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2017 International Conference on Computer Science and Engineering (UBMK)最新文献

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An order based hybrid metaheuristic algorithm for solving optimization problems 一种基于顺序的混合元启发式算法求解优化问题
2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093477
O. Gokalp, Aybars Uğur
{"title":"An order based hybrid metaheuristic algorithm for solving optimization problems","authors":"O. Gokalp, Aybars Uğur","doi":"10.1109/UBMK.2017.8093477","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093477","url":null,"abstract":"In this work, a hybrid method that includes metaheuristic algorithms has been proposed for solving optimization problems. The proposed method was implemented as employing three metaheuristic algorithms which are Artificial Bee Colony, Differential Evolution and Particle Swarm Optimization in an order. The success of the developed method is presented by testing on 12 continuous optimization test functions which are widely used in the literature. The experimental results show that the proposed hybrid method gives better results than the individual algorithms that make up it.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122329545","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
An analysis of automatic gender detection by first-order configural relations 基于一阶构形关系的性别自动检测分析
2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093524
M. Atici
{"title":"An analysis of automatic gender detection by first-order configural relations","authors":"M. Atici","doi":"10.1109/UBMK.2017.8093524","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093524","url":null,"abstract":"Automatic gender detection from face images is a challenging problem. In the literature, different techniques have been applied so far on face images for gender detection. In contrast to these existing methods, we have analyzed the usage of first-order configural relations of the face to predict gender from images by using machine learning algorithms. In experiments on the dataset of Wikipedia profile pictures, 83% of general accuracy, 83.3% detection rate for male faces and 82.7% detection rate for female faces have been achieved by Logistic Regression. These results indicate that first-order configural relations are effective in automatic gender prediction from digital face images.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126895897","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
On the design strategies of diffusion layers and key schedule in lightweight block ciphers 轻量级分组密码中扩散层和密钥调度的设计策略
2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093436
M. K. Pehlivanoglu, S. Akleylek, M. T. Sakalli, N. Duru
{"title":"On the design strategies of diffusion layers and key schedule in lightweight block ciphers","authors":"M. K. Pehlivanoglu, S. Akleylek, M. T. Sakalli, N. Duru","doi":"10.1109/UBMK.2017.8093436","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093436","url":null,"abstract":"In recent years, lightweight cryptography has become essential especially for the resource-constrained devices to ensure data protection and security. The selection of suitable cryptographic algorithm which is directly linked to requirements of the system will have dynamically effect on following such metrics like performance of the device, hardware resource cost, the area, speed, efficiency, computation latency, communication bandwidth. This paper aims to provide a comprehensive survey on the lightweight block ciphers that were given in the literature and throw a light on the future research directions. Then, the focus is given to the diffusion layers in view of construction methods and efficiency. A new metric based on the order of the matrix to measure the security of diffusion layer consisting MDS matrix over a finite field extension is proposed and related experimental results are given. Key schedule of the lightweight block ciphers is analyzed.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124028673","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
Predicting financial market in big data: Deep learning 用大数据预测金融市场:深度学习
2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093449
Afan Hasan, O. Kalipsiz, S. Akyokuş
{"title":"Predicting financial market in big data: Deep learning","authors":"Afan Hasan, O. Kalipsiz, S. Akyokuş","doi":"10.1109/UBMK.2017.8093449","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093449","url":null,"abstract":"Deep Learning is appealing for learning from large amounts of unlabeled/unsupervised data, making it attractive for extracting meaningful representations and patterns from big data. Deep learning, by its simplest definition, is expressed as the application of machine learning methods to the big data. In this study, it was investigated how to apply hierarchical deep learning models for the problems in finance such as prediction and classification. The Design and pricing of securities, construction of portfolios, risk management and stock market forecasting are some of important prediction problems in finance. These kind of problems include large data sets with complex relationship among data and events. It is very difficult or sometimes impossible to represent these complex relationships in a full economic model. Deep learning methods, by representing complex relationships among data, allows the production of more useful results than standard methods in finance. In this study, we introduced and applied deep learning methods to stock market prediction problem and obtained successful results.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129019947","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: 15
Visual program application for supplier selection using fuzzy 应用模糊可视化程序进行供应商选择
2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093457
E. A. Tarim, Emel Kuruoğlu Kandemir
{"title":"Visual program application for supplier selection using fuzzy","authors":"E. A. Tarim, Emel Kuruoğlu Kandemir","doi":"10.1109/UBMK.2017.8093457","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093457","url":null,"abstract":"The choice of supplier in the supply chain network is a very important and quick issue for decision makers. When we work in line with these requirements, a multi-criteria decision support system focusing on supplier importance and ordering is being established. The fuzzy analytical hierarchy process (AHP) and fuzzy DEMATEL methods are used in the decision support system. With Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) method, the criterias required for supplier selection are listed according to the values they affect the system and affect the system. By using the fuzzy DEMATEL method, the criterion of the criterion is defined and the criterion which the decision maker should consider most is determined. Suppliers are ranked for supplier selection through comparison matrices using the fuzzy AHP method. All these methods are applied with the data obtained through the experts and their results are shown. At the last stage of the work, an interface is created by which all these methods can be used, and a visual program, which is a decision support system and a decision support system, is included.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084192","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
