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2018 3rd International Conference on Computer Science and Engineering (UBMK)最新文献

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Neural Text Normalization for Turkish Social Media 土耳其社交媒体的神经文本规范化
2018 3rd International Conference on Computer Science and Engineering (UBMK) Pub Date : 2018-09-01 DOI: 10.1109/UBMK.2018.8566406
Sinan Göker, Burcu Can
{"title":"Neural Text Normalization for Turkish Social Media","authors":"Sinan Göker, Burcu Can","doi":"10.1109/UBMK.2018.8566406","DOIUrl":"https://doi.org/10.1109/UBMK.2018.8566406","url":null,"abstract":"Social media has become a rich data source for natural language processing tasks with its worldwide use; however, it is hard to process social media data due to its informal nature. Text normalization is the task of transforming the noisy text into its canonical form. It generally serves as a preprocessing task in other NLP tasks that are applied to noisy text. In this study, we apply two approaches for Turkish text normalization: Contextual Normalization approach using distributed representations of words and Sequence-to-Sequence Normalization approach using neural encoder-decoder models. As the approaches applied to Turkish and also other languages are mostly rule-based, additional rules are required to be added to the normalization model in order to detect new error patterns arising from the change of the language use in social media. In contrast to rule-based approaches, the proposed approaches provide the advantage of normalizing different error patterns that change over time by training with a new dataset and updating the normalization model. Therefore, the proposed methods provide a solution to language change dependency in social media by updating the normalization model without defining new rules.","PeriodicalId":293249,"journal":{"name":"2018 3rd International Conference on Computer Science and Engineering (UBMK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127030968","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
Animal Sound Classification Using A Convolutional Neural Network 基于卷积神经网络的动物声音分类
2018 3rd International Conference on Computer Science and Engineering (UBMK) Pub Date : 2018-09-01 DOI: 10.1109/UBMK.2018.8566449
Emre Sasmaz, F. Tek
{"title":"Animal Sound Classification Using A Convolutional Neural Network","authors":"Emre Sasmaz, F. Tek","doi":"10.1109/UBMK.2018.8566449","DOIUrl":"https://doi.org/10.1109/UBMK.2018.8566449","url":null,"abstract":"In this paper, we investigate the problem of animal sound classification using deep learning and propose a system based on convolutional neural network architecture. As the input to the network, sound files were preprocessed to extract Mel Frequency Cepstral Coefficients (MFCC) using LibROSA library. To train and test the system we have collected 875 animal sound samples from an online sound source site for 10 different animal types. We report classification confusion matrices and the results obtained by different gradient descent optimizers. The best accuracy of 75% was obtained by Nesterov-accelerated Adaptive Moment Estimation (Nadam).","PeriodicalId":293249,"journal":{"name":"2018 3rd International Conference on Computer Science and Engineering (UBMK)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115416771","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: 21
Performance Analysis of Artificial Neural Network Based Classfiers for Cyberbulling Detection 基于人工神经网络分类器的网络欺凌检测性能分析
2018 3rd International Conference on Computer Science and Engineering (UBMK) Pub Date : 2018-09-01 DOI: 10.1109/UBMK.2018.8566566
Eren Çürük, C. Aci, Esra Saraç Eşsiz
{"title":"Performance Analysis of Artificial Neural Network Based Classfiers for Cyberbulling Detection","authors":"Eren Çürük, C. Aci, Esra Saraç Eşsiz","doi":"10.1109/UBMK.2018.8566566","DOIUrl":"https://doi.org/10.1109/UBMK.2018.8566566","url":null,"abstract":"In this study, analyzes were performed to detection of cyberbullying by Artificial Neural Network (ANN) based classifiers. In contrast to the general classifiers used in the detection of cyberbullying in the literature, ANN basis classifiers as Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), Radial Basis Function (RBF) and Logistic Regression (LR) classifiers have been tested. The performances of the classifiers mentioned in the study were tested with comments from Formspring.me and Myspace media. N-gram model was used for the qualitative derivation and N = 1 was chosen because we wanted to measure the overall performance of the classifiers, also stop-words have been removed from features. In these studies, the F-measure value was taken over than 0.90. Given the accuracy and time performance of the classifiers, it has been observed that the most appropriate classifier for cyberbullying detection is the SGD classifier.","PeriodicalId":293249,"journal":{"name":"2018 3rd International Conference on Computer Science and Engineering (UBMK)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116313965","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
