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2018 14th Symposium on Neural Networks and Applications (NEUREL)最新文献

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NEUREL 2018 TOC
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/neurel.2018.8586981
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引用次数: 0
Blind DIBR-synthesized Image Quality Assessment using multi-scale DoG and GRNN 基于多尺度DoG和GRNN的盲dibr合成图像质量评价
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587020
Dragana D. Sandić-Stanković, Dejan Bokan, Dragan D. Kukolj
{"title":"Blind DIBR-synthesized Image Quality Assessment using multi-scale DoG and GRNN","authors":"Dragana D. Sandić-Stanković, Dejan Bokan, Dragan D. Kukolj","doi":"10.1109/NEUREL.2018.8587020","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587020","url":null,"abstract":"In this paper, we explore the suitability of multi-resolution and multi-scale band-pass image representation generated by difference of Gaussian (DoG) operator for blind image quality assessment model. The developed model is based on general regression neural network (GRNN). The proposed model is consistent with human perception when evaluated on DIBR-synthesized image dataset.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124234358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Survey of Deep Neural Networks: Deployment Location and Underlying Hardware 深度神经网络综述:部署位置和底层硬件
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587006
Miloš Kotlar, D. Bojic, Marija Punt, V. Milutinovic
{"title":"A Survey of Deep Neural Networks: Deployment Location and Underlying Hardware","authors":"Miloš Kotlar, D. Bojic, Marija Punt, V. Milutinovic","doi":"10.1109/NEUREL.2018.8587006","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587006","url":null,"abstract":"This survey paper overviews the landscape of emerging deep neural networks (neural networks for deep analytics) and explores what type of underlying hardware is likely to be used at various deployment locations: in dew, fog, and cloud computing (dew computing is performed by edge devices). The paper discusses how different architecture approaches could be used on different deployment locations, for implementing deep neural networks. These include multicore processors, manycore processors, field programmable gate arrays, and application specific integrated circuits. The classification proposed in this paper divides the existing solutions into twelve different categories. Our two-dimensional classification enables comparing the existing architectures, which are predominantly cloud based, and anticipated future architectures, which are expected to be hybrid cloud-fog-dew architectures for internet of things applications. This classification enables its users to make trade-offs between data processing bandwidth, data processing latency, and power consumption.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128001896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network 基于LSTM和Bi-LSTM的深度神经网络预测血糖
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-09-11 DOI: 10.1109/NEUREL.2018.8586990
Qingnan Sun, Marko V. Jankovic, L. Bally, S. Mougiakakou
{"title":"Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network","authors":"Qingnan Sun, Marko V. Jankovic, L. Bally, S. Mougiakakou","doi":"10.1109/NEUREL.2018.8586990","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586990","url":null,"abstract":"A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia. A sequential model with one long-short-term memory (LSTM) layer, one bidirectional LSTM layer and several fully connected layers was used to predict blood glucose levels for different prediction horizons. The method was trained and tested on 26 retrospectively analysed datasets from 20 real patients. The proposed network outperforms the baseline methods in terms of all evaluation criteria.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131269841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 94
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