2022 7th International Conference on Computer Science and Engineering (UBMK)最新文献_第9页

IT Support Ticket Completion Time Prediction IT支持票据完成时间预测

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919591

Mihra Yıldız, Ali Alsaç, Taner Ulusinan, M. Ganiz, M. M. Yenisey

{"title":"IT Support Ticket Completion Time Prediction","authors":"Mihra Yıldız, Ali Alsaç, Taner Ulusinan, M. Ganiz, M. M. Yenisey","doi":"10.1109/UBMK55850.2022.9919591","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919591","url":null,"abstract":"Prediction of the time that will be spent on IT support tickets is very important for planning and optimization of IT support services that are usually bound with service level agreements. Predicting completion time of a ticket is a difficult problem, which requires substantial experience and technical expertise if done manually by a human. However, it is possible to automate this task using supervised machine learning models given we have a large amount of labeled data. In this study, we employ supervised machine learning algorithms to predict completion time of tickets for IT support. We use a real-world dataset that includes about 17 thousand tickets. We employ data science approaches to preprocess and transform the input and feed to supervised machine learning algorithms for learning models for ticket completion time prediction. More specifically we use Linear Regression, Decision Trees Regression, Random Forest Regression, Support Vector Machines Regression, and Multiple Regression algorithms. For the evaluation of these supervised models, we use several metrics such as MAE, MSE, and MAPE. Our results show varying success levels with different supervised machine learning algorithms for this difficult task. Among the models we train, the Decision Trees and Random Forest Regression show promising results.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"24 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":"116795706","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

Anthropometric Measurements with 2D Images 人体测量与二维图像

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919504

Rumeysa Ashhan Ertürk, Mustafa Ersel Karnaşak

{"title":"Anthropometric Measurements with 2D Images","authors":"Rumeysa Ashhan Ertürk, Mustafa Ersel Karnaşak","doi":"10.1109/UBMK55850.2022.9919504","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919504","url":null,"abstract":"In this work, we designed and provided a proof-of-concept study for a novel system that takes several anthro-pometric measurements (bust, waist, and hip circumferences) simultaneously using only two (frontal and side) 2D images of a human subject. The system has two components: a specific camera setup with lasers and image analysis software. Towards this purpose, we compare body measurements of the proposed system and manual measurements on a limited number of subjects. For automatic measurements, we took one frontal and one side image from each subject. Body segmentation and pose estimation are applied to these images using pre-trained deep neural networks. Using the laser positions on the image, pixel sizes are estimated in terms of physical length (ie. centimeter). Using physical widths of the bust, waist, and hip in the images, their circumferences are estimated automatically. On three subjects, we obtained less than 10% measurement error. We concluded that anthropometric measurements could be obtained using a camera and laser setup. However, the number of subjects should be increased with a more precise laser to determine a better margin for measurement error.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"6 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":"116829960","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

Model for Estimating the Probability of a Customer to Have a Transaction 估计客户进行交易概率的模型

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919439

A. Sayar, Tunaban Bozkan, Tuna Çakar, Seyit Ertugrul

{"title":"Model for Estimating the Probability of a Customer to Have a Transaction","authors":"A. Sayar, Tunaban Bozkan, Tuna Çakar, Seyit Ertugrul","doi":"10.1109/UBMK55850.2022.9919439","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919439","url":null,"abstract":"In this study, it is aimed to estimate the probability of a customer who comes to the institution for the first time to make a transaction in the next 3 months, using data-driven machine learning models, in order to provide financing to the seller company by assigning the receivables arising from the sale of goods and services in a company actively operating in the factoring sector. Accordingly, it was aimed to directly contribute to the transaction volume on a business basis by acting and taking action with more effective, efficient and correct approaches by finding high-potential and low-potential customers. In this context, provided by KKB (Credit Registration Bureau); The data set to he used in machine learning models was created with feature engineering and exploratory data analysis, using the Risk, Mersis, GIB information of the prospective customers and the historical information of the customers, check issuers, customer representatives and branches kept in the database. Since the leads coming to the institution are in two different types of organizations (Individual and Legal), two different forecasting models were applied. Multiple classification models were tried, and the highest F1-Score of 86% for private companies was obtained with the Random Forest model, and the highest F1- Score for commercial companies was obtained with the Random Forest model with 82%.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"1 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":"129632645","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

A GA-Based CNN Model for Brain Tumor Classification 一种基于ga的脑肿瘤分类CNN模型

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919461

Kevser Özdem, Çağin Özkaya, Yilmaz Atay, E. Çeltikçi, A. Börcek, Umut M. Demirezen, Ş. Sağiroğlu

{"title":"A GA-Based CNN Model for Brain Tumor Classification","authors":"Kevser Özdem, Çağin Özkaya, Yilmaz Atay, E. Çeltikçi, A. Börcek, Umut M. Demirezen, Ş. Sağiroğlu","doi":"10.1109/UBMK55850.2022.9919461","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919461","url":null,"abstract":"Detection and classification of tumor types generally cover problem-specific algorithm developments. The problems of detecting tumors with the analysis of standard brain images obtained with different medical imaging tools and frequently used in the literature are always desired, developed, and discussed. This study focuses on identifying tumors, extracting different characteristics, and associating them with cancer types. The standard approach of convolutional neural networks (CNN) was used primarily for the identification of tumors. Then, the genetic algorithm (GA) approach was designed and used for hyperparameter optimization in CNN to increase the performance in all datasets. Thus, a CNN+GA hybrid model was proposed and analyzed with different tests. In this process, the results were examined in detail and the standard CNN algorithm and some machine learning methods suggested in the literature were analyzed comparatively. In addition, the data set called Gazi Brains 2020 Dataset, which was obtained within the scope of the Turkish Brain Project, is also used to test the proposed system. Here, literature reviews of the previous studies in which different machine/deep learning approaches are used together with optimization algorithms are presented. The different comparison scores obtained according to the experimental studies were presented in the tables and the outputs were evaluated in terms of significance. The results have shown that the proposed hybrid models are successful in achieving better accuracies not only with different datasets available in the literature but also DL/ML models trained with Gazi Brain 2020 Dataset. It should be concluded that the proposed method might be also used for other deep/machine learning models and applications.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"33 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":"129806579","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