Predicting IMDb Ratings of Pre-release Movies with Factorization Machines Using Social Media 使用社交媒体的分解机器预测预发行电影的IMDb评级
2018 3rd International Conference on Computer Science and Engineering (UBMK) Pub Date : 2018-09-01 DOI: 10.1109/UBMK.2018.8566661
Beyza Cizmeci, Ş. Öğüdücü
{"title":"Predicting IMDb Ratings of Pre-release Movies with Factorization Machines Using Social Media","authors":"Beyza Cizmeci, Ş. Öğüdücü","doi":"10.1109/UBMK.2018.8566661","DOIUrl":"https://doi.org/10.1109/UBMK.2018.8566661","url":null,"abstract":"The film industry has always been a very important sector in the global market. Therefore, it is very important to maximize the profit by predicting the movie success before its release. Although several studies have been done in this field, it is still needed to improve the prediction performance and collect more data. This study aims to explore the use of Factorization Machines approach in order to predict movie success by predicting IMDb ratings for newly released movies using social media data and compare it to current studies. Also, a framework has been developed in order to gather the movie data from different sources including social media. Comparison of the Factorization Machines to the current models shows that there are promising results.","PeriodicalId":293249,"journal":{"name":"2018 3rd International Conference on Computer Science and Engineering (UBMK)","volume":"461 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115738196","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: 12
Stock Price Forecast using Wavelet Transformations in Multiple Time Windows and Neural Networks 基于多时间窗和神经网络的小波变换股票价格预测
2018 3rd International Conference on Computer Science and Engineering (UBMK) Pub Date : 2018-09-01 DOI: 10.1109/UBMK.2018.8566614
Ajla Kulaglic, B. Üstündağ
{"title":"Stock Price Forecast using Wavelet Transformations in Multiple Time Windows and Neural Networks","authors":"Ajla Kulaglic, B. Üstündağ","doi":"10.1109/UBMK.2018.8566614","DOIUrl":"https://doi.org/10.1109/UBMK.2018.8566614","url":null,"abstract":"This paper presents a highly reliable and accurate stock-price prediction model. We aim to anticipate the stock price with respect to multiple patterns in different time scales. The stock price time-series are decomposed, using discrete wavelet transform (DWT), into temporal resolution of varying scales. Then, each subseries is used to predict the stock price using two types of neural network (NN) models with one and two hidden layers. Results show that having multiple time windows in input datasets together with DWT decrease the RMSE of NN models below 10%.","PeriodicalId":293249,"journal":{"name":"2018 3rd International Conference on Computer Science and Engineering (UBMK)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114466781","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: 5
Towards Developing Fuzzy Neighborhood Based Clustering Algorithms for High Performance Distributed Memory Computing Environments 高性能分布式内存计算环境中基于模糊邻域的聚类算法研究
2018 3rd International Conference on Computer Science and Engineering (UBMK) Pub Date : 2018-09-01 DOI: 10.1109/UBMK.2018.8566594
C. Atilgan, Baris Tekin Tezel, E. Nasibov
{"title":"Towards Developing Fuzzy Neighborhood Based Clustering Algorithms for High Performance Distributed Memory Computing Environments","authors":"C. Atilgan, Baris Tekin Tezel, E. Nasibov","doi":"10.1109/UBMK.2018.8566594","DOIUrl":"https://doi.org/10.1109/UBMK.2018.8566594","url":null,"abstract":"Fuzzy neighborhood-based clustering algorithms overcome the parameter selection problem of classical neighborhood based clustering algorithms and offer fully unsupervised, i.e., parameter free clustering. On the other hand, due to the inherent fuzzy-calculation-overhead, they demand higher processing time and memory compared to classical clustering algorithms. In some recent studies, these fuzzy algorithms have been improved, especially in terms of speed, such that they became applicable to large data sets. Nonetheless, they need to be adapted to multi-computer systems in order to be used in today's big data applications. The aim of this study is developing fuzzy neighborhood-based clustering algorithms which are designed to run on high performance distributed memory computing environments and revealing their effectiveness by testing them in a real big-data application.","PeriodicalId":293249,"journal":{"name":"2018 3rd International Conference on Computer Science and Engineering (UBMK)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128588296","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
Real Time Data Analytics Architecture for ECG 心电图实时数据分析体系结构
2018 3rd International Conference on Computer Science and Engineering (UBMK) Pub Date : 2018-09-01 DOI: 10.1109/UBMK.2018.8566300
Nur Banu Oğur, C. Çeken
