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

An effective method determining the initial cluster centers for K-means for clustering gene expression data 一种确定基因表达数据k均值初始聚类中心的有效方法

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093520

D. Tanir, F. Nuriyeva

引用次数: 1

Open source slurm computer cluster system design and a sample application 开源slurm计算机集群系统设计及示例应用程序

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093424

N. Azginoglu, M. Atasever, Z. Aydın, Mete Celik, H. Erbay

引用次数: 2

PageRank based semantic similarity measure on a graph based Turkish WordNet 基于PageRank的基于图的土耳其语WordNet语义相似度度量

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093438

C. Tulu, Umut Orhan

引用次数: 2

Voice command controlled mobile vehicle application 语音命令控制移动车辆的应用程序

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093565

A. Iskender, Hakan Üçgün, Uğur Yüzgeç, M. Kesler

引用次数: 7

A gender recognition system from facial images using SURF based BoW method 基于SURF的人脸图像性别识别系统

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093405

Bahar Hatipoglu, Cemal Kose

引用次数: 5

Comparison of heuristic algorithms with performance metrics on multimodal benchmark functions 启发式算法与多模态基准函数性能指标的比较

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093476

Ayşe Baştuğ, C. Karakuzu

{"title":"Comparison of heuristic algorithms with performance metrics on multimodal benchmark functions","authors":"Ayşe Baştuğ, C. Karakuzu","doi":"10.1109/UBMK.2017.8093476","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093476","url":null,"abstract":"The solution of difficult problems can be realized in shorter time with heuristic algorithms. There are many heuristic algorithms. In this study, artificial bee colony (ABC), biogeography based optimization (BBO), cuckoo bird search algorithm (CSO), differential evolution (DE), imperialist competitive algorithm (ICA) and particle swarm algorithm (PSO) have been chosen due to reasons such as the widespread use in the literature and the large number of open source code applications to use it widely in the literature and to have a lot of open source code applications. Each of these preferred algorithms has been run 30 times in a 10-dimensional search space with the same initial positions and conditions to find the global minimum point on the 5 benchmark function, which is also frequently used in the scientific world. The performance of the algorithms based on the results obtained from the runs has been determined by the best cost, worst cost, accuracy, stability, time and standard deviation performance metrics. The performance scores of the algorithms are evaluated based on the cumulative average ranking value generated from the results of these performance metrics.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"143 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":"122676485","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

Deep learning based traffic direction sign detection and determining driving style 基于深度学习的交通方向标志检测和驾驶风格确定

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093453

Mucahit Karaduman, H. Eren

{"title":"Deep learning based traffic direction sign detection and determining driving style","authors":"Mucahit Karaduman, H. Eren","doi":"10.1109/UBMK.2017.8093453","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093453","url":null,"abstract":"Intelligent automobiles and advanced driver assistance systems (ADAS) are some of the major technological developments that affect human daily life. Today, many studies are being generated to develop state of the art transportation systems. The general objective in these studies is to cope with negative effects of traffic. In this work, our aim is to contribute to the development of ADAS by determining driver behavior and traffic direction sign detection. The data employed are acquired by smartphone sensors, which are accelerometer, gyroscope, GPS, and camera, while the subject car moves between two specific points. The proposed method consists of two simultaneously running algorithms. The first one determines driver maneuvers, and the second one is the deep learning based algorithm that detects traffic direction sign using Convolution Neural Network (CNN). Here, the results of these two simultaneously running algorithms are assessed, and driving type is determined. GPS data is used for synchronization. Consequently, it is determined whether riding style is safe or aggressive, involving in traffic direction sign detection.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"2 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":"129480286","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: 10

Sentiment analysis on the hotel reviews in the Kazakh language 哈萨克语酒店评论的情感分析

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093531

B. Yergesh, G. Bekmanova, A. Sharipbay

引用次数: 10

Implicit feature detection by ontology aided feature-based opinion summarization 基于本体的隐式特征检测方法

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093501

Derviş Kanbur, M. Aktaş

{"title":"Implicit feature detection by ontology aided feature-based opinion summarization","authors":"Derviş Kanbur, M. Aktaş","doi":"10.1109/UBMK.2017.8093501","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093501","url":null,"abstract":"Thanks to e-commerce in an increasingly developing structure the number of customer feedbacks grows rapidly. Due to the great increase in the number of e-commerce enterprises and customers, it becomes difficult for a potential customer to read these feedbacks while decision-making. It becomes almost impossible for the producer to monitor these feedbacks, as well. Product feature extraction from customer reviews is an important sub-research area in opinion mining. The extracted features help to assess the opinions written by customers who have purchased specific products and they provide opinions of customers regarding their positive/negative experiences. Because most of customer reviews are asyntactic plain texts, methods should be developed for extraction of implicit and explicit product features expressed in customer reviews and comments. In this research, we aim at developing a system which reviews and summarizes feedbacks given in Turkish language. Our study differs from others in that it combines synonym word/word groups, that it uses ontology including product features and that it combines Turkish abbreviations/loan words and it increases the success in extraction of product features. Our test results using feedbacks of particular products on the web indicates the impact of our study.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"23 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":"127789947","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

Solution to the maximum independent set problem with genetic algorithm 用遗传算法求解最大独立集问题

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI: 10.1109/UBMK.2017.8093516

M. Gencer, M. Berberler

{"title":"Solution to the maximum independent set problem with genetic algorithm","authors":"M. Gencer, M. Berberler","doi":"10.1109/UBMK.2017.8093516","DOIUrl":"https://doi.org/10.1109/UBMK.2017.8093516","url":null,"abstract":"In this study, from the problems of graph theory to the Maximum Independent set problem belonging to NP-Hard complexity class, were searched solutions close to optimal quality by using genetic algorithms from artificial intelligence techniques. Unlike most of the studies in the literature, the initial population of the genetic algorithm has not been determined at random and has been created with various heuristic approaches. In the heuristic approaches discussed, the vertex order and sum of neighborhood vertex order sequences techniques are used and the performance ratios of both are compared. These two techniques were found to be effective against different problems, and two algorithms were combined to form a much more successful initial population. It was found experimentally on the small size problems where the merging process is done, and the big size problems were obtained. In the next step, problems which have different edge densities and with large peak numbers for computational experiments were selected from randomly generated problems used in a literature study, and these problems were solved by genetic algorithm generated by intuitive approach of the initial population, and performance ratios and resolution times were investigated.","PeriodicalId":201903,"journal":{"name":"2017 International Conference on Computer Science and Engineering (UBMK)","volume":"76 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":"115857075","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