Sentiment analysis on Twitter data with semi-supervised Doc2Vec 基于半监督Doc2Vec的Twitter数据情感分析
2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093492
Metin Bilgin, İzzet Fatih Şentürk
{"title":"Sentiment analysis on Twitter data with semi-supervised Doc2Vec","authors":"Metin Bilgin, İzzet Fatih Şentürk","doi":"10.1109/UBMK.2017.8093492","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093492","url":null,"abstract":"Twitter is one of the most popular microblog sites developed in recent years. Feelings are analysed on the messages shared on Twitter so that users ideas on the products and companies can be determined. Sentiment analysis helps companies to improve their products and services based on the feedback obtained from the users through Twitter. In this study, it was aimed to perform sentiment analysis on Turkish and English Twitter messages using Doc2Vec. The Doc2Vec algorithm was run on Positive, Negative and Neutral tagged data using the Semi-Supervised learning method and the results were recorded.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130654021","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: 42
A K-medoids based clustering scheme with an application to document clustering 基于k -媒质的聚类方案及其在文档聚类中的应用
2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093409
Aytuğ Onan
{"title":"A K-medoids based clustering scheme with an application to document clustering","authors":"Aytuğ Onan","doi":"10.1109/UBMK.2017.8093409","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093409","url":null,"abstract":"Clustering is an important unsupervised data analysis technique, which divides data objects into clusters based on similarity. Clustering has been studied and applied in many different fields, including pattern recognition, data mining, decision science and statistics. Clustering algorithms can be mainly classified as hierarchical and partitional clustering approaches. Partitioning around medoids (PAM) is a partitional clustering algorithms, which is less sensitive to outliers, but greatly affected by the poor initialization of medoids. In this paper, we augment the randomized seeding technique to overcome problem of poor initialization of medoids in PAM algorithm. The proposed approach (PAM++) is compared with other partitional clustering algorithms, such as K-means and K-means++ on text document clustering benchmarks and evaluated in terms of F-measure. The results for experiments indicate that the randomized seeding can improve the performance of PAM algorithm on text document clustering.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128283158","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: 17
Engineering a BDI agent-based semantic e-barter system 设计一个基于BDI代理的语义电子易货系统
2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093474
Yunus Emre Cakmaz, Omer Faruk Alaca, Caglar Durmaz, Berkay Akdal, Baris Tekin Tezel, Moharram Challenger, G. Kardas
{"title":"Engineering a BDI agent-based semantic e-barter system","authors":"Yunus Emre Cakmaz, Omer Faruk Alaca, Caglar Durmaz, Berkay Akdal, Baris Tekin Tezel, Moharram Challenger, G. Kardas","doi":"10.1109/UBMK.2017.8093474","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093474","url":null,"abstract":"Barter system is an alternative commerce approach where customers meet at a marketplace in order to exchange their goods or services without currency. E-barter systems, also gain attention with the rise of e-commerce. Barterers search databases for goods and services they need. In this paper, the integration of ontology and agent systems is proposed as a solution for searching in diverse barter databases semantically and bargaining on behalf of the customers. Systematic design of the semantic e-barter system based on the Belief-Desire-Intention (BDI) agents is performed with using Prometheus methodology. The system is implemented on JACK intelligent agent platform. The implemented system is benefited from matchmaking reasoning by employing ontologies.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128424553","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: 4
Improving prediction performance using ensemble neural networks in textile sector 利用集成神经网络改进纺织行业预测性能
2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093487
P. Yıldırım, Derya Birant, Tuba Alpyildiz
{"title":"Improving prediction performance using ensemble neural networks in textile sector","authors":"P. Yıldırım, Derya Birant, Tuba Alpyildiz","doi":"10.1109/UBMK.2017.8093487","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093487","url":null,"abstract":"Neural network technique has been recently preferred in textile sector for the prediction task because the traditional mathematical and statistical methods can be inadequate to derive complex relations within textile datasets. Meanwhile ensemble learning has become a popular machine learning approach in recent years due to the high prediction performance it provides. Therefore, this study proposes an ensemble learning approach that combines neural networks with different parameter values (the number of hidden layers, learning rate and momentum coefficient) to improve prediction performance in textile sector. In the experimental studies, the proposed model was tested on ten different real-world textile datasets. The results show that ensemble neural networks usually achieve better prediction performance than an individual neural network in terms of correlation coefficient and relative absolute error measures.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134142320","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
A hybrid approach for improving the classification performance 一种提高分类性能的混合方法
2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093498
Ibrahim Kök, Murat Emre Davarci, S. Özdemir
{"title":"A hybrid approach for improving the classification performance","authors":"Ibrahim Kök, Murat Emre Davarci, S. Özdemir","doi":"10.1109/UBMK.2017.8093498","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093498","url":null,"abstract":"There are many factors that affect the performance of classification. The volume, size, type of data and classification methods are the most obvious factors. For the exact same data set, it is possible to achieve different classification performance values by using different classification methods Hence, the development of classification models that are more accurate and applicable to many areas for classification problems has a great importance. In this work, a hybrid classification model combining Naïve Bayes, Perceptron and KNN is proposed. In this model, a novel parameter called Decision Function is used. The proposed decision function aims to increase the classification success by considering the classification results of the three algorithms The performance evaluation results show that the proposed decision function significantly improves the classification success.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134195010","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
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