{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T15:55:33Z","timestamp":1720799733871},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1016\/j.eswa.2019.01.066","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T18:11:44Z","timestamp":1548785504000},"page":"271-281","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":26,"special_numbering":"C","title":["Enhancing batch normalized convolutional networks using displaced rectifier linear units: A systematic comparative study"],"prefix":"10.1016","volume":"124","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-2527-4548","authenticated-orcid":false,"given":"David","family":"Mac\u00eado","sequence":"first","affiliation":[]},{"given":"Cleber","family":"Zanchettin","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5614-229X","authenticated-orcid":false,"given":"Adriano L.I.","family":"Oliveira","sequence":"additional","affiliation":[]},{"given":"Teresa","family":"Ludermir","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2019.01.066_bib0001","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.eswa.2018.07.053","article-title":"Exudate detection for diabetic retinopathy with circular hough transformation and convolutional neural networks","volume":"114","author":"Adem","year":"2018","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.eswa.2019.01.066_bib0002","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1162\/089976698300017746","article-title":"Natural gradient works efficiently in learning","volume":"10","author":"Amari","year":"1998","journal-title":"Neural Computation"},{"key":"10.1016\/j.eswa.2019.01.066_bib0003","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.eswa.2017.02.002","article-title":"Enhancing deep learning sentiment analysis with ensemble techniques in social applications","volume":"77","author":"Araque","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2016.05.022","article-title":"Deep learning with adaptive learning rate using laplacian score","volume":"63","author":"Chandra","year":"2016","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0005","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.eswa.2018.06.032","article-title":"Forecasting stock market crisis events using deep and statistical machine learning techniques","volume":"112","author":"Chatzis","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0006","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.eswa.2017.04.030","article-title":"Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies","volume":"83","author":"Chong","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0007","article-title":"Fast and accurate deep network learning by exponential linear units (elus)","author":"Clevert","year":"2015","journal-title":"CoRR"},{"key":"10.1016\/j.eswa.2019.01.066_bib0008","series-title":"Proceedings of the joint statistical meetings, Houston Texas, August","first-page":"1","article-title":"On multiple-comparisons procedures","volume":"1","author":"Conover","year":"1979"},{"key":"10.1016\/j.eswa.2019.01.066_bib0009","unstructured":"Cunningham, R. J., Harding, P. J., & Loram, I. D. (2017). The application of deep convolutional neural networks to ultrasound for modelling of dynamic states within human skeletal muscle. arXiv:1706.09450."},{"key":"10.1016\/j.eswa.2019.01.066_bib0010","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.eswa.2018.10.003","article-title":"Deep learning for aspect-based sentiment analysis: A comparative review","volume":"118","author":"Do","year":"2019","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0011","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.eswa.2017.10.052","article-title":"Designing architectures of convolutional neural networks to solve practical problems","volume":"94","author":"Ferreira","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_sbref0011","series-title":"AISTATS \u201911: Proceedings of the 14th international conference on artificial intelligence and statistics","first-page":"315","article-title":"Deep sparse rectifier neural networks","volume":"15","author":"Glorot","year":"2011"},{"key":"10.1016\/j.eswa.2019.01.066_bib0013","series-title":"2016 IEEE conference on computer vision and pattern recognition (CVPR)","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.eswa.2019.01.066_bib0014","series-title":"Proceedings of the IEEE international conference on computer vision","first-page":"1026","article-title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","volume":"11-18-Dece","author":"He","year":"2016"},{"key":"10.1016\/j.eswa.2019.01.066_bib0015","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","article-title":"Identity mappings in deep residual networks","author":"He","year":"2016","journal-title":"Lecture Notes in Computer Science"},{"key":"10.1016\/j.eswa.2019.01.066_sbref0015","series-title":"ICCV 2015","article-title":"Elu-networks - fast and accurate cnn learning on imagenet","author":"Heusel","year":"2015"},{"key":"10.1016\/j.eswa.2019.01.066_bib0017","series-title":"Untersuchungen zu dynamischen neuronalen Netzen","author":"Hochreiter","year":"1991"},{"key":"10.1016\/j.eswa.2019.01.066_bib0018","series-title":"2017 IEEE conference on computer vision and pattern recognition (CVPR)","first-page":"1","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.eswa.2019.01.066_bib0019","unstructured":"Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167."},{"key":"10.1016\/j.eswa.2019.01.066_bib0020","unstructured":"Karpathy, A. (2017). Convolutional neural networks for visual recognition. URL: http:\/\/www.cs231n.github.io\/neural-networks-1\/."