检索结果-内蒙古大学图书馆

distributed learning automata based approach to inferring urban structure via traffic flow

APPLIED INTELLIGENCE 2022年第2期52卷 1338-1350页

作者： Yasinian, Hamid Esmaeilpour, Mansour Islamic Azad Univ Cent Tehran Branch Dept Comp Engn Tehran Iran Islamic Azad Univ Hamedan Branch Dept Comp Engn Hamadan Hamadan Iran

Traffic flow can be used as a reference for knowledge generation, which is highly important in urban planning. One of the significant applications of traffic data is decision making about the structure of roads connecting zones of a city. It leads us to an optimal connection between important areas like business centers, shopping malls, construction sites, residential complexes, and other parts of a city which is the motivation of this research. The main question is how to infer the optimal connectivity network considering the current structure of an urban area and time-varying traffic dynamics. Therefore a novel formulation is created in this paper to solve the optimization problem using available data. A proposed algorithm is presented to infer the optimal structure that is a distributed learning automata-based approach. A matrix called estimated optimal connectivity represents the favorite structure and it is optimized utilizing signals about the current system and traffic dynamics from the environment. Two types of data, including synthetic and real-world, are used to show the algorithm's ability. After many experiments, the algorithm showed capability of optimizing the structure by finding new paths connecting the most correlated areas.

关键词： Urban structure Traffic dynamics Optimal connectivity structure distributed learning automata

来源：评论

学校读者我要写书评

暂无评论

Navigation in the social internet-of-things (SIoT) for discovering the influential service-providers using distributed learning automata

引用

JOURNAL OF SUPERCOMPUTING 2021年第10期77卷 11004-11031页

作者： Pashaei Barbin, Javad Yousefi, Saleh Masoumi, Behrooz Islamic Azad Univ Qazvin Branch Fac Comp & Informat Technol Engn Qazvin Iran Urmia Univ Dept Comp Engn Orumiyeh *** Iran

The integration of the social network concepts and IoT has led to the creation of a new concept called Social IoT, wherein objects or things can interact and provide services to one another. Efficient and distributed service navigation in such systems is essential due to the potentially huge number of services that are provided. This paper proposes a distributed learning automata-based algorithm called DLA_N, which aims at finding service-providers (i.e., objects) with high popularity or influence. The main idea is that an object can use its friends or friends of its friends to search for the desired service provider. Taking into account the SIoT's graph properties (i.e., the topology of the network), we define a new centrality metric that indicates the importance degree of a node in SIoT. Embedding a learning automata in each object, a distributed LA approach is proposed for the selection of the most influential nodes in the network. Starting from a requesting object, the proposed DLA_N algorithm learns to select a path containing objects with a high centrality metric. The distributed nature of our navigation results in high scalability and low navigation time. The results of performance evaluation indicate that the proposed method outperforms existing methods in the literature.

关键词： Social IoT distributed learning automata Service navigation Susceptible-infected-recovered

来源：评论

学校读者我要写书评

暂无评论

An iterative stochastic algorithm based on distributed learning automata for finding the stochastic shortest path in stochastic graphs

引用

JOURNAL OF SUPERCOMPUTING 2020年第7期76卷 5540-5562页

作者： Beigy, Hamid Meybodi, Mohammad Reza Sharif Univ Technol Dept Comp Engn Tehran Iran Amirkabir Univ Technol Dept Comp Engn Tehran Iran

In this paper, we study the problem of finding the shortest path in stochastic graphs and propose an iterative algorithm for solving it. This algorithm is based on distributed learning automata (DLA), and its objective is to use a DLA for finding the shortest path from the given source node to the given destination node whose weight is minimal in expected sense. At each stage of this algorithm, DLA specifies edges needed to be sampled. We show that the given algorithm finds the shortest path with minimum expected weight in stochastic graphs with high probability which can be close to unity as much as possible. We compare the given algorithm with some distributed learning automata-based iterative algorithms, a particle swarm optimization-based algorithm, an ant colony-based algorithm, a Q-learning-based algorithm, and an actor-critic-based algorithm for finding the shortest path. Computer experiments show that the proposed algorithm requires fewer edge samples to find the shortest path than the previously introduced DLA-based algorithms.