Adaptive Slot-Filling for Turkish Natural Language Understanding 土耳其语自然语言理解的自适应槽填充

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919492

A. Balcioglu

{"title":"Adaptive Slot-Filling for Turkish Natural Language Understanding","authors":"A. Balcioglu","doi":"10.1109/UBMK55850.2022.9919492","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919492","url":null,"abstract":"Slot-filling is a key part of natural language under-standing that aims to extract words which hold certain attributes for the dialogue system. Although slot-filling is traditionally considered to be a data demanding and expensive task, advances in transformer models can help to solve this problem via transfer learning. In this paper, we propose an adaptive transfer-learning based slot filling model using BERT and conditional random fields (CRFs). We also introduce and discuss the stemming problem for agglutinative languages in slot-filling, which we define as the ambiguity of meaning between extracting the whole word or extracting a part of the word for the slot. We propose a novel definition of stemming specifically for wordpiece tokenizers used in transformer models and use it to solve the stemming issue. Our experiments with the BERT-CRF model out perform previous models on Turkish slot filling. We also show that under the new definition, wordpiece tokenizers perform on par with current state-of-the-art stemming models. Finally, we contend transformer based models like ours can overcome the stemming issue with the help of labelling.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"39 5-6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114031275","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

Business Process Modeling That Distinguishes Homonymy Within Three Parts of Speechs in The Uzbek Language 区分乌兹别克语三词性同音异义的业务流程建模

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919453

Elov Botir Boltayevich, Axmedova Xolisxon Ilxomovna

{"title":"Business Process Modeling That Distinguishes Homonymy Within Three Parts of Speechs in The Uzbek Language","authors":"Elov Botir Boltayevich, Axmedova Xolisxon Ilxomovna","doi":"10.1109/UBMK55850.2022.9919453","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919453","url":null,"abstract":"One of the processes of natural language processing is the semantic analysis of texts. An important task of semantic analysis is to distinguish between the meanings of the words in the text and to distinguish their meanings. For the purpose of semantic analysis of homonymous words, they are divided into groups such as homonyms within 2 parts of speechs, homonyms within 3 parts of speechs and homonyms within 4 parts of speechs according to their occurrence within categories. In the Uzbek language, words that form a homonym are divided into 11 groups within 3 parts of speechs. In this article analyzes the linguistic factors that differentiate homonomy words in the Uzbek language, such as adjective or noun or adverb, noun or pronoun or verb, noun or adjective or verb, noun or verb or pronoun, noun or adjective or predicate word, noun or adverb or imitation word, noun or exclamation word or imitation word, noun or adjective or auxiliary, noun or number or verb, noun or verb or imitation word, exclamation word or verb or adverb develops a total of 7 mathematical models.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"97 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":"131657350","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

What Does Industry Suggest to Academia on Software Engineering Education? 工业界对软件工程教育的建议是什么?

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919527

Deniz Akdur

引用次数: 2

Keyless Entry System and Campus Office Management System 无钥匙进入系统及校园办公管理系统

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919517

Sila Dagtekin, Yigit Ergan Gümüş, Emre Olca

引用次数: 0

Linguistic Modeling of Polysemous Words for the Semantic Analyzer of The Uzbek Language 乌兹别克语语义分析器中多义词的语言建模

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919553

Bakhtiyor Mengliev, S. Gulyamova

{"title":"Linguistic Modeling of Polysemous Words for the Semantic Analyzer of The Uzbek Language","authors":"Bakhtiyor Mengliev, S. Gulyamova","doi":"10.1109/UBMK55850.2022.9919553","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919553","url":null,"abstract":"This article focuses on the semantic analyzer linguistic modeling of polysemous words. A semantic analyzer is a program that semantically processes the syntactic structure of a query using conceptual graphs. Conceptual graphics are semantic or, in other words, conceptual representations of situations and knowledge in natural language comprehension models. The object highlighted in the work had an indirect and direct relation to semantics, the modeling of which was of considerable complexity. In the course of the study, 10 models were compiled to determine some polysemous words in the Uzbek language. Structuring these language models is the first step toward the linguistic foundations of a semantic analyzer. In the future, individual models of each language basis can be developed and applied in practice, and for this purpose, the models presented in the work can serve as models. To launch the semantic analyzer of the Uzbek language, the issue of linguistic modeling of the environment of homonymous, multifunctional, and polysemous words is important. Linguistic modeling of polysemous words for a semantic analyzer lays the foundation for the development of their mathematical algorithms and the creation of a semantic analyzer in the future.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"54 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":"134259467","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

Transfer Learning for Land Cover Semantic Segmentation 土地覆盖语义分割的迁移学习

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919601

A. Kındıroglu, Metehan Yalçin, F. Bagci, Ufuk Uyan, Mahiye Uluyagmur Öztürk

引用次数: 1