2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)最新文献_第3页

Tracking Hammerhead Sharks With Deep Learning 用深度学习追踪双髻鲨

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247911

Alvaro Peña, Noel Pérez, D. Benítez, A. Hearn

{"title":"Tracking Hammerhead Sharks With Deep Learning","authors":"Alvaro Peña, Noel Pérez, D. Benítez, A. Hearn","doi":"10.1109/ColCACI50549.2020.9247911","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247911","url":null,"abstract":"In this study, we propose a new automated method based on deep convolutional neural networks to detect and track endangered hammerhead sharks in video sequences. The proposed method improved the standard YOLOv3 deep architecture by adding 18 more layers (16 convolutional and 2 Yolo layers), which increased the model performance in detecting the species under analysis at different scales. According to the frame analysis based validation, the proposed method outperformed the standard YOLOv3 architecture in terms of accuracy scores for the majority of inspected frames. Also, the mean of precision and recall on an experimental frames dataset formed using the 10-fold cross-validation method highlighted that the proposed method was better than the standard YOLOv3 architecture, reaching scores of 0.99 and 0.93 versus 0.95 and 0.89 for the mean of precision and recall, respectively. Furthermore, both methods were able to avoid introducing false positive detections. However, they were unable to handle the problem of species occlusion. Our results indicate that the proposed method is a feasible alternative tool that could help to monitor relative abundance of hammerhead sharks in the wild.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"80 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":"126872732","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

Object Classification Using Spectral Images and Deep Learning 利用光谱图像和深度学习进行目标分类

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9248726

C. López, Roman Jacome, Hans Garcia, H. Arguello

{"title":"Object Classification Using Spectral Images and Deep Learning","authors":"C. López, Roman Jacome, Hans Garcia, H. Arguello","doi":"10.1109/ColCACI50549.2020.9248726","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9248726","url":null,"abstract":"Spectral images contain valuable information across the electromagnetic spectrum, which provides a useful tool for classification tasks. Most of the traditional machine learning algorithms for spectral images classification such as support vector machine (SVM), k-nearest neighbor, or random forest required complex handcrafted features extraction of the data, in contrast with these approaches deep learning-based methods realize the feature extraction automatically. This paper proposes a procedure to classify spectral images with a Convolutional Neural Network (CNN) approach which consists in the experimental acquisition of two datasets, medicines and honey, the pre-processing of the raw data, the design of the (CNN) and finally the classification results performed by the designed CNN. The results of the first simulation of the proposed CNN-Med show accuracy in the validation set of up to 97.3% for the medicines dataset compared with 94.6% ResNet-18 architecture accuracy and 89.2% AlexNet architecture accuracy. The results of the proposed CNN-Honey show an accuracy, by patches, in the validation set of up to 92.11% for the honey dataset compared with 86.84% ResNet-18 architecture accuracy.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"5 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":"121080087","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

Comparison between HOG and Haar descriptors in the detection of abnormal tissue in mammograms HOG和Haar描述符在乳房x光检查异常组织中的比较

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247852

Jesica Talero, R. Espinosa

{"title":"Comparison between HOG and Haar descriptors in the detection of abnormal tissue in mammograms","authors":"Jesica Talero, R. Espinosa","doi":"10.1109/ColCACI50549.2020.9247852","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247852","url":null,"abstract":"The design and development of artificial intelligence and machine learning models applied to medical images are an alternative for the detection and classification of abnormal clinical patterns. Mammography images help identify abnormal areas of dense breast tissue that indicate risk factors for breast cancer. In this article, we compare the HOG and Haar descriptors by varying the negative sample factor parameter in training a machine learning based cascade object detector using MATLAB®. The objective was to identify the best descriptor and parameters that allow increasing the precision of detection and labeling of regions of clinical interest due to the presence of abnormal regions in digital mammograms. The images used in the training were obtained from the free database of the United Kingdom Mammography Image Analysis Society (MIAS) and tests were performed with images of Breast Cancer Digital Repository (BCDR). The HOG and Haar descriptors were used in 15 stages, with a different value in the negative sample factor parameter in each descriptor. The results showed that using the HOG descriptor with a low value of negative sample factor, the precision in detecting abnormal tissue in mammography was higher compared to the use of Haar descriptor.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"34 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":"129927757","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

Welcome Letter 欢迎信

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/colcaci50549.2020.9247934

引用次数: 0

Critical Diagnosis in Brain MRI Studies based on Image Signal Intensity and Supervised Learning 基于图像信号强度和监督学习的脑MRI关键诊断

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247930

Natalia Santamaria-Macias, J. F. Orejuela-Zapata, J. Pulgarin-Giraldo, A. M. Granados-Sánchez

