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Titlebook: Computer Vision and Image Processing; 4th International Co Neeta Nain,Santosh Kumar Vipparthi,Balasubramanian Conference proceedings 2020 S

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https://doi.org/10.1007/978-981-15-4018-9artificial intelligence; computer hardware; computer networks; computer systems; computer vision; correla
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978-981-15-4017-2Springer Nature Singapore Pte Ltd. 2020
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1865-0929 al Network; Object Detection; Object Recognition; Online Handwriting Recognition; Optical Character Recognition; Security and Privacy; Unsupervised Clustering. .978-981-15-4017-2978-981-15-4018-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
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The Definitive Guide to PCI DSS Version 4on scores that are combined using a weighted sum rule. Extensive experiments are carried out on the publicly available OULU-NPU database and performance of the proposed method is benchmarked with fifteen different state-of-the-art techniques. Obtained results have indicated the outstanding performance of the proposed method on OULU-NPU database.
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Maurizio Delmonte,Davide Moro,Andy McKayhe model performed better achieving an accuracy of 97% and 95% for the two malware datasets including benign samples. They did not require more hardware resources and model is trained with a low computational cost. The model was evaluated on Malimg dataset and Kaggle’s Microsoft Malware Classification Challenge (BIG 2015) dataset.
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Integrating Plone with Other Systemsnlinear changes. So kernel variants of ELPP are proposed to handle non-linearity present in the data. This article addressed kernel variants of the ELPP which explored the complex non-linear changes of the facial expression recognition. The proposed kernel variants of the ELPP is applied for face recognition on some benchmark databases.
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Integrating Plone with Other Systemsters (width) of the model and hence, learned the inter-channel dependencies in a better way. The proposed network outperforms the state-of-the-art demosaicing methods on both sRGB and linear datasets.
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