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

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Feature Selection for Image Distortion Classification 图像失真分类的特征选择
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586989
Saad Merrouche, Dimitrije Bujaković, M. Andric, Boban P. Bondzulic
{"title":"Feature Selection for Image Distortion Classification","authors":"Saad Merrouche, Dimitrije Bujaković, M. Andric, Boban P. Bondzulic","doi":"10.1109/NEUREL.2018.8586989","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586989","url":null,"abstract":"In this research, the spatial local mean subtraction contrast normalized coefficients are selected in order to achieve the classification of image distortion type. In order to achieve the feature selection, these coefficients and their products are calculated through four spatial orientations, which gives 18 features. Two methods for feature selection are applied in order to select a smaller number of features. Bhattacharyya distance is used as a measure that quantifies separability in the feature domain. The obtained results show that using methods for feature selection reduce the number of features and make the image distortion classification more robust.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"138 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":"114249863","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: 1
Detecting missing products in commercial refrigerators using convolutional neural networks 利用卷积神经网络检测商用冰箱中的缺失产品
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587005
Luka Šećerović, V. Papic
{"title":"Detecting missing products in commercial refrigerators using convolutional neural networks","authors":"Luka Šećerović, V. Papic","doi":"10.1109/NEUREL.2018.8587005","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587005","url":null,"abstract":"Out of stock (OOS) is a problem all stores are facing and it reduces their profit. Standard procedures for solving OOS are mostly manual and not scalable. This paper analyzes and proposes an automated and scalable solution for solving OOS problem inside commercial refrigerators. Small, low resolution cameras are placed inside refrigerators. Images taken with those cameras are analyzed with Faster R-CNN and Single Shot Multibox (SSD) models for object detection. Models were trained using transfer learning and their performances were analyzed and compared. After object detection, K-mean clustering algorithm is used to group objects on same shelves. Distance between objects on the same shelf determines if and where the OOS problem is present.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"1 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":"124238488","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: 2
Supervised and Unsupervised Learning of Fetal Heart Rate Tracings with Deep Gaussian Processes 基于深度高斯过程的胎儿心率跟踪的监督和无监督学习
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586992
Guanchao Feng, J. G. Quirk, P. Djurić
{"title":"Supervised and Unsupervised Learning of Fetal Heart Rate Tracings with Deep Gaussian Processes","authors":"Guanchao Feng, J. G. Quirk, P. Djurić","doi":"10.1109/NEUREL.2018.8586992","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586992","url":null,"abstract":"Cardiotocography (CTG) comprises of fetal heart rate (FHR) and uterine activity (UA) monitoring during pregnancy. It is used in hospitals on a regular basis because FHR and UA tracings contain important information about fetal well-being. Despite the CTG’s long history of use (of almost 50 years), the benefits it brings to the daily practice remain unsatisfying. The interpretation of CTG recordings by obstetricians suffer from high inter- and intra-variability, while their computerized analysis still remains difficult. In this paper, we propose both supervised and unsupervised learning by deep Gaussian processes (DGPs) for classification of FHR tracings. In working with real FHR signals, we obtained promising results which demonstrate the potential of the DGPs methodology. Further, we showed that the performance of the DGPs was improved by utilizing corresponding UA signals.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"81 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":"114320192","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: 22
2018 14th Symposium on Neural Networks and Applications (NEUREL) [Copyright notice] 2018第十四届神经网络与应用学术研讨会(NEUREL)[版权声明]
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/neurel.2018.8587010
{"title":"2018 14th Symposium on Neural Networks and Applications (NEUREL) [Copyright notice]","authors":"","doi":"10.1109/neurel.2018.8587010","DOIUrl":"https://doi.org/10.1109/neurel.2018.8587010","url":null,"abstract":"","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"27 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":"123567682","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: 1
Automatic Detection of Dehydration using Support Vector Machines 支持向量机的脱水自动检测
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587008
N. Reljin, Yelena Malyuta, Gary Zimmer, Y. Mendelson, D. Blehar, C. Darling, K. Chon
{"title":"Automatic Detection of Dehydration using Support Vector Machines","authors":"N. Reljin, Yelena Malyuta, Gary Zimmer, Y. Mendelson, D. Blehar, C. Darling, K. Chon","doi":"10.1109/NEUREL.2018.8587008","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587008","url":null,"abstract":"There is a high demand for techniques that can detect dehydration automatically and accurately. In this study we collected photoplethysmographic (PPG) signals with miniature, wearable pulse oximeters from dehydrated patients being treated in the emergency department of tertiary care medical center. We used a set of features based on the variable frequency complex demodulation (VFCDM) to track changes in the amplitudes of the PPG recordings in the heart rate frequency range over time. These features were fed to support vector machines (SVM) with radial basis function (RBF) kernel for automatic classification. The optimal overall accuracy for classifying dehydration, sensitivity and specificity were 67.91%, 72.77% and 64.31% respectively. These results are promising, and suggest that automatic distinction between dehydration and rehydration is potentially possible even in clinical setting.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"20 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":"128495594","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: 3
Automatic monitoring of honeybees’ activity outside of the hive from UHD video 通过超高清视频自动监控蜜蜂在蜂巢外的活动
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587026