引用次数: 0

Deep-Learning for Volcanic Seismic Events Classification 火山地震事件分类的深度学习

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247848

A. Salazar, Rodrigo Arroyo, Noel Pérez, D. Benítez

{"title":"Deep-Learning for Volcanic Seismic Events Classification","authors":"A. Salazar, Rodrigo Arroyo, Noel Pérez, D. Benítez","doi":"10.1109/ColCACI50549.2020.9247848","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247848","url":null,"abstract":"In this work, we proposed a new method to classify long-period and volcano-tectonic spectrogram images using six different deep learning architectures. The developed method used three deep convolutional neural networks named: DCNN1, DCNN2, and DCNN3. Also, three deep convolutional neural networks combined with deep recurrent neural networks named DCNN-RNN1, DCNN-RNN2, and DCNN-RNN3 to maximize the area under the curve of the receiver operating characteristic scores on a dataset of volcano seismic spectrogram images. The three deep recurrent neural network-based models reached the worst results due to the overfitting produced by the small number of samples in the training sets. The DCNN1 overcame the remaining models by touching an area under the curve of the receiver operating characteristic and accuracy scores of 0.98 and 95%, respectively. Although these values were not the highest values per metric, they did not represent statistical differences against other results obtained by more algorithmically complex models. The proposed DCNN1 model showed similar or superior performance when compared to the majority of the state of the art methods in terms of the accuracy metric. Therefore it can be considered a successful scheme to classify LP and VT seismic events based on their spectrogram images.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"21 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":"130454735","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

Preliminary machine learning model for citrus greening disease (Huanglongbing-HLB) prediction in Colombia 哥伦比亚柑橘黄龙冰- hlb病预测的初步机器学习模型

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247900

Edisson Chavarro-Mesa, Enrique Delahoz-Domínguez, Mary Fennix-Agudelo, Wendy Miranda-Castro, J. Ángel-Diaz

引用次数: 0

Buy & Sell Trends Analysis Using Decision Trees 使用决策树进行买卖趋势分析

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247907

Carlos Vaca, Daniel Riofrío, Noel Pérez, D. Benítez

{"title":"Buy & Sell Trends Analysis Using Decision Trees","authors":"Carlos Vaca, Daniel Riofrío, Noel Pérez, D. Benítez","doi":"10.1109/ColCACI50549.2020.9247907","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247907","url":null,"abstract":"Present artificial intelligence advances tend to be focused on customized deep learning techniques which are computational expensive and require costly infrastructure. These techniques have shown to be particularly effective in highly complex environments such as image processing, natural language processing and market price predictions. On the other hand, small companies are requiring more and more access to artificial intelligence to predict customer behavior and hence to avoid to be affected by the highly volatility and variance of the market. Unfortunately, most of these companies may not be able to afford the costs of current artificial intelligence advanced methods. Hence, in this paper we study a low-cost known alternative: decision tree classifiers. In particular, we focus our analysis on the benefits to use them to analyze market predictions with high area under the receiver operating characteristic curve over three databases: Social Network Advertising Sells, Organic Purchased Indicator, and Online Shoppers Purchasing Intention. The best decision tree models obtained were those that produced an area under the receiver operating characteristic curve score from 0.81 to 0.96. In addition, we report the accuracy of our models which provided results ranging from 79.80& to 89.80&. These results show that simple models like decision trees are good to understand the fluctuation and trends from market data, and since its simplicity are an alternative for small businesses willing to try artificial intelligence predictions.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"43 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":"130228579","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

Video-Tensor Completion using a Deep Learning approach 使用深度学习方法的视频张量补全

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/ColCACI50549.2020.9247929

Paula Arguello, David Morales, Y. Fonseca, H. Arguello

{"title":"Video-Tensor Completion using a Deep Learning approach","authors":"Paula Arguello, David Morales, Y. Fonseca, H. Arguello","doi":"10.1109/ColCACI50549.2020.9247929","DOIUrl":"https://doi.org/10.1109/ColCACI50549.2020.9247929","url":null,"abstract":"The tensor completion problem solves the recovery of corrupted data in a multidimensional array named as a tensor. The traditional approaches in tensor completion are based on the transform tensor singular value decomposition(tt-SVD). These approaches minimize the tensor nuclear norm in a domain of an orthogonal transformation to induce low tensorial rank representation. Hence, they require previous knowledge of the data to ensure a low tensor rank representation and, therefore, to ensure a good quality reconstruction. On the other hand, based on the wide progress of deep learning in diverse contexts, this paper presents a 3DU-Net architecture for tensor data recovery in the problem of grayscale videos. The proposed method consists of convolutional layers with 3D filters to take advantage of the information at the spatio-temporal dimensions. The experimental results show that the proposed method has better performance in relative error (RE), peak-to-signal-ratio (PSNR), and less runtime compared with the state-of-the-art solutions. In particular, in the presence of noise, our proposed approach improves the recovery in up to 5.99 dB, and 0.09 in the RE with an 85% of corrupted pixels. In the noiseless case, the proposed architecture improves in 4.39 dB and 0.07 in the RE, when an 85% of the data is lost. Furthermore, the proposed method shows to be faster than the state-of-the-art in the reconstruction time in at least 2.5 times.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"22 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":"129657306","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

2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) Pub Date : 2020-08-07 DOI: 10.1109/colcaci50549.2020.9247899

引用次数: 0