2021 6th International Conference on Computer Science and Engineering (UBMK)最新文献

Turkish Speech Recognition in Call Centers 呼叫中心中的土耳其语语音识别

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558890

Hatice Altınok, Serdar Kurdal, Murtaza Mehdi Yucal

{"title":"Turkish Speech Recognition in Call Centers","authors":"Hatice Altınok, Serdar Kurdal, Murtaza Mehdi Yucal","doi":"10.1109/UBMK52708.2021.9558890","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558890","url":null,"abstract":"Bu çalışmada çagrı merkezlerinde kullanılan otomatik konuşma tanıma sisteminde Türkçe ve yabancı kökenli sözeükleri taniyabilen bir yapıdan bahsedilmektedir. Çagrı merkezlerinin farkli sektör ve alanlara hizmet vermesinden kaynaklı Türkçe kökenli konuşmalarin yanı sıra birçok yabancı kökenli kelimelerin geçtigi konuşmalara da sahiptir. Sadece Türkçe için geliştirilen konuşma tanıma sistemleri bu merkezlerin ihtiyaeını tam anlamıyla karşılayamadigi için, Türkçe ve yabancı kelimelerin bir arada yer aldığı hibrit bir yapı oluşturulmuştur. Bu yapıda seslere karşılık gelen metinler okunuldugu gibi yazılmak yerine, okunuşun düzgün söylendigi varsayilarak yazilmiştir. Böylelikle metine çevrilen konuşmanın anlamlandırılmasındaki geçen zaman azalmıştir. Söyleyiş sözlügünde düzgün söylemlerin karşısına okunuş varyasyonları eklenerek degişik agızdaki konuşmalarin ve yabancı kökenli kelimelerin tanınması saglanmıştır. Gerçekleştirilen çalişma sonueunda uygulanan yöntemler sayesinde öneeki sisteme göre başarım oramnin arttığı gözlemlenmiştir.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115816111","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

Research of Cluster Analysis Methods for Group Solutions of the Pattern Recognition Problem 模式识别问题群解的聚类分析方法研究

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558884

L. Cherikbayeva, A. Yerimbetova, Elmira Daiyrbayeva

引用次数: 2

Learning and Predicting Asset Management 学习和预测资产管理

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558904

Kağan Küçük, Fatih Kahraman, M. Kamasak, E. Adali

{"title":"Learning and Predicting Asset Management","authors":"Kağan Küçük, Fatih Kahraman, M. Kamasak, E. Adali","doi":"10.1109/UBMK52708.2021.9558904","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558904","url":null,"abstract":"Instant exchange rates offered to customers are the most critical issues in the banking industry. It is very important for both the bank and the customer that the offers given are at the appropriate level. In this study, approximately 5 months of data were used and estimation models were designed for the estimation of the currency offers given to the customers. The study was conducted over 18 different currencies. In the study, dependent variables were determined as customer segment, instant exchange rate, day information, time information and volatility value. The independent variable is the exchange rate margin. The training was carried out with daily data and using RF, GBM, ANN, DNN and CNN algorithms. Random search algorithm was used to find the hyperparameters of the algorithms and the results of the model training were compared. The models with the lowest error values were selected to be used in the estimation phase. Mean Square Error (MSE) and Mean Absolute Error (MAE) functions were used to measure performance. It has been observed that artificial neural networks and convolutional neural network algorithms reveal better results than other algorithms according to the trainings carried out on three different models. Estimation time for 18 currencies is about 3 seconds.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126780242","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

Classification of Covid-19 X-ray Images Using Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) 基于三对角矩阵增强多方差乘积表示(TMEMPR)的Covid-19 x射线图像分类

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558982

Furkan Eren, Zeynep Gündoğar

{"title":"Classification of Covid-19 X-ray Images Using Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR)","authors":"Furkan Eren, Zeynep Gündoğar","doi":"10.1109/UBMK52708.2021.9558982","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558982","url":null,"abstract":"Medical images are crucial data sources for diseases that can not be diagnosed easily. X-rays, one of the medical images, have high resolution. Processing high-resolution images leads to a few problems such as difficulties in data storage, computational load, and the time required to process high-dimensional data. It is vital to be able to diagnose diseases fast and accurately. In this study, a data set consisting of lung X-rays of patients with and without COVID-19 symptoms was taken into consideration. Disease diagnosis from these images can be summarized in two steps as preprocessing and classification. The preprocessing step covers the feature extraction process and for this the recently developed decomposition-based method, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR), is proposed as a feature extraction method. The classification of images is the second step where the methods of Random Forests and Support Vector Machines are applied. Also, the X-ray images have been reduced by 99,9% with TMEMPR and with several state-of-the-art feature extraction methods such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT). The results are examined with regard to different feature extraction methods and it is observed that a higher accuracy rate is achieved when the TMEMPR method is used.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126951108","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

