2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)最新文献

Machine Learning and Social Media Harvesting for Wildfire Prevention 预防野火的机器学习和社交媒体收获

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179001

A. Laksito, Kusrini Kusrini, A. Setyanto, Muhammad Zuhdi Fikri Johari, Zauvik Rizaldi Maruf, K. A. Yuana, G. B. Adninda, Renindya Kartikakirana, F. Nucifera, Wiwin Widayani, K. Chandramouli, Ebroul Ezquierdo

{"title":"Machine Learning and Social Media Harvesting for Wildfire Prevention","authors":"A. Laksito, Kusrini Kusrini, A. Setyanto, Muhammad Zuhdi Fikri Johari, Zauvik Rizaldi Maruf, K. A. Yuana, G. B. Adninda, Renindya Kartikakirana, F. Nucifera, Wiwin Widayani, K. Chandramouli, Ebroul Ezquierdo","doi":"10.1109/ICPRS58416.2023.10179001","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179001","url":null,"abstract":"Social media can be exploited to ease or better understand many problems in science and technology. Social media sensing become one of the methods to get the update information from crowd. Text is the most unstructured information sources available on that media comes from their users. Taking useful information from the unstructured text however, still challenging. The conversion from unstructured data to a vector need to be carried out texts analysis. Machine learning offer an opportunity overcome the challenges. However, it needs massive amount of labelled data to train the machine learning. The research community pose the fact that the availability of the dataset for non - English languages are limited. Forest fire as one of topics discussed in many social media platforms in many languages including Indonesian language. This work fills the gap by providing a labelled dataset in forest fire topics. Moreover, an exercise of the dataset with some available machine learning techniques are compared. Due to the limited number of data rows, researchers perform data augmentation and observe the impact of the augmentation towards the classifier performance. According to our experiments, we found that Random oversampling (ROS), in general, improves the accuracy score for all models we employed. Bidirectional LSTM with ROS overperforms other classifier and achieves 91%, 91% and 90.7%of recall, precision and f1-score respectively.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"47 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125921347","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

Beam Recognition for Headlamps Aiming 前照灯瞄准的光束识别

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179083

Aymeric Chaumont, Paul Creusy, Agathe Plu, Yasser Almehio, Hafid El-Idrissi

{"title":"Beam Recognition for Headlamps Aiming","authors":"Aymeric Chaumont, Paul Creusy, Agathe Plu, Yasser Almehio, Hafid El-Idrissi","doi":"10.1109/ICPRS58416.2023.10179083","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179083","url":null,"abstract":"In this paper, a novel approach is introduced to address the issue of vehicle headlamps aiming. Instead of handling it with traditional methods, it is considered as a beam pattern recognition problem. Unlike conventional techniques, which primarily rely on offline measurements of specific reference points, which are then projected onto a wall or panel, our method suggests an automatic aiming process using the Surround View Camera (SVC) via two main steps. First, checking the alignment of headlamps, where it detects whether it is “conform” or not according to the automotive lighting regulations. Second, estimating the deviation from optimal aiming regarding vertical and horizontal angles. A Deep Learning model is proposed to detect whether the headlamp is still within the regulation requirements, and another model for regression with a normalized loss function is designed to predict the errors of both orientations. Our two-step approach, tested with multiple architectures, shows high accuracy. To evaluate the performance of the proposed models, photometrical datasets of three different products are tested for both classification and regression. We computed several metrics on experimental results to evaluate both steps and obtained with the best considered architecture a mean accuracy of 94.46% for the classification part and a normalized error interval of 3.53% for the regression one.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122360122","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

Granulometric Analysis of Maltodextrin Particles Observed by Scanning Electron Microscopy 麦芽糖糊精颗粒的扫描电镜粒度分析

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179067

Antoine Bottenmuller, L. Théodon, J. Debayle, Daniel Tobón Vélez, M. Tourbin, C. Frances, Y. Gavet

{"title":"Granulometric Analysis of Maltodextrin Particles Observed by Scanning Electron Microscopy","authors":"Antoine Bottenmuller, L. Théodon, J. Debayle, Daniel Tobón Vélez, M. Tourbin, C. Frances, Y. Gavet","doi":"10.1109/ICPRS58416.2023.10179067","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179067","url":null,"abstract":"Maltodextrin is a substance that is being increasingly used to preserve the physicochemical and biological properties of many active compounds. Therefore, determining the particle size distribution (PSD) of the particles having maltodextrin matrices is a crucial issue to control their end-use properties. This can be done directly by laser diffraction (LD) in a dry way or via SEM image analysis. In this paper, a new method of segmentation of quasi-circular particles from grayscale images called curvature analysis method (CAM) is proposed. This method is compared to two other widely used methods: Circular Hough Transform (CHT) and Stochastic Watershed (SW). It aims in particular to reduce the drawbacks of these two methods: a large number of false detections and an inaccuracy with respect to the mean particle size in case of a large number of overlapping particles. The method is validated using synthetic images generated with a model allowing to simulate gray level images similar to the maltodextrin particle images from the SEM. The CAM method is then applied to real images and the resulting PSD is compared to the one provided by the LD technique. Overall, the results obtained by the CAM method are much better than those proposed by CHT and SW on synthetic images and than the LD method on real images.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129876992","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

