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International Journal of Software Science and Computational Intelligence, 2022
Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance.
Zhiwei Zhan +6 more
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Emotion is a feeling that can be expressed by different mediums. Emotion analysis is a key task in NLP which is responsible for judging the emotional tendency of texts. Currently, in a complex multi-semantic environment, it still suffers from poor performance.
Zhiwei Zhan +6 more
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G-CNN and F-CNN: Two CNN based architectures for face recognition
2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), 2017In the recent past, deployment of Convolutional Neural Networks (CNN) has led to prodigious success in many pattern recognition tasks. This is mainly due to the very nature of CNN, that is its ability to work in a similar manner to that of the visual system of the human brain.
A Vinay +7 more
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Nonlinear CNN: improving CNNs with quadratic convolutions
Neural Computing and Applications, 2019In this work, instead of designing deeper convolutional neural networks, we investigate the relationship between the nonlinearity of convolution layer and the performance of the network. We modify the normal convolution layer by inserting quadratic convolution units which can map linear features to a higher-dimensional space in a single layer so as to ...
Yiyang Jiang +4 more
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CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications
Journal of Ambient Intelligence and Humanized Computing, 2019Computer vision based patient activity monitoring systems can be attractive for various unobtrusive clinical applications. Such a monitoring system can be developed using movement information derived from the skeleton model of the current body pose, e.g. obtained using a depth camera.
Luis A. Zavala-Mondragon +3 more
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Proceedings of the 7th International Conference on Computational Systems-Biology and Bioinformatics, 2016
Unravelling gene expression has become a critical procedure in bioinformatics world today and required continuous efforts to form a complete picture of enhancers. Enhancers are explicit patterns of gene expression that bound by activators to stimulate transcription.
Yu, Shiong Wong +2 more
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Unravelling gene expression has become a critical procedure in bioinformatics world today and required continuous efforts to form a complete picture of enhancers. Enhancers are explicit patterns of gene expression that bound by activators to stimulate transcription.
Yu, Shiong Wong +2 more
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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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