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Explanation-Guided Fairness Testing through Genetic Algorithm [article]

Ming Fan, Wenying Wei, Wuxia Jin, Zijiang Yang, Ting Liu
<span title="2022-05-16">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Moreover, ExpGA only requires prediction probabilities of the tested model, resulting in a better generalization capability to various models.  ...  Augmented with the discriminatory samples generated by ExpGA, the fairness of tested models has a substantial improvement through retraining.  ...  The evaluation experiments demonstrate that ExpGA can detect discriminatory samples much faster with a higher success rate than four state-of-the-art methods, both on the text and tabular benchmarks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.08335v1">arXiv:2205.08335v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kwcxbsoif5ct3cq4m4i77rwee4">fatcat:kwcxbsoif5ct3cq4m4i77rwee4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220520182318/https://arxiv.org/pdf/2205.08335v1.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/3f/51/3f51b93c66ddc6563e00e451ba8cf33081ec8402.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.08335v1" 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>

Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network

Lihui Tang, Junjian Li, Wenming Lu, Peiqing Lian, Hao Wang, Hanqiao Jiang, Fulong Wang, Hongge Jia
<span title="">2021</span> <i title="Hindawi-Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xyl5x4yszzchnnjs4fcvduq4dq" style="color: black;">Geofluids</a> </i> &nbsp;
This paper proposes a new method of a well control optimization method based on a multi-input deep neural network.  ...  This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming  ...  As can be seen from the table, compared with the singleinput production dynamic prediction model, the MRE of the Geofluids production dynamic prediction model based on the multiinput deep neural network  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/8873782">doi:10.1155/2021/8873782</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/fe6c59d3be3b4c21a3eed13994534ce3">doaj:fe6c59d3be3b4c21a3eed13994534ce3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tfkvktjxczcrbh66thbyzkgx7i">fatcat:tfkvktjxczcrbh66thbyzkgx7i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210611060313/https://downloads.hindawi.com/journals/geofluids/2021/8873782.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/2f/23/2f23b75ef28ba5331635d57cfe8744e4cf8d7f78.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/8873782"> <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>

GO-Deep: novel gene annotation prediction using deep neural networks [article]

Cole Lyman
<span title="2018-01-17">2018</span> <i title="Figshare"> Figshare </i> &nbsp;
Deep recurrent network* ■ Learns features of a gene sequence and predicts appropriate annotations.  ...  *in preprint, and can only predict four annotations Our Solution Recurrent Neural Network (RNN)  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.5797083.v1">doi:10.6084/m9.figshare.5797083.v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6olfkfa44rhs3nooh4lmvfb3yq">fatcat:6olfkfa44rhs3nooh4lmvfb3yq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200214164234/https://s3-eu-west-1.amazonaws.com/pfigshare-u-files/10238592/SRC2017GODeep.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/11/9a/119a0b5bef4029468bc19e5c6a33b39b41f4ce13.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.6084/m9.figshare.5797083.v1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> figshare.com </button> </a>

