发布求助
文献互助 智能选刊 最新文献

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

筛选
英文 中文
Improving Performance of Vehicle Routing Algorithms using GPS Data 利用GPS数据改进车辆路径算法的性能
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586982
E. Žunić, Harun Hindija, A. Besirevic, K. Hodzic, Sead Delalic
{"title":"Improving Performance of Vehicle Routing Algorithms using GPS Data","authors":"E. Žunić, Harun Hindija, A. Besirevic, K. Hodzic, Sead Delalic","doi":"10.1109/NEUREL.2018.8586982","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586982","url":null,"abstract":"Two important problems distribution companies face on a daily basis are the routing and tracking of a vehicle fleet. The former is being overcome by solving the famous vehicle routing problem (VRP), a generalization of the traveling salesman problem (TSP), and the later analyses GPS data to get information of the moving vehicles. In this paper a system which uses GPS data to track the vehicles, analyze their routes and improve input data needed for the algorithm for the vehicle routing problem is described. In a real-world scenario, implementing an VRP algorithm is not enough. Algorithms which analyze GPS data ensure that the VRP algorithm takes correct input data and that the driven routes are those that the algorithm proposed.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"33 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":"126633721","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: 10
Machine-learning based Blind Visual Quality Assessment with Content-aware Data Partitioning 基于机器学习的盲视觉质量评估与内容感知数据划分
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587018
A. Gavrovska, G. Zajic, M. Milivojević, I. Reljin
{"title":"Machine-learning based Blind Visual Quality Assessment with Content-aware Data Partitioning","authors":"A. Gavrovska, G. Zajic, M. Milivojević, I. Reljin","doi":"10.1109/NEUREL.2018.8587018","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587018","url":null,"abstract":"Over the years different machine-learning based image quality assessment models have been proposed. In this paper, we analyze data partitioning. Since statistical data partitioning may affect the results due to the number of iterations, we analyze the effect of content-aware partitioning. The results are analyzed for different partitioning methods and models using publicly available dataset and difference mean opinion scores.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"24 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":"123735571","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
Deep Learning for Two-Step Classification of Malignant Pigmented Skin Lesions 深度学习用于恶性色素皮肤病变的两步分类
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587019
S. Kaymak, P. Esmaili, Ali Serener
{"title":"Deep Learning for Two-Step Classification of Malignant Pigmented Skin Lesions","authors":"S. Kaymak, P. Esmaili, Ali Serener","doi":"10.1109/NEUREL.2018.8587019","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587019","url":null,"abstract":"Skin cancer is one of the most common types of cancer. Its early detection drastically improves outcomes and saves human lives. Well known skin cancer types are melanoma, basal cell carcinoma and squamous cell carcinoma. Melanoma is melanocytic malignant while basal cell carcinoma and squamous cell carcinoma are non-melanocytic malignant. Even though the diagnosis of these cancer types is done by a skin biopsy, automatic detection of skin cancer using computerized methods may lead to a faster and a more accurate diagnosis. The majority of automated skin cancer detection methods proposed by researchers so far concentrated only on melanocytic malignant type melanoma. Non-melanocytic malignant skin lesions could not be investigated in detail due to the lack of available datasets with different lesion classes. In this paper, an automatic detection of malignant pigmented skin lesions is investigated