N. Saparkhojayev, Lazzat Zholayeva, Yerzhan Tashkenbayev, D. Tokseit
{"title":"Abnormality Detection in Chest X-ray Images Using Uncertainty Prediction Algorithms","authors":"N. Saparkhojayev, Lazzat Zholayeva, Yerzhan Tashkenbayev, D. Tokseit","doi":"10.1109/icecco53203.2021.9663852","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663852","url":null,"abstract":"Histogram of Oriented Gradient (HOG) is one of the popular algorithms for recognizing objects in images with a very high success rate. In image processing techniques hardware reinforcement is one of the key features of studying the large size and complex images to perform. In this study, HOG features were extracted from all locations of a dense grid on an image region and used linear Support Vector Machine (SVM) to classify the combined features.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128766866","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}
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":null,"pages":null},"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}
A. Kuandykov, D. Kozhamzharova, Nurlan Karimzhan, M. Aitimov
{"title":"MAS agents development for mining industry","authors":"A. Kuandykov, D. Kozhamzharova, Nurlan Karimzhan, M. Aitimov","doi":"10.1109/icecco53203.2021.9663861","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663861","url":null,"abstract":"The essence of multi-agent technology is a fundamentally new method of solving problems. In contrast to the classical method, when a search is carried out a well-defined (deterministic) algorithm, which allows find the best solution to the problem in multi-technology solution is obtained automatically as a result of the interaction of many self-parking enforcement targeted software modules - the so-called software agents ants. Often classical methods for solving problems are not applicable in real life. There are various fields where MAS could be implemented, for the research of this paper the mining industry where taken.This paper explains MAS, its classification, shows the possibility of use of agent modeling in real industry. The article describes the steps of the development of agents, and the testing of them on a build-up layout of quarry and on working prototypes of the dumper and excavator robots.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123571175","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}
{"title":"What Factors at School Influence Student Academic Performance at University","authors":"N. Ibragimov, Asmina Barkhandinova, Nurzat Shayakhmetov, Aruzhan Akkoziyeva, Sultanmakhmud Bazarbayev, Zhandos Tangatar","doi":"10.1109/icecco53203.2021.9663856","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663856","url":null,"abstract":"This research demonstrates the use of some statistical tests popular in Data Science applied in Social Sciences. The usage is shown on the case of investigation of what factors might affect students’ performance. Most recent studies focus mostly on short-term results such as performance in high school. This paper examines these factors in the long-term run, taking the factors in schools that might affect a person in the future university. Analytical approach is used to derive most significant factors. The results approve and disapprove some stereotypes that were present before the research. The derived factors give motivation for further investigation of educational success.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126409092","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}
{"title":"Review of Electronic Industry in Kazakhstan: Conditions and Opportunities","authors":"Gulfarida Tulemissova, O. Baimuratov","doi":"10.1109/icecco53203.2021.9663826","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663826","url":null,"abstract":"This article is devoted to analysis of the development of the electronic industry in the leading countries of the world, where the influence of the globalism effect on global trends in the development of economic sectors is noted. Particular attention is paid to changing the paradigm of chip design, development of quantum microelectronics, use of new materials, at deepening the specialization of companies and developing the market for service companies. As a result of research and analysis conditions for the development of the electronic industry in Kazakhstan, identified options for the development strategy of the electronic industry in Kazakhstan with the introduction of Fabless enterprises, recommendations for the development of the electronic industry using the Foresight method (roadmap) are given.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133425284","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}
{"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":null,"pages":null},"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}
{"title":"Automatic Classification of University Staff Enquiries in Russian and English","authors":"Abylay Omar, S. Kadyrov, Yerbol Baigarayev","doi":"10.1109/icecco53203.2021.9663851","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663851","url":null,"abstract":"Document or text classification is a typical task in supervised machine learning. In this study we consider a multi-label text classification problem of helpdesk enquiries made by a university staff. To this end, we collect our data and consider the enquiries made in either Russian or in English. The dataset is categorized into eight different labels and underwent a preprocessing stage. A classical Term Frequency-Inverse Document Frequency algorithm is applied to the preprocessed data for feature extraction. For classification and prediction the Support Vector Machine and Multinomial Naive Bayes algorithms were utilized and the findings of experiments were compared. The experimental results show that in both languages, Support Vector Machine algorithm outperforms.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127209212","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}
Zh. U. Abuova, Aizada Vakhitova, Anargul Bekenova, Zhenis Bagisov, I. Bapiyev
{"title":"Experience of using SMART technologies in university education","authors":"Zh. U. Abuova, Aizada Vakhitova, Anargul Bekenova, Zhenis Bagisov, I. Bapiyev","doi":"10.1109/icecco53203.2021.9663820","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663820","url":null,"abstract":"The article summarizes the experience of using SMART technologies in the educational process of the university. The concept and the main components of SMART technologies (forms, methods, hardware and software) are identified. Specific examples of the use of SMART technologies (mobile learning, Interactive Whiteboard, Prezi and Kahoot! Web services) are considered to support face-to-face classes, improve the efficiency of students’ independent work, and systematically monitor knowledge and skills. The intellectual software used in the university to support active forms and methods of teaching (didactic games, discussions and project method) is presented.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121405242","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}
{"title":"Building Information Modeling for rural road design: a case study","authors":"Idrissi Gartoumi Khalil, Aboussaleh Mohamed, Zaki Smail","doi":"10.1109/icecco53203.2021.9663761","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663761","url":null,"abstract":"Much of the scientific research that has been conducted has focused on the contribution of advanced technologies to improving the performance of the construction industry. However, little of this work has focused on road projects. The emergence of modelling and simulation tools has led to an explosion in the cost effectiveness, durability, serviceability and safety of roads. Building information modelling (BIM) is one of the revolutionary digital processes emerging from information and communication technologies for civil engineering design. Scientifically, this paper aims to explore and measure the contribution of BIM in infrastructure projects and on the selected project to improve the level of service and safety of the rural road in Morocco. The objective of this study is to demonstrate the benefits and facilities offered. The geometric design is carried out using AUTODESK® CIVIL3D software. 70% of the total project cost is spent on earthworks and construction materials. These results show the criticality of the geometric design both at the technical and budgetary level; the simple error threatens the safety of the users and will lead to huge additional costs. With this BIM tool, the project is designed overcoming multiple constraints, well estimated, visualised on the real environmental context in 3D, and validated in a collaborative way on the basis of the generated dynamic 3D models.","PeriodicalId":331369,"journal":{"name":"2021 16th International Conference on Electronics Computer and Computation (ICECCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130002385","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}
{"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":null,"pages":null},"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}