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Variational LSTM Enhanced Anomaly Detection for Industrial Big Data

Xiaokang Zhou, Yiyong Hu, Wei Liang, Jianhua Ma, Qun Jin
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lxfdtuyn4nhhtevm3datmysrpa" style="color: black;">IEEE Transactions on Industrial Informatics</a> </i> &nbsp;
An encoder-decoder neural network associated with a variational reparameterization scheme is designed to learn the low-dimensional feature representation from high-dimensional raw data.  ...  anomaly detection based on reconstructed feature representation.  ...  A reparameterization scheme based on variational Bayes was proposed to reconstruct a hidden variable for low-dimensional feature representation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tii.2020.3022432">doi:10.1109/tii.2020.3022432</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cgagy6kxhbe3jalnngde4m6gri">fatcat:cgagy6kxhbe3jalnngde4m6gri</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717193208/https://ieeexplore.ieee.org/ielx7/9424/9361473/09195000.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/70/a5/70a5ec8e4c7782ddc4b644ad437338a216528f92.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tii.2020.3022432"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach

Md. Nazmul Hasan, Rafia Nishat Toma, Abdullah-Al Nahid, M M Manjurul Islam, Jong-Myon Kim
<span title="2019-08-28">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/a2yvk5xhdnhpxjnk6yd33uudqq" style="color: black;">Energies</a> </i> &nbsp;
Since the power consumption signature is time-series data, we were led to build a CNN-based LSTM (CNN-LSTM) model for smart grid data classification.  ...  In this paper, an electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture.  ...  In the future, we will explore abnormal consumptions based on datasets that have a time attribute and extend our method to real-time energy fraud detection.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/en12173310">doi:10.3390/en12173310</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wgshofbvwfgrbebyrauwp57lsa">fatcat:wgshofbvwfgrbebyrauwp57lsa</a> </span>
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RICNN: A ResNet&Inception convolutional neural network for intrusion detection of abnormal traffic

Benhui Xia, Dezhi Han, Ximing Yin, Gao Na
<span title="">2021</span> <i title="National Library of Serbia"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rhtuh2ifczhapmhplqzald63za" style="color: black;">Computer Science and Information Systems</a> </i> &nbsp;
Therefore, this paper proposes a ResNet &Inception-based convolutional neural network (RICNN) model to abnormal traffic classification.  ...  To secure cloud computing and outsourced data while meeting the requirements of automation, many intrusion detection schemes based on deep learn ing are proposed.  ...  And to investigate the effectiveness of our model in extracting features directly on the original traffic, we also compare it with LSTM and CNN+LSTM models that identify the timing properties of the samples  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2298/csis210617055x">doi:10.2298/csis210617055x</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g6f3e4pbsbguzgb32el74ntgbe">fatcat:g6f3e4pbsbguzgb32el74ntgbe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211108155355/http://www.doiserbia.nb.rs/img/doi/1820-0214/2021%20OnLine-First/1820-02142100055X.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/5e/7f/5e7feae51f845db52cc5fab4292cb52f7349311a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2298/csis210617055x"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

A Hybrid Deep Neural Network for Electricity Theft Detection Using Intelligent Antenna-Based Smart Meters

Ashraf Ullah, Nadeem Javaid, Adamu Sani Yahaya, Tanzeela Sultana, Fahad Ahmad Al-Zahrani, Fawad Zaman, Daniele Pinchera
<span title="2021-08-24">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;
An electric utility company gathers the data from the intelligent antenna-based smart meters installed at the consumers' end. The dataset contains real-time data with missing values and outliers.  ...  This paper presents a hybrid model, named as hybrid deep neural network, which combines convolutional neural network, particle swarm optimization, and gated recurrent unit, termed as convolutional neural  ...  Whereas the other models have a low AUC score. Moreover, it is shown that the proposed model beats the existing ones regarding AUC in the presence of imbalanced dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/9933111">doi:10.1155/2021/9933111</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nkkshmchgbfnhakhtf6j65v7sa">fatcat:nkkshmchgbfnhakhtf6j65v7sa</a> </span>
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RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm

Hamid Gharagozlou, Javad Mohammadzadeh, Azam Bastanfard, Saeed Shiry Ghidary, Ripon Chakrabortty
<span title="2022-05-06">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3wwzxqpotbc73bzpemzybzg7ee" style="color: black;">Computational Intelligence and Neuroscience</a> </i> &nbsp;
The present paper proposes a method called RLAS-BIABC for AS, which is established on attention mechanism-based long short-term memory (LSTM) and the bidirectional encoder representations from transformers  ...  We tested our model on three datasets, LegalQA, TrecQA, and WikiQA, and the results show that RLAS-BIABC can be recognized as a state-of-the-art method.  ...  In [81] , the authors employed a method based on inter-weighted alignment networks to determine the similarity between a question-answer pair. e article [82] suggested a scheme based on a bidirectional  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/7839840">doi:10.1155/2022/7839840</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35571722">pmid:35571722</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9106472/">pmcid:PMC9106472</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bq4mfgdomrdzbbfuhanwn6g2vi">fatcat:bq4mfgdomrdzbbfuhanwn6g2vi</a> </span>
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Effect Improved for High-Dimensional and Unbalanced Data Anomaly Detection Model Based on KNN-SMOTE-LSTM

