2021 16th International Conference on Electronics Computer and Computation (ICECCO)最新文献_第5页

MAS agents development for mining industry MAS代理矿业开发

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663861

A. Kuandykov, D. Kozhamzharova, Nurlan Karimzhan, M. Aitimov

引用次数: 0

Evaluation of Collision Resolution Methods Using Asymptotic Analysis 用渐近分析评价碰撞解决方法

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663778

Ahmed Dalhatu Yusuf, S. Abdullahi, Moussa Mahamat Boukar, Salisu Ibrahim Yusuf

引用次数: 0

Abnormality Detection in Chest X-ray Images Using Uncertainty Prediction Algorithms 基于不确定性预测算法的胸部x线图像异常检测

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663852

N. Saparkhojayev, Lazzat Zholayeva, Yerzhan Tashkenbayev, D. Tokseit

引用次数: 1

Few-Shot Learning Approach for COVID-19 Detection from X-Ray Images 基于x射线图像的COVID-19检测的少镜头学习方法

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663860

R. Abdrakhmanov, M. Altynbekov, Assanali Abu, A. Shomanov, D. Viderman, Minho Lee

{"title":"Few-Shot Learning Approach for COVID-19 Detection from X-Ray Images","authors":"R. Abdrakhmanov, M. Altynbekov, Assanali Abu, A. Shomanov, D. Viderman, Minho Lee","doi":"10.1109/icecco53203.2021.9663860","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663860","url":null,"abstract":"The end of 2019 and the beginning of 2020 were accompanied by an exponential spread of COVID-19 infection (a viral disease). This later led to a pandemic situation all over the planet. Such a rapid infection of people with the virus (SARS-CoV-2) from each other was caused by the fact that the symptoms of this disease are very similar to ordinary ARVI (acute respiratory viral infection). This in turn complicates the identification of a patient with a new virus. In order to isolate and contain the further spread of the virus, effective and rapid methods are needed to identify patients at an early stage. In our research work, we propose to use the few-shot method. This method is effective with a small amount of input data, training with few-shot is aimed at creating accurate machine learning models with less training data. Since the size of the input data is a factor determining the cost of resources (such as time costs), it is possible to reduce the cost of data analysis by using few-shot learning. The obtained results include the highest accuracy of 97.7% for 10 shots of COVID-19 X-ray images, which implies the effectiveness of the proposed approach. Notably, it was discovered that the accuracy of the approach directly correlates with the number of COVID-19 samples used for training.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131004547","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

Review of Electronic Industry in Kazakhstan: Conditions and Opportunities 哈萨克斯坦电子工业:条件与机遇

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663826

Gulfarida Tulemissova, O. Baimuratov

引用次数: 0

Experience of using SMART technologies in university education 在大学教育中使用智能技术的经验

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663820

Zh. U. Abuova, Aizada Vakhitova, Anargul Bekenova, Zhenis Bagisov, I. Bapiyev

引用次数: 0

Handling data imbalance using CNN and LSTM in financial news sentiment analysis 利用CNN和LSTM处理财经新闻情感分析中的数据不平衡

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663802

Moldir Omarkhan, Gulnur Kissymova, Iskander Akhmetov

{"title":"Handling data imbalance using CNN and LSTM in financial news sentiment analysis","authors":"Moldir Omarkhan, Gulnur Kissymova, Iskander Akhmetov","doi":"10.1109/icecco53203.2021.9663802","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663802","url":null,"abstract":"With a speedy development in Natural Language processing, the financial sector meets the demand of analyzing a large quantity of financial text data. Several recent research has focused on the subject of Financial Sentiment Analysis (FSA). In this article, we worked on sentiment analysis which is one of the most popular areas of natural language processing. We tried to use the sentiment analysis of news in the financial market, as sometimes news has a very strong impact on the stock market. We used the data of P. Malo [18] containing the 5,000 sentences of the finance news with labels of the sentiment. This study uses machine learning and deep learning algorithms as a research approach to develop a comprehensive comparative study on Financial News Sentiment Analysis that includes data sources. We compared the classification accuracy performance of machine learning and deep learning algorithms such as SVM, KNN, Decision Tree, Random Forest, XGBoost, CNN, and LSTM in a sentiment analysis of financial news. Our inspirations in the future direction such as handling data imbalance also discussed and applied for algorithms. The experiments demonstrate that the CNN algorithm, based on accuracy, consistently outperforms the other models in the performance of sentiment analysis of financial news.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130392402","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

Automatic Classification of University Staff Enquiries in Russian and English 俄语和英语的大学职员查询自动分类

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663851

Abylay Omar, S. Kadyrov, Yerbol Baigarayev

引用次数: 0

3D U-Net for brain stroke lesion segmentation on ISLES 2018 dataset 基于ISLES 2018数据集的脑卒中病灶三维U-Net分割

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663825

A. Tursynova, B. Omarov

{"title":"3D U-Net for brain stroke lesion segmentation on ISLES 2018 dataset","authors":"A. Tursynova, B. Omarov","doi":"10.1109/icecco53203.2021.9663825","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663825","url":null,"abstract":"Brain stroke is one of the global problems today. An image such as a CT scan helps to visually see the whole picture of the brain. Segmentation of the affected brain regions requires a qualified specialist. However, manual segmentation requires a lot of time and a good expert. The role and support of trained neural networks for segmentation tasks is considered as one of the best assistants. Among the neural network models, the models based on U-Net are recognized as the leading ones. The U-Net architecture can work with a small number of datasets and is considered advanced for the segmentation method. In this paper, we use the classical U-Net architecture for the experiment. As datasets, we use 3D computed tomography images of the brain taken from ISLES 2018 the public domain. Using the classical U-Net architecture, we found that U-Net is considered the best architecture for segmentation methods. This study presents experiment results of 3D U-Net model for brain stroke lesion segmentation, and gives future perspectives for researchers who is going to segment brain strokes and create modified U-Net model for improvement. The developed model is useful for brain stroke segmentation when there is little number of images for train and testing the model.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122865224","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: 18

Majorizing model of a ∑-frequency pulse system for automatic temperature control of the optical fiber drawing process 光纤拉拔过程温度自动控制∑-频率脉冲系统的优化模型

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663746

B. Aitchanov, A. Tergeussizova

引用次数: 0