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LA-CNN: Load-Adjusted Video-on-Demand Prediction using CNNs
2024 35th Irish Signals and Systems Conference (ISSC)The ability to predict RTP packet counts accurately is needed if network managers are to be able to manage Video-on-Demand (VoD) sessions. We contribute a new algorithm called Load-Adjusted Convolutional Neural Networks (LA-CNNs) which addresses the task of accurately predicting the number of RTP packets received by a VoD client.
Kangogo, Kimeli, de Fréin, Ruairí
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Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, 2019
We present an interdisciplinary study to tackle the memory bottleneck of training deep convolutional neural networks (CNN). Firstly, we introduce Split Convolutional Neural Network (Split-CNN) that is derived from the automatic transformation of the state-of-the-art CNN models.
Tian Jin, Seokin Hong
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We present an interdisciplinary study to tackle the memory bottleneck of training deep convolutional neural networks (CNN). Firstly, we introduce Split Convolutional Neural Network (Split-CNN) that is derived from the automatic transformation of the state-of-the-art CNN models.
Tian Jin, Seokin Hong
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2017
The CNN effect is a term that emerged in journalistic jargon and academic debate in the early 1990s. It has been employed in a variety of contexts by scholars, journalists, and policy makers to describe a fairly broad spectrum of phenomena having to do with the impact of news media on politics. Here, we discuss whether and how the CNN effect influences
VALERIANI, AUGUSTO, ZAMBERNARDI, LORENZO
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The CNN effect is a term that emerged in journalistic jargon and academic debate in the early 1990s. It has been employed in a variety of contexts by scholars, journalists, and policy makers to describe a fairly broad spectrum of phenomena having to do with the impact of news media on politics. Here, we discuss whether and how the CNN effect influences
VALERIANI, AUGUSTO, ZAMBERNARDI, LORENZO
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OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN
IEEE Journal of Biomedical and Health InformaticsThe global increase in lung cancer cases, often marked by pulmonary nodules, underscores the critical importance of timely detection to mitigate cancer progression and reduce morbidity and mortality. The Faster R-CNN approach is a two-stage, high-precision nodule detection method designed for detecting small nodules, particularly in computed tomography
Yadollah Zamanidoost +2 more
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Assamese document classification using CNN, multi-channel CNN and CNN-SVM
AIP Conference Proceedings, 2023Chayanika Talukdar, Shikhar Kumar Sarma
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Proceedings of the 30th ACM International Conference on Multimedia, 2022
Fuyan Ma, Ziyu Ma, Bin Sun, Shutao Li
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Fuyan Ma, Ziyu Ma, Bin Sun, Shutao Li
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