2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)最新文献_第4页

Intention to use biometrie systems among international students in Cyprus 有意在塞浦路斯的国际学生中使用生物识别系统

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319391

Issa Djeni, Meryem Erbilek

{"title":"Intention to use biometrie systems among international students in Cyprus","authors":"Issa Djeni, Meryem Erbilek","doi":"10.1109/CICN.2017.8319391","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319391","url":null,"abstract":"In biometrie authentication system the false acceptance rate (FAR) and false rejection rate (FRR) are two signifieant performanee metries used to illustrate the seeurities level of the system. All the biometric systems do not have an equal FAR and FRR. De facto, some biometric technologies are mathematically more vulnerable than the others. These disparities affect the behavioral intention toward biometric technology. This research aims to investigate the factors affecting the intention to use biometric system among international students staying on campus and those enrolled in graduate course in biometric system engineering. To address this problem, a proposed technology acceptance model will be tested empirically with 98 international students in two universities in Cyprus. The results of these studies have shown that among privacy concerns, biometrics system quality and personal innovativeness in IT, only performance expectancy and perceived securities are the main drivers of biometric system adoption. Additionally, the attendance of biometric course does affect the intention to use. The predictors investigated account for 40% of behavioral intentions. These findings could be extended to the intention to use biometric system in developing countries with infrastructure similar to Cyprus. Finally, organizations planning to use biometric in the future should carry out a non-professional training on how biometric system works.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133738065","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

Store products recognition and counting system using computer vision 店内产品识别与计数系统采用计算机视觉

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319389

Muhanad H. Algburi, S. Albayrak

引用次数: 1

Implementing Diffie-Hellman key exchange method on logical key hierarchy for secure broadcast transmission 实现了基于逻辑密钥层次的Diffie-Hellman密钥交换方法，实现了安全广播传输

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319374

H. Bodur, R. Kara

引用次数: 9

Thresholding neural network (TNN) with smooth sigmoid based shrinkage (SSBS) function for image de-noising 基于平滑s形收缩(SSBS)函数的阈值神经网络(TNN)用于图像去噪

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319358

Noorbakhsh Amiri Golilarz, H. Demirel

引用次数: 18

Fuzzy SVM for 3D facial expression classification using sequential forward feature selection 基于序列前向特征选择的模糊支持向量机三维面部表情分类

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319371

Payam Zarbakhsh, H. Demirel

引用次数: 9

Testing of algorithms for anomaly detection in Big data using apache spark 使用apache spark测试大数据异常检测算法

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319364

S. Lighari, D. Hussain

{"title":"Testing of algorithms for anomaly detection in Big data using apache spark","authors":"S. Lighari, D. Hussain","doi":"10.1109/CICN.2017.8319364","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319364","url":null,"abstract":"The constant upsurge in the size of networks and the data massively produced by them has made the data analysis very challenging principally the data attaining the boundaries of big data and it becomes even more difficult to detect intrusions in the case of big data. In this era, the experts find very limited tools and methods to analyze big data for security reasons. Either we need to device new tools or we can use existing tools in a novel manner to achieve the purpose of big data security analysis. In this paper, we are using apache spark a big data tool for analyzing the big dataset for anomaly detection. The anomaly detection is performed by using different machine learning algorithms like Logistic regression, Support vector machine, Naïve bayes, Decision trees, Random forest, and Kmeans. More or less all the aforementioned algorithms are capable to detect anomalies in big data but we need to know how efficiently each performs. The main objective of this investigation is to find the most efficient algorithm in the context of anomaly detection. In this regard, we set to compare their training time, prediction time, and the rate of accuracy. The analysis was implemented on Kddcup99 dataset. Although this dataset is of size in megabytes but it meets our purpose here for big data security analytics.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116275258","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: 13

Improved antlion optimization algorithm via tournament selection 通过比赛选择改进了antlion优化算法

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319385

Haydar Kiliç, U. Yuzgec

引用次数: 4

Image de-noising using un-decimated wavelet transform (UWT) with soft thresholding technique 基于软阈值技术的非抽取小波变换图像去噪方法

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319347

Noorbakhsh Amiri Golilarz, H. Demirel

引用次数: 22

Artificial neural networks for predicting the racing time of cross-country skiers from survey-based data 基于调查数据预测越野滑雪运动员比赛时间的人工神经网络

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319368

F. Abut, M. Akay, S. Daneshvar, D. Heil

{"title":"Artificial neural networks for predicting the racing time of cross-country skiers from survey-based data","authors":"F. Abut, M. Akay, S. Daneshvar, D. Heil","doi":"10.1109/CICN.2017.8319368","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319368","url":null,"abstract":"This paper proposes for the first time in literature to use machine learning methods and survey-based data for predicting the racing times of cross-country skiers. Particularly, three popular types of artificial neural networks (ANN) including Multilayer Feed-Forward Artificial Neural Network (MFANN), General Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) have been used for model development. The utilized dataset is made up of samples related to 370 cross-country skiers with heterogeneous properties, and includes physiological variables such as gender, age, height, weight and body mass index (BMI) along with a rich set of survey-based data. The results reveal that in general, the three ANN-based methods show comparable performance, and can be categorized as feasible tools to predict the racing time of cross-country skiers with acceptable error rates. Furthermore, significant advantages such as the non-exercise-based usage and the applicability to a broader range of cross-country skiers make the prediction models proposed in this study easy-to-use and more valuable.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125445402","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

Assessment of source data vulnerability to reproduction in Android applications 评估源数据在Android应用程序中的复制漏洞

2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2017-09-01 DOI: 10.1109/CICN.2017.8319369

M. Shafi, Muhammad Israr, Muhammad Sohail Khan, M. I. Khattak, Togeer Ali Syed

引用次数: 0