A. Avramović, Vedran Jovanovic, Ratko Pilipović, Vladan Stojnić, V. Risojevic, Slavica Gajić, M. Simić, Igor Sevo, M. Mustra, Z. Babic, Janja Filipi
{"title":"Automatic monitoring of honeybees’ activity outside of the hive from UHD video","authors":"A. Avramović, Vedran Jovanovic, Ratko Pilipović, Vladan Stojnić, V. Risojevic, Slavica Gajić, M. Simić, Igor Sevo, M. Mustra, Z. Babic, Janja Filipi","doi":"10.1109/NEUREL.2018.8587026","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587026","url":null,"abstract":"Studying the behavior of social insect using computer vision algorithms is an interesting topic for both biological and signal processing communities. One of the most interesting aspects in the field is tracking of honeybees. Regarding computer vision method, honeybees’ behavior has been mostly monitored inside and at the entrance of the hive. In this research we are proposing the method for automatic monitoring of honeybees’ activity outside of the hive. Experiments showed that the activity of honeybees outside the hive can estimated using an ultra-high definition video captured with UAV from distance of 10 meters. Specific spots where honeybees are gathered can be detected using heat maps which represent the density of their occurrence in the observed time interval.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"31 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":"131973404","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: 2
Splash Detection in Fish Plants Surveillance Videos Using Deep Learning 使用深度学习的鱼类植物监控视频中的飞溅检测
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586984
Vedran Jovanovic, E. Svendsen, V. Risojevic, Z. Babic
{"title":"Splash Detection in Fish Plants Surveillance Videos Using Deep Learning","authors":"Vedran Jovanovic, E. Svendsen, V. Risojevic, Z. Babic","doi":"10.1109/NEUREL.2018.8586984","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586984","url":null,"abstract":"The objective of this paper is to present and evaluate an improved method for automatic splash detection in surveillance videos of offshore fish production plants. In fishing and aquaculture industry one of the main challenges is production loss, that is, among the other things, caused by poor handling of the fish during operations such as crowding and delousing. This operations are very stressful for fish, and may trigger an increase in mortality, which is directly correlated with the production and profit loss. Because of this, improved solutions based on new technologies are being investigated, in order to decrease the risk of unnecessary stress, and improve the quality of production. One of the main parameters used for remote visual inspection of fish state is surface activity, which can be observed in a form of fish jumping and splashing. For that reason, in this paper, a novel algorithm based on using of Convolutional Neural Networks (CNNs) for splash detection is presented, which outperforms all existing algorithms based on local descriptors and linear classifiers. Using this approach we obtained splash detection accuracy of 99.9%.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"20 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":"126212402","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: 6
Towards Stochasticity of Regularization in Deep Neural Networks 深度神经网络正则化的随机性研究
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587027
Ljubinka Sandjakoska, A. Bogdanova
{"title":"Towards Stochasticity of Regularization in Deep Neural Networks","authors":"Ljubinka Sandjakoska, A. Bogdanova","doi":"10.1109/NEUREL.2018.8587027","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587027","url":null,"abstract":"The high capacity of deep neural networks, developed for complex data, evokes its proneness to overfitting. A lot of attention is paid on finding flexible solutions to this problem. To achieve flexibility, as a very challenging issue in improving the ability of generalization, deep networks have to deal with the stochastic effects of regularization. In this paper we propose a methodological framework for dealing with the stochasticity in regularized deep neural network. Basics of dropout as ensemble method for regularization are presented, followed by introducing new method for dropout regularization and its application in molecular dynamics simulations. Results from the simulation show that, the stochastic behavior cannot be avoided but we have to find way to deal with it. The proposed dropout method improves the state-of-the-art of applied deep neural networks on the benchmark dataset.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"21 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":"126587100","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
Smart Warehouse Management System Concept with Implementation 智能仓库管理系统的概念与实现
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587004
E. Žunić, Sead Delalic, K. Hodzic, A. Besirevic, Harun Hindija
{"title":"Smart Warehouse Management System Concept with Implementation","authors":"E. Žunić, Sead Delalic, K. Hodzic, A. Besirevic, Harun Hindija","doi":"10.1109/NEUREL.2018.8587004","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587004","url":null,"abstract":"Distribution companies use complex software systems called WMS (Warehouse Management System). The WMS is an important part of the company’s business and it can make processes simple to keep track of. Smart WMS optimizes processes to save resources and to create a more efficient working place. This paper describes the concept of a smart WMS that is implemented in one of the largest distribution companies in Bosnia and Herzegovina. The system uses artificial intelligence and optimization algorithms to improve working process. The paper describes the complete warehouse workflow that includes stock planning, initial product placement, transfer from stock to pick zone, order picking process, transport and tracking. The anomaly detection is used in some processes to improve the whole system. The main contribution of this paper is the presentation of an efficient and in the real world used smart WMS concept.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"4 5-6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120928871","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: 36
NEUREL 2018 Breaker Page NEUREL 2018断路器页
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/neurel.2018.8587022
{"title":"NEUREL 2018 Breaker Page","authors":"","doi":"10.1109/neurel.2018.8587022","DOIUrl":"https://doi.org/10.1109/neurel.2018.8587022","url":null,"abstract":"","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"18 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":"120974382","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
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