2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献_第9页

Efficient Energy Consumption in Wireless Sensor Networks Using an Improved Differential Evolution Algorithm 基于改进差分进化算法的无线传感器网络节能研究

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303713

Milad Ghahramani, Abolfazl Laakdashti

{"title":"Efficient Energy Consumption in Wireless Sensor Networks Using an Improved Differential Evolution Algorithm","authors":"Milad Ghahramani, Abolfazl Laakdashti","doi":"10.1109/ICCKE50421.2020.9303713","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303713","url":null,"abstract":"The recent advancements in the wireless sensor network field have caused researchers to be interested in using this tool in different applications including military, environmental, medical, commercial, and domestic applications. One of the most important challenges in wireless sensor networks is that the power supplies of the wireless sensor nodes are not rechargeable because of their distribution in points inaccessible by people. In recent years, various methods have been presented for efficient energy consumption by wireless sensor nodes. One of the efficient methods is the clustering method. In this paper, a new clustering algorithm based on the metaheuristic differential evolution algorithm is presented. In the proposed algorithm, a new evaluation function is used so that the algorithm can increase the lifetime of the wireless sensor nodes and the cluster head nodes and therefore the lifetime of the entire wireless sensor network by presenting appropriate answers which are the correct assignment of wireless sensor nodes to cluster head nodes. The clustering algorithm simulation results and its comparison with some of the other methods are indicative of its high performance, such that this method can be used for clustering sensor networks with a large number of wireless sensor nodes.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"45 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":"128554437","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: 3

Using a Novel Method for Trust Evaluation to Enhance ABAC Capabilities 利用一种新的信任评估方法增强ABAC能力

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303631

M. Arasteh, S. Alizadeh

{"title":"Using a Novel Method for Trust Evaluation to Enhance ABAC Capabilities","authors":"M. Arasteh, S. Alizadeh","doi":"10.1109/ICCKE50421.2020.9303631","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303631","url":null,"abstract":"Access control is a security mechanism that prevents unauthorized access to sensitive resources. Attribute-Based Access Control model (ABAC) makes decisions by the considerations of subjects’ attributes. Although it has many advantages, it is not dynamic. In dynamic environments, the system should be able to change the users’ permissions according to their manner of activities. So, this paper proposes using trust besides ABAC. The introduced method for the evaluation of trust employs both the Fuzzy Inference System (FIS) and Neural Networks (NN), which is called Fuzzy-Neural based trust (FNT). As trust is evaluated according to some predefined parameters, the proposed model uses FIS to assess each parameter. Next, the assessed parameters should be mixed to generate a single result. Since the definition of an exact function might be difficult and complicated, the proposed model employs the NN, which acts as a black box and generates an expected output after its learning process. For the evaluation of trust, the assessed parameters are fed to the NN to produce a final result. Whenever a subject’s trust is evaluated, then the proposed model makes the final AC decision by the consideration of both ABAC’s result and the amount of trust. Afterwards, we evaluate the proposed model and then highlight its advantages by comparing with some other famous AC models.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"21 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":"124269907","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: 1

Turn off/on Base Stations with CSO approach using Simulated Annealing Algorithm in 5G Networks 5G网络中使用模拟退火算法的CSO方法关闭/打开基站

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303715

Leili Mortazavi, M. Alishahi, Ameneh Rajabi Darbandiolya, Amir Mohammad Nazemi

{"title":"Turn off/on Base Stations with CSO approach using Simulated Annealing Algorithm in 5G Networks","authors":"Leili Mortazavi, M. Alishahi, Ameneh Rajabi Darbandiolya, Amir Mohammad Nazemi","doi":"10.1109/ICCKE50421.2020.9303715","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303715","url":null,"abstract":"The demand of users and the increase of devices connected to cellular networks has increased accordingly, since the advent of different generations of mobile phones and the communication between people through mobile phones. Over the years, researchers and mobile operators have come to the conclusion that current technologies do not meet the needs of users. Therefore, to meet the needs of users, the number of micro-cells is increasing sharply, but with this volume of increase, mobile operators are facing the challenge of energy consumption. Using the cell shutdown approach, key and very useful solutions can be provided to optimize energy consumption in mobile cellular networks. In the cell shutdown method, shutting down a number of cells, moving them to an adjacent cell, without compromising the quality of service and reducing the covered areas, is a requirement of this approach. This paper simulates a solution to reduce energy consumption in base stations by clustering and selecting a base station based on the annealing algorithm. The simulation results show that the proposed model, compared to the model that is performed only with respect to the node Euclidean distance to the base station, has improved an operational power of more than 5%, a power consumption of more than 2% and a network lifetime of more than 3%. While the average of inactive base stations in the proposed model has been up to 8% higher.","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":"117232761","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: 1

