2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)最新文献

Work of Breathing Estimation during Spontaneous Breathing Test using Machine Learning Techniques 基于机器学习技术的自主呼吸测试中呼吸估计工作

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247855

Luis Felipe Buitrago Castro, Luis Fernando Enriquez Santacruz, M. B. S. Sánchez

{"title":"Work of Breathing Estimation during Spontaneous Breathing Test using Machine Learning Techniques","authors":"Luis Felipe Buitrago Castro, Luis Fernando Enriquez Santacruz, M. B. S. Sánchez","doi":"10.1109/ColCACI50549.2020.9247855","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247855","url":null,"abstract":"Prolonged support or premature weaning of mechanical ventilation leads to several complications of cardiopulmonary physiology. Recently, work of breathing is proposed as an alternative that provides objective information about the weaning process. However, the availability and ease of use of techniques for its estimation in a clinical context are limited. Thus, the application of computerized methods for work of breathing estimation becomes necessary to assist professionals. In this article, we compare the performance of different machine learning techniques in the work of breathing estimation tasks. The problem is divided into two classes: high and low work of breathing, based on information extracted from the pressure, volume, and flow signals recorded by the mechanical ventilator. The classification algorithms used were: support vector machines, neural networks, k nearest neighbors, which were trained and tested on ventilatory signals of subjects with high and low work of breathing. The results show that the classification system can recognize the work of breathing levels with an accuracy of up to 80%.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115242435","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

Convolutional neural network proposal for wrist position classification from electromyography signals 基于肌电信号的腕部位置分类的卷积神经网络方案

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247924

A. Orjuela-Cañón, O. J. Perdomo-Charry, C. H. Valencia-Niño, Leonardo Forero

引用次数: 0

Echo State Network Performance Analysis using Non-random Topologies 基于非随机拓扑的回声状态网络性能分析

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9248714

D. C. R. Arroyo, A. Florez, D. Flores, R. Romero, Liang Zhao

引用次数: 2

Locomotion Control of PhantomX Hexapod Robot with Touch-Pressure Sensor and RoboComp 基于触摸压力传感器和RoboComp的PhantomX六足机器人运动控制

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247874

John Euler Chamorro Fuertes, Jairo Jose Marin Arciniegas, Pablo Bustos García de Castro, Oscar Andrés Vivas Albán

引用次数: 1

MRI Brain Tumour Segmentation using a CNN Over a Multi-parametric Feature Extraction 基于CNN多参数特征提取的MRI脑肿瘤分割

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247926

Elizabeth Martinez, C. Calderón, Hans Garcia, H. Arguello

{"title":"MRI Brain Tumour Segmentation using a CNN Over a Multi-parametric Feature Extraction","authors":"Elizabeth Martinez, C. Calderón, Hans Garcia, H. Arguello","doi":"10.1109/ColCACI50549.2020.9247926","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247926","url":null,"abstract":"A Brain tumour is a collection of an abnormal mass of tissue that can be grown as cancerous. This pathology can be detected using noninvasive techniques such as CT and MR. Despite CT can form a three-dimensional computer model by taking multiple X-rays shots, the MRI scans are highly preferred since they do not use ionizing energy on its captures and they also provide sufficient information to confirm a diagnosis, however, MRI scans have a lot of noise which can reduce the accuracy of the diagnosis. Therefore, many works in the state of the art try to solve these issue using first a filtering method to clear the noise and then a semantic classification algorithm such feature pyramid network, mask R CNN and random forest classifiers trained over the images acquired with MRI technique extracting grayscale intensity, spatial proximity and texture similarity features, however, segmentation image using these methods does not have sufficient accuracy. Thus, this work proposes to look forward over the FLAIR images on the BRATS 2015 training dataset that is composed by 155 captures of axial cuts from where the principal and adjacent layers that have the highest amount of information are used to reformulate and increase data features that lead on a pixel-based classifier U-net proposed performs a semantic segmentation with a precision of 76%, which improves in up to 23% precision compared with the random forest-based method that obtained a 53% of precision.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116231781","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

[ColCACI 2020 Front cover] [ColCACI 2020年封面]

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/colcaci50549.2020.9247851

引用次数: 0

MosCla app: An android app to classify Culicoides species mocla应用程序:一款安卓应用程序，用于分类库蠓物种