A Static Dictionary-Based Approach To Compressing Short Texts 基于静态字典的短文本压缩方法

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9559035

Murat Aslanyürek, A. Mesut

引用次数: 0

Early Stage Fault Prediction via Inter-Project Rule Transfer 基于项目间规则传递的早期故障预测

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558920

Nazli Ece Uykur, Begum Mutlu, E. Sezer

{"title":"Early Stage Fault Prediction via Inter-Project Rule Transfer","authors":"Nazli Ece Uykur, Begum Mutlu, E. Sezer","doi":"10.1109/UBMK52708.2021.9558920","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558920","url":null,"abstract":"Software fault protection can be first achieved by fault prediction. The earlier the fault prediction can be done in the software development life-cycle, the lower the damage and repair costs caused by the defects that will occur. Machine learning is one well-known method for the decision-making part of automatic software fault prediction. However, the applicability of machine learning methods is low due to the lack of data in the early stages of development processes. In this study, the data needed in the design of rule-base was obtained from counterpart projects, and the fault prediction problem was evaluated by the fuzzy rule-based systems’ point of view since these systems have portability utility which allows rule transfer between different problems with similar goals in the same domain. Briefly, this study aims to show that early-stage fault prediction is possible with the portability characteristics of fuzzy systems sourced from the inter-project rule transfer. Several experiments have been performed by using the software metrics datasets of 5 software projects to support this idea. Fuzzy systems obtained from several combinations of these datasets were evaluated by their prediction accuracy. The results show that more accurate rules can be obtained from previously completed software projects, and the use of rule bases gathered from those projects’ software metrics repositories can be transfered to predict the faulty modules of the current software project.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114437004","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

Finite State Machine Model for Uzbek Language Morphological Analyzer 乌兹别克语形态分析器的有限状态机模型

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9559023

Khamroyeva Shahlo Mirdjanovna

引用次数: 0

Diagnosing Autism Spectrum Disorder Using Machine Learning Techniques 使用机器学习技术诊断自闭症谱系障碍

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558975

Hidayet Takçi, Saliha Yeşilyurt

{"title":"Diagnosing Autism Spectrum Disorder Using Machine Learning Techniques","authors":"Hidayet Takçi, Saliha Yeşilyurt","doi":"10.1109/UBMK52708.2021.9558975","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558975","url":null,"abstract":"Autism is a generalized pervasive developmental disorder that can be characterized by language and communication disorders. Screening tests are often used to diagnose such a disorder; however, they are usually time-consuming and costly tests. In recent years, machine learning methods have been frequently utilized for this purpose due to their performance and efficiency. This paper employs the most eight prominent machine learning algorithms and presents an empirical evaluation of their performances in diagnosing autism disorder on four different benchmark datasets, which are up-to-date and originate from the QCHAT, AQ-10-child, and AQ-10-adult screening tests. In doing so, we also utilize precision, sensitivity, specificity, and classification accuracy metrics to scrutinize their performances. According to the experimental results, the best outcomes are obtained with C-SVC, a classifier based on a support vector machine. More importantly, in terms of C-SVC performance metrics even lead to 100% in all datasets. Multivariate logistic regression has been taken second place. On the other hand, the lowest results are obtained with the C4.5 algorithm, a decision tree-based algorithm.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116311617","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

3D Video Quality Evaluation Based on SSIM Model Improvement 基于SSIM模型改进的三维视频质量评价

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9558955

G. Yilmaz, G. Akar

{"title":"3D Video Quality Evaluation Based on SSIM Model Improvement","authors":"G. Yilmaz, G. Akar","doi":"10.1109/UBMK52708.2021.9558955","DOIUrl":"https://doi.org/10.1109/UBMK52708.2021.9558955","url":null,"abstract":"In order to provide improved multimedia services to the end users, developing objective models efficiently predicting 3 Dimensional (3D) video Quality of Experience (QoE) can currently be considered as one of the most significant research areas. Nevertheless, there is currently no model standardized and widely utilized by the researchers due to its efficient and reliable assessment of the 3D video quality. Therefore, highly exploited 2 Dimensional (2D) video quality assessment models such as Structural SIMilarity Index (SSIM) are preferred for the 3D video quality evaluation. However, providing efficiency and reliability for the 3D video quality assessment using the 2D video quality assessment models can only be ensured if they include 3D video related features effecting Human Visual System (HVS). Under the light of these information, the SSIM model is improved for the 3D video quality assessment using perceptually significant feature, contrast and motion characteristics having impact on the HVS in this study. The results obtained by utilizing the improved SSIM model clearly present that the model is quite competent to provide enhanced multimedia services to the end users.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125724234","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

Detection of Malware with Deep Learning Method 基于深度学习方法的恶意软件检测

2021 6th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-09-15 DOI: 10.1109/UBMK52708.2021.9559020

Ümit Emre Köse, R. Samet

引用次数: 1