WiFi Fingerprinting based Floor Detection with Hierarchical Extreme Learning Machine 基于WiFi指纹的分层极限学习机地板检测

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303624

Atefe Alitaleshi, H. Jazayeriy, S. J. Kazemitabar

引用次数: 11

A Deep Convolutional Neural Network for Melanoma Recognition in Dermoscopy Images 皮肤镜图像中黑色素瘤识别的深度卷积神经网络

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303684

S. Haghighi, H. Danyali, M. Helfroush, Mohammad Hasan Karami

{"title":"A Deep Convolutional Neural Network for Melanoma Recognition in Dermoscopy Images","authors":"S. Haghighi, H. Danyali, M. Helfroush, Mohammad Hasan Karami","doi":"10.1109/ICCKE50421.2020.9303684","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303684","url":null,"abstract":"Automated melanoma recognition in dermoscopy images is a challenging task due to a set of hindrances including low contrast skin images, the resemblance of melanoma and non-melanoma skin lesions, and the great variety in this type of skin cancer. However, in this study, a fully automated method is proposed which recognizes the melanoma lesions from the non-melanoma lesions with high accuracy. Convolutional Neural Networks (CNNs) have made great strides in the field of recognition and classification of medical images. Based on this ground, a deep convolutional neural network is proposed that acts as the central pillar of the proposed melanoma recognition method. In order to compensate for the lack of training data, data augmentation techniques have been employed. The proposed method is a merger of the features elicited from the proposed Convolutional Neural Network architecture and a Support Vector Machine (SVM) classifier. The classifier categorizes the input dermoscopy images into two main classes of Melanoma and non-Melanoma skin lesion images with a promising accuracy of 89.52%, which outperforms the state-of-art methods.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"37 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":"132097252","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: 1

Feedback Error Learning Controller based on RMSprop and Salp Swarm Algorithm for Automatic Voltage Regulator System 基于RMSprop和Salp群算法的自动调压系统反馈误差学习控制器

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303718

Mohammad Ali Labbaf Khaniki, Mohammad Behzad Hadi, M. Manthouri

{"title":"Feedback Error Learning Controller based on RMSprop and Salp Swarm Algorithm for Automatic Voltage Regulator System","authors":"Mohammad Ali Labbaf Khaniki, Mohammad Behzad Hadi, M. Manthouri","doi":"10.1109/ICCKE50421.2020.9303718","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303718","url":null,"abstract":"The primary goal of the Automatic Voltage Regulator (AVR) is to control the terminal voltage at the desired level. The controller used in AVR must be capable of maintaining generator terminal voltage in various operating conditions and also withstand uncertainty. In this paper, Feedback Error Learning (FEL) controller has been proposed to control the AVR system. FEL structure consists of the classical controller (PD controller) and the intelligent controller (MLP neural network controller). This control strategy has been employed to control unknown and uncertain plant model. Salp Swarm Algorithm (SSA) has been used to obtain the initial weights, biases and the number of neurons of the MLP neural network. The training methods used in this research are Stochastic Gradient Descend (SGD) and Root Mean Square propagation (RMSprop) that in particular; these methods are used in deep learning. The robustness and effectiveness of the proposed method has been studied in different operating conditions. The results demonstrate that the proposed strategy outperforms other methods.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"31 3 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":"134628775","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: 6

Biological Network Alignment Using Hybrid Genetic Algorithm and Simulated Annealing 基于混合遗传算法和模拟退火的生物网络定位

