2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献_第7页

Adaptive Neural Backstepping Control for a Class of Strict Feedback Nonlinear Full-State Constrained System with Sensor and Actuator Faults 一类具有传感器和执行器故障的严格反馈非线性全状态约束系统的自适应神经反步控制

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303708

Parisa Abdar, B. Rezaei, Safa Khari

{"title":"Adaptive Neural Backstepping Control for a Class of Strict Feedback Nonlinear Full-State Constrained System with Sensor and Actuator Faults","authors":"Parisa Abdar, B. Rezaei, Safa Khari","doi":"10.1109/ICCKE50421.2020.9303708","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303708","url":null,"abstract":"The aim of the current article is dealing with the adaptive neural fault tolerant control subject for a class of strict feed-back nonlinear full state constrained systems with faults in actuators and sensors. The faults which are taken into account in the current study are bias, drift, loos of accuracy, and misfortune of impression faults. In order to reduce the computational effort, only one parameter law is updated at each step. Besides, it is guaranteed that the states stay inside their constraint sets based on Barrier Lyapaunov Functions (BLF). In order to reach stability and tracking performance of the system, the controller parameter adaptive law was designed according to Lyapunov stability theory. It was found that, the Lyapunov theory demonstrates that the devised method can guarantee the closed loop stability of the control system, and all signals within the closed-loop framework are semi-globally uniformly bounded and the boundary of states are never damaged and the following blunder can converge to small desired value by the proper choose of design parameters. The simulation study have shown that the proposed control strategy was proven to be effective.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134346487","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}

引用次数: 0

Attributed Graph Clustering via Deep Adaptive Graph Maximization 基于深度自适应图最大化的属性图聚类

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303694

Bahare Fatemi, Soheila Molaei, Hadi Zare, H. Veisi

{"title":"Attributed Graph Clustering via Deep Adaptive Graph Maximization","authors":"Bahare Fatemi, Soheila Molaei, Hadi Zare, H. Veisi","doi":"10.1109/ICCKE50421.2020.9303694","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303694","url":null,"abstract":"Due to the increasing popularity of the social networks, the detection and discovery of the hidden building blocks and their community structures are considered as the primary tasks on the graph (network) based data structures. Graph clustering is considered as a challenging task as it requires contribution of input graph’s topological and content data jointly. Graph Convolutional Neural Networks (GCNs) have demonstrated remarkable power in the domain of graph representation learning by merging both structural and content information of networks. While GCN based clustering methods are being used as the state-of-the-art alternative solution for graph clustering, these methods fail to capture global structural information of networks, considering a local neighborhood of each node. Here we propose an integrated novel graph convolutional clustering approach that enables us to extract the local and global structures of the graph based data along with the nodes content. Experimental studies on three real-world benchmark information networks approve our approach and confirm that our proposed method outperforms baseline methods significantly in graph clustering and link prediction tasks.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129317013","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}

引用次数: 0

An Efficient, Current-Mode Full-Adder Based on Majority Logic in CNFET Technology CNFET技术中基于多数逻辑的高效电流模式全加法器

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303623

Mostafa Parvizi, S. M. Ali Zanjani

引用次数: 1

Multi-Attribute Decision Making using Competitive Neural Networks 基于竞争神经网络的多属性决策

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303699

M. Abdoos

引用次数: 0

Ransomware Detection with Semi-Supervised Learning 基于半监督学习的勒索软件检测

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303689

Fakhroddin Noorbehbahani, Mohammad Saberi

引用次数: 5

Low Complexity Method for Parent Selection based on Rank Increase Enhancement in RPL routing protocol RPL路由协议中基于秩递增的低复杂度父级选择方法

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303674

Mohammad Koosha, Mina Namdar, Emad Alizadeh

{"title":"Low Complexity Method for Parent Selection based on Rank Increase Enhancement in RPL routing protocol","authors":"Mohammad Koosha, Mina Namdar, Emad Alizadeh","doi":"10.1109/ICCKE50421.2020.9303674","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303674","url":null,"abstract":"6LoWPAN is regarded as the most prominent stack of protocols used for the Internet of Things or IoT and RPL has confirmed itself as the most commonly used network layer protocol for 6LoWPAN. But still, there are dozens of subjects open to research in RPL. One of these issues is the proper route establishment in a network of nodes. This task is preliminarily done by Objective Functions like OF0 and MRHOF. However, these OFs do not seem enough sophisticated since they do not take all the network metrics into consideration and consequently cause fast energy depletion of nodes or poor QoS properties. In this piece of research, we propose a method based on Fuzzy logic to modify the Objective Functions OF0 and MRHOf with regard to almost all the network parameters involved. Then our method is tested and simulated by the Cooja simulator that is based on Contiki OS to prove its effectiveness in comparison to the original OFs. By the results obtained from the simulations we show that our Fuzzy model has helped prolonging the life span of the networks by reducing energy consumption and other network properties like Throughput and PDR have also been improved.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125092798","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}

