2022 7th International Conference on Computer Science and Engineering (UBMK)最新文献_第10页

Incremental Machine Learning: Incremental Classification 增量机器学习:增量分类

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919487

Engin Baysal, C. Bayilmis

引用次数: 0

Brain Computer Interaction and Cybersecurity of the Brain 脑机交互与脑网络安全

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919551

Mustafa Senol

引用次数: 0

The Task of Identifying Morphological Errors of Words in the Kazakh Language in Social Networks 社交网络中哈萨克语词法错误的识别任务

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919516

D. Rakhimova, Yntymak Abdrazakh

{"title":"The Task of Identifying Morphological Errors of Words in the Kazakh Language in Social Networks","authors":"D. Rakhimova, Yntymak Abdrazakh","doi":"10.1109/UBMK55850.2022.9919516","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919516","url":null,"abstract":"On the Internet and on Instagram, Vkontakte, Twitter and other social networks, applications are very attractive in terms of receiving and analyzing information in messages, because the information in these systems is real. However, text posts, user comments in social. networks often differs from the generally accepted norms of the language. There are mistakes in it, deliberate distortions of words. Unfortunately, such inaccurate data cannot be taken into account in content analysis, statistical or sentiment analysis of data. Words with such errors, i.e. incorrect words can be processed and analyzed, identifying and correcting them into the correct form. This paper presents research and implementation of a model for determining incorrect words of the Kazakh language in semi-structured data, using the example of comments and posts on social networks. To solve and analyze the task, the following was done: a comparative analysis of text correction systems was carried out; explored various spell-checking technologies; the most frequently used errors are identified and the classification of errors in words is presented. A dictionary of the basics of the Kazakh language from the electronic corpus has been developed. The authors have developed an approach to identify incorrect words and auto-replace with a suitable candidate object (word) for the Kazakh language. To solve the problem, a approach is presented to detect incorrect words in the Kazakh language. An approach has been developed for identifying incorrect words from semi-structured data, which is based on the stemming algorithm with lexicon stems according to the CSE (Complete Set of Endings) Experimental calculations and evaluation of the results have been carried out.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"18 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":"129375735","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

Server Busy Times Finder and Synthetic User Log Generator On-Demand Architecture 服务器繁忙时间查找和合成用户日志生成器按需架构

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919538

Batiray Erbay, Tolga Büyüktanir, Mert Altun, Harun Uz

引用次数: 1

A Study on the Effect of Virtualization on Productivity 虚拟化对生产力影响的研究

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919452

Ali Yildirim, H. Kilinç

引用次数: 0

Robotic Grasping in Simulation Using Deep Reinforcement Learning 基于深度强化学习的机器人抓取仿真

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919482

Musab Coskun, Ozal Yildirim, Y. Demir

引用次数: 0

Applying Web Crawler Technologies for Compiling Parallel Corpora as one Stage of Natural Language Processing 应用网络爬虫技术编译并行语料库作为自然语言处理的一个阶段

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919521

Nilufar Abdurakhmonovaa, Ismailov Alisher, Guli Toirovaa

引用次数: 0

Diagnosing The Breathing Sounds as COPD or Asthma 诊断呼吸声音为慢性阻塞性肺病或哮喘

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2022-09-14 DOI: 10.1109/UBMK55850.2022.9919567

Burak Türkan, Ahmet Gökay Ateş, Özgür Özdemir, Elena Battini Sönmez

引用次数: 0

Benchmark Static API Call Datasets for Malware Family Classification 恶意软件家族分类的基准静态API调用数据集

2022 7th International Conference on Computer Science and Engineering (UBMK) Pub Date : 2021-11-30 DOI: 10.1109/UBMK55850.2022.9919580

Buket Gençaydın, Ceyda Nur Kahya, Ferhat Demirkiran, Berkant Düzgün, Aykut Çayir, Hasan Dag

{"title":"Benchmark Static API Call Datasets for Malware Family Classification","authors":"Buket Gençaydın, Ceyda Nur Kahya, Ferhat Demirkiran, Berkant Düzgün, Aykut Çayir, Hasan Dag","doi":"10.1109/UBMK55850.2022.9919580","DOIUrl":"https://doi.org/10.1109/UBMK55850.2022.9919580","url":null,"abstract":"Nowadays, malware and malware incidents are increasing daily, even with various antivirus systems and malware detection or classification methodologies. Machine learning techniques have been the main focus of the security experts to detect malware and determine their families. Many static, dynamic, and hybrid techniques have been presented for that purpose. In this study, the static analysis technique has been applied to malware samples to extract API calls, which is one of the most used features in machine/deep learning models as it represents the behavior of malware samples. Since the rapid increase and continuous evolution of malware affect the detection capacity of antivirus scanners, recent and updated datasets of malicious software became necessary to overcome this drawback. This paper introduces two new datasets: One with 14,616 samples obtained and compiled from VirusShare and one with 9,795 samples from VirusSample. In addition, benchmark results based on static API calls of malware samples are presented using several machine and deep learning models on these datasets. We believe that these two datasets and benchmark results enable researchers to test and validate their methods and approaches in this field.","PeriodicalId":417604,"journal":{"name":"2022 7th International Conference on Computer Science and Engineering (UBMK)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131461674","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