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2017 25th Signal Processing and Communications Applications Conference (SIU)最新文献

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Turkish cuisine: A benchmark dataset with Turkish meals for food recognition 土耳其美食:一个用于食物识别的土耳其餐基准数据集
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960494
C. Gungor, Fatih Baltaci, Aykut Erdem, Erkut Erdem
{"title":"Turkish cuisine: A benchmark dataset with Turkish meals for food recognition","authors":"C. Gungor, Fatih Baltaci, Aykut Erdem, Erkut Erdem","doi":"10.1109/SIU.2017.7960494","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960494","url":null,"abstract":"Food recognition in still images is a problem that has been recently introduced in computer vision. The benchmark data sets used in training and evaluation of food recognition methods contain sample images of popular foods from the globe. However, when they are examined thoroughly, it can be observed that very few of them are Turkish dishes. In this study, we first carry out a data collection process for Turkish dishes and construct a new dataset named \"TurkishFoods-15\" containing 500 images in each food class. In addition, we introduce a novel food recognition approach that depends on fine-tuning Google Inception v3 deep neural network model based on transfer lear­ning. For this purpose, our Turkish cuisine dataset was combined with the widely used Food-101 dataset from the literature and the performance analysis of the developed deep learning-based approach is carried out on this combined dataset containing 113 food classes. Our results show that the recognition of Turkish dishes can be achieved with certain success even though it does not have certain difficulties.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128335336","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: 19
Error performance analysis of multi-hop space shift keying with transmit antenna selection 发射天线选择下多跳空间移位键控的误差性能分析
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960381
Ferhat Yarkin, I. Altunbas, E. Başar
{"title":"Error performance analysis of multi-hop space shift keying with transmit antenna selection","authors":"Ferhat Yarkin, I. Altunbas, E. Başar","doi":"10.1109/SIU.2017.7960381","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960381","url":null,"abstract":"In this paper, symbol error probability analysis is performed for multihop space shift keying (SSK) system with transmit antenna selection (TAS). In this scheme, SSK is applied on the selected antennas at each hop. It assumed that there is no line of sight transmission between the source and the destination and each receiving terminal decodes and forwards the received signal from the previous terminal and maps the decided information bits to the selected antennas according to SSK technique. For the proposed system, approximate and asymptotic symbol error probabilities are calculated and the theoretical results are verified by computer simulations. It is shown that the proposed system provides better error performance than the multihop SSK system without TAS, and it is also indicated that the proposed system outperforms conventional M-QAM system for especially high data rates and sufficient number of receive antennas at the destination.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"528 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114001744","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
FPAA implementation of CNN based chaos generator 基于CNN的FPAA混沌发生器实现
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960281
Enis Günay, Kenan Altun
{"title":"FPAA implementation of CNN based chaos generator","authors":"Enis Günay, Kenan Altun","doi":"10.1109/SIU.2017.7960281","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960281","url":null,"abstract":"Recent times Field Programmable Analog Arrays (FPAAs) attracts attention among the reprogrammable and reconfigurable hardwares because of their flexible structure and analog outputs. Thus, a Celluar Neural Network (CNN) based chaos generator, which was realized by using discrete circuit elements, is reactualized in FPAA platform. Experimental results are compared with numerical results and the discrete ones.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133194599","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
Call center text mining approach 呼叫中心文本挖掘方法
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960138
Ibrahim Onuralp Yigit, A. F. Ates, Mehmet Güvercin, H. Ferhatosmanoğlu, B. Gedik
{"title":"Call center text mining approach","authors":"Ibrahim Onuralp Yigit, A. F. Ates, Mehmet Güvercin, H. Ferhatosmanoğlu, B. Gedik","doi":"10.1109/SIU.2017.7960138","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960138","url":null,"abstract":"Nowadays, the ability to convert call records from voice to text makes it possible to apply text mining methods to extract information from calls. In this study, it is aimed not only to evaluate the sentiment (positive/negative) of the calls in general, but also to measure the customer satisfaction and representative's performance by using call record texts. New features have been extracted from texts using text mining methods. Using the features extracted, prediction models were developed to evaluate the contents of call records by classification and regression methods. As a result of this study, it is planned to utilize the prediction models developed in Turk Telekom's call centers.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130539639","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: 2
Movie rating prediction using ensemble learning and mixed type attributes 基于集成学习和混合类型属性的电影评分预测
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960604
Aysegül Özkaya Eren, M. Sert
{"title":"Movie rating prediction using ensemble learning and mixed type attributes","authors":"Aysegül Özkaya Eren, M. Sert","doi":"10.1109/SIU.2017.7960604","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960604","url":null,"abstract":"Nowadays, audience can easily share their rating about a movie on the internet. Predicting movie user ratings automatically is specifically valuable for prediction box office gross in the cinema sector. As a result, movie rating prediction has been a popular application area for machine learning researchers. Although most of the recent studies consider using mostly numerical features in analyses, handling nominal features is still an open problem. In this study, we propose a method for predicting movie user ratings based on numerical and nominal feature collaboration and ensemble learning. The effectiveness and the performance of the proposed approach is validated on Internet Movie Database (IMDb) performance dataset by comparing with different methods in the literature. Results show that, using mixed data types along with the ensemble learning improves the movie rating prediction.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125817459","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
