Rumor detection based on a Source-Replies conversation Tree Convolutional Neural Net

Computing ◽  
2022 ◽  
Author(s):  
Na Bai ◽  
Fanrong Meng ◽  
Xiaobin Rui ◽  
Zhixiao Wang
Keyword(s):  
Neural Net ◽  
Rumor Detection

Communications Neural Net Processors.

1992 ◽  
Author(s):  
H. T. Kung
Keyword(s):  
Neural Net

Using artificial neural network condensation to facilitate adaption of machine learning in medical settings by reducing computational burden (Preprint)

2020 ◽  
Author(s):  
Dianbo Liu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Third World ◽  
Mortality Prediction ◽  
Neural Net ◽  
Medical Settings ◽  
Hidden Layer ◽  
Applications Of Machine Learning ◽  
Computational Resources ◽  
Developed Nations

BACKGROUND Applications of machine learning (ML) on health care can have a great impact on people’s lives. At the same time, medical data is usually big, requiring a significant amount of computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations and on mobile devices. This can prevent many people from benefiting of the advancement in ML applications for healthcare. OBJECTIVE In this paper we explored three methods to increase computational efficiency of either recurrent neural net-work(RNN) or feedforward (deep) neural network (DNN) while not compromising its accuracy. We used in-patient mortality prediction as our case analysis upon intensive care dataset. METHODS We reduced the size of RNN and DNN by applying pruning of “unused” neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the RNN cell but reduce the total number of recurrent layers to accomplish a reduction of total parameters in the network. Finally, we implemented quantization on DNN—forcing the weights to be 8-bits instead of 32-bits. RESULTS We found that all methods increased implementation efficiency–including training speed, memory size and inference speed–without reducing the accuracy of mortality prediction. CONCLUSIONS This improvements allow the implementation of sophisticated NN algorithms on devices with lower computational resources.

Utilizing semantic segmentation method with convolutional neural net to model a partial nephrectomy simulator for 3D printing

2019 ◽  
Vol 18 (1) ◽  
pp. e1747-e1748
Author(s):  
Y.S. Kyung ◽  
B. Lim ◽  
S.Y. Choi ◽  
J.H. Han ◽  
W. Lee ◽  
...  
Keyword(s):  
3D Printing ◽  
Partial Nephrectomy ◽  
Semantic Segmentation ◽  
Neural Net ◽  
Segmentation Method

scholarly journals Catch me if you can: A participant-level rumor detection framework via fine-grained user representation learning

2021 ◽  
Vol 58 (5) ◽  
pp. 102678
Author(s):  
Xueqin Chen ◽  
Fan Zhou ◽  
Fengli Zhang ◽  
Marcello Bonsangue
Keyword(s):  
Representation Learning ◽  
Fine Grained ◽  
Rumor Detection

scholarly journals Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis

2021 ◽  
Vol 16 (5) ◽  
pp. 1317-1330
Author(s):  
David A. Joy ◽  
Ashley R.G. Libby ◽  
Todd C. McDevitt
Keyword(s):  
Stem Cells ◽  
Pluripotent Stem Cells ◽  
Human Pluripotent Stem Cells ◽  
Neural Net

Transfer learning based convolution neural net for authentication and classification of emotions from natural and stimulated speech signals

2021 ◽  
pp. 1-12
Author(s):  
Mukul Kumar ◽  
Nipun Katyal ◽  
Nersisson Ruban ◽  
Elena Lyakso ◽  
A. Mary Mekala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Research Work ◽  
Oral Communication ◽  
Neural Net ◽  
Emotion Classification ◽  
Style Transfer ◽  
Conventional Machine ◽  
Neural Network Algorithm

Over the years the need for differentiating various emotions from oral communication plays an important role in emotion based studies. There have been different algorithms to classify the kinds of emotion. Although there is no measure of fidelity of the emotion under consideration, which is primarily due to the reason that most of the readily available datasets that are annotated are produced by actors and not generated in real-world scenarios. Therefore, the predicted emotion lacks an important aspect called authenticity, which is whether an emotion is actual or stimulated. In this research work, we have developed a transfer learning and style transfer based hybrid convolutional neural network algorithm to classify the emotion as well as the fidelity of the emotion. The model is trained on features extracted from a dataset that contains stimulated as well as actual utterances. We have compared the developed algorithm with conventional machine learning and deep learning techniques by few metrics like accuracy, Precision, Recall and F1 score. The developed model performs much better than the conventional machine learning and deep learning models. The research aims to dive deeper into human emotion and make a model that understands it like humans do with precision, recall, F1 score values of 0.994, 0.996, 0.995 for speech authenticity and 0.992, 0.989, 0.99 for speech emotion classification respectively.

Prediction of cognitive function based on structural MRI images using a 3D convolutional neural net among cognitively normal older adults

2021 ◽  
Vol 29 (4) ◽  
pp. S65-S66
Author(s):  
Ashti Shah ◽  
Akiko Mizuno ◽  
Linghai Wang ◽  
Andrea Weinstein ◽  
Howard Aizenstein
Keyword(s):  
Older Adults ◽  
Cognitive Function ◽  
Structural Mri ◽  
Neural Net ◽  
Cognitively Normal
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