2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)最新文献_第3页

Age Detection from Brain MRI Images Using the Deep Learning 基于深度学习的脑MRI图像年龄检测

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964911

Masoumeh Siar, M. Teshnehlab

{"title":"Age Detection from Brain MRI Images Using the Deep Learning","authors":"Masoumeh Siar, M. Teshnehlab","doi":"10.1109/ICCKE48569.2019.8964911","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964911","url":null,"abstract":"Estimating the age of the brains of individuals from brain images can be very useful in many applications. The brain’s age has greatly contributed to predicting and preventing early deaths in the medical community. It can also be very useful for diagnosing diseases, such as Alzheimer’s. According to the authors knowledge, this paper is one of the first researches that have been done in age detection by brain images using Deep Learning (DL). In this paper, the convolution neural network (CNN), used for age detection from brain magnetic resonance images (MRI). The images used in this paper are from the imaging centers and collected by the author of the paper. In this paper 1290 images have been collected, 941 images for train data and 349 images for test images. Images collected at the centers were labeled age. In this paper, the Alexnet model is used in CNN architecture. The used architecture of the architecture has 5 Convolutional layers and 3 Sub-sampling layers that the last layer has been used to categorize the image. The CNN that the last layer has been used to categorize the images into five age classes.The accuracy of the CNN is obtained by the Softmax classifier 79%, Support Vector Machine (SVM) classifier 75% and the Decision Tree (DT) classifier, 49%. In addition to the accuracy criterion, we use the benchmarks of Recall, Precision and F1-Score to evaluate network performance.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"34 1","pages":"369-374"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89204174","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

Communication-Critical Task Duplication for Cloud Workflow Scheduling with Time and Budget Concerns 通信关键任务复制的云工作流程调度与时间和预算问题

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8965163

Mohammad Kamyar Arbab, Mahmoud Naghibzadeh, S. R. Kamel Tabbakh

{"title":"Communication-Critical Task Duplication for Cloud Workflow Scheduling with Time and Budget Concerns","authors":"Mohammad Kamyar Arbab, Mahmoud Naghibzadeh, S. R. Kamel Tabbakh","doi":"10.1109/ICCKE48569.2019.8965163","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965163","url":null,"abstract":"scientific workflows are suitable models in many applications for recognition and execution of tasks in parallel, especially in the Cloud. Different aspects of resource provisioning and the resource pricing model in the cloud environment cause the scheduling problem of workflow very complex. One of the main aims of the scheduling algorithms is to satisfy the users’ different quality of service requirements. Communication delay between tasks is an important affecting factor in optimal scheduling of workflows. It can also highly increase the cost of workflow scheduling if proper actions are not taken. To solve this problem, we propose a tasks duplication-based list scheduling algorithm called Communication-Critical Task Duplication (CCTD). We first define the concept of communication critical task (CCT) for a workflow. Then, by presenting a ranking-based approach, we identify communication critical tasks in a workflow as well as duplicating candidates. Task duplication in idle time slots of leased virtual machines which their children tasks are mapped to. This idea, while eliminating the cost and time of data transfer between parent-child tasks, reduces the time of execution of tasks and effectively uses leased time intervals of resources. According to the proposed scheduling algorithm, a new heuristic method has been proposed for the budget distribution. This method distribute overall budget to tasks in proportional to the workload and duplication rank of each task. The proposed algorithm was evaluated and verified using four well-known scientific workflows. The simulation results show that the CCTD algorithm, while respecting the user budget constraint, reduces the workflow overall completion time.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"24 1","pages":"255-262"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87529148","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

Increasing The Network Control Ability and Flexibility In Incremental Switch Deployment for Hybrid Software-Defined Networks 在混合型软件定义网络中增加交换机部署的网络控制能力和灵活性

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8965051

Amir Hossein Fakhteh, Vahid Sattari-Naeini, H. Naji

{"title":"Increasing The Network Control Ability and Flexibility In Incremental Switch Deployment for Hybrid Software-Defined Networks","authors":"Amir Hossein Fakhteh, Vahid Sattari-Naeini, H. Naji","doi":"10.1109/ICCKE48569.2019.8965051","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8965051","url":null,"abstract":"Software-Defined Network is a new networking generation to increase network flexibility and performance improvement. However, budget constraints, technical limitations and the risk of architectural changes that organizations are encountered with, are barriers to the full implementation of the SDN. For these reasons, Internet Service Providers (ISPs) should replace only a limited number of conventional switches with SDN switches at one time. Put both switches together in a topology, create hybrid SDN. In this paper, we first cluster the switches into two clusters, based on a graph concept which is called closeness-centrality. Afterward, we have one cluster with low closeness centrality and another with high; then, we propose a heuristic migration scheme for deploying SDN switches in hybrid SDNs. We first allocate all existing budget to a cluster at low closeness-centrality and then we upgrade the switches according to the proposed scheme. There are four main issues in this paper: (1) improving the network control ability with a given upgrading budget constraint, (2) minimizing the upgrading cost to achieve the best network control ability, (3) increasing hybrid SDN flexibility and (4) improving network fault tolerance by deploying a link fault recovery algorithm. Also, in order to perform controller placement, we propose a simple solution to provide an acceptable delay between controller and SDN switches. We evaluate our scheme in three real topologies and one synthetic topology. The results show that our scheme can achieve better network control ability and flexibility with the help of graph theory parameters compared to other related papers according to budget constraint.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"87 1","pages":"263-268"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83773948","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

