the feature distribution is introduced to guild the view weight learning; 2) Views with noisy weights are automatically filtered by a reduction principle which is generated according to the training data. The proposed multi-view fuzzy system is finally applied for epileptic EEG signals detection.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"357 1","pages":"387-392"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76504341","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 : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002966
Jialin Liu, X. Yao
The Capacitated Arc Routing Problem (CARP) is a NP-hard combinatorial optimisation problem with numerous real-world applications. Several divide-and-conquer approaches, controlled by one or more hyperparameters, have been proposed to tackle large-scale CARPs. The tuning of hyperparameters can be computationally expensive due to the lack of priori knowledge, the size of the configuration space, and the time required for solving a CARP instance. Motivated by this time consuming task, we propose a scalable approach based on self-adaptive hierarchical decomposition (SASAHiD) to scale up existing methods. We take a state-of-the-art decomposition method for large-scale CARPs called SAHiD as an example to carry out experiments on two sets of real-world CARP instances with hundreds to thousands of tasks. The results demonstrate that SASAHiD outperforms SAHiD significantly with fewer hyperparameters, thus the dimension of associated configuration space is reduced. Moreover, we propose an incremental hyperparameter tuning approach across multiple problem instances to learn the hyperparameters of SASAHiD on a set of instances with different sizes. SASAHiD with optimised hyperparameters achieves better or competitive results with the SASAHiD using default hyperparameters when solving problem instances that it has never seen in the training set.
{"title":"Self-adaptive Decomposition and Incremental Hyperparameter Tuning Across Multiple Problems","authors":"Jialin Liu, X. Yao","doi":"10.1109/SSCI44817.2019.9002966","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002966","url":null,"abstract":"The Capacitated Arc Routing Problem (CARP) is a NP-hard combinatorial optimisation problem with numerous real-world applications. Several divide-and-conquer approaches, controlled by one or more hyperparameters, have been proposed to tackle large-scale CARPs. The tuning of hyperparameters can be computationally expensive due to the lack of priori knowledge, the size of the configuration space, and the time required for solving a CARP instance. Motivated by this time consuming task, we propose a scalable approach based on self-adaptive hierarchical decomposition (SASAHiD) to scale up existing methods. We take a state-of-the-art decomposition method for large-scale CARPs called SAHiD as an example to carry out experiments on two sets of real-world CARP instances with hundreds to thousands of tasks. The results demonstrate that SASAHiD outperforms SAHiD significantly with fewer hyperparameters, thus the dimension of associated configuration space is reduced. Moreover, we propose an incremental hyperparameter tuning approach across multiple problem instances to learn the hyperparameters of SASAHiD on a set of instances with different sizes. SASAHiD with optimised hyperparameters achieves better or competitive results with the SASAHiD using default hyperparameters when solving problem instances that it has never seen in the training set.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"48 1","pages":"1590-1597"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76915396","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 : 2019-12-01DOI: 10.1109/SSCI44817.2019.9003127
Y. Taniguchi, Y. Kubota, S. Tsuruta, Yoshiyuki Mizuno, T. Muranushi, Yuko Hada Muranushi, Yoshitaka Sakurai, R. Knauf, Andrea Kutics
Unusually intense solar flares may cause serious calamities such as damages of electric/nuclear power plants. It is thereupon highly demanded, but is quite difficult, to predict intense solar flares due to the imbalanced character of the available data. To cope with this problem, we have heretofore developed and applied a Case Based Genetic Algorithm (CBGALO) that contains a Local Optimizer, which is a Support Vector Machine (SVM). However, the prediction performance significantly depends on input data for learning. Hereupon, CBGALO is further extended by a Case Based automatically restartable Good combination searching GA for both learning features and input data (CBRsGcmbGA). Even the powerful but computationally expensive Deep Learning cannot automatically (evolutionarily, in our approach) search the learning data. Our approach solved this problem a little better by the case-based approach. However, it became obvious that even this work suffers from the typical GA effect in falling into local optima. To improve the results, we hence developed newly a diversity maintenance approach that inserts good individuals with large Hamming distance into the case base as elite individuals in GA’s population. In 2 out of 3 classes of solar flares, the performance of our new approach became as high as the best ones among the conventional world top records. Namely, even in those ≥ C class solar flares, our approach applying the Hamming distance to increase diversity had as high a performance 0.662 as compared with the conventional world top record 0.650.
