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Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

Mohd Hider Kamarudin, Zool Hilmi Ismail, Noor Baity Saidi
<span title="2021-02-04">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
variations that occur within the same species but cultivated from different locations.  ...  Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers.  ...  Acknowledgments: The authors gratefully thank Raudah Yunus for her assistance with the preparation of the manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11041403">doi:10.3390/app11041403</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bt53jh7idjct7lxxjfx247gza4">fatcat:bt53jh7idjct7lxxjfx247gza4</a> </span>
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Prediction of sea temperature using temporal convolutional network and LSTM-GRU network

Yu Jiang, Minghao Zhao, Wanting Zhao, Hongde Qin, Hong Qi, Kai Wang, Chong Wang
<span title="">2021</span> <i title="OAE Publishing Inc."> Complex Engineering Systems </i> &nbsp;
Some scholars also put 78 forward Long Short-Term Memory Network (LSTM) and the Gated Recurrent Unit 79 (GRU), their improved algorithm, making a breakthrough in temperature prediction. 80 Based on those  ...  One is 32 LSTM-GRU hybrid neural network model, and the other is the Temporal Convolutional 33 Network model.  ...  Long Short-Term Memory Network 177 (LSTM) is a special RNN model whose structural design solves long-term dependence 178 issues.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.20517/ces.2021.03">doi:10.20517/ces.2021.03</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ywsc3fabm5clfolageswont7d4">fatcat:ywsc3fabm5clfolageswont7d4</a> </span>
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Seismic Events Prediction Using Deep Temporal Convolution Networks

Yue Geng, Lingling Su, Yunhong Jia, Ce Han
<span title="2019-04-02">2019</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ak4drtjatbcoxpgx5twsmzrwve" style="color: black;">Journal of Electrical and Computer Engineering</a> </i> &nbsp;
We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events.  ...  This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN).  ...  of long-term historical dependence and gradient vanishing. e long short-term memory module is illustrated in Figure 5 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2019/7343784">doi:10.1155/2019/7343784</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ofisadursbderefrynn6o7lh6m">fatcat:ofisadursbderefrynn6o7lh6m</a> </span>
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Human mitochondrial genome compression using machine learning techniques

Rongjie Wang, Tianyi Zang, Yadong Wang
<span title="2019-10-22">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oqcgtjrrffbixmejmuqswbwhd4" style="color: black;">Human Genomics</a> </i> &nbsp;
dependence of sequences through long short-term memory network (LSTM).  ...  Long Short-Term Memory Networks (LSTM) [17] , as a special recurrent neural network, was proposed in 1997 to solve the problem that recurrent neural network (RNNs) cannot learn long-term dependent correlation  ...  Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s40246-019-0225-3">doi:10.1186/s40246-019-0225-3</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31639043">pmid:31639043</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6805717/">pmcid:PMC6805717</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4kskwxcdavcxvfv7yfhomsqrb4">fatcat:4kskwxcdavcxvfv7yfhomsqrb4</a> </span>
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DEEPGONET: Multi-label Prediction of GO Annotation for Protein from Sequence Using Cascaded Convolutional and Recurrent Network [article]

Sheikh Muhammad Saiful Islam, Md Mahedi Hasan
<span title="2018-10-31">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we present DEEPGONET, a novel cascaded convolutional and recurrent neural network, to predict the top-level hierarchy of GO ontology.  ...  for proteins where only primary sequence is available.  ...  We extract local features of amino acid by using several convolutional layers with different kernel size which is followed by bidirectional long short term recurrent neural network (Bi-GRU) to extract  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1811.00053v1">arXiv:1811.00053v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2ocsawjqjbcr7ggse4gk5eawye">fatcat:2ocsawjqjbcr7ggse4gk5eawye</a> </span>
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Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives

Tanvir Alam, Hamada R. H. Al-Absi, Sebastian Schmeier
<span title="2020-11-30">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5mjgjqh44vhvrky7ivzpsdhc6e" style="color: black;">Non-Coding RNA</a> </i> &nbsp;
Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes.  ...  DL-based modeling techniques have now become a choice for many modeling tasks in the field of lncRNAome as well.  ...  LSTM: long short-term memory. MLP: multi-layer perceptron. RNN: recursive neural network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ncrna6040047">doi:10.3390/ncrna6040047</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33266128">pmid:33266128</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n7c7kn5dh5gpjhfmlkylrscmmu">fatcat:n7c7kn5dh5gpjhfmlkylrscmmu</a> </span>
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A Multifeature Fusion Short-Term Traffic Flow Prediction Model Based on Deep Learnings

