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

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Sentiment analysis on microblog data based on word embedding and fusion techniques 基于词嵌入和融合技术的微博数据情感分析
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960519
Ahmet Hayran, M. Sert
{"title":"Sentiment analysis on microblog data based on word embedding and fusion techniques","authors":"Ahmet Hayran, M. Sert","doi":"10.1109/SIU.2017.7960519","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960519","url":null,"abstract":"People often use social platforms to state their views and desires. Twitter is one of the most popular microblog service used for this purpose. In this study, we propose a new approach for automatically classifying the sentiment of microblog messages. The proposed approach is based on utilizing robust feature representation and fusion. We make use of word embedding technique as the feature representation and the Support Vector Machine as the classifier. In our approach, we first calculate statistical measures from word embedding representations and fuse them using different combinations. Learning is performed using these fused features and tested on the Turkish tweet dataset. Results show that the proposed approach significantly reduces the dimension of tweet representation and enhances sentiment classification accuracy. Best performance is attained by the proposed Dvot fusion technique with an accuracy of %80.05.","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":"129045797","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: 16
Stock market direction prediction using deep neural networks 基于深度神经网络的股票市场方向预测
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960512
Hakan Gunduz, Z. Cataltepe, Y. Yaslan
{"title":"Stock market direction prediction using deep neural networks","authors":"Hakan Gunduz, Z. Cataltepe, Y. Yaslan","doi":"10.1109/SIU.2017.7960512","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960512","url":null,"abstract":"In this study, the daily movement directions of three frequently traded stocks (GARAN, THYAO and ISCTR) in Borsa Istanbul were predicted using deep neural networks. Technical indicators obtained from individual stock prices and dollar-gold prices were used as features in the prediction. Class labels indicating the movement direction were found using daily close prices of the stocks and they were aligned with the feature vectors. In order to perform the prediction process, the type of deep neural network, Convolutional Neural Network, was trained and the performance of the classification was evaluated by the accuracy and F-measure metrics. In the experiments performed, using both price and dollar-gold features, the movement directions in GARAN, THYAO and ISCTR stocks were predicted with the accuracy rates of 0.61, 0.578 and 0.574 respectively. Compared to using the price based features only, the use of dollar-gold features improved the classification performance.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"56 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":"129272854","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
Comparison of feature selection methods for sentiment analysis on Turkish Twitter data 土耳其Twitter数据情感分析特征选择方法比较
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960388
Tuba Parlar, E. Saraç, S. A. Özel
{"title":"Comparison of feature selection methods for sentiment analysis on Turkish Twitter data","authors":"Tuba Parlar, E. Saraç, S. A. Özel","doi":"10.1109/SIU.2017.7960388","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960388","url":null,"abstract":"The Internet and social media provide a major source of information about people's opinions. Due to the rapidly growing number of online documents, it becomes both time-consuming and hard task to obtain and analyze the desired opinionated information. Sentiment analysis is the classification of sentiments expressed in documents. To improve classification perfromance feature selection methods which help to identify the most valuable features are generally applied. In this paper, we compare the performance of four feature selection methods namely Chi-square, Information Gain, Query Expansion Ranking, and Ant Colony Optimization using Maximum Entropi Modeling classification algorithm over Turkish Twitter dataset. Therefore, the effects of feature selection methods over the performance of sentiment analysis of Turkish Twitter data are evaluated. Experimental results show that Query Expansion Ranking and Ant Colony Optimization methods outperform other traditional feature selection methods for sentiment analysis.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"222 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":"126120307","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: 7
Improvement of signal to noise ratio in Fiber Bragg Grating based sensor systems 光纤光栅传感器系统信噪比的提高
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960227
Murat Yücel, M. Torun, M. Burunkaya
{"title":"Improvement of signal to noise ratio in Fiber Bragg Grating based sensor systems","authors":"Murat Yücel, M. Torun, M. Burunkaya","doi":"10.1109/SIU.2017.7960227","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960227","url":null,"abstract":"Fiber Bragg Gratings (FBG) draw considerable interests for their specifications as low sizes, easy mounting, remote sensing, sufficient to sense more than one or more parameters in the same line and low cost when used several. The main specification of FBG is that they reflect measured parameter directly and linearly in the centre Bragg wavelength. In this study, it is shown that noisy FBG band can be boosted high ranges of signal to noise ratio (SNR) with signal processing techniques. Maximum finding, centroid and Gauss fitting techniques are examined with this purpose. In experimental studies, high linearity is observed among measured parameters and wavelength.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"30 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":"121025356","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
Design and performance analysis of information centric network for Internet of Things 面向物联网的信息中心网络设计与性能分析
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960565
Y. Yengi, S. Khan, K. Küçük
{"title":"Design and performance analysis of information centric network for Internet of Things","authors":"Y. Yengi, S. Khan, K. Küçük","doi":"10.1109/SIU.2017.7960565","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960565","url":null,"abstract":"Billions of objects connect to the internet by using Internet of Things (IoT). Current trends of IoT are developing protocols, platforms make objects accessible across domains. The purpose of these studies is to combine all IoT devices on main host systems. However, the host systems have a problem about the mismatch between devices and the host. To solve this problem we have designed the Information Centric Network (ICN) based on IoT platform in this paper. ICN provides service delivery for support mobility, efficient and scalability. In addition to this, we have discussed the requirements that meet ICN based IoT solutions. Also, we have presented the analysis of results with regards to energy requirements for IoT applications under varying cache size.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"35 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":"124072336","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
Could we create a training set for image captioning using automatic translation? 我们能否创建一个使用自动翻译的图像字幕训练集?