Video classification based on ConvNet collaboration and feature selection 基于卷积神经网络协作和特征选择的视频分类
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960515
Emel Boyaci, M. Sert
{"title":"Video classification based on ConvNet collaboration and feature selection","authors":"Emel Boyaci, M. Sert","doi":"10.1109/SIU.2017.7960515","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960515","url":null,"abstract":"Today, video data, as a powerful multimedia component, is accompanied by some problems with increasing usage in communication, health, education, and social media in particular. Classification and detection of concepts in video data by automatic methods are some of these challenging problems. In this study, we propose a video classification system, which incorporates deep convolutional neural networks (CNNs) by leveraging feature selection and data fusion techniques to improve the accuracy of the classification. Principal Component Analysis (PCA) as a feature selection method and Discriminant Correlation Analysis (DCA) technique, which incorporates class associations into the correlation analysis of feature sets for data fusion, are applied to the problem at the feature level. Support Vector Machines (SVMs) have been trained with new feature vectors obtained from different deep convolutional neural networks by feature selection and data fusion methods. The proposed method is tested for 38 concepts on TRECVID 2013 SIN video task dataset and the results are evaluated. Our results show that the classification accuracy is improved by 4% with an accuracy of 50.27% when the proposed data fusion and feature selection techniques are used.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124789882","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
Forecasting the annual electricity consumption of Turkey using a hybrid model 使用混合模型预测土耳其的年用电量
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960283
Gokhan Aydogdu, O. Yıldız
{"title":"Forecasting the annual electricity consumption of Turkey using a hybrid model","authors":"Gokhan Aydogdu, O. Yıldız","doi":"10.1109/SIU.2017.7960283","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960283","url":null,"abstract":"In this study, we implemented traditional, artificial intelligence and hybrid methods to predict electricity consumption of Turkey. While traditional method is multiple linear regression and artificial intelligence method is artificial neural network, hybrid method is a new method combining these two methods. The data used in the study was provided from Turkish Electricity Transmission Company, Turkish Electricity Distribution Company, Ministry of Energy and Natural Resources and Turkish Statistical Institute which are the public institutions in Turkey. The performance was evaluated using mean absolute percentage error (MAPE), mean square error (MSE) and root mean square error (RMSE). The test of the proposed hybrid model resulted in an average absolute forecast error of 2.25 percent.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125583954","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
Optimum number of antennas for energy efficiency versus user location in massive MIMO systems 大规模MIMO系统中能源效率与用户位置的最佳天线数量
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960155
Mohammed A. Abuibaid, S. Çolak
{"title":"Optimum number of antennas for energy efficiency versus user location in massive MIMO systems","authors":"Mohammed A. Abuibaid, S. Çolak","doi":"10.1109/SIU.2017.7960155","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960155","url":null,"abstract":"The large scale (massive) MIMO system is one of the candidate technologies for use in 5G due to its high energy efficiency (EE) and offered high data rate. In this paper, energy efficiency and proposed data rate of the large scale MIMO system in downlink mode are investigated according to the traffic load in the cellular network. In addition, three different user distribution models have been proposed for understanding the effect of users' position within the cell on EE. Under equal traffic load in all cells, simulations show that BS requires fewer antennas if most of the users are close to the cell center. As a result of simulations, it has been shown that the energy efficiency can be increased by changing the number of antennas that BS will use depending on the traffic load on the cell and user locations.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130446075","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
Stem-based PoS tagging for agglutinative languages 基于词干的黏着性语言词性标注
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960386
Necva Bölücü, Burcu Can
{"title":"Stem-based PoS tagging for agglutinative languages","authors":"Necva Bölücü, Burcu Can","doi":"10.1109/SIU.2017.7960386","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960386","url":null,"abstract":"Words are made up of morphemes being glued together in agglutinative languages. This makes it difficult to perform part-of-speech tagging for these languages due to sparsity. In this paper, we present two Hidden Markov Model based Bayesian PoS tagging models for agglutinative languages. Our first model is word-based and the second model is stem-based where the stems of the words are obtained from other two unsupervised stemmers: HPS stemmer and Morfessor FlatCat. The results show that stemming improves the accuracy in PoS tagging. We present the results for Turkish as an agglutinative language and English as a morphologically poor language.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"1966 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129719608","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: 6
Emotion recognition from EEG signals through one electrode device 通过单电极装置对脑电图信号进行情绪识别
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960390
M. Sarikaya, G. Ince
{"title":"Emotion recognition from EEG signals through one electrode device","authors":"M. Sarikaya, G. Ince","doi":"10.1109/SIU.2017.7960390","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960390","url":null,"abstract":"In recent years, researchers have concentrated on the development of ElectroEncephaloGraphy (EEG) based Brain-Computer Interfaces (BCI) to increase the quality of life using medical applications. BCIs can also be used for marketing, gaming, and entertainment to provide users with a more personalized experience. Both medical and non-medical applications require the ability to interpret the user's multimedia-induced perception and emotional experience. This paper presents a novel method to detect human emotion with a single-channel commercial BCI device. The proposed EEG-based emotion recognition system was tested on human test subjects using a deep learning neural network and an accuracy above 87% was achieved.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117238530","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
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