Agricultural Fruit Prediction Using Deep Neural Networks

Tamoor Khan, Jiangtao Qiu, Muhammad Asim Ali Qureshi, Muhammad Shahid Iqbal, Rashid Mehmood, Waqar Hussain
<span title="">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cx3f4s3qmfe6bg4qvuy2cxezyu" style="color: black;">Procedia Computer Science</a> </i> &nbsp;
Agricultural production prediction is a challenging task in the deep neural network field.  ...  fruits using deep neural networks.  ...  In our study, we employ a technical analysis using a deep neural network algorithm to predict fruit production.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.procs.2020.06.058">doi:10.1016/j.procs.2020.06.058</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xadi4wlndbeglpl3gwb7csxhwi">fatcat:xadi4wlndbeglpl3gwb7csxhwi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210527111738/https://pdf.sciencedirectassets.com/280203/1-s2.0-S1877050920X00123/1-s2.0-S1877050920315726/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEJv%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIAqzH7ucDysuTKee5Sc2ns2Brlfc8%2FQwWccSznZwzD9YAiBEsPt3voqs8Sde2HMaYhhe%2FrItwaW6%2BBJOkx0tkK3Goyr6AwhDEAMaDDA1OTAwMzU0Njg2NSIMyPL9g8X7732aVdZYKtcDRtVvTvRBQhqOFpASn0gdD1%2FGOF%2BuqFuabXh6Vc9oo4uzKyPIqMHiCzp5Duxgk1bSVdxSrBRRaAL6PhV6PpHY%2BIH4zZK0k50BKwDdvmjhBhQqenVQr5vZkC9Y9nki1%2FvueeZW%2FuIuJUb%2FFPAvv7MhvpgOtdCkFTnyxJMf2LZt472c38DzTMCtj18BOXZBFlEOwhToZVxATZZOIueYSXU6HewPTy6om%2BOsFIvdipglSj8duMq%2FGJzPoTlI%2F6e5swAjh4he9BqcbSUpBk98U7xH1dW5ryZngF%2FI6lGCHgVYbs0Wg1Zqt7XGhjX1qAv2TwNoLKBhzn%2FAvCkTtJ4HeQNakVkbp5p%2B1m9dAR3Qq8W6a5zB6bHTOQCiSseMQcxxWYsZH9wH5QJIUEde4CkeoL3tb07QkcjimbADE%2BTMG7DgCnx3QJkG7VFjA4wDW%2FO1CphgKkc%2Bvu6HBA484E6K%2FytjOfRNdPatmM3zkfMRGbyA7xUr8CcuggmutqojM591YFqAkWJCy0DtlTJUkDR%2B979rASmXc5MTp0G49T%2BG8WRTRVfpTF3s7ffA1cTcLD7ivneQlGwSUDUUUXHFfTMEm0mmh2L7Pb79f21UogiHlnTGA4jIA3hO5WArMOzfvYUGOqYB5bmY1vhfxIuo%2BrUV7iTWPW0NNm%2FclnMLCRtyGreCtlXz9oSxKYjMOiYqBL1h4JIViTi7Rd17jv%2Bt4f%2Bvmhr1q9x6J0pNiIR1Ytr14pbcQdaSp6C03Z%2F2z3i7ZNXFKes91UYiz3r2E9ijgq4S5CouaikHKY67DbY6hZl9thwk8mve%2BlHvYnadKsziexV%2BX09bGcHnvlVryTbQQJx%2BLzCGF354uV5pOA%3D%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20210527T111714Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTYX3P2EPTX%2F20210527%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=108a575afbddc3ed98154cb93b8dbcb3bc1db6bc7ec597cf1418ec148054007e&amp;hash=2b5ee4b622c5cfa8acec1a0b3f797a386f54b1e53ec58daa43fde0aa81d6ce17&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S1877050920315726&amp;tid=spdf-874ab65f-12c2-4476-bec2-c9208100a764&amp;sid=b22d63091abf3942316a0ff515ed8e4b9e18gxrqa&amp;type=client" 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/e0/1f/e01f10629ade9425a913ec8b889e2fbfc511ba40.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.procs.2020.06.058"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Prediction of Bioprocess Production Using Deep Neural Network Method

Amirah Baharin, Afnizanfaizal Abdullah, Siti Noorain Mohmad Yousoff
<span title="2017-03-01">2017</span> <i title="Universitas Ahmad Dahlan"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/avuzjspx3nh5lboz3nsmpd3ba4" style="color: black;">TELKOMNIKA (Telecommunication Computing Electronics and Control)</a> </i> &nbsp;
Deep learning enhanced the state-of-the-art methods in genomics allows it to be used in analysing the biological data with high prediction.  ...  In this study, deep neural network has been to identify any set of gene deletion models that offers optimal results in xylitol production and its growth yield.  ...  While in section 2 described the implementation of deep neural network in predicting the bioprocess product.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12928/telkomnika.v15i1.6124">doi:10.12928/telkomnika.v15i1.6124</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3qfflicx6bhghmfcmv4njczv6m">fatcat:3qfflicx6bhghmfcmv4njczv6m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180417162742/http://journal.uad.ac.id/index.php/TELKOMNIKA/article/viewFile/6124/pdf_437" 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/da/27/da2720987389ccd719871459204679f54a7b7cb3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12928/telkomnika.v15i1.6124"> <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>

Deep Neural Network Method for the Prediction of Xylitol Production

Siti Noorain Mohmad Yousoff, 'Amirah Baharin, Afnizanfaizal Abdullah
<span title="2017-03-01">2017</span> <i title="Institute of Advanced Engineering and Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/trvfti3jm5hnxhei7rl7owpcqq" style="color: black;">Indonesian Journal of Electrical Engineering and Computer Science</a> </i> &nbsp;
Artificial intelligence methods such as deep neural network offer an efficient and powerful approach to be used to analyse the xylitol production value and at the same time to predict which genes and pathway  ...  Results show that, with an absence of genes pgi, tkt and tala, xylitol production can be boosted up to the higher level.</p>  ...  In this experiment, deep neural network has been used. Deep neural network is one of the methods from deep learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijeecs.v5.i3.pp691-696">doi:10.11591/ijeecs.v5.i3.pp691-696</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s5pk4bhkffftraot2ztllrdwb4">fatcat:s5pk4bhkffftraot2ztllrdwb4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180721192944/http://iaescore.com/journals/index.php/IJEECS/article/download/6524/6264" 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/cc/b0/ccb065d85f1935d6441c95849d2b6dec78c84d5f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/ijeecs.v5.i3.pp691-696"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Feature Interaction based Neural Network for Click-Through Rate Prediction [article]