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303703

Elham Mahdipour, M. Ghasemzadeh

{"title":"Biological Network Alignment Using Hybrid Genetic Algorithm and Simulated Annealing","authors":"Elham Mahdipour, M. Ghasemzadeh","doi":"10.1109/ICCKE50421.2020.9303703","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303703","url":null,"abstract":"This research demonstrates how we can improve the efficiency of protein-protein interaction (PPI) network alignment using soft computing. In Bioinformatics, biological network alignment is particularly important for its use in identifying cellular pathways, discovering new drugs, and detecting disease progression. Also, network alignment is used in social networks, ontology matching, pattern recognition, and natural language processing. In this regard, the main challenge is that the problem of finding the alignments in two graphs is NP-hard, therefore, accurate algorithms can only be used for very small instances. For real and relatively large cases, typically (meta)heuristic methods, which can find approximate solutions in reasonable time, are used. In this regard, we propose a new hybrid metaheuristic algorithm, called SAGA. The SAGA proposed method is applied the simulated annealing in the crossover operation of genetic algorithm. Concerning the integrated network alignment, SAGA first finds the local alignments and then it discovers the existing global network alignments. We implement the SAGA network aligner on python 3.6 and obtained experimental results on five eukaryotic species of the Biogrid dataset. The experimental results show that SAGA network aligner can achieve a better mapping than some of the state-of-the-art algorithms. Based on the experimental results, the proposed integrated network aligner can balance the functional quality and topological quality criteria that are significant in Bioinformatics.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"256 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":"124015799","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

Regional Selection Mechanism for Traffic-balanced Adaptive Routing Algorithms in Mesh-based NoC Architectures 基于网格的NoC架构中流量均衡自适应路由算法的区域选择机制

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303650

Nooshin Nosrati, H. Shahhoseini

{"title":"Regional Selection Mechanism for Traffic-balanced Adaptive Routing Algorithms in Mesh-based NoC Architectures","authors":"Nooshin Nosrati, H. Shahhoseini","doi":"10.1109/ICCKE50421.2020.9303650","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303650","url":null,"abstract":"With deep submicron scaling and integrating multi and many cores on a single chip, Network-on-Chip (NoC) has been proposed as a scalable and cost-effective solution for inter-core communication. Data exchange between cores does not follow a uniform traffic pattern, which frequently leads to congestion in central routers of a mesh-based NoC. Congestion limits the performance of NoC and results in larger packets transmission latency, higher thermal, and more power consumption. This paper proposes a traffic-balanced selection mechanism to distribute traffic load over the network and mitigate the blocking of packets. The proposed scheme based on congestion information of neighbors routers estimates congestion of all possible minimal paths for a packet, then conduct packet through a non-congested path. It uses congestion information of a stair-shaped region in routing decision. The stair-shaped region provides adequate and up-to-date congestion information by taking into account local and non-local traffic conditions. Experimental results show the proposed method improves both saturation throughput and average latency of 9.91%-17.45% and 30.48%-64.23%, respectively, compared to similar approaches for all considered traffic patterns.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"35 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":"117234471","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

Software-defined Control of Emergency Vehicles in Smart Cities 智慧城市应急车辆的软件定义控制

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303706

Nazila Bagheri, S. Yousefi, G. Ferrari

{"title":"Software-defined Control of Emergency Vehicles in Smart Cities","authors":"Nazila Bagheri, S. Yousefi, G. Ferrari","doi":"10.1109/ICCKE50421.2020.9303706","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303706","url":null,"abstract":"One of the most fundamental challenges in nowadays transportation systems is the appropriate management of emergencies resulting from accidents. The purpose of this paper is to utilize vehicular communication technologies and integrate them with the software defined idea to reduce the time required by the emergency vehicle to arrive at the accident scene from the emergency center (i.e., rescue time). In this context, one of the main approaches is traffic light preemption in favor of emergency vehicles: the timing of traffic lights along the rescue route is dynamically adjusted to minimize the number of RED lights met by the emergency vehicles. Most of existing methods of preemption are based on local decision making at each individual traffic light. However, in this paper, the use of a central controller for traffic light scheduling leads to higher efficiency due to the higher knowledge of street traffic and intersection conditions. The proposed method is evaluated using the OMNET++ and SUMO tools over part of the city of Tabriz, Iran. The simulation results demonstrate that the proposed method can reduce the average rescue time even by more than 50% in some cases.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"19 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":"115513231","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: 3

Zero-Shot Classification by Large-Margin Distance Learning 大边际远程学习的零射击分类

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303620

Mohammad Reza Zarei, M. Taheri

引用次数: 0