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

COVID-19 classification based on CNNs models in CT image datasets CT图像数据集中基于cnn模型的COVID-19分类

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663749

Dina Kushenchirekova, Andrey Kurenkov, D. Mamyrov, D. Viderman, Seong-Jun Lee, Min-Ho Lee

{"title":"COVID-19 classification based on CNNs models in CT image datasets","authors":"Dina Kushenchirekova, Andrey Kurenkov, D. Mamyrov, D. Viderman, Seong-Jun Lee, Min-Ho Lee","doi":"10.1109/icecco53203.2021.9663749","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663749","url":null,"abstract":"The COVID-19 coronavirus pandemic was a global challenge to the whole society and at the same time created a unique situation for the development of science, scientific communication and open access to scientific information. At the beginning of 2019 the world has faced a pandemic of Covid-19 coronavirus. The coronavirus impacted dramatically lives of majority people around the globe. Deep learning methods allow automatic classification of the coronavirus disease from the computer tomography (CT) scans of the lung. In our work we test several popular convolutional neural network (CNN) models to classify slices of the CT scans. In this study we indicate that the VGG-19 model gives the best classification accuracy among the other tested models such as DenseNet201, MobileNetV2, Xception, VGG-16 and ResNet50v2. In particular, the model achieves the accuracy of 99.08% for CovidX CT Dataset and 98.44% for SARS-CoV-2 CT dataset and 92.30% for UCSD COVID-CT dataset. Additionally, our results include 3D heatmaps that explain classification results for each individual model, showing regions of the lung affected by the coronavirus.","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":"115666247","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

Application of k-means in the perception of supervisors from students’ side k-means在学生对导师感知中的应用

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663859

Azamat Serek, Adil Akhmetov, Nurbek Ismagulov, Bekarys Rysbek, Beibarys Rysbek, Sanzhar Alim

引用次数: 1

Review of violent extremism detection techniques on social media 社交媒体上的暴力极端主义侦查技术综述

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663813

Kydyrkhanov Zhaksylyk, Omarov Batyrkhan, Mussiraliyeva Shynar

引用次数: 0

Simulation Of An Intelligent Detection System Virus SARS-CoV-2 In Enclosed Spaces 封闭空间中SARS-CoV-2病毒智能检测系统的模拟

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663840

Zhanar S. Moldabayeva, Peter Schmidt, Zhexen Y. Seitbattalov, Tanzharyk A. Amankeldinov

{"title":"Simulation Of An Intelligent Detection System Virus SARS-CoV-2 In Enclosed Spaces","authors":"Zhanar S. Moldabayeva, Peter Schmidt, Zhexen Y. Seitbattalov, Tanzharyk A. Amankeldinov","doi":"10.1109/icecco53203.2021.9663840","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663840","url":null,"abstract":"During the COVID-19 pandemic, methods to determine the presence of coronavirus are very relevant. In this article, a new method has been developed to detect the presence of the SARS-CoV-2 virus in the air. The article analyzes in detail the signs indicating favorable conditions for the spread of the virus. The influence of changes in temperature, humidity, the presence of particles in the air, the level of CO2 and the ventilation of the room on the probability of infection with the virus is considered.Based on the studied parameters, a model was built. It allows to detect the probability of the threat of the spread of the virus. The model was implemented as a fuzzy controller in the Matlab environment. The studied parameters were taken as input parameters for constructing the model. The output parameter was the detector value indicating the presence or absence of a virus in the air.Thus, the built system allows detecting situations in which coronavirus infection is most likely. The system makes a conclusion about the degree of risk of the spread of the virus and, in case of danger, informs about the need for disinfection actions. The use of this system will reduce the cost of processing enclosed spaces, since it will not be carried out according to schedule, but in the case of a system signal.","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":"133775192","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

