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Augmented Neural Networks for Modelling Consumer Indebtness [article]

Alexandros Ladas, Jonathan M. Garibaldi, Rodrigo Scarpel, Uwe Aickelin
2014 arXiv   pre-print
In particular, as our results show, Neural Networks achieve the best performance in modelling consumer indebtness, especially when they manage to incorporate the significant and experimentally verified  ...  results of the Data Mining process in the model, exploiting the flexibility Neural Networks offer in designing their topology.  ...  As Neural Networks have not been used so far for the purposes of Consumer Debt Analysis, in this work we exploit the many advantages they offer in order to achieve a better modelling of consumer indebtness  ... 
arXiv:1409.1057v1 fatcat:wc76n47npbekfjsbwbsed2xd6m

Augmented Neural Networks for modelling consumer indebtness

Alexandras Ladas, Jon Garibaldi, Rodrigo Scarpel, Uwe Aickelin
2014 2014 International Joint Conference on Neural Networks (IJCNN)  
In particular, as our results show, Neural Networks achieve the best performance in modelling consumer indebtness, especially when they manage to incorporate the significant and experimentally verified  ...  results of the Data Mining process in the model, exploiting the flexibility Neural Networks offer in designing their topology.  ...  As Neural Networks have not been used so far for the purposes of Consumer Debt Analysis, in this work we exploit the many advantages they offer in order to achieve a better modelling of consumer indebtness  ... 
doi:10.1109/ijcnn.2014.6889760 dblp:conf/ijcnn/LadasGSA14 fatcat:uignkakfb5gohau7npytrc665i

Augmented Neural Networks for Modelling Consumer Indebtness

Alexandros Ladas, Jonathan M. Garibaldi, Rodrigo Arnaldo Scarpel, Uwe Aickelin
2014 Social Science Research Network  
Please see the repository url above for details on accessing the published version and note that access may require a subscription. For more information, please contact eprints@nottingham.ac.uk  ...  Augmented Neural Networks for Modelling Consumer Indebtness Alexandros Ladas, Jon Garibaldi, Rodrigo Scarpel and Uwe Aickelin Abstract-Consumer Debt has risen to be an important problem of modern societies  ...  As Neural Networks have not been used so far for the purposes of Consumer Debt Analysis, in this work we exploit the many advantages they offer in order to achieve a better modelling of consumer indebtness  ... 
doi:10.2139/ssrn.2828081 fatcat:qkno4bnmevfp5jkqyhxgsxjpqe

An Efficient Deep Learning Model for Olive Diseases Detection

Madallah Alruwaili, Saad Alanazi, Sameh Abd, Abdulaziz Shehab
2019 International Journal of Advanced Computer Science and Applications  
It utilizes an efficient parameterized transfer learning model, a smart data augmentation with balanced number of images in every category, and it functions in more complex environments with enlarged and  ...  However, the detection of plant diseases either via the farmers' naked eyes or their traditional tools or even within laboratories is still an error prone and time consuming process.  ...  ACKNOWLEDGMENT We are indebted to the Deanship of Scientific Research at Jouf University for funding this work through General Research Project under grant number (39/766).  ... 
doi:10.14569/ijacsa.2019.0100863 fatcat:lxavnhlwnfeyxkfflddksll6je

GoogleNet CNN Neural Network towards Chest CT-Coronavirus Medical Image Classification

Nesreen Alsharman, Ibrahim Jawarneh
2020 Journal of Computer Science  
This paper aims to improve the accuracy of detection for CT-Coronavirus images using deep learning for Convolutional Neural Networks (CNNs) that helps medical staffs for classification chest CT-Coronavirus  ...  This research retrains GoogleNet CNN architecture over the COVIDCT-Dataset for classification CT-Coronavirus image.  ...  Acknowledgment The authors are indebted to editor and reviewers for their support and for their helpful comments from which this article has benefited much.  ... 
doi:10.3844/jcssp.2020.620.625 fatcat:gbqilxd2wnaafkmb6vjzyl4aba

Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks

Dmitry A. Konovalov, Suzanne Hillcoat, Genevieve Williams, R. Alastair Birtles, Naomi Gardiner, Matthew I. Curnock
2018 Journal of Geoscience and Environment Protection  
This study reports the first proof of concept for recognizing individual dwarf minke whales using the Deep Learning Convolutional Neural Networks (CNN).The "off-the-shelf" Image net-trained VGG16 CNN was  ...  Training and image augmentation procedures were developed to compensate for the small number of available images.  ...  We are particularly indebted to  ... 
doi:10.4236/gep.2018.65003 fatcat:kbazdp565bcrfplsoynamuzliy

Pose estimation of anime/manga characters

Pramook Khungurn, Derek Chou
2016 Proceedings of the 1st International Workshop on coMics ANalysis, Processing and Understanding - MANPU '16  
To alleviate data scarcity, we propose using a database of 3D character models and poses to generate synthetic training data for 2D pose estimators of anime/manga characters based on convolutional neural  ...  networks (CNN).  ...  We are also indebted to the illustrators, 3D modelers, and animators for making their works available on the Internet. Kavita Bala would like to thank Adobe for their donation.  ... 
doi:10.1145/3011549.3011552 dblp:conf/icpr/KhungurnC16 fatcat:edhcuyjszrf4lezchum5whu4ma

Automated Identification of Cell Populations in Flow Cytometry Data with Transformers [article]