},{"key":"10.1016\/j.eswa.2019.01.066_sbref0018","series-title":"Learning multiple layers of features from tiny images","first-page":"1","author":"Krizhevsky","year":"2009"},{"key":"10.1016\/j.eswa.2019.01.066_bib0022","series-title":"Advances in neural information processing systems","article-title":"ImageNet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012"},{"issue":"260","key":"10.1016\/j.eswa.2019.01.066_bib0023","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","article-title":"Use of ranks in one-criterion variance analysis","volume":"47","author":"Kruskal","year":"1952","journal-title":"Journal of the American Statistical Association"},{"issue":"11","key":"10.1016\/j.eswa.2019.01.066_bib0024","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proceedings of the IEEE"},{"key":"10.1016\/j.eswa.2019.01.066_bib0025","unstructured":"LeCun, Y., Bottou, L., Orr, G. B., & M\u00fcller, K. R. (2012). Efficient backprop. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)7700 LECTU, 9\u201348. doi:10.1007\/978-3-642-35289-8-3."},{"key":"10.1016\/j.eswa.2019.01.066_sbref0022","series-title":"Proceedings of the 30 th international conference on machine learning","first-page":"6","article-title":"Rectifier nonlinearities improve neural network acoustic models","author":"Maas","year":"2013"},{"key":"10.1016\/j.eswa.2019.01.066_bib0027","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.eswa.2018.10.012","article-title":"Sparsemaps: Convolutional networks with sparse feature maps for tiny image classification","volume":"119","author":"Moradi","year":"2019","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0028","series-title":"Proceedings of the 27th international conference on machine learning","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"Nair","year":"2010"},{"key":"10.1016\/j.eswa.2019.01.066_bib0029","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.eswa.2018.07.070","article-title":"A deep active survival analysis approach for precision treatment recommendations: Application of prostate cancer","volume":"115","author":"Nezhad","year":"2019","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0030","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.eswa.2017.07.018","article-title":"Face alignment using a deep neural network with local feature learning and recurrent regression","volume":"89","author":"Park","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0031","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.eswa.2018.09.040","article-title":"A structure-enriched neural network for network embedding","volume":"117","author":"Qiao","year":"2019","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0032","article-title":"Unsupervised representation learning with deep Convolutional generative adversarial networks","author":"Radford","year":"2015","journal-title":"CoRR"},{"key":"10.1016\/j.eswa.2019.01.066_bib0033","doi-asserted-by":"crossref","unstructured":"Shah, A., Kadam, E., Shah, H., & Shinde, S. (2016). Deep residual networks with exponential linear unit. arXiv:1604.04112, (p.\u00a07). doi:10.1145\/2983402.2983406.","DOI":"10.1145\/2983402.2983406"},{"key":"10.1016\/j.eswa.2019.01.066_bib0034","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"CoRR"},{"key":"10.1016\/j.eswa.2019.01.066_bib0035","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2019.01.066_bib0036","series-title":"Conference of the advanced school for computing and imaging","article-title":"Feature scaling in support vector data descriptions","author":"Tax","year":"2002"},{"key":"10.1016\/j.eswa.2019.01.066_bib0037","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.eswa.2018.04.021","article-title":"An automated system for epilepsy detection using eeg brain signals based on deep learning approach","volume":"107","author":"Ullah","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0038","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.eswa.2018.08.038","article-title":"Feed-forward neural network training using sparse representation","volume":"116","author":"Yang","year":"2019","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0039","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.eswa.2018.04.007","article-title":"Building feedforward neural networks with random weights for large scale datasets","volume":"106","author":"Ye","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0040","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eswa.2016.10.017","article-title":"Text summarization using unsupervised deep learning","volume":"68","author":"Yousefi-Azar","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2019.01.066_bib0041","unstructured":"Zagoruyko, S., & Komodakis, N. (2016). Wide residual networks. arXiv:1605.07146."},{"key":"10.1016\/j.eswa.2019.01.066_bib0042","unstructured":"Zagoruyko, S., & Komodakis, N. (2017). Diracnets: Training very deep neural networks without skip-connections."}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417419300855?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417419300855?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,2,22]],"date-time":"2019-02-22T08:02:57Z","timestamp":1550822577000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417419300855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6]]},"references-count":42,"alternative-id":["S0957417419300855"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2019.01.066","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2019,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing batch normalized convolutional networks using displaced rectifier linear units: A systematic comparative study","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2019.01.066","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}