Chunxu Chai, Chuanxiang Ren, Changchang Yin, Hui Xu, Qiu Meng, Juan Teng, Ge Gao, Inhi Kim
<span title="2022-05-30">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mizko6kwlndydeoyklg52vn3oi" style="color: black;">Journal of Advanced Transportation</a> </i> &nbsp;
Short-term traffic flow prediction is an important component of intelligent transportation systems, which can support traffic trip planning and traffic management.  ...  Although existing predicting methods have been applied in the field of traffic flow prediction, they cannot capture the complex multifeatures of traffic flows resulting in unsatisfactory short-term traffic  ...  GRU and LSTM networks have not only the function of short-term memory, but also the function of long-term memory.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/1702766">doi:10.1155/2022/1702766</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3wzek6femverzghdoojrdfevtm">fatcat:3wzek6femverzghdoojrdfevtm</a> </span>
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ISRITI 2019 Paper Titles

<span title="">2019</span> <i title="IEEE"> 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) </i> &nbsp;
and Long-Short Term Memory M 2 3 A B C D E F H I L M N O P R S T U V W Z Melanoma Cancer Classification Using ResNet with Data Augmentation Method for Linear Satellite Pay Television Censor Inappropriate  ...  Digital Image Processing Determining NPC Behavior in Maze Chase Game using Naïve Bayes Algorithm Developing a Complete Dialogue System Using Long Short-Term Memory Discrete Firefly Algorithm for an Examination  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isriti48646.2019.9034586">doi:10.1109/isriti48646.2019.9034586</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ftkc6ofpmrcufnnaaxdssk2wgu">fatcat:ftkc6ofpmrcufnnaaxdssk2wgu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201103051252/https://ieeexplore.ieee.org/ielx7/9027964/9034556/09034586.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/52/86/52868d72cebacbed4d33a34163ce0124d29fc371.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/isriti48646.2019.9034586"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

An Improved Convolutional Neural Network with LSTM Approach for Texture Classification

Subba Rao M
<span title="2020-07-25">2020</span> <i title="The World Academy of Research in Science and Engineering"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/l2ghh6vzxbh45awjxwipefhnue" style="color: black;">International Journal of Emerging Trends in Engineering Research</a> </i> &nbsp;
The Convolutional Neural Network (CNN) in combination with Long Short-Term Memory (LSTM) forms a robust selection between a powerful invariant feature extractor and an accurate classifier.  ...  Texture classification is a problem that has several applications, such as remote detection and recognition of forest species.  ...  Figure 2 LSTM (Long Short-Term Memory) Long Short-Term Memory (LSTM) is a model of artificial recurrent neural network (RNN) that is used in deep learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.30534/ijeter/2020/148872020">doi:10.30534/ijeter/2020/148872020</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7szrrlcd7be77fnbntsvezoodm">fatcat:7szrrlcd7be77fnbntsvezoodm</a> </span>
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Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses

Ryan Calmus, Benjamin Wilson, Yukiko Kikuchi, Christopher I. Petkov
<span title="2019-12-16">2019</span> <i title="The Royal Society"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/p7stgfom7rbkheuw5cc6avanwu" style="color: black;">Philosophical Transactions of the Royal Society of London. Biological Sciences</a> </i> &nbsp;
The model integrates established vector symbolic additive and conjunctive binding operators with neurobiologically plausible oscillatory dynamics, and is compatible with modern spiking neural network simulation  ...  The neural mechanisms for segmenting continuous streams of sensory input and establishing representations of dependencies remain largely unknown, as do the transformations and computations occurring between  ...  We thank Elizabeth Buffalo, Pascal Fries, Karl Friston, Tim Griffiths, David Poeppel, Mark Stokes and Chris Summerfield for inspiring discussions on complex combinatorial binding.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1098/rstb.2019.0304">doi:10.1098/rstb.2019.0304</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31840585">pmid:31840585</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6939361/">pmcid:PMC6939361</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3arw5gklwvc5jaxrxfhilya6le">fatcat:3arw5gklwvc5jaxrxfhilya6le</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200506083900/https://eprint.ncl.ac.uk/file_store/production/262568/A15B619A-5F9F-4B26-A1D5-D6113ED75FB8.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d5/e9/d5e968093377129c95114ff213527eee1f4886d3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1098/rstb.2019.0304"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6939361" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Principal Component Analysis and Prediction of Students' Physical Health Standard Test Results Based on Recurrent Convolution Neural Network

