International Work-Conference on Artificial and Natural Neural Networks最新文献

On the Use of First and Second Derivative Approximations for Biometric Online Signature Recognition 论生物特征在线签名识别中一阶和二阶衍生近似的使用

International Work-Conference on Artificial and Natural Neural Networks Pub Date : 2024-06-01 DOI: 10.1007/978-3-031-43085-5_36

Marcos Faúndez-Zanuy, Moisés Díaz

引用次数: 1

Adversarial Attacks on Leakage Detectors in Water Distribution Networks 输水管网泄漏检测器的对抗性攻击

International Work-Conference on Artificial and Natural Neural Networks Pub Date : 2023-05-25 DOI: 10.48550/arXiv.2306.06107

Paul Stahlhofen, André Artelt, L. Hermes, Barbara Hammer

引用次数: 1

Long-term hail risk assessment with deep neural networks 基于深度神经网络的长期冰雹风险评估

International Work-Conference on Artificial and Natural Neural Networks Pub Date : 2022-08-31 DOI: 10.48550/arXiv.2209.01191

Ivan Lukyanenko, Mikhail Mozikov, Yury Maximov, Ilya Makarov Moscow Institute of Physics, Technologies, Skolkovo Institute of Science, Technology, Los Alamos National Laboratory, A. I. R. Institute

{"title":"Long-term hail risk assessment with deep neural networks","authors":"Ivan Lukyanenko, Mikhail Mozikov, Yury Maximov, Ilya Makarov Moscow Institute of Physics, Technologies, Skolkovo Institute of Science, Technology, Los Alamos National Laboratory, A. I. R. Institute","doi":"10.48550/arXiv.2209.01191","DOIUrl":"https://doi.org/10.48550/arXiv.2209.01191","url":null,"abstract":"Hail risk assessment is necessary to estimate and reduce damage to crops, orchards, and infrastructure. Also, it helps to estimate and reduce consequent losses for businesses and, particularly, insurance companies. But hail forecasting is challenging. Data used for designing models for this purpose are tree-dimensional geospatial time series. Hail is a very local event with respect to the resolution of available datasets. Also, hail events are rare - only 1% of targets in observations are marked as\"hail\". Models for nowcasting and short-term hail forecasts are improving. Introducing machine learning models to the meteorology field is not new. There are also various climate models reflecting possible scenarios of climate change in the future. But there are no machine learning models for data-driven forecasting of changes in hail frequency for a given area. The first possible approach for the latter task is to ignore spatial and temporal structure and develop a model capable of classifying a given vertical profile of meteorological variables as favorable to hail formation or not. Although such an approach certainly neglects important information, it is very light weighted and easily scalable because it treats observations as independent from each other. The more advanced approach is to design a neural network capable to process geospatial data. Our idea here is to combine convolutional layers responsible for the processing of spatial data with recurrent neural network blocks capable to work with temporal structure. This study compares two approaches and introduces a model suitable for the task of forecasting changes in hail frequency for ongoing decades.","PeriodicalId":103356,"journal":{"name":"International Work-Conference on Artificial and Natural Neural Networks","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127944383","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

Learning without Forgetting for 3D Point Cloud Objects 学习不忘3D点云对象

International Work-Conference on Artificial and Natural Neural Networks Pub Date : 2021-06-27 DOI: 10.1007/978-3-030-85030-2_40

T. Chowdhury, Mahira Jalisha, A. Cheraghian, Shafin Rahman

引用次数: 3

On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks 分布式随机神经网络中超维计算对压缩的影响

International Work-Conference on Artificial and Natural Neural Networks Pub Date : 2021-06-17 DOI: 10.1007/978-3-030-85099-9_13

A. Rosato, M. Panella, Evgeny Osipov, D. Kleyko

引用次数: 2

Hotel Recognition via Latent Image Embedding 基于潜在图像嵌入的酒店识别

International Work-Conference on Artificial and Natural Neural Networks Pub Date : 2021-06-15 DOI: 10.1007/978-3-030-85099-9_24

B. Tseytlin, Ilya Makarov

引用次数: 5

Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images 在全卷积网络中增强形态学信息以改善荧光显微镜图像中的细胞实例分割

International Work-Conference on Artificial and Natural Neural Networks Pub Date : 2021-06-10 DOI: 10.1007/978-3-030-85030-2_4

W. Zamora-Cardenas, Mauro Mendez, S. C. Ramírez, Martin Vargas, Gerardo Monge, S. Quirós, D. Elizondo, Miguel A. Molina-Cabello

引用次数: 3

Temporal EigenPAC for dyslexia diagnosis 时间特征pac在阅读障碍诊断中的应用

International Work-Conference on Artificial and Natural Neural Networks Pub Date : 2021-04-13 DOI: 10.1007/978-3-030-85099-9_4

N. Gallego-Molina, M. Formoso, A. Ortiz, Francisco J. Mart'inez-Murcia, J. Luque

引用次数: 1

Contrastive Explanations for Explaining Model Adaptations 解释模式适应的对比解释

International Work-Conference on Artificial and Natural Neural Networks Pub Date : 2021-04-06 DOI: 10.1007/978-3-030-85030-2_9

André Artelt, Fabian Hinder, Valerie Vaquet, Robert Feldhans, B. Hammer

引用次数: 4

Optimization of Convolutional Neural Network Ensemble Classifiers by Genetic Algorithms 基于遗传算法的卷积神经网络集成分类器优化

International Work-Conference on Artificial and Natural Neural Networks Pub Date : 2019-06-12 DOI: 10.1007/978-3-030-20518-8_14

Miguel A. Molina-Cabello, Cristian Accino, Ezequiel López-Rubio, Karl Thurnhofer-Hemsi

引用次数: 3