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Titlebook: ICT Innovations 2019. Big Data Processing and Mining; 11th International C Sonja Gievska,Gjorgji Madjarov Conference proceedings 2019 Sprin

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發(fā)表于 2025-3-21 17:58:01 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱ICT Innovations 2019. Big Data Processing and Mining
副標(biāo)題11th International C
編輯Sonja Gievska,Gjorgji Madjarov
視頻videohttp://file.papertrans.cn/461/460161/460161.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: ICT Innovations 2019. Big Data Processing and Mining; 11th International C Sonja Gievska,Gjorgji Madjarov Conference proceedings 2019 Sprin
描述This book constitutes the refereed proceedings of the 11th International ICT Innovations Conference, ICT Innovations 2019, held in Ohrid, Macedonia, in October 2019..The 18 full papers presented were carefully reviewed and selected from 75 submissions. They cover the following topics: sensor applications and deployments, embedded and cyber-physical systems, robotics, network architectures, cloud computing, software infrastructure, software creation and management, models of computation, computational complexity and cryptography, design and analysis of algorithms, mathematical optimization, probability and statistics, data management systems, data mining, human computer interaction (HCI), artificial intelligence, machine learning, life and medical sciences, health care information systems, bioinformatics..
出版日期Conference proceedings 2019
關(guān)鍵詞artificial intelligence; data mining; information retrieval; Natural Language Processing (NLP); natural
版次1
doihttps://doi.org/10.1007/978-3-030-33110-8
isbn_softcover978-3-030-33109-2
isbn_ebook978-3-030-33110-8Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Communications in Computer and Information Sciencehttp://image.papertrans.cn/i/image/460161.jpg
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,Detection of Toy Soldiers Taken from a Bird’s Perspective Using Convolutional Neural Networks,
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Diatom Ecological Modelling with Weighted Pattern Tree Algorithm by Using Polygonal and Gaussian Me while the models that are built with low number of MFs are excellent for making predictions for unseen data. The results from this research can be used for ecological modelling of diatoms, to classify a given diatom into a particular water quality class.
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Ski Injury Predictions with Explanations,on about reasons for specific decision. The proposed models were created on Mt. Kopaonik, Serbia ski resort and it is shown that ski injury in the following hour on specific ski slope can be predicted with AUC ~0.76, which is better up to ~15% compared to classical approaches such as logistic regres
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Image Augmentation with Neural Style Transfer,s which are frequently used in computer vision: ImageNet and Painter by Numbers (PBN). Afterwards, the model is used to generate new images from the CIFAR-100 and Tiny-ImageNet-200 datasets. The performance of the augmentation model is evaluated by a separate convolutional neural network. The evalua
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