Matthias Wödlinger, Michael Reiter, Lisa Weijler, Margarita Maurer-Granofszky, Angela Schumich, Michael Dworzak
2021 arXiv   pre-print
We present a novel neural network approach based on the transformer architecture that learns to directly identify blast cells in a sample.  ...  Manual MRD assessment from Multiparameter Flow Cytometry (FCM) data after treatment is time-consuming and subjective.  ...  We are indebted to Melanie Gau, Roxane Licandro, Florian Kleber, Paolo Rota and Guohui Qiao (all from TU Vienna) for valuable contributions to the AutoFLOW project.  ... 
arXiv:2108.10072v1 fatcat:bhb2x6ybv5h7dfxi4txg4zlaoy

Multilingual Neural Network Acoustic Modelling for ASR of Under-Resourced English-isiZulu Code-Switched Speech

Astik Biswas, Febe de Wet, Ewald van der Westhuizen, Emre Yılmaz, Thomas Niesler
2018 Interspeech 2018  
We used this corpus to evaluate the application of multilingual neural network acoustic modelling to English-isiZulu code-switched speech recognition.  ...  Although isiZulu speakers code-switch with English as a matter of course, extremely little appropriate data is available for acoustic modelling.  ...  We are also indebted to Dr Armin Saeb and Dr Raghav Menon for their valuable insights.  ... 
doi:10.21437/interspeech.2018-1711 dblp:conf/interspeech/BiswasWWYN18 fatcat:sgusnkmqvvemtnnxzytmwtdh7m

GEOSPATIAL MACHINE LEARNING DATASETS STRUCTURING AND CLASSIFICATION TOOL: CASE STUDY FOR MAPPING LULC FROM RASAT SATELLITE IMAGES

S. K. M. Abujayyab, I. R. Karaş
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
TensorFlow-Keras library employed to perform the classification using neural networks. A case study using RASAT satellite image in Ankara-Turkey utilized to perform the analysis.  ...  Tools of steps developed in two programing environments, which are ArcGIS for geospatial datasets structuring and Anaconda for ML training and classification.  ...  We are indebted for their supports.  ... 
doi:10.5194/isprs-archives-xlii-4-w16-39-2019 fatcat:mhvs7y62z5cflghgw6ptvilyda

Remaining phosphorus estimated by pedotransfer function

Joice Cagliari, Maurício Roberto Veronez, Marcelo Eduardo Alves
2011 Revista Brasileira de Ciência do Solo  
A pedotransfer function was developed by artificial neural networks (ANN) from a database of Prem values, pH values measured in 1 mol L-1 NaF solution (pH NaF) and soil chemical and physical properties  ...  Although the database used in this study was not comprehensive enough for the establishment of a definitive pedotrasnfer function for Prem estimation, results indicated the inclusion of Prem and pH NaF  ...  ACKNOWLEDGEMENTS The authors are indebted to the Brazilian Federal Agency for Support and Evaluation of Graduate Education -CAPES for the financial support and grateful to the researchers Mariana Cantoni  ... 
doi:10.1590/s0100-06832011000100019 fatcat:54xgrvyu6bbcfhirgw23xkz654

Dental Impression Tray Selection From Maxillary Arch Images Using Multi-Feature Fusion and Ensemble Classifier

Muhammad Asif Hasan, Norli Anida Abdullah, Mohammad Mustaneer Rahman, Mohd Yamani Idna Bin Idris, Omar F. Tawfiq
2021 IEEE Access  
Besides, the performance of a deep learning based multilayer perceptron neural network is also investigated.  ...  Dental impression tray is frequently used in dentistry to record the patient's oral structure for clinical oral diagnosis and treatment planning.  ...  ACKNOWLEDGMENT The authors are indebted to the anonymous reviewers for their valuable observations and suggestions.  ... 
doi:10.1109/access.2021.3059785 fatcat:vs7epml745edfkygdzeuy3kaqi

NeuroAnimator

Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey Hinton
1998 Proceedings of the 25th annual conference on Computer graphics and interactive techniques - SIGGRAPH '98  
Furthermore, by exploiting the network structure of the NeuroAnimator, we introduce a fast algorithm for learning controllers that enables either physics-based models or their neural network emulators  ...  Depending on the model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conventional numerical simulation.  ...  We are indebted to Steve Hunt for procuring the equipment that we needed to carry out our research at Intel.  ... 
doi:10.1145/280814.280816 dblp:conf/siggraph/GrzeszczukTH98 fatcat:kjjczayu5ja43e5a4gbz33v7aa

On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval

Mikhail Krinitskiy, Marina Aleksandrova, Polina Verezemskaya, Sergey Gulev, Alexey Sinitsyn, Nadezhda Kovaleva, Alexander Gavrikov
2021 Remote Sensing  
As a result, we demonstrate that our models based on convolutional neural networks deliver a superior quality compared to all previously published approaches.  ...  We present several new algorithms that are based on deep learning techniques: A model for outliers filtering, and a few models for TCC retrieval from all-sky imagery.  ...  The approaches presented in the state-of-the-art studies at the moment are of several types: Data augmentation strategies, strategies of training of neural networks, approaches for neural architecture  ... 
doi:10.3390/rs13020326 fatcat:ae5do63kl5bszn7mqg3ruciav4

The role of convolutional neural networks in scanning probe microscopy: a review

Ido Azuri, Irit Rosenhek-Goldian, Neta Regev-Rudzki, Georg Fantner, Sidney R Cohen
2021 Beilstein Journal of Nanotechnology  
In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.  ...  We further are deeply indebted to the reviewers of this manuscript for providing useful and comprehensive feedback.  ...  Acknowledgements We thank Sergei Kalinin for commenting on the manuscript and providing a preprint of his perspectives article.  ... 
doi:10.3762/bjnano.12.66 pmid:34476169 pmcid:PMC8372315 fatcat:dwl2mqxfjnanve363my7qr4try
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