Detecting Multi Thoracic Diseases in Chest X-Ray Images Using Deep Learning Techniques 利用深度学习技术检测胸部x射线图像中的多种胸部疾病

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179041

Sebastian Quevedo, Federico Domíngez, Enrique Peláez

引用次数: 1

Several Kinds of Residual Neural Networks Driven by Partial Differential Equations and Their Applications in Image Classification and Denoising 几种偏微分方程驱动残差神经网络及其在图像分类和去噪中的应用

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179052

Haowei Ji, Haoyuan Xu, Yi Zhang, Jinchun He

{"title":"Several Kinds of Residual Neural Networks Driven by Partial Differential Equations and Their Applications in Image Classification and Denoising","authors":"Haowei Ji, Haoyuan Xu, Yi Zhang, Jinchun He","doi":"10.1109/ICPRS58416.2023.10179052","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179052","url":null,"abstract":"In the past few years, many scholars gradually began to interpret the conventional residual neural Networks (ResNet) as the partial differential equations. Some scholars called it Neural PDE. Ruthotto, Haber, Lin and others have done relevant researches on this interpretation. This paper mainly studies the residual neural network model driven by the heat equation (HERN) and the convection-diffusion equation (CDERN). Experiment I and experiment II mainly used these two networks mentioned above to compare with the traditional ResNet for image recognition under the interference of the Gaussian noise and the salt and pepper noise. In experiment III, under the interference of the Gaussian noise, the HERN model and the CDERN model are used to compare with the denoising effects of the traditional ResNet, the Gaussian filtering algorithm and Perona-Malik Model. The results show that the denoising effect of the Gaussian noise with the square error greater than 0.6 based on the HERN model is better than Gaussian filter, the CDERN model and the traditional ResNet model; In addition, it also shows that CDERN and HERN are more stable than the traditional ResNet.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133592389","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

ML-based Vegetative Drought Prediction employing Satellite Remote Sensing and Precipitation Datasets 基于ml的卫星遥感和降水植被干旱预测

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179057

Jyoti S. Shukla, R. Pandya

{"title":"ML-based Vegetative Drought Prediction employing Satellite Remote Sensing and Precipitation Datasets","authors":"Jyoti S. Shukla, R. Pandya","doi":"10.1109/ICPRS58416.2023.10179057","DOIUrl":"https://doi.org/10.1109/ICPRS58416.2023.10179057","url":null,"abstract":"In an agriculturally-dominant economy such as India, gross crop production and adequate land use constitute essential variables to contemplate, when assessing the socioeco-nomic well-being of all societal segments. Indubitably, agriculture growth relies on climatic factors and the availability of natural resources to suffice the demand-supply chain. Drought, a natural hazard, grievously impairs land conditions and water availability determinants, bringing in a crisis in agriculture productivity and several other interlinked spheres. In this paper, we demonstrate the effective implementation of Linear Regression, a widely favored Machine Learning (ML) - based model, to capture the short-term drought severity, incorporating two datasets com-prising NOAA/AVHRR satellite-data-derived Vegetation Health Index (VHI) and VHI+Rainfall readings as the features. The Karnataka state of India is selected as the Region of Interest (ROI), and the compiled dataset spans the period 1981 through 2021. A thorough comparison of the proposed model with several anointed reference models is proffered. The model is further evaluated on datasets for various timescales, and the outcomes are documented. The proposed ML model outperforms the other conventional models as validated through the performance metrics, inclusive of coefficient of determination (R2), mean squared error, and mean absolute error, obtaining values of 0.98, 0.0076, and 0.048 for the VHI dataset and 0.97, 0.0148, and 0.067 for VHI+Rainfall datasets respectively on the selected ROI.","PeriodicalId":253231,"journal":{"name":"2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128740001","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

Climatic Challenges of Quito 基多的气候挑战

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179029

R. Llugsi

引用次数: 0

Telugu Spoken Digits Modeling using Convolutional Neural Networks 使用卷积神经网络的泰卢固语口语数字建模

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10178989

Parabattina Bhagath, A. U. M. Rao, B. S. Ram, M. A. K. Reddy

引用次数: 0

A Novel Kernel Extension for the Nearest Feature Line Classifier 最近特征线分类器的一种新的核扩展

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179028

M. Bicego, Eduardo-José Villegas-Jaramillo, M. Orozco-Alzate

引用次数: 0

A Deep Learning-Based Algorithm for ECG Arrhythmia Classification 基于深度学习的心电心律失常分类算法

2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS) Pub Date : 2023-07-04 DOI: 10.1109/ICPRS58416.2023.10179058

Daniela Espin-Ramos, Vicente Alvarado, E. Añazco, Erick Flores, Bolívar Nuñez, J. Santos, Sara Guerrero, Jonathan Abraham Aviles-Cedeno

引用次数: 0