关键词： learning automata Stochastic shortest path distributed learning automata

来源：评论

学校读者我要写书评

暂无评论

A sampling method based on distributed learning automata for solving stochastic shortest path problem

引用

KNOWLEDGE-BASED SYSTEMS 2021年 212卷 106638-106638页

作者： Beigy, Hamid Meybodi, Mohammad Reza Sharif Univ Technol Dept Comp Engn Sharif Intelligent Syst Lab Tehran Iran Amirkabir Univ Technol Dept Comp Engn Soft Comp Lab Tehran Iran

This paper studies an iterative stochastic algorithm for solving the stochastic shortest path problem. This algorithm, which uses a distributed learning automata, tries to find the shortest path by taking a sufficient number of samples from the edges of the graph. In this algorithm, which edges to be sampled are determined dynamically as the algorithm proceeds. At each iteration of this algorithm, a distributed learning automata used to determine which edges to be sampled. This sampling method, which uses distributed learning automata, reduces the number of samplings from those edges, which may not be along the shortest path, and resulting in a reduction in the number of the edges to be sampled. In this paper, we propose a new method for analysis of the algorithm. The method proposed in this paper, unlike the previous ones, which were based on the Martingale theory, is based on the sampling. The proof given in this paper, unlike the previous ones, which were for a specific input graph, is not restricted to the input graph and is general. We also show that as the number of samples taken from the edges increases, the probability of finding the shortest path also increases. Experimental results obtained from some benchmark stochastic graphs and some random graphs also confirm the theoretical results. (C) 2020 Elsevier B.V. All rights reserved.

关键词： distributed learning automata learning automata Shortest path problem Stochastic graph

来源：评论

学校读者我要写书评

暂无评论

HIDDEN WEBPAGES DETECTION USING distributed learning automata

引用

JOURNAL OF WEB ENGINEERING 2018年第3-4期17卷 270-283页

作者： Kumar, Manish Bhatia, Rajesh PEC Univ Technol Chandigarh India

Webpages directly connected to each other on the Web can be reached easily by following hyperlinks. Those webpages that are not linked by hyperlinks comprises hidden Web and it is challenging to find them. Furthermore, most of webpages in hidden Web are generated dynamically. This paper proposes first time an algorithm to find webpages in hidden Web using distributed learning automata. learning automata use its self-learning characteristic of taking action based on the action probabilities using pairs. These actions may lead the current webpage to hidden webpages that are generated dynamically. At each stage of the proposed algorithm, we determine the edge that should be chosen to reach webpage of interest. The proposed algorithm is validated on four different websites from ***. Precision-recall curve and coverage plot in the results section shows the effectiveness of the proposed algorithm.

关键词： hidden Web learning automata distributed learning automata DLA

来源：评论

学校读者我要写书评

暂无评论

distributed learning automata-based algorithm for community detection in complex networks

引用

INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2016年第8期30卷 1650042-1650042页

作者： Khomami, Mohammad Mehdi Daliri Rozvanian, Alireza Meybodi, Mohammed Reza Islamic Azad Univ Comp Engn & Informat Technol Dept Qazvin Branch Qazvin Iran Amirkabir Univ Technol Tehran Polytech Comp Engn & Informat Technol Dept Comp Lab Tehran Iran

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

关键词： Complex networks onlien social networks social network analysis community detection distributed learning automata

来源：评论

学校读者我要写书评

暂无评论

Finding Maximum Clique in Stochastic Graphs Using distributed learning automata

引用

INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2015年第1期23卷 1-31页

作者： Rezvanian, Alireza Meybodi, Mohammad Reza Amirkabir Univ Technol Tehran Polytech Comp Engn & Informat Technol Dept Soft Comp Lab Tehran Iran