Dafang Zou and Leiming Zhang and Jiafa Mao and Weiguo Sheng
<span title="2020-06-07">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper aims to fully utilize the information between features and improve the performance of deep neural networks in the CTR prediction task.  ...  We evaluate our models on CTR prediction tasks compared with classical baselines and show that our deep FINN model outperforms other state-of-the-art deep models such as PNN and DeepFM.  ...  At last, we feed the cross features into a deep neural network and the network outputs the prediction score. A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.05312v1">arXiv:2006.05312v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gskgtn3kifeoxoy56h7p4apxoq">fatcat:gskgtn3kifeoxoy56h7p4apxoq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200623142709/https://arxiv.org/pdf/2006.05312v1.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] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.05312v1" 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>

Deep Learning for Distribution Channels' Management

Sabina-Cristiana NECULA
<span title="2017-12-30">2017</span> <i title="ECO-INFOSOC Research Center"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lmfo33l5fnd6hpja5rybf7qc5a" style="color: black;">Informatică economică</a> </i> &nbsp;
We present an approach that combines self-organizing maps with artificial neural network with multiple hidden layers in order to identify the potential sales that might be addressed for channel distribution  ...  This paper presents an experiment of using deep learning models for distribution channel management.  ...  Deep learning relates to artificial neural networks with multiple hidden layers, convolutional neural networks, recurrent neural networks, self-organized maps, Boltzmann machine and auto encoders.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12948/issn14531305/21.4.2017.06">doi:10.12948/issn14531305/21.4.2017.06</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5tkuomqqkjcyvmtr43uo636pxu">fatcat:5tkuomqqkjcyvmtr43uo636pxu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428233352/http://revistaie.ase.ro/content/84/06%20-%20necula.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/52/24/5224d92f6899fcd3e3539bc21e16291ea5e4df86.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12948/issn14531305/21.4.2017.06"> <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>

Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period

Aljaž Ferencek, Davorin Kofjač, Andrej Škraba, Blaž Sašek, Mirjana Kljajić Borštnar
<span title="2020-10-01">2020</span> <i title="Walter de Gruyter GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dgbyfatk75gfthywgmv7zz5jsa" style="color: black;">Business Systems Research</a> </i> &nbsp;
, and we have analysed their quality and performance.Results: Results showed that a deep neural network with 6 layers and a convolutional neural network gave the best results.Conclusions: This paper suggests  ...  AbstractBackground: This paper addresses the problem of products' terminal call rate (TCR) prediction during the warranty period.  ...  The results showed that the best two models, deep neural network with 6 layers and a convolutional neural network differed in 1% when predicting TCR at 12 months.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2478/bsrj-2020-0014">doi:10.2478/bsrj-2020-0014</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3myjohi7v5hj7lixddbmeu6gti">fatcat:3myjohi7v5hj7lixddbmeu6gti</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201105231807/https://content.sciendo.com/downloadpdf/journals/bsrj/11/2/article-p36.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/30/aa/30aaf482046356a883d2ef1313c0637bda406631.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.2478/bsrj-2020-0014"> <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 deep-learning-based prediction method of the estimated ultimate recovery (EUR) of shale gas wells