Fuguang Bao, Yongqiang Wu, Zhaogang Li, Yongzhao Li, Lili Liu, Guanyu Chen
<span title="">2020</span> <i title="Hindawi-Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y3fh56bfunh5fgneywwba6d4ke" style="color: black;">Complexity</a> </i> &nbsp;
(kNN), and it designs and constructs an anomaly detection network model based on kNN-SMOTE-LSTM in accordance with the data characteristic of being unbalanced.  ...  A significant research issue related to the data analysis of the sensor is the detection of anomalies. The anomaly detection is essentially an unbalanced sequence binary classification.  ...  for modeling complex and imbalanced data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2020/9084704">doi:10.1155/2020/9084704</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/692df8c6c7cd4442a07b754c0b8ed357">doaj:692df8c6c7cd4442a07b754c0b8ed357</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ofo3rkd65zaytcmqzlxkgshifu">fatcat:ofo3rkd65zaytcmqzlxkgshifu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201104014445/http://downloads.hindawi.com/journals/complexity/2020/9084704.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/06/14/0614c9fe2aefb8728535a689207b09d5ad58cab3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2020/9084704"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction

Wolfgang Kratsch, Jonas Manderscheid, Maximilian Röglinger, Johannes Seyfried
<span title="2020-04-08">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2bq67muntvahlgzwn7lfoncyt4" style="color: black;">Business &amp; Information Systems Engineering</a> </i> &nbsp;
Second, DL techniques perform more stably in case of imbalanced target variables, especially for logs with a high event-to-activity ratio (i.e., many loops in the control flow).  ...  ., random forests and support vector machines) based on five publicly available event logs. It could be observed that DL generally outperforms classical ML techniques.  ...  To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s12599-020-00645-0">doi:10.1007/s12599-020-00645-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4swzksz6g5hibhjg2svohhxybm">fatcat:4swzksz6g5hibhjg2svohhxybm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715082810/https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1628&amp;context=bise" 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/a6/e7/a6e7f9725fc425ef2a3b41e7a48b068bd85e0f81.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s12599-020-00645-0"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

Sheo Yon Jhin, Jaehoon Lee, Minju Jo, Seungji Kook, Jinsung Jeon, Jihyeon Hyeong, Jayoung Kim, Noseong Park
<span title="2022-04-25">2022</span> <i title="ACM"> Proceedings of the ACM Web Conference 2022 </i> &nbsp;
In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins.  ...  Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough.  ...  We use the accuracy for balanced classification datasets and AUROC for imbalanced datasets. PhysioNet Sepsis.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3485447.3512030">doi:10.1145/3485447.3512030</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/apwdvcpi3zdejjbcxvxxad46dm">fatcat:apwdvcpi3zdejjbcxvxxad46dm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220503131713/https://dl.acm.org/doi/pdf/10.1145/3485447.3512030" 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/f8/6e/f86eec423da153982404e08f5faca8dfcd15ec14.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3485447.3512030"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

DE-PNN: Differential Evolution-Based Feature Optimization with Probabilistic Neural Network for Imbalanced Arrhythmia Classification

Amnah Nasim, Yoon Sang Kim
<span title="2022-06-12">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
, especially for imbalanced datasets.  ...  In this research, a heartbeat classification method is presented based on evolutionary feature optimization using differential evolution (DE) and classification using a probabilistic neural network (PNN  ...  Detailed methodology: Differential Evolution-based feature optimization with Probabilistic Neural Network for imbalanced arrhythmia classification. Figure 5 . 5 Figure 5.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s22124450">doi:10.3390/s22124450</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yuwvwrlxrbcifcefu2pv5mfiti">fatcat:yuwvwrlxrbcifcefu2pv5mfiti</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220618112742/https://mdpi-res.com/d_attachment/sensors/sensors-22-04450/article_deploy/sensors-22-04450.pdf?version=1655025719" 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/5b/88/5b88c2cb6c4476aa37f17a20ece02c874d7df3dc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s22124450"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data [article]

Weiwei Jiang, Jiayun Luo
<span title="2021-07-06">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To answer this question, a real-world public dataset is leveraged in this study and different drought levels are predicted using the last 90 days of 18 meteorological indicators as the predictors.  ...  Drought is a serious natural disaster that has a long duration and a wide range of influence.  ...  Similar with GB, XGBoost fits Classification and Regression Trees (CARTs) to the residuals. The data split scheme for each node of a tree is based on the similarity score.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.02517v1">arXiv:2107.02517v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/utxukmhetzbvpod3kmcezlzp6m">fatcat:utxukmhetzbvpod3kmcezlzp6m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210710045510/https://arxiv.org/pdf/2107.02517v1.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/f4/e2/f4e254352fe04e2e86144faf217662b6b9a70059.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.02517v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Classification of Arrhythmia in Heartbeat Detection Using Deep Learning