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247857

S. Gutiérrez, Noel Pérez, D. Benítez, S. Zapata, D. Augot

{"title":"MosCla app: An android app to classify Culicoides species","authors":"S. Gutiérrez, Noel Pérez, D. Benítez, S. Zapata, D. Augot","doi":"10.1109/ColCACI50549.2020.9247857","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247857","url":null,"abstract":"Culicoides biting midges are transmission vectors of various diseases affecting humans and animals around the world. An optimal and fast classification method for these and other species have been a challenge and a necessity, especially in areas with limited resources and public health problems. In this work, we developed a mobile application to classify two Culicoides species using the morphological pattern analysis of their wings. The app implemented an automatic classification method based on the calculation of seven morphological features extracted from the wing images and a support vector machine classifier to produce the final classification of Pusillus or Obsoletus class. The proposed app was validated on an experimental dataset with 87 samples, reaching an outstanding mean of AUC score of 0.98 in the classification stage. Besides, we assessed the app feasibility using the mean of time and battery consumption metrics on two different emulators. The obtained scores of 12 and 7 s and 0.11 and 0.03 mAh for the phone and tablet emulators are satisfactory when developing mobile applications. Finally, reducing the feature space using an external wrapper method provided us a considerable improvement in the classification performance, AUC scores from 0.95 to 0.98, and decreasing the volume of information in training stages. Thus, these results enable the proposed app as an excellent approximation to those specialists that need a practical tool to classify Pussillus or Obsoletus species in wildlife environments.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123866912","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

Systematic Literature Review: Artificial Neural Networks Applied in Satellite Images 系统文献综述:人工神经网络在卫星图像中的应用

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247916

Paola Andrea Zárate Luna, Jesús Alfonso López Sotelo

{"title":"Systematic Literature Review: Artificial Neural Networks Applied in Satellite Images","authors":"Paola Andrea Zárate Luna, Jesús Alfonso López Sotelo","doi":"10.1109/ColCACI50549.2020.9247916","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247916","url":null,"abstract":"For approximately 50 years, artificial neural networks have been playing a decisive role in the technological advances of the world, however, their application in the treatment of satellite images has not reached the expected potential since researchers have had to face to several problems such as object recognition, classification and semantic segmentation in images of low spatial resolution due to the high costs generated by building an optimal training and testing data set. This article presents the systematic review of large research literature and the most relevant papers presented in the last decade. The main sources chosen for the review were the IEEE digital library, the indexing of the SCOPUS system database and the Science Direct repository, with a total search of 386 articles related to the case study that after applying different filters, Inclusion and exclusion criteria are deepened in detail with 30 of them, finding an ascending scale in the amount of research developed in recent years, demonstrating the great interest and growth of this type of artificial intelligence technique.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121096083","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

Machine learning techniques for detecting motor imagery in upper limbs 用于检测上肢运动图像的机器学习技术

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247869

J. Archila, A. Orjuela-Cañón

引用次数: 0

Snapshot compressive spectral video via a monocular optical system 快照压缩光谱视频通过单目光学系统

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9248717

David Morales, Paula Arguello, M. Márquez, H. Arguello

{"title":"Snapshot compressive spectral video via a monocular optical system","authors":"David Morales, Paula Arguello, M. Márquez, H. Arguello","doi":"10.1109/ColCACI50549.2020.9248717","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9248717","url":null,"abstract":"This work introduces an imaging device that efficiently captures high-speed spectral videos along with a mathematical model that allows reconstructs them from far fewer measurements than those required by conventional scanning devices. This imaging architecture modulates and multiplexes the spectral-temporal information into a single compressed measurement by introducing a Dynamic Vision Sensor (SCAMP5) as a detector in a conventional compressive snapshot spectral image (CASSI) system. SCAMP5 sensor embeds processing and data storage capability into the pixels, which allows developed a high-speed temporal codification. The results of the numerical experiments through high-speed spectral videos shows reliable performance reconstructing spectral videos for a different amount of reconstructed frames. Comparing this proposal approach of snapshot spectral video with the conventional capture of spectral videos with multishot systems, our work arises very close results additionally our system outperforme the temporal spectral compression, more fully, the proposal approach captures a 8 times less samples obtaining a difference of 2.86 in SAM, 0.08 in SSIM, 2.9 in PSNR and 0.03 for RMSE. Therefore, the proposed architecture is an efficient and alternative high-speed spectral video acquisition system.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117083756","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