{"title":"Real Time Data Analytics Architecture for ECG","authors":"Nur Banu Oğur, C. Çeken","doi":"10.1109/UBMK.2018.8566300","DOIUrl":"https://doi.org/10.1109/UBMK.2018.8566300","url":null,"abstract":"The concept of big data emerging from the expansion of data volumes on the Internet has begun to talk about its name in medicine as well as in many fields of life. Big data analytics, which also require the use of machine learning methods, enable the use of decision-making processes by extracting useful information from large and complex data sets. Implementing machine learning strategies on data sets within big data is an expensive process because it requires extensive use of resources such as CPU and memory. For this reason, platforms specially developed for big data analytics are designed. One of these systems, Apache Spark, has built-in machine learning algorithms ranging from regression to classification and clustering, and is a very powerful engine for real time stream processing. In this study, the first results of a system that provides real-time disease diagnosis from ECG data using Logistic Regression are presented. The first findings obtained show that this architecture, built with Apache Kafka and Apache Spark, can be an important design option in real time processing of ECG data.","PeriodicalId":293249,"journal":{"name":"2018 3rd International Conference on Computer Science and Engineering (UBMK)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130542169","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
Analysis and Comparison of Opcode-based Malware Detection Approaches 基于操作码的恶意软件检测方法分析与比较
2018 3rd International Conference on Computer Science and Engineering (UBMK) Pub Date : 2018-09-01 DOI: 10.1109/UBMK.2018.8566292
Mert Nar, A. Kakisim, Necmettin Çarkaci, Melek Nurten Yavuz, I. Sogukpinar
{"title":"Analysis and Comparison of Opcode-based Malware Detection Approaches","authors":"Mert Nar, A. Kakisim, Necmettin Çarkaci, Melek Nurten Yavuz, I. Sogukpinar","doi":"10.1109/UBMK.2018.8566292","DOIUrl":"https://doi.org/10.1109/UBMK.2018.8566292","url":null,"abstract":"Malicious software (Malwares) become major threats for digital assets in the digital environment. Traditional malware detection systems use the signatures of the malware executables to detect them. However, the complexity and diversity of malwares increases day by day with metamorphic ones that quickly change its structure and signature. Therefore, most of the researches have focused on the detection of these kinds of malwares. In this work, five different malware detection approaches have been implemented and tested on real and synthetic malware and benign samples. We have collected a new malware data set including 6857 benign and 8701 malicious samples. Experiments have shown that the real malware executables decrease the performance of the methods.","PeriodicalId":293249,"journal":{"name":"2018 3rd International Conference on Computer Science and Engineering (UBMK)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128828790","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
Fusion of Multi-focus Image by Blocks Optimal Positions 基于分块最优位置的多焦点图像融合
2018 3rd International Conference on Computer Science and Engineering (UBMK) Pub Date : 2018-09-01 DOI: 10.1109/UBMK.2018.8566416
Ahmet Nusret Toprak, V. Aslantaş
{"title":"Fusion of Multi-focus Image by Blocks Optimal Positions","authors":"Ahmet Nusret Toprak, V. Aslantaş","doi":"10.1109/UBMK.2018.8566416","DOIUrl":"https://doi.org/10.1109/UBMK.2018.8566416","url":null,"abstract":"Digital imaging systems suffer from limited depth of field due to the nature of the optics involved. As a consequence, an object will appear in focus when it is on the focus plane and will appear blurred as it deviates from the focus plane. Therefore, in a single image only part of the plane within the depth of field can be sharp, while the rest areas are blurred. To overcome this problem, a series of multi-focus images are taken by gradually moving the focal plane. Then, all the in-focus regions are merged together through a process called multi-focus image fusion to generate an all-in-focus image of the scene. In this paper, a novel multi-focus image fusion method based on placing the blocks in the optimal positions considering sharp regions of source images. The experimental results show that the proposed method can fuse the multi-focus source images and extend the depth of field efficiently.","PeriodicalId":293249,"journal":{"name":"2018 3rd International Conference on Computer Science and Engineering (UBMK)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122150079","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
UBMK 2018 Title Page UBMK 2018标题页
2018 3rd International Conference on Computer Science and Engineering (UBMK) Pub Date : 2018-09-01 DOI: 10.1109/ubmk.2018.8566447
{"title":"UBMK 2018 Title Page","authors":"","doi":"10.1109/ubmk.2018.8566447","DOIUrl":"https://doi.org/10.1109/ubmk.2018.8566447","url":null,"abstract":"","PeriodicalId":293249,"journal":{"name":"2018 3rd International Conference on Computer Science and Engineering (UBMK)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122600201","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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