Yu-Yang Liu, Xin-Hua Ma, Xiao-Wei Zhang, Wei Guo, Li-Xia Kang, Rong-Ze Yu, Yu-Ping Sun
<span title="">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/c3m5nplcmfc2ppgervj3tfmxcm" style="color: black;">Petroleum Science</a> </i> &nbsp;
First, the existing EUR evaluation methods of shale gas wells and the deep feedforward neural network algorithm was systematically analyzed.  ...  The results show that the EUR prediction with high accuracy. In addition, the results are affected by the variety and number of input parameters, the network structure and hyperparameters.  ...  Second, with the help of the trained deep neural network model, combined with the J o u r n a l P r e -p r o o f prediction data set, prediction of the normalized EUR of shale gas wells was conducted.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.petsci.2021.08.007">doi:10.1016/j.petsci.2021.08.007</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hfiplj63yrfwdlitbgqfwv4pt4">fatcat:hfiplj63yrfwdlitbgqfwv4pt4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210819012647/https://pdf.sciencedirectassets.com/780151/AIP/1-s2.0-S1995822621000285/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHEaCXVzLWVhc3QtMSJHMEUCIQDypRsZ1z8ygcL%2FwT3jw4qIs7JYLYt1%2F6D%2F%2FcWeVNZbUQIgLpi0U0AiV7RjV1LrGkHa3I8VCC3HXIMci9ZzKiBrEiIqgwQImv%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARAEGgwwNTkwMDM1NDY4NjUiDF3C95DByTtZyPahTSrXA3IW%2FsjlGN3UlvnRqplnIQVmVcAbphdkk6TE5jrPytXm8SLRPuRCfiTC3N%2FsG%2Fw7PGphJazWYh%2FuqqATStvfesbkvr%2BmE4CkYhCfwpieVarbUjCbvcgLLLeyVHEdYfCfXAq9nHMOM7KQXzK3AQwppRj08OVzeUmI3nO4eTRQj%2FLRqZh9DSJICqYKbXYnKj6koemyAbDoLY4GUGMGfWIQ%2FscwuS%2F4W8o3OL5N8wi59caUDW0ts%2FOSSyMsmEQumXkG7rFQRv5%2BS%2FtMiRWpIjXEfjwIxWPRWoBJdrp6jqIbcWbhIdJRKF1One3i45rk0h2ni97wQWbFyxOd%2FZQ8ygDX80CZuBVtLSho5zfdAsrpi2gL0%2BZ%2BHpBJFYexm3gkViaqcljLpK777BVT4IWDdzTlxFP0yllsUvEDEtfSo2K8nk6cFlicZeAob05SWefd%2Bz4Gm83XUJWRF1Kk7My9wCWi0FCPY3ZA%2BzMCnfItINd08sNSkvTs0KmOvx8%2FaB9Z2qDzMctB2d4z8ZaPOk8Nzrc4mIsVSmE4UUEP4Gektcmw84a6wdgqtASaOZzHRwhge3w5Ca02rNE%2FEuVBrL6dOJYhW9202xvuDhlgneM5TZ9j%2FE5k9K81tPG93DCE0faIBjqlAT7tD%2FMyRuAkNXjYPkOdz%2FzGjtBpjiFxl2xodkbk3%2FfRDaRZ%2FdBnVSk47TrCjxIOh0o3VO3OPZa3oEjenb0Xw3YIaA%2Brdqqg1UI9hwI9%2FYYxDXWvmeKfBFkyBJBeWYa5TXfkYjL2CntT5saXLWskeRyiMhrCJlOeCCi1RyOzgVIrETUipf4UHSNB6gff%2FFCh0hJg1AzAu8OFNQ3tlEDyX4KacfTehg%3D%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20210819T012637Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTYV4YDXPD3%2F20210819%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=1164de7940c1db43ae9c0effcc680e21e0c7edd69b66ac3402b86c38ca9ca136&amp;hash=d5d6eab447a8820b38cded0d39e49440d06ceb76f19ffa9ce7a6bcc4d75c6e31&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S1995822621000285&amp;tid=spdf-116bddc0-d765-48aa-ad4f-8426066205e2&amp;sid=53222f0f935f424fdb3b6d936cd412bf4734gxrqa&amp;type=client" 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/d0/75/d075d9636d9fd32a8fecadc3ad6d92849ca4cfd6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.petsci.2021.08.007"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Comparative study on traditional recommender systems and deep learning based recommender systems

N.L. Anantha, Bhanu Bathula
<span title="2018-06-30">2018</span> <i title="International Information and Engineering Technology Association"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yuzurvebivazlptvcltftrxxxy" style="color: black;">Advances in Modelling and Analysis B</a> </i> &nbsp;
Product recommendation is challenging task to e-commerce companies. Traditional Recommender Systems provided the solutions in recommending the products.  ...  Now a day Deep Learning is using in every domain. Deep Learning techniques in the field of Recommender Systems can be directly applied. Deep Learning has ample number of algorithms.  ...  CNN based recommender system Deep Cooperative Neural Network [DeepCoNN] [12] is Convolutional Neural Network uses factorization Machines provide users rating predictions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18280/ama_b.610202">doi:10.18280/ama_b.610202</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4iur3pjuujdkha6dyt3v6ntequ">fatcat:4iur3pjuujdkha6dyt3v6ntequ</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200320080431/http://iieta.org/sites/default/files/Journals/AMA/AMA_B/61.02_02.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/a8/33/a8337719cddd0d88474b9bf4146af67d45bcab87.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18280/ama_b.610202"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Power Consumption Prediction Based on Deep Learning