[ICCKE 2019 Authors]

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/iccke48569.2019.8964957

引用次数: 0

Epileptic seizures detection in EEG signals using TQWT and ensemble learning 基于TQWT和集成学习的脑电信号癫痫发作检测

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964826

Navid Ghassemi, A. Shoeibi, M. Rouhani, Hossein Hosseini-Nejad

{"title":"Epileptic seizures detection in EEG signals using TQWT and ensemble learning","authors":"Navid Ghassemi, A. Shoeibi, M. Rouhani, Hossein Hosseini-Nejad","doi":"10.1109/ICCKE48569.2019.8964826","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964826","url":null,"abstract":"In this paper, a new scheme for diagnosis of epileptic seizures in EEG signals using Tunable-Q wavelet transform (TQWT) framework is proposed and benchmarked with Bonn dataset. First, a segmentation of the EEG signals into smaller windows is performed, then a high-pass Butterworth filter with a cutoff frequency of 0.5 Hz is applied to eliminate possible noise. After that, a TQWT with parameters J=8, r=3, Q=1 has been utilized for decomposing the segmented EEG Signals into nine sub-bands. Next, a combination of statistical, entropy-based, and fractal dimension features are extracted from each sub-band. Finally, different ensemble learning-based classifiers, specifically, Adaboost, Gradient Boosting (GB), Hist Gradient Boosting (HistGB), and Random Forest (RF) are employed to classify signals. Also, a feature ranking is driven from classifiers to further analyze the importance of each feature in this particular task. Comparing our method to previous ones, introduced scheme outperforms most of the state-of-the-art works in this field, indicating the effectiveness of the proposed epileptic seizures detection method.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"48 1","pages":"403-408"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91352687","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: 25

Electronic voting through DE-PBFT consensus and DAG data structure 通过DE-PBFT共识和DAG数据结构进行电子投票

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8965093

J. Bahri, Hamid Reza Shayegh Borojeni

引用次数: 2

A Brief Review on Vessel Extraction and Tracking Methods 血管提取与跟踪方法综述

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964824

F. Z. Boroujeni, Simindokht Jahangard, R. Rahmat

引用次数: 0

Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm 基于深度神经网络和机器学习算法的脑肿瘤检测

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964846

Masoumeh Siar, M. Teshnehlab

{"title":"Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm","authors":"Masoumeh Siar, M. Teshnehlab","doi":"10.1109/ICCKE48569.2019.8964846","DOIUrl":"https://doi.org/10.1109/ICCKE48569.2019.8964846","url":null,"abstract":"Brain tumor can be classified into two types: benign and malignant. Timely and prompt disease detection and treatment plan leads to improved quality of life and increased life expectancy in these patients. One of the most practical and important methods is to use Deep Neural Network (DNN). In this paper, a Convolutional Neural Network (CNN) has been used to detect a tumor through brain Magnetic Resonance Imaging (MRI) images. Images were first applied to the CNN. The accuracy of Softmax Fully Connected layer used to classify images obtained 98.67%. Also, the accuracy of the CNN is obtained with the Radial Basis Function (RBF) classifier 97.34% and the Decision Tree (DT) classifier, is 94.24%. In addition to the accuracy criterion, we use the benchmarks of Sensitivity, Specificity and Precision evaluate network performance. According to the results obtained from the categorizers, the Softmax classifier has the best accuracy in the CNN according to the results obtained from network accuracy on the image testing. This is a new method based on the combination of feature extraction techniques with the CNN for tumor detection from brain images. The method proposed accuracy 99.12% on the test data. Due to the importance of the diagnosis given by the physician, the accuracy of the doctors help in diagnosing the tumor and treating the patient increased.","PeriodicalId":6685,"journal":{"name":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"1 1","pages":"363-368"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89761090","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: 87

A Ring Oscillator PUF Architecture With Enhanced Challenge-Response Set 一种具有增强挑战响应集的环形振荡器PUF结构

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964942

Mohammad Javad Parvardeh, Shahriar Baradaran Shokouhi

引用次数: 5

A Novel Area-Delay Efficient Carry Select Adder Based on New Add-one Circuit 一种基于新型加一电路的区域延迟高效进位选择加法器

2019 9th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2019-10-01 DOI: 10.1109/ICCKE48569.2019.8964968

Maytham Allahi Roodposhti, M. Valinataj

引用次数: 3