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New Hybrid Deep Learning Method to Recognize Human Action from Video
2021
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
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We introduce a new deep learning architecture, a 3D DenseNet with the spatial pyramid pooling (3D-DenseNet), that can work on classification tasks and is influenced by the hierarchical feature pooling ...
The first contribution is to utilize spatial pooling with normalization in 3D-CNN. The next contribution is to select and handle important features. ...
The model uses a fully connected network (DenseNet) extension, with time information added to all convolutional and pooling layers, as well as a hierarchical spatial structure. ...
doi:10.26555/jiteki.v7i2.21499
fatcat:huva5lbeobelhnzrju7fx6ceoy
A BERT-based Hybrid Short Text Classification Model Incorporating CNN and Attention-based BiGRU
2021
Journal of Organizational and End User Computing
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Short text classification is a research focus for natural language processing (NLP), which is widely used in news classification, sentiment analysis, mail filtering and other fields. ...
Convolutional neural network (CNN) capture static features. As a supplement, a bi-gated recurrent neural network (BiGRU) is adopted to capture contextual features. ...
CNN captures the static information of the text from a spatial perspective and retains the most contributing features through max pooling. ...
doi:10.4018/joeuc.294580
fatcat:gn6oxlmbsbe2jktbghuejyp2qq
Deep Learning Based Text Classification: A Comprehensive Review
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2004.03705v1 fatcat:2jhz46jj6zbctidnhtsfv5fioqOther Versions
Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural ...
and strengths. ...
ACKNOWLEDGMENTS The authors would like to thank Richard Socher, Kristina Toutanova, and Brooke Cowan for reviewing this work, and providing very insightful comments. ...
arXiv:2004.03705v3
fatcat:al5hstylsbhfpldvokuvlpomam
A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts
2020
Proceedings of the 7th ACM IKDD CoDS and 25th COMAD
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2020 pre-print arXiv:2001.05493v1 fatcat:v3iucv6brvcillvu7vsskzmikaOther Versions
Our multi-modal deep learning architecture contains, Deep Pyramid CNN, Pooled BiLSTM, and Disconnected RNN(with Glove and FastText embedding, both). ...
, uses psycho-linguistic features and very ba-sic linguistic features. ...
In Sections 3.3, 3.4 and 3.5 we have described the architecture of different deep learning models like Deep Pyramid CNN, Disconnected RNN and Pooled BiLSTM respectively. ...
doi:10.1145/3371158.3371165
dblp:conf/comad/KhandelwalK20
fatcat:ka3zmz4h3ffejdn3ezztnrkxsy
Enhancing Local Feature Extraction with Global Representation for Neural Text Classification
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling. ...
In particular, En-coder1 serves as a global information provider, while Encoder2 performs as a local feature extractor and is directly fed into the classifier. ...
Introduction Text classification is a fundamental task in natural language processing, which is widely used in various applications such as spam detection, sentiment analysis and topic classification. ...
doi:10.18653/v1/d19-1047
dblp:conf/emnlp/NiuXHXWG19
fatcat:amlyt7vgnvb7vili5b5zl3qo7u
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances
[article]
2022
arXiv
pre-print
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., emotion recognition and sentiment analysis). ...
Finally, we discuss some important aspects on affective computing and their applications and conclude this review with an indication of the most promising future directions, such as the establishment of ...
The GCAE uses two parallel CNNs, which output results combined with the gated unit and extended with the third CNN, extracting contextual information of aspect terms. ...
arXiv:2203.06935v3
fatcat:h4t3omkzjvcejn2kpvxns7n2qe
Deep Learning Methods for Human Behavior Recognition
2020
2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)
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With the decreased costs of security monitoring equipment such as cameras, video surveillance has been broadly applied to our communities and public places. ...
We proposed Selective Kernel Network (SKNet) and ResNeXt with attention mechanism, which generate positive results to recognize human behaviours. ...