引用次数: 2

A Hybrid Scheme for Spatio-Textual Recommender System 一种空间文本推荐系统的混合方案

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303668

Seyede Masoome Shafiee, Mohammad Reza Moosavi, M. Z. Jahromi

{"title":"A Hybrid Scheme for Spatio-Textual Recommender System","authors":"Seyede Masoome Shafiee, Mohammad Reza Moosavi, M. Z. Jahromi","doi":"10.1109/ICCKE50421.2020.9303668","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303668","url":null,"abstract":"Location Based Social Networks (LBSNs) enable their user to share their check-ins and post reviews about them. The availability of spatial and textual information in LBSNs offers an opportunity to explore user’s history and preferences to find the locations that the user might be interested in. Point-Of-Interests (POIs) spatial features are one of the most important data available on LBSNs as it has a huge impact on user's choice of new location to visit. Users’ reviews and POIs’ categories are another valuable resources of information in LBSNs which help infer users’ interest and POIs’ features. Recent researches attempt to improve the performance of POI recommendation models by making use of different information sources available in social network. In this paper, we examine the impact of using this information on the accuracy of recommendation task. Our major contribution is proposing the model which use heterogeneous context information in the form of a weighted linear combination. We argue that the weights of this combination should be learned for each user separately instead of using the same set of weights for all users. We provide an algorithm for learning the weights for each user such that recommendation accuracy is improved. In addition, it is enable to incorporate extra information source to our proposed model without requirement of changing the model completely or adding extra complexity to it. Experiments conducted on two large datasets of real world, Yelp and Foursquare, shows the effectiveness of the proposed method.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128562955","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}

引用次数: 0

An Entanglement-Inspired Action Selection and Knowledge Sharing Scheme for Cooperative Multi-agent Q-Learning Algorithm used in Robot Navigation 机器人导航中协作多智能体q -学习算法的纠缠启发行为选择和知识共享方案

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303636

Mohammad Hasan Karami, Hossein Aghababa, A. Keyhanipour

{"title":"An Entanglement-Inspired Action Selection and Knowledge Sharing Scheme for Cooperative Multi-agent Q-Learning Algorithm used in Robot Navigation","authors":"Mohammad Hasan Karami, Hossein Aghababa, A. Keyhanipour","doi":"10.1109/ICCKE50421.2020.9303636","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303636","url":null,"abstract":"Multi-agent reinforcement learning, especially learning in unknown complex environments, requires new algorithms. In this work, our focus is on adopting the concept of the quantum entanglement phenomena to the action selection procedure of multi-agent Q-learning, aiming to enhance the learning speed, collision avoidance, and also providing full coverage of the environment. The exploration procedure is exclusively induced by a memory-based probabilistic sequential action selection method acting as a knowledge hub, shared among the agents, which is the central pillar of this work. This causes enhancing the parallelism of the learning process, plus, building an effective yet simple communicating-bridge between the learning agents; that is, they can signal and guide one another through sharing their gained experience and knowledge in order not to repeat the same mistake that the other agents have already run into. The simulation results demonstrated the effectiveness of our proposed algorithm in terms of reducing the learning time, significant reduction of collision occurrence, and providing full coverage of big complex clutter environments.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125296155","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}

引用次数: 0

E-Commerce Customer Churn Prediction By Gradient Boosted Trees 基于梯度提升树的电子商务客户流失预测

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303661

Shamim Raeisi, H. Sajedi

引用次数: 2

An Evolutionary Hybrid Feature Selection Approach for Biomedical Data Classification 生物医学数据分类的进化混合特征选择方法

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303648

Fariba Moeini, S. J. Mousavirad

{"title":"An Evolutionary Hybrid Feature Selection Approach for Biomedical Data Classification","authors":"Fariba Moeini, S. J. Mousavirad","doi":"10.1109/ICCKE50421.2020.9303648","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303648","url":null,"abstract":"Feature selection is an important function/subject in machine learning. It involves separating the relevant features of the data set and reducing its dimension by eliminating unnecessary data, leading to predictive performance. To this end, researchers use specific search methods to find an optimal subset of features. The aim of this study is developing a hybrid algorithm according to simulated annealing (SA) and grey wolf optimizer (GWO) to be applied in feature selection for biomedical data. Grey wolf algorithm optimizer is an innovative, bio-inspired method of optimization and, as the name suggests, reproduces the actual pattern of how grey wolves hunt in their natural habitat. Two feature selection methods (BGWO1-SA and BGWO2-SA) are presented here. For greater intensification of the suggested algorithm, the SA algorithm receives the inputs of the wolves’ updated position in the last phase of both above-mentioned approaches. The proposed methods are compared with four competitors: particle swarm optimization, genetic algorithms, and two versions of GWO algorithm. The assessments were based on a set of challenging biomedical benchmarks, and the results showed that the presented methods outperform their rivals.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116771949","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}

引用次数: 0