Kai Hou, Yuanpeng Zhang
<span title="2021-09-04">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6o4hgxplrbehxg4t53ub7zmfha" style="color: black;">Wireless Communications and Mobile Computing</a> </i> &nbsp;
In this paper, the convolutional neural network and the recurrent neural network are combined to establish a recurrent convolutional neural network model composed of anomalies, LSTM (Long Short-Term Memory  ...  The recurrent convolutional neural network is an advanced neural network that integrates deep structure and convolution calculation.  ...  With the development of recurrent convolutional neural networks, recurrent convolutional neural networks and principal component analysis have been widely used in various practices, such as predicting  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/2438656">doi:10.1155/2021/2438656</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tpj675ebijb5bboz57dwcmdpf4">fatcat:tpj675ebijb5bboz57dwcmdpf4</a> </span>
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Deep Learning Based Text Classification: A Comprehensive Review [article]

Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, Jianfeng Gao
<span title="2021-01-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We also provide a summary of more than 40 popular datasets widely used for text classification.  ...  In this paper, we provide a comprehensive review of more than 150 deep learning based models for text classification developed in recent years, and discuss their technical contributions, similarities,  ...  ACKNOWLEDGMENTS The authors would like to thank Richard Socher, Kristina Toutanova, and Brooke Cowan for reviewing this work, and providing very insightful comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.03705v3">arXiv:2004.03705v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/al5hstylsbhfpldvokuvlpomam">fatcat:al5hstylsbhfpldvokuvlpomam</a> </span>
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Understanding Sequence Conservation With Deep Learning

Yi Li, Daniel Quang, Xiaohui Xie
<span title="">2017</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7y7p3qrae5bnlok65yvbqtexc4" style="color: black;">Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics - ACM-BCB &#39;17</a> </i> &nbsp;
DeepCons is a convolutional neural network trained to predict whether a given DNA sequence is conserved or not.  ...  neural networks, and recurrent neural networks).  ...  Long short term memory (LSTM) networks [28] , on the other hand, are specifically designed to capture long term sequential patterns, and have been widely applied to analysis natural languages [29] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3107411.3107425">doi:10.1145/3107411.3107425</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/bcb/LiQX17.html">dblp:conf/bcb/LiQX17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gyub746ntncldmfyuet3rw3eiu">fatcat:gyub746ntncldmfyuet3rw3eiu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180719072603/https://www.biorxiv.org/content/biorxiv/early/2017/01/28/103929.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f8/e7/f8e77cb0f78a50575cf7eccf4788d4781c519b71.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3107411.3107425"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Understanding sequence conservation with deep learning [article]

Yi Li, Daniel Quang, Xiaohui Xie
<span title="2017-01-28">2017</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
DeepCons is a convolutional neural net (CNN) that receives a short segment of DNA sequence as input and outputs the probability of the sequence of being evolutionary conserved.  ...  Motivation: Comparing the human genome to the genomes of closely related mammalian species has been a powerful tool for discovering functional elements in the human genome.  ...  Long short term memory (LSTM) networks [28] , on the other hand, are specifically designed to capture long term sequential patterns, and have been widely applied to analysis natural languages [29] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/103929">doi:10.1101/103929</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s4o3npazqjfcjnlvyblc56u3hy">fatcat:s4o3npazqjfcjnlvyblc56u3hy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430182528/https://www.biorxiv.org/content/biorxiv/early/2017/01/28/103929.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/29/50/295097a59caf3390a835a8df16b6af371e6e0913.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/103929"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

An Introduction to Neural Architecture Search for Convolutional Networks [article]

George Kyriakides, Konstantinos Margaritis
<span title="2020-05-22">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work, we provide an introduction to the basic concepts of NAS for convolutional networks, along with the major advances in search spaces, algorithms and evaluation techniques.  ...  Neural Architecture Search (NAS) is a research field concerned with utilizing optimization algorithms to design optimal neural network architectures.  ...  (Cai et al., 2018a) also utilize REINFORCE to train a bidirectional Long Short-Term Memory (Bi-LSTM) RNN, with a simple two-actions action space; widening or deepening each layer.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.11074v1">arXiv:2005.11074v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dlbggh7f5vfgvirjklonhudx24">fatcat:dlbggh7f5vfgvirjklonhudx24</a> </span>
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