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960638
Nermin Samet, Samet Hicsonmez, P. D. Sahin, Emre Akbas
{"title":"Could we create a training set for image captioning using automatic translation?","authors":"Nermin Samet, Samet Hicsonmez, P. D. Sahin, Emre Akbas","doi":"10.1109/SIU.2017.7960638","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960638","url":null,"abstract":"Automatic image captioning has received increasing attention in recent years. Although there are many English datasets developed for this problem, there is only one Turkish dataset and it is very small compared to its English counterparts. Creating a new dataset for image captioning is a very costly and time consuming task. This work is a first step towards transferring the available, large English datasets into Turkish. We translated English captioning datasets into Turkish by using an automated translation tool and we trained an image captioning model on the automatically obtained Turkish captions. Our experiments show that this model yields the best performance so far on Turkish captioning.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"36 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":"123691457","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
Spectrum handoff process with aging solution for secondary users in priority based cognitive networks 基于优先级的认知网络中二次用户频谱切换的老化解决方案
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960158
M. E. Bayrakdar, A. Çalhan
{"title":"Spectrum handoff process with aging solution for secondary users in priority based cognitive networks","authors":"M. E. Bayrakdar, A. Çalhan","doi":"10.1109/SIU.2017.7960158","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960158","url":null,"abstract":"Spectrum handoff is a process performed by secondary users in cognitive radio networks. To accomplish this, the base station that coordinates the secondary users decides which user will make the spectrum handoff process according to certain criteria. The priority classes of the secondary users are at the top of these criteria. In traditional priority based queues, packets with higher priority are transmitted first, and lower priority packets waits for the other higher-priority packet transmissions to finish. In this study, priority data traffic is used in queue structure in order to meet the different requirements of secondary users. In addition, we have added the aging solution to the spectrum handoff mechanism in order to shorten the long wait times of low priority packets. The aging solution is defined as the increase of the priority of lower priority packets waiting for too long in the queue over certain waiting periods. Analytical and simulation models of the aging solution have been designed in order to prove validation. It has been shown that the total number of spectrum handoff on the network for different priority packets and different loads is reduced significantly.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"41 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":"131467469","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
RSS based localization of an emitter using a single mini UAV 使用单个微型无人机的基于RSS的发射器定位
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960239
Seçkin Uluskan, Mustafa Gokce, T. Filik
{"title":"RSS based localization of an emitter using a single mini UAV","authors":"Seçkin Uluskan, Mustafa Gokce, T. Filik","doi":"10.1109/SIU.2017.7960239","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960239","url":null,"abstract":"In this study, it is aimed to find the position of a signal emitter by the means of a mini unmanned aerial vehicle (mUAV) with a sensor node that can record the received signal strength (RSS) and the global positioning system (GPS) data. The RSS and GPS data are instantaneously transferred to the central node in order to establish a real-time positioning and tracking system. A cumulative maximum likelihood solution has been proposed to best estimate the location of the emitter. By the means of the experiments using the real data, the localization system performance is shown to be in line with Cramer Rao Lower Bound (CRLB).","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"18 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":"126478610","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: 7
Detection of knee abnormality from surface EMG signals by artificial neural networks 基于人工神经网络的膝关节表面肌电信号异常检测
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960160
O. Erkaymaz, Irem Senyer, Rukiye Uzun
{"title":"Detection of knee abnormality from surface EMG signals by artificial neural networks","authors":"O. Erkaymaz, Irem Senyer, Rukiye Uzun","doi":"10.1109/SIU.2017.7960160","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960160","url":null,"abstract":"Using surface EMG signals is a non-invasive measurement method obtained as a result of muscle activity. In this study, surface EMG data have been used for classification, taken from healthy individuals or individuals with knee abnormalities in gait position. For this purpose, first feature extraction was realized by discrete wavelet transform from the data. Then, extracted features were classified by artificial neural network approach that is widely used in the literature. In classification process, artificial neural networks were trained by using simple cross-validation algorithm. During training the optimal network topology was determined. The highest classification performance of proposed model was obtained in rate fiction 80%–20% and 70%–30% of data set. Our results revealed that proposed artificial neural network model is able to detect knee abnormality from surface EMG signals.","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":"130480935","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
Producing the location information with the Kalman filter on the GPS data for autonomous vehicles 对自动驾驶汽车的GPS数据进行卡尔曼滤波生成位置信息
2017 25th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2017-05-15 DOI: 10.1109/SIU.2017.7960151
K. Korkmaz
{"title":"Producing the location information with the Kalman filter on the GPS data for autonomous vehicles","authors":"K. Korkmaz","doi":"10.1109/SIU.2017.7960151","DOIUrl":"https://doi.org/10.1109/SIU.2017.7960151","url":null,"abstract":"In today's vehicles, nearly 70% of driver-vehicle interaction takes place through digital systems. This interaction, which is increasing day by day, is provided by many intelligent applications running at the bottom. Applications such as lanecontrol, emergency brake assist, adaptive cruise control, which become standard equipment on vehicles, can be listed as a few of them. Vehicles providing autonomous driving support with the autopilot feature have begun to be used on developed country roads. In this study, correction of the GPS data, which is the main source of the vehicle location information, was done with the Kalman filter. The study began with the extraction of the vehicle model, which was entered into MATLAB environment and tested. Then, in MATLAB environment, the KALMAN fitler was implemented through this vehicle model and coefficient matrices were determined. Finally, the determined coefficient matrices and method are adapted to the real vehicle and field tests are performed.","PeriodicalId":217576,"journal":{"name":"2017 25th Signal Processing and Communications Applications Conference (SIU)","volume":"8 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":"122332091","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
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