ICECCO 2021 Table of contents ICECCO 2021目录

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663807

引用次数: 0

Optimization of CNN Model for Breast Cancer Classification 乳腺癌分类CNN模型的优化

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663847

N. Mikhailov, M. Shakeel, A. Urmanov, Min-Ho Lee, M. Demirci

引用次数: 2

Development of a software simulator for small satellite swarm control 小卫星群控软件模拟器的研制

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663828

K. Moldamurat, A.S. Utegen, S. Brimzhanova, D. Kalmanova, N. G. Yryskeldi

{"title":"Development of a software simulator for small satellite swarm control","authors":"K. Moldamurat, A.S. Utegen, S. Brimzhanova, D. Kalmanova, N. G. Yryskeldi","doi":"10.1109/icecco53203.2021.9663828","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663828","url":null,"abstract":"This article is devoted to creating a control code to simulate the complicated type of flight of a group of small spacecraft in formation, called swarms of spacecraft. The Swarm project with integrated Fem satellites on a silicon wafer (SWIFT) Jet Propulsion Laboratories is a new, a paradigm-changing definition of spacecraft technology that could provide swarms of fully capable femtosatellites to fly. These swarms have the capacity to be used as optical relays, distributed antennas, or for mass distribution probing applications, among others. In this article, a swarm is defined as a collection of hundreds to thousands of homogeneous spacecraft, and a femtosat is defined as a satellite weighing about 100 g. Because of their small size, femtosats have limited probing, actuation, and computational capabilities, requiring that the swarm guidance and control algorithms be both motor and computationally efficient.In the article for testing the SS swarm control system, a virtual software simulator is developed. This simulator is written in Matlab R2015b in C ++ programming language. The simulator, using flight parameters, creates a simulation and shows the reaction of the SS in different situations.The simulator checks the flight path of the SS according to the task algorithm. During testing of the SSA flight orientation, the accuracy of hitting the target as well as the reduction and elimination of collision with other objects during the flight path is calculated.This virtual software simulator is designed for virtual testing of any group of SS.The virtual software simulator is used in the field of space research activities by space industry specialists with higher education.","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":"127409080","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: 3

Coordination of movement of multiagent robotic systems 多智能体机器人系统的运动协调

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663796

Abzal E. Kyzyrkanov, Sabyrzhan Atanov, Shadi A. Aljawarneh

引用次数: 10

ICECCO 2021 Cover Page ICECCO 2021封面

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663849

引用次数: 0

Analysis of Prostate Cancer DNA Sequences Using Bi-direction Long Short Term Memory Model 双向长短期记忆模型分析前列腺癌DNA序列

2021 16th International Conference on Electronics Computer and Computation (ICECCO) Pub Date : 2021-11-25 DOI: 10.1109/icecco53203.2021.9663839

Yusuf Aleshinloye Abass, Steve A. Adeshina, N. N. Agwu, Moussa Mahamat Boukar

{"title":"Analysis of Prostate Cancer DNA Sequences Using Bi-direction Long Short Term Memory Model","authors":"Yusuf Aleshinloye Abass, Steve A. Adeshina, N. N. Agwu, Moussa Mahamat Boukar","doi":"10.1109/icecco53203.2021.9663839","DOIUrl":"https://doi.org/10.1109/icecco53203.2021.9663839","url":null,"abstract":"Machine and deep learning-based models are the emerging techniques in addressing prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that requires huge attention in the biomedical domain. These techniques have been shown to provide better accurate results when compared to the conventional regression-based models. Prediction of the gene sequence that leads to cancerous diseases such as prostate cancer is very crucial. Identifying the most important features in a gene sequence is one of the most challenging tasks and extracting the components of the gene sequence that can give an insight into the kind of mutation in the gene is very important, it will lead to effective drug design and promote the new concept of personalized medicine. In this work we have extracted the exons in the various prostate gene sequence that was used in the experiment, we built a bi-LSTM model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The bi-LSTM model was evaluated on different classification metrics. Our experimental results show that the model prediction offers a training accuracy and validation accuracy of 95 percent and 91 percent respectively.","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":"129760643","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