Wusat Ullah, Imran Siddique, Rana Muhammad Zulqarnain, Mohammad Mahtab Alam, Irfan Ahmad, Usman Ahmad Raza, Ahmed Mostafa Khalil
<span title="2021-10-19">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3wwzxqpotbc73bzpemzybzg7ee" style="color: black;">Computational Intelligence and Neuroscience</a> </i> &nbsp;
One dataset is the MIT-BIH arrhythmia database, with a sampling frequency of 125 Hz with 1,09,446 ECG beats. The classes included in this first dataset are N, S, V, F, and Q.  ...  This paper aims to apply deep learning techniques on the publicly available dataset to classify arrhythmia. We have used two kinds of the dataset in our research paper.  ...  [27] presented a precise classification system based on the intelligent ECG classification with the aid of fast residual convolutionary neural networks (FCResNet) proposed to promote smart classification  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/2195922">doi:10.1155/2021/2195922</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34712316">pmid:34712316</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8548158/">pmcid:PMC8548158</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hanpigmzrbbazazpqehzcyxawa">fatcat:hanpigmzrbbazazpqehzcyxawa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211024110834/https://downloads.hindawi.com/journals/cin/2021/2195922.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/72/bc/72bc8e45639f33804dce8d8ad39bd88858c87076.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/2195922"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548158" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Accelerometer-based Bed Occupancy Detection for Automatic, Non-invasive Long-term Cough Monitoring [article]

Madhurananda Pahar, Igor Miranda, Andreas Diacon, Thomas Niesler
<span title="2022-03-13">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This provides a first indication that automatic cough monitoring based on bed-mounted accelerometer measurements may present a non-invasive, non-intrusive and cost-effective means of monitoring long-term  ...  We present a new machine learning based bed-occupancy detection system that uses the accelerometer signal captured by a bed-attached consumer smartphone.  ...  In this scheme, one patient is held out as a test-patient in an outer loop.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.03936v2">arXiv:2202.03936v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q3f7xo54e5e6hi3bakma3f5xvy">fatcat:q3f7xo54e5e6hi3bakma3f5xvy</a> </span>
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HARNAS: Human Activity Recognition Based on Automatic Neural Architecture Search Using Evolutionary Algorithms

Xiaojuan Wang, Xinlei Wang, Tianqi Lv, Lei Jin, Mingshu He
<span title="2021-10-19">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
Human activity recognition (HAR) based on wearable sensors is a promising research direction.  ...  However, the computation speed of a model not only depends on the complexity, but is also related to the memory access cost (MAC).  ...  These two objectives conflict with that of reducing the model classification error, thus reducing the probability of finding a model with a low classification error.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21206927">doi:10.3390/s21206927</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34696140">pmid:34696140</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ttpg7rai4fdz3cpv74yx7o2rvq">fatcat:ttpg7rai4fdz3cpv74yx7o2rvq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211026083043/https://mdpi-res.com/d_attachment/sensors/sensors-21-06927/article_deploy/sensors-21-06927-v2.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/67/6c/676c45c7cf4cd4f1f83445c3af488193a9536861.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21206927"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures

Sheraz Naseer, Rao Faizan Ali, P.D.D Dominic, Yasir Saleem
<span title="2020-11-16">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nzoj5rayr5hutlurimhzyjlory" style="color: black;">Symmetry</a> </i> &nbsp;
A total of sixty anomaly detectors were trained by authors using twelve conventional Machine Learning algorithms to compare the performance of aforementioned deep representations with that of a human-engineered  ...  Oil and Gas organizations are dependent on their IT infrastructure, which is a small part of their industrial automation infrastructure, to function effectively.  ...  [30] explored the use of RNNs with LSTMs to elucidate intrusion detection problem on the KDDCup99 dataset. A recent work by Tuor et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/sym12111882">doi:10.3390/sym12111882</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gs2oqpz525hifldtegp3ecp5oe">fatcat:gs2oqpz525hifldtegp3ecp5oe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201118030142/https://res.mdpi.com/d_attachment/symmetry/symmetry-12-01882/article_deploy/symmetry-12-01882.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/3c/e3/3ce3033a0fb86cfb9a0af1f9415d21d445ea57a3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/sym12111882"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

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,  ...  MT-LSTM has been reported to outperform a set of baselines, including the models based on LSTM and RNN, on text classification.  ... 
<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>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200415221309/https://arxiv.org/pdf/2004.03705v1.pdf" title="fulltext PDF download [not primary version]" 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] <span style="color: #f43e3e;">&#10033;</span> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.03705v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>
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