2021 29th Signal Processing and Communications Applications Conference (SIU)最新文献_第8页

Video Colorizing with Automatic Reference Image Selection 视频着色与自动参考图像选择

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477833

Ali Asaf Polat, Mehmet Furkan Şahın, M. Karsligil

引用次数: 0

Performance Comparison of Machine Learning Models for Diabetes Prediction 糖尿病预测机器学习模型的性能比较

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477824

Pınar Cihan, H. Coskun

{"title":"Performance Comparison of Machine Learning Models for Diabetes Prediction","authors":"Pınar Cihan, H. Coskun","doi":"10.1109/SIU53274.2021.9477824","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477824","url":null,"abstract":"Diabetes is a chronic disease that causes blood sugar to rise. This chronic disease can be cause mortality. There are different diagnoses and treatment methods for diabetes in the medical field. In addition, with the developing technology, diagnosis of the disease can be made computer-aided. Computeraided diagnostic methods are a successful, fast, and alternative method that supports the doctor's decision. The use of computeraided diagnosis approach for diabetes and many other diseases is increasing day by day. Machine learning classification methods are the most commonly used methods for computer-aided diagnostics. The aim of this study is to design a model to detect the probability of diabetes in patients at an early stage with maximum accuracy. Therefore, seven machine learning classification algorithms were used, namely Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Gaussian Naive Bayes, Decision Tree, Random Forest, and Artificial Neural Network. The study was carried out on the Pima Indians Diabetes Database (PIDD) taken from the Kaggle database. The performances of machine learning methods were evaluated according to precision, recall, ROC curve, and PRC criteria. According to the results, the Logistic Regression method is more successful than other methods in classifying diabetes disease accurately.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127953162","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: 9

Collecting Parts by Indoor Mapping Method 室内制图法收集部件

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477922

Müzeyyennur Yilgin, D. Erol, O. Yıldız

引用次数: 0

PCB Component Recognition with Semi-supervised Image Clustering 基于半监督图像聚类的PCB元件识别

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477791

Ahmet Emin Ünal, Kasim Tasdemir, Akif Bahçebaşi

引用次数: 0

Performance of Downlink NOMA Network Using Space-Time Line Coding Technique 采用空时线编码技术的下行NOMA网络性能研究

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9478000

Tayfun Yilmaz, A. Ayranci, H. Ilhan

引用次数: 1

Adaptive Boosting of DNN Ensembles for Brain-Computer Interface Spellers 脑机接口拼写器中DNN集成的自适应增强

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477841

O. B. Güney, Emirhan Koç, Can Aksoy, Yigit Çatak, Suayb S. Arslan, H. Özkan

{"title":"Adaptive Boosting of DNN Ensembles for Brain-Computer Interface Spellers","authors":"O. B. Güney, Emirhan Koç, Can Aksoy, Yigit Çatak, Suayb S. Arslan, H. Özkan","doi":"10.1109/SIU53274.2021.9477841","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477841","url":null,"abstract":"Steady-state visual evoked potentials (SSVEP) are commonly used in brain computer interface (BCI) applications such as spelling systems, due to their advantages over other paradigms. In this study, we develop a method for SSVEP-based BCI speller systems, using a known deep neural network (DNN), which includes transfer and ensemble learning techniques. We test performance of our method on publicly available benchmark and BETA datasets with leave-one-subject-out procedure. Our method consists of two stages. In the first stage, a global DNN is trained using data from all subjects except one subject that is excluded for testing. In the second stage, the global model is fine-tuned to each subject whose data are used in the training. Combining the responses of trained DNNs with different weights for each test subject, rather than an equal weight, provide better performance as brain signals may differ significantly between individuals. To this end, weights of DNNs are learnt with SAMME algorithm with using data belonging to the test subject. Our method significantly outperforms canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126338538","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

Auto-tagging of Short Conversational Sentences using Natural Language Processing Methods 使用自然语言处理方法的短会话句自动标注

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477994

¸Sükrü Ozan, D. E. Ta¸sar, ˙I zmir Türkiye AdresGezgini A.¸S. Ar-Ge Merkezi

引用次数: 1

Driver Identification Using Vehicle Diagnostic Data with Fully Convolutional Neural Network 基于全卷积神经网络的车辆诊断数据驾驶员识别

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477850

Caner Aslan, Yakup Genç

{"title":"Driver Identification Using Vehicle Diagnostic Data with Fully Convolutional Neural Network","authors":"Caner Aslan, Yakup Genç","doi":"10.1109/SIU53274.2021.9477850","DOIUrl":"https://doi.org/10.1109/SIU53274.2021.9477850","url":null,"abstract":"In this study, driver identification task was performed using artificial neural networks and vehicle diagnosis data without feature engineering which is one of the most challenging steps of machine learning methods. Along with the data sets used in the literature, the data set including rural road, urban and highway routes with 5 different drivers was collected and used experimentally. Driver identification problem is considered as multivariate time series classification problem. With the FCNN (Fully Convolutional Neural Network) architecture established to solve the problem, the success rate of RNN (Recurrent Neural Network)-based architectures and traditional ML methods in the literature has been exceeded. The window size in the sliding window method has been reduced in training and validation. At the same time, the space occupied by the model on the memory and the number of floating point operations were reduced, increasing the possibility of use in in-vehicle systems. While the results are obtained with the stratified fold validation, they are compared with the time series segmentation method in data sets containing a single drive on the route.","PeriodicalId":160693,"journal":{"name":"2021 29th Signal Processing and Communications Applications Conference (SIU)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114451676","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

Gender Detection with Smartphone Motion Sensors Using Convolutional Neural Networks 使用卷积神经网络的智能手机运动传感器的性别检测

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9478004

Erhan Davarci, E. Anarim

引用次数: 2

Antenna Scanning Type Classification with Autoencoder Based Deep Clustering 基于深度聚类的自编码器天线扫描类型分类

2021 29th Signal Processing and Communications Applications Conference (SIU) Pub Date : 2021-06-09 DOI: 10.1109/SIU53274.2021.9477860

Emirhan Özmen, Yakup S. Özkazanç

引用次数: 0