Xuanwen Zhang, Li Liu
<span title="">2019</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wxgp7pobnrfetfizidmpebi4qy" style="color: black;">Journal of Physics, Conference Series</a> </i> &nbsp;
The purpose of this paper is to select a power consumption forecasting method with high accuracy and low error.  ...  In this paper, the origin of deep learning technology is introduced, and the LSTMs model of deep learning is built, and the short-term electricity consumption forecasting model is built, which can complete  ...  SHORT-TERM ELECTRICITY CONSUMPTION PREDICTION MODEL BASED ON DEEP LEARNING In this paper, the long-short-term memory neural network model LSTMs is used to build the electricity consumption prediction model  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1325/1/012207">doi:10.1088/1742-6596/1325/1/012207</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5vbhzgpihbc4zjmod5elkbqctm">fatcat:5vbhzgpihbc4zjmod5elkbqctm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220129165753/https://iopscience.iop.org/article/10.1088/1742-6596/1325/1/012207/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/39/bf/39bf205381ae7f59642e6cc2201f622374c2b27a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1325/1/012207"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> iop.org </button> </a>

Hybrid-DNNs: Hybrid Deep Neural Networks for Mixed Inputs [article]

Zhenyu Yuan, Yuxin Jiang, Jingjing Li, Handong Huang
<span title="2020-05-18">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We develop a general architecture of hybrid deep neural networks (HDNNs) to support mixed inputs.  ...  Concentrating on reservoir production prediction, a specific HDNN model is configured and applied to an oil development block.  ...  Qiu for valuable discussions on production characterization of the adopted oil block.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.08419v1">arXiv:2005.08419v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jqi3jbsukve6laa365jzfexcea">fatcat:jqi3jbsukve6laa365jzfexcea</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200527195923/https://arxiv.org/ftp/arxiv/papers/2005/2005.08419.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] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.08419v1" 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>

Development of Oil Production Forecasting Method based on Deep Learning

Fargana Abdullayeva, Yadigar Imamverdiyev
<span title="2019-12-01">2019</span> <i title="International Academic Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zh64mt6o3jauhlhx2og4r37yoy" style="color: black;">Statistics, Optimization and Information Computing</a> </i> &nbsp;
The main purpose of this work is to develop a method that can forecast oil production with high accuracy, using Deep neural networks based on the debt data of wells.  ...  In this paper, a hybrid model based on a combination of the CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) networks, called CNN-LSTM is proposed for the forecasting of oil production  ...  The main purpose of this work is to develop a method that can predict the oil production with high accuracy using the Deep neural networks based on the debt data of wells.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.19139/soic-2310-5070-651">doi:10.19139/soic-2310-5070-651</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7ioxxctuobdmnhmcxyhwdon3wq">fatcat:7ioxxctuobdmnhmcxyhwdon3wq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200507140947/http://iapress.org/index.php/soic/article/download/soic.v7i4.1215/581" 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/58/ea/58eaed2cd64f64098054823c8a346ef2c879a1f9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.19139/soic-2310-5070-651"> <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>

Performance comparison of machine learning methods for prediction of estimating water production

A P Widowo, E A Sarwoko, Suhartono
<span title="">2019</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wxgp7pobnrfetfizidmpebi4qy" style="color: black;">Journal of Physics, Conference Series</a> </i> &nbsp;
The methods used in the prediction are Neural Network, Deep Learning, and k-Nearest Neighbor.  ...  Therefore, a study is needed to consider the problem-solving in the form of clean water production prediction that can help PDAM determine policy regarding water production.  ...  The methods used in the prediction were Neural Network, Deep Learning, and k-Nearest Neighbor.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1217/1/012122">doi:10.1088/1742-6596/1217/1/012122</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s2yfceu3mbcbhetfhfh7ppp3ku">fatcat:s2yfceu3mbcbhetfhfh7ppp3ku</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200310093038/https://iopscience.iop.org/article/10.1088/1742-6596/1217/1/012122/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/1d/2c/1d2c54a8162c40e8b75818360370e25d5adc9405.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1217/1/012122"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> iop.org </button> </a>
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