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New Hybrid Deep Learning Method to Recognize Human Action from Video

Md Shofiqul Islam, Sunjida Sultana, Md Jabbarul Islam
2021 Jurnal Ilmiah Teknik Elektro Komputer dan Informatika  
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  
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]

Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao
2021 arXiv   pre-print
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

Anant Khandelwal, Niraj Kumar
2020 Proceedings of the 7th ACM IKDD CoDS and 25th COMAD  
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

Guocheng Niu, Hengru Xu, Bolei He, Xinyan Xiao, Hua Wu, Sheng GAO
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)  
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]

Yan Wang, Wei Song, Wei Tao, Antonio Liotta, Dawei Yang, Xinlei Li, Shuyong Gao, Yixuan Sun, Weifeng Ge, Wei Zhang, Wenqiang Zhang
2022 arXiv   pre-print
., 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

Jia Lu, Minh Nguyen, Wei Qi Yan
2020 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ)  
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]

Israa Khalaf Salman Al-Tameemi, Mohammad-Reza Feizi-Derakhshi, Saeed Pashazadeh, Mohammad Asadpour
2022 arXiv   pre-print
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

Yongjie Guo, Feng Wang, Yuming Xiang, Hongjian You
2021 Remote Sensing  
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]

Depeng Liang, Yongdong Zhang
2017 arXiv   pre-print
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

Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari
2019 Electronics  
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]

Hojjat Salehinejad, Shahrokh Valaee, Aren Mnatzakanian, Tim Dowdell, Joseph Barfett, Errol Colak
2017 arXiv   pre-print
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

Muhammad Zubair Khan, Mohan Kumar Gajendran, Yugyung Lee, Muazzam A. Khan
2021 IEEE Access  
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

Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J. Santamaría, Mohammed A. Fadhel, Muthana Al-Amidie, Laith Farhan
2021 Journal of Big Data  
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]

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie
2020 arXiv   pre-print
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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