Because of unpredictable, uncertain and time-varying nature of real networks it seems that stochastic graphs, in which weights associated to the edges are random variables, may be a better candidate as a graph model for real world networks. Once the graph model is chosen to be a stochastic graph, every feature of the graph such as path, clique, spanning tree and dominating set, to mention a few, should be treated as a stochastic feature. For example, choosing stochastic graph as the graph model of an online social network and defining community structure in terms of clique, and the associations among the individuals within the community as random variables, the concept of stochastic clique may be used to study community structure properties. In this paper maximum clique in stochastic graph is first defined and then several learning automata-based algorithms are proposed for solving maximum clique problem in stochastic graph where the probability distribution functions of the weights associated with the edges of the graph are unknown. It is shown that by a proper choice of the parameters of the proposed algorithms, one can make the probability of finding maximum clique in stochastic graph as close to unity as possible. Experimental results show that the proposed algorithms significantly reduce the number of samples needed to be taken from the edges of the stochastic graph as compared to the number of samples needed by standard sampling method at a given confidence level.

关键词： Clique problem maximum clique stochastic graph distributed learning automata social networks

来源：评论

学校读者我要写书评

暂无评论

Sampling from complex networks using distributed learning automata

引用

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2014年 396卷 224-234页

作者： Rezvanian, Alireza Rahmati, Mohammad Meybodi, Mohammad Reza Amirkabir Univ Technol Tehran Polytech Comp Engn & Informat Technol Dept Soft Comp Lab Tehran Iran Amirkabir Univ Technol Tehran Polytech Comp Engn & Informat Technol Dept Image Proc & Pattern Recognit Lab Tehran Iran

A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Complex networks Social networks Network sampling distributed learning automata

来源：评论

学校读者我要写书评

暂无评论

Utilizing distributed learning automata to solve the connected target coverage problem in directional sensor networks

引用

SENSORS AND ACTUATORS A-PHYSICAL 2013年 198卷 21-30页

作者： Mohamadi, Hosein Ismail, Abdul Samad Salleh, Shaharuddin Univ Teknol Malaysia Fac Comp Johor Baharu 81310 Malaysia Univ Teknol Malaysia Fac Sci Johor Baharu 81310 Malaysia

Sensor networks have been employed in a variety of applications. Directional sensor networks (DSNs) are a class of sensor networks that have emerged more recently and received noticeable attention from scholars. One of the most significant challenges associated with DSNs is designing an effective algorithm to cover all the targets and, at the same time, retain connectivity with the sink. As sensors are often densely deployed, employing scheduling algorithms can be considered as a promising approach. In this paper, we use distributed learning automata (DLA) to design a new scheduling algorithm for solving the connected target coverage problem in DSNs. The proposed algorithm employs DLA to determine the sensors that should be activated at each stage for monitoring all the targets and transmitting the sensing data to the sink. In addition, we devise several pruning rules in order to maximize network lifetime. Extensive simulation experiments were carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrated the superiority of the proposed algorithm over a greedy-based algorithm in terms of extending network lifetime. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Directional sensor networks Cover set formation distributed learning automata Scheduling algorithms

来源：评论

学校读者我要写书评

暂无评论

A learning automata based approach for module placement in fog computing environment

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期237卷

作者： Abofathi, Yousef Anari, Babak Masdari, Mohammad Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Islamic Azad Univ Dept Comp Engn Shabestar Branch Shabestar Iran

Today, fog computing is an emerging technology to support resource-constrained Internet of Things (IoT) applications. The scalability, geographic distribution, and heterogeneity of edge computing nodes, as well as the diversity of users' expectations, have made optimal module placement, considering the maximum use of fog resources, a challenging optimization problem. This paper proposes a method based on distributed learning automata to reduce the search space by using the maximum capacity of fogs in a heterogeneous fog network. In this method, fog topology is mapped to a distributed learning automata. With the cooperation of the automaton in this DLA, the problem of module placement has been solved to reduce energy consumption and delay of applications. To evaluate the amount of energy consumption and the execution time of IoT applications, two single-objective cost functions for energy and delay and another single-objective function with simultaneous consideration of energy and delay have been used. The results indicate that the average efficiency of the proposed method is 15.99%, 18.21%, and 15.53%, respectively, compared to other methods.

关键词： Fog Computing IoT Applications Module Placement learning automata distributed learning automata

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：