{"title":"Maintaining Diversity in an SVM integrated Case Based GA for Solar Flare Prediction","authors":"Y. Taniguchi, Y. Kubota, S. Tsuruta, Yoshiyuki Mizuno, T. Muranushi, Yuko Hada Muranushi, Yoshitaka Sakurai, R. Knauf, Andrea Kutics","doi":"10.1109/SSCI44817.2019.9003127","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9003127","url":null,"abstract":"Unusually intense solar flares may cause serious calamities such as damages of electric/nuclear power plants. It is thereupon highly demanded, but is quite difficult, to predict intense solar flares due to the imbalanced character of the available data. To cope with this problem, we have heretofore developed and applied a Case Based Genetic Algorithm (CBGALO) that contains a Local Optimizer, which is a Support Vector Machine (SVM). However, the prediction performance significantly depends on input data for learning. Hereupon, CBGALO is further extended by a Case Based automatically restartable Good combination searching GA for both learning features and input data (CBRsGcmbGA). Even the powerful but computationally expensive Deep Learning cannot automatically (evolutionarily, in our approach) search the learning data. Our approach solved this problem a little better by the case-based approach. However, it became obvious that even this work suffers from the typical GA effect in falling into local optima. To improve the results, we hence developed newly a diversity maintenance approach that inserts good individuals with large Hamming distance into the case base as elite individuals in GA’s population. In 2 out of 3 classes of solar flares, the performance of our new approach became as high as the best ones among the conventional world top records. Namely, even in those ≥ C class solar flares, our approach applying the Hamming distance to increase diversity had as high a performance 0.662 as compared with the conventional world top record 0.650.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"150 1","pages":"353-360"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86149634","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 : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002792
Yanli Huang, Jie Hou, Shun-Yan Ren, Erfu Yang
The coupled complex-valued memristive neural networks (CCVMNNs) are investigated in this study. First, we analyze the passivity of the proposed network model by designing an appropriate controller and using certain inequalities as well as Lyapunov functional method, and provide a passivity condition for the considered CCVMNNs. In addition, a criterion for guaranteeing synchronization of this kind of network is established. Finally, the effectiveness and correctness of the acquired theoretical results are verified by a numerical example.
{"title":"Passivity and Synchronization of Coupled Complex-Valued Memristive Neural Networks","authors":"Yanli Huang, Jie Hou, Shun-Yan Ren, Erfu Yang","doi":"10.1109/SSCI44817.2019.9002792","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002792","url":null,"abstract":"The coupled complex-valued memristive neural networks (CCVMNNs) are investigated in this study. First, we analyze the passivity of the proposed network model by designing an appropriate controller and using certain inequalities as well as Lyapunov functional method, and provide a passivity condition for the considered CCVMNNs. In addition, a criterion for guaranteeing synchronization of this kind of network is established. Finally, the effectiveness and correctness of the acquired theoretical results are verified by a numerical example.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"2 1","pages":"2152-2159"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88526599","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 : 2019-12-01DOI: 10.1109/SSCI44817.2019.9002885
Aaisha Makkar, Neeraj Kumar, M. Guizani
In the modern era, Internet of Things(IoT) plays an important role in connecting the people across the globe. The IoT objects enable the communication and data exchange among each other irrespective of their geographical locations. In such an environment, the Web of Things (WoT) provides the Internet service to the IoT objects. The Internet is mostly accessed by the search engines. The success of search engine depends upon the ranking algorithm. Although, Google is preferred by the maximum Internet users, but still the Google’s ranking algorithm, PageRank experiences the occurrence of spam web pages. In this paper, the webpage filtering algorithm is proposed which automatically detects the spam web pages. The spam webpages are detected before these are processed by the ranking module of search engines. The machine learning model, i.e., decision tree is used for the validation of the proposed scheme. The ten fold cross validation approach is used to improve the accuracy of model, i.e., 98.2%. The results obtained demonstrate that the proposed scheme has the power of preventing the spam web pages in Cognitive Internet of Things (CIoT) environment.
{"title":"The Power of AI in IoT : Cognitive IoT-based Scheme for Web Spam Detection","authors":"Aaisha Makkar, Neeraj Kumar, M. Guizani","doi":"10.1109/SSCI44817.2019.9002885","DOIUrl":"https://doi.org/10.1109/SSCI44817.2019.9002885","url":null,"abstract":"In the modern era, Internet of Things(IoT) plays an important role in connecting the people across the globe. The IoT objects enable the communication and data exchange among each other irrespective of their geographical locations. In such an environment, the Web of Things (WoT) provides the Internet service to the IoT objects. The Internet is mostly accessed by the search engines. The success of search engine depends upon the ranking algorithm. Although, Google is preferred by the maximum Internet users, but still the Google’s ranking algorithm, PageRank experiences the occurrence of spam web pages. In this paper, the webpage filtering algorithm is proposed which automatically detects the spam web pages. The spam webpages are detected before these are processed by the ranking module of search engines. The machine learning model, i.e., decision tree is used for the validation of the proposed scheme. The ten fold cross validation approach is used to improve the accuracy of model, i.e., 98.2%. The results obtained demonstrate that the proposed scheme has the power of preventing the spam web pages in Cognitive Internet of Things (CIoT) environment.","PeriodicalId":6729,"journal":{"name":"2019 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"3132-3138"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88839831","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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