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

A Modified Grey Wolf Optimization for Energy Efficiency and Resource Wastage Balancing in Cloud Data-Centers 云数据中心能源效率与资源浪费平衡的改进灰狼优化

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303615

Atiyeh Ansari, M. Asghari, S. Gorgin, D. Rahmati

{"title":"A Modified Grey Wolf Optimization for Energy Efficiency and Resource Wastage Balancing in Cloud Data-Centers","authors":"Atiyeh Ansari, M. Asghari, S. Gorgin, D. Rahmati","doi":"10.1109/ICCKE50421.2020.9303615","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303615","url":null,"abstract":"Virtual Machine Placement (VMP) process is one of the significant issues in Cloud data-centers. This operation can manage VMs to be placed on the best available Physical Machine (PM) and it has a notable impact on the performance, resource utilization, and power consumption of the data-centers. Meta-heuristics swarm intelligence methods are a common approach of solving these types of optimization problems. Grey Wolf Optimization (GWO) is a meta-heuristic algorithm that has proved its advantages. In this paper, we propose a Modified Grey Wolf Optimizer (MGWO) to balance power consumption and memory resource wastage using virtual machine placement. The suggested algorithm compared with two existing greedy algorithms, First Fit (FF) and First Fit Decreasing (FFD). The simulation results revealed the effectiveness of the proposed MGWO to provide a tradeoff between energy efficiency and memory resource wastage. The average of resource wastage of the MGWO is less than 10 percent, considering the tradeoff between energy consumption and the memory wastage parameter. In addition, the convergence test of the MGWO has been compared with a Genetic Algorithm (GA). The proposed solution is useful for defining some constraints for controlling resource wastage, such as cores and memory wastage.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"2674 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":"124387741","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

Distributed synchronization for charging sensors based on service priority in WSAN WSAN中基于服务优先级的计费传感器分布式同步

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303688

Rahim Abri Ligvan, Reza Soitani, S. Pashazadeh

引用次数: 0

Spiking Neural Controller for Autonomous Robot Navigation in Dynamic Environments 动态环境下自主机器人导航的脉冲神经控制器

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303687

M. T. Ramezanlou, V. Azimirad, Saleh Valizadeh Sotubadi, F. Janabi-Sharifi

引用次数: 2

Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees 利用抽样技术、套袋和提升树处理电信行业客户流失预测中的阶层不平衡

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303698

Sajjad Shumaly, Pedram Neysaryan, Yanhui Guo

{"title":"Handling Class Imbalance in Customer Churn Prediction in Telecom Sector Using Sampling Techniques, Bagging and Boosting Trees","authors":"Sajjad Shumaly, Pedram Neysaryan, Yanhui Guo","doi":"10.1109/ICCKE50421.2020.9303698","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303698","url":null,"abstract":"Customer churn is a serious problem in the telecommunications industry and occurs more often. The cost of maintaining existing customers is much lower than attracting new customers, and the literature stated that five times the cost of maintaining existing customers have to be spent on attracting new customers. In this article, we have identified customers who intend to stop using the organization's services. One of the most important problems in predicting customer churn is the imbalanced data, which has been tried to be solved and compared with different methods. The machine learning algorithms used in this paper are Decision Tree, Support Vector Machine, Multi-Layer Perceptron, Random Forest, and Gradient Boosting. Data was balanced by random over-sampling, random under-sampling and SMOTE methods. The methods of over-sampling and under-sampling had appropriate and almost similar results in terms of the area under the receiver character curve (AUC) index, the method of under-sampling has shown the better specificity, and the method over-sampling has shown the better sensitivity. Also, the performance of random forest and gradient boosting algorithms were better than other algorithms.","PeriodicalId":402043,"journal":{"name":"2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"46 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":"127058431","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

Noise Margin Calculation in Multiple-Valued Logic 多值逻辑中的噪声余量计算

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303638

Mehdi Takbiri, K. Navi, R. F. Mirzaee

引用次数: 3

A Large-Scale Application Mapping in Reconfigurable Hardware Using Deep Graph Convolutional Network 基于深度图卷积网络的可重构硬件中的大规模应用映射

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303679

S. M. Mohtavipour, H. Shahhoseini

引用次数: 2

Improved MDNET Tracker in Better Localization Accuracy 改进MDNET跟踪器，提高定位精度

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303727

Zahra Soleimanitaleb, Mohammad Ali Keyvanrad, Ali Jafari

{"title":"Improved MDNET Tracker in Better Localization Accuracy","authors":"Zahra Soleimanitaleb, Mohammad Ali Keyvanrad, Ali Jafari","doi":"10.1109/ICCKE50421.2020.9303727","DOIUrl":"https://doi.org/10.1109/ICCKE50421.2020.9303727","url":null,"abstract":"Object tracking is one of the most important issues in the field of computer vision, which has many applications in the automotive, defense, robotics, medicine, industries, etc. In recent years, various researches have been done in this field and due to its many applications, research in this field continues. In this paper, a deep end-to-end, multi-domain method, or MDNET was examined. The MDNET method works well in tracking video sequences, but has the following problems: The first problem is finding the target position, where the candidate is selected as the target with the highest score. To solve this problem, a new way to find the target position is provided. In this way, the average of the top five candidates is selected as the target position, which was able to increase the IOU standard of the basic method on the OTB100 dataset from 69 to 70.3 and also the results based on the OTB50 and the various challenges are investigated. The second problem is the loss of target in some frames, In this case, instead of drawing the candidates around the target, the candidates are drawn in the whole image and the candidate with the highest score is selected as the target. This method was able to increase the IOU standard on the OTB100 dataset from 70.3 to 72.2 and the results based on the OTB50 and the various challenges are investigated.","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":"125887957","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

Reconstruction of Worm Propagation Path Using a Trace-back Approach 用回溯法重建蠕虫传播路径

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303712

Sara Asgari, B. Sadeghiyan

引用次数: 0

Sing Network Slicing And NFV Technology Sing网络切片和NFV技术

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303683

Z. Mohammady, R. Azmi

引用次数: 0

Reducing Search Space in Subgraph Matching Problem 子图匹配问题中搜索空间的缩减

2020 10th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2020-10-29 DOI: 10.1109/ICCKE50421.2020.9303627

Hojjat Moayed, E. Mansoori

引用次数: 0