2022 7th International Conference on Computer Science and Engineering (UBMK)最新文献_第8页

EMG-based BCI for PiCar Mobilization 基于肌电图的脑机接口用于PiCar动员

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919502

Efe Ertekin, Burak Bahir Günden, Y. Yilmaz, Alperen Sayar, Tuna Çakar, Sefik Şuayb Arslan

{"title":"EMG-based BCI for PiCar Mobilization","authors":"Efe Ertekin, Burak Bahir Günden, Y. Yilmaz, Alperen Sayar, Tuna Çakar, Sefik Şuayb Arslan","doi":"10.1109/UBMK55850.2022.9919502","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919502","url":null,"abstract":"In this study, the main scope was to develop a brain-computer interface (BCI) with the use of PiCar and EEG/ERP devices. Thus, it is aimed to facilitate the lives of people with certain diseases and disabilities. The ultimate goal of this project has been to direct and control a BCI-based PiCar concerning the signals captured via the EEG/ERP device. With the EEG headset, the EMG signals of the gestures (facial expressions) of the participant were captured. With the collected data, filtering and other preprocessing methods were applied to have noise-free signals. In the preprocessing, the detrending method was used to clean the data set which showed a constantly increasing trend, to a certain range, and zero trends. The denoising (Wavelet Denoising) and outlier detection/elimination methods (OneClassSVM) were used for noise elimination. The SMOTE oversampling method was used for data augmentation. Welch's method was used to get band powers from the signals. With the use of augmented data, several machine learning algorithms were applied such as Support Vector Machine, Logistic Regression, Linear Discriminant Analysis, Random forest Classifier, Gradient Boosting Classifier, Multinomial Naive Bayes, Decision tree, K-Nearest Neighbor, and voting classifier. The developed models were used to predict the direction that is passed as an input to PiCar's API. After that, PiCar was controlled concerning the predicted direction with HTTP GET requests. In this project, the OpenBCI headset and the Brainflow library for EEG/EMG signal obtaining and processing were used. Also, the Tkinter library was used for the Graphical user interface and Django for establishing a server on PiCar's brain which is RaspberryPi.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"278 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115253080","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

EEG Signals and Spectrogram with Deep Learning Approaches Emotion Analysis with Images 基于深度学习方法的脑电信号和频谱图

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919468

Ayşe Gül Eker, N. Duru, Kadir Eker

引用次数: 0

Multivariate Modeling and Analysis for Cellular Traffic Prediction Using Call Detail Records 基于呼叫详细记录的蜂窝通信量预测的多元建模与分析

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919559

Senem Tanberk, O. Demir

引用次数: 0

Distribution Planning and Route Optimization in Cargo Delivery 货物配送规划与路线优化

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919508

Selin Bostan, C. Güney, Ümit Emre Köse, Seda Aslan, M. Kamaşak

{"title":"Distribution Planning and Route Optimization in Cargo Delivery","authors":"Selin Bostan, C. Güney, Ümit Emre Köse, Seda Aslan, M. Kamaşak","doi":"10.1109/UBMK55850.2022.9919508","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919508","url":null,"abstract":"In this study, in the cargo transportation sector in last mile delivery service, route optimization integrated with distribution planning for vehicles and couriers is determined and the results obtained with a sample application are examined. A Geographical Information Systems(GIS) supported and map-based system is developed for distribution planning, which determines which unit will deliver the cargo according to the delivery points.In this system, the distribution area of each unit is visualized on the map with a geographical data set. In this study, the criteria for the most suitable routing solution are determined and an application is proposed. With this application, it is aimed to save vehicles and fuel in distribution activities, which is the highest cost in the cargo sector, and to need-oriented vehicle capacity management. With the routing optimization solution, screens are developed for the applications where buyer customers can track their cargo, and screens where they can monitor the distribution live, and see the estimated delivery time of the cargo will be delivered in which time zone.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125458786","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

Implementing of Transfer Learning Method in the Diagnosis of Skin Diseases with Convolutional Neural Networks 卷积神经网络在皮肤病诊断中的迁移学习方法实现

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919472

Ayhan Sarı, A. Nizam, M. Aydın

{"title":"Implementing of Transfer Learning Method in the Diagnosis of Skin Diseases with Convolutional Neural Networks","authors":"Ayhan Sarı, A. Nizam, M. Aydın","doi":"10.1109/UBMK55850.2022.9919472","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919472","url":null,"abstract":"Millions of people are diagnosed with skin cancer every year around the world, and many people die from this disease. Early diagnosis is important in skin diseases. For this reason, studies on identifying skin diseases with high accuracy using computer-assisted machine learning-based algorithms have gained importance. Convolutional neural networks are frequently used to detect skin diseases quickly and with high accuracy using medical images. In this study, a method using transfer learning is proposed to classify the HAM10000 dataset with high accuracy. Pre-trained models with the ImageNet dataset were transferred and used for classification of the HAM10000 dataset. To demonstrate the effectiveness of the proposed method, Xception and DenseNet201 convolutional neural network models are used separately. In experimental studies, the number of images in the dataset was increased by real-time data augmentation method. In the study, better classification results were obtained in the Xcepiton model compared to the DenseNet201 model, according to the test accuracy, precision, sensitivity and fl-score criteria. It has been observed that higher performances are obtained when the results in this study are compared with similar studies in the literature.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116968535","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

Morphological Annotation System in The Corpus of Internet Information Texts in The Uzbek Language 乌兹别克语网络信息文本语料库中的形态标注系统

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/ubmk55850.2022.9919433

Abdullayeva Oqila Xolmo'minovna

{"title":"Morphological Annotation System in The Corpus of Internet Information Texts in The Uzbek Language","authors":"Abdullayeva Oqila Xolmo'minovna","doi":"10.1109/ubmk55850.2022.9919433","DOIUrl":"https://doi.org/10.1109/ubmk55850.2022.9919433","url":null,"abstract":"Morphological and syntactic annotation is one of the most important types of annotation at the current stage of development of text corpora because it is clearly applied in areas such as lexical and grammatical development. Morphological annotation of language units in Uzbek was done manually and semi-automatically. We offered special tags for the language corpus, if we identify the most common affixes of combinations speech parts and suffixes in words in Uzbek and analyze them with marked tags, it can serve as a foundation for the further development of language corpora. More than 70 special tags were selected for morphological annotation, and speech units were annotated in the corpus. Search and analysis results are displayed in 3 different ways in the user interface: 1) only by word search: in this case, any word is written in the search line. If the analyzed word is tagged in the corpus, it will appear with all its grammatical markers. 2) word+tag search: at this stage, the morphological or semantic feature of a word in a certain word group can be analyzed. For example, if a word with a specific morphological indicator is analyzed, a word is written in the search line, and a tag is selected from the list of morphological tags. At the time of choosing a morphological indicator, a word group must be specified. 3) search by tag only: this option is used when the researcher needs speech units belonging to a certain morphological index or a semantic group in a certain word group.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124544359","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

Linguistic-based Data Augmentation Approach for Offensive Language Detection 基于语言的攻击性语言检测数据增强方法

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919562

Toygar Tanyel, Besher Alkurdi, S. Ayvaz

{"title":"Linguistic-based Data Augmentation Approach for Offensive Language Detection","authors":"Toygar Tanyel, Besher Alkurdi, S. Ayvaz","doi":"10.1109/UBMK55850.2022.9919562","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919562","url":null,"abstract":"The massive amount of data generated by social media possess a great deal of toxic content that lead to serious content filtering problems including hate speech, cyberbullying and insulting. Offensive content even without profanity may result in psychological and physical harms to, particularly children and sensitive people. As of 2022, Turkey houses 7th largest Twitter community among all countries in terms of the active user size exceeding 16 million users, which represents a high diversity of people considering its population. That said, there is a growing need for a comprehensive and high-quality dataset in Turkish that can be utilized in development of NLP models for robust detection of offensive language usage in social media. Related studies in literature have mostly focused on small, synthetic and label-imbalanced datasets. Machine learning models trained on such datasets tend to favor majority class for accuracy and possess generalizability issues. However, it is challenging to create an unbiased dataset containing hate speech without offensive words, and build an accurate detection model to identify the actual hate speech Tweets. The models may lack sufficient context due to the absence of swear words. Therefore, we propose a data augmentation approach based on data mining methods utilizing the linguistic features of Turkish that can help enhance the generalizability of machine learning models without further human interaction. Furthermore, we evaluated the impact of our comprehensive dataset in detection of offensive language in social media. The NLP models training using the augmented dataset improved the macro average detection accuracy by 7.60% in comparison to the baseline approach.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124574550","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

Comparison of Uzbek-Turkish Inflection Suffixes 乌兹别克-土耳其语屈折后缀的比较

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919603

Eçref Adalı, Mengliyev Bakhtiyor Rajabovich, Khamroyeva Shahlo Mirdjonovna

引用次数: 0

EAFT: Evolutionary Algorithms for GCC Flag Tuning GCC标志调优的进化算法

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919557

Burak Tagtekin, Tuna Çakar

引用次数: 0

Quality of Experience Prediction for VoIP Calls Using Audio MFCCs and Multilayer Perceptron 基于音频mfc和多层感知机的VoIP呼叫体验质量预测

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919483

Faruk Kaledibi, H. Kilinç, C. O. Sakar

{"title":"Quality of Experience Prediction for VoIP Calls Using Audio MFCCs and Multilayer Perceptron","authors":"Faruk Kaledibi, H. Kilinç, C. O. Sakar","doi":"10.1109/UBMK55850.2022.9919483","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919483","url":null,"abstract":"To provide a high-quality communication service to their users, VoIP service providers use some monitoring and warning systems that notify them of any malfunctions that may occur in the system. Because the VoIP service is delivered over the internet, issues with the internet infrastructure and related hardware have a direct impact on the quality of service (QoS) and experience provided. In such cases, service providers analyze the QoS reports to analyze the incidents. The QoS reports consist of various parameters such as packet loss, delay, jitter, and codec information extracted from the related VoIP call. However, in some cases, these parameters may be insufficient or corrupted. Therefore, real sound recordings are used to determine the source of the complaint. However, listening to audio recordings made by third parties is not preferred when the content is sensitive. Thus, a computer-based analysis is an important requirement in such cases. In this study, a machine learning-based model was developed that can classify a given packet loss into six classes, which is one of the most important factors affecting the quality of experience. The audio recordings were represented with Mel Frequency Cepstrum Coefficients (MFCCs). The model trained using 9000 5-second audio recordings from 15 different speakers can predict the packet loss rate and the mean opinion score (MOS) with an accuracy of 87%.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131285034","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