Spatial pyramid pooling networks (SPP-Nets) were adopted to overcome the information loss and usage problems of R-CNNs by replacing it with the last pooling layer (He, Zhang, Ren, & Sun, 2015) . ...
doi:10.1109/ivcnz51579.2020.9290640
fatcat:sq4fni6z2nfz5okecnsbmzum6e
A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks
[article]
2022
arXiv
pre-print
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benchmark datasets, and the efficacy of multiple classification methodologies suited to each field. ...
Finally, we highlight the most significant challenges and investigate several important sentiment applications. ...
LSTM, gated recurrent unit (GRU), bidirectional LSTM (BiLSTM) and CNN). ...
arXiv:2207.02160v1
fatcat:l3vxpjnqkrfthkvhdldwonpoe4
DGFNet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images
2021
Remote Sensing
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The encoder captures semantic representation by stacking convolution layers and shrinking image spatial resolution, while the decoder restores the spatial information by an upsampling operation and combines ...
Firstly, the FEM combines local information with global contents and strengthens the feature representation in the encoder. ...
For aspect-category sentiment analysis (ACSA) and aspect-term sentiment analysis (ATSA) tasks, Xue and Li [51] proposed an efficient convolutional neural network with gating mechanisms to achieve state-of-art ...
doi:10.3390/rs13183755
fatcat:jjizsdld5vc7npyc2va6brd2re
AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification
[article]
2017
arXiv
pre-print
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Experiment results demonstrate that our model achieves state-of-the-art results on five tasks, including sentiment analysis, question type classification, and subjectivity classification. ...
Recently deeplearning models have been shown to be capable of making remarkable performance in sentences and documents classification tasks. ...
NSC+LA: Neural sentiment classification model with local semantic attention (Chen et al., 2016a) . ...
arXiv:1611.01884v3
fatcat:mbxbbdcws5drdmcfqk3qe45fyi
A State-of-the-Art Survey on Deep Learning Theory and Architectures
2019
Electronics
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The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network ...
This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). ...
Acknowledgments: We would like to thank all authors mentioned in the reference of this paper from whom we have learned a lot and thus made this review paper possible. ...
doi:10.3390/electronics8030292
fatcat:2i64q7g6kjbjvfalvzwgiggnyq
Interpretation of Mammogram and Chest X-Ray Reports Using Deep Neural Networks - Preliminary Results
[article]
2017
arXiv
pre-print
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In this paper, we propose a Bi-directional convolutional neural network (Bi-CNN) model for the interpretation and classification of mammograms based on breast density and chest radiographic radiology reports ...
Our study revealed that the proposed Bi-CNN outperforms the random forest and the support vector machine methods. ...
ACKNOWLEDGEMENT The authors thank the support of NVIDIA Corporation with the donation of the Titan X GPUs used for this project. ...
arXiv:1708.09254v3
fatcat:jyihlrcthvalpdmwgm5dqmar2q
Deep Neural Architectures for Medical Image Semantic Segmentation: Review
2021
IEEE Access
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Numerous pooling techniques have been developed such as max pooling, average pooling, stochastic pooling [88] , spatial pyramid pooling [89] , multiscale orderless pooling [90] , and spectral pooling ...
The design of networks with modules like spatial-channel attention and spatial pyramid provides task-specific feature extraction more objectively. ...
doi:10.1109/access.2021.3086530
fatcat:hacpqwdxybh63j5ygebqszm7qq
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
2021
Journal of Big Data
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It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with ...
The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion. ...
Acknowledgements We would like to thank the professors from the Queensland University of Technology and the University of Information Technology and Communications who gave their feedback on the paper. ...
doi:10.1186/s40537-021-00444-8
pmid:33816053
pmcid:PMC8010506
fatcat:x2h5qs5c2jbntipu7oi5hfnb6u
A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing
[article]
2020
arXiv
pre-print
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This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends. ...
Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. ...
To concern about the estimation of depression angle and azimuth angle of targets in SAR-ATR tasks, the authors proposed a new CNN architecture with spatial pyramid pooling(SPP) in [385] , which could ...
arXiv:2009.13702v1
fatcat:m6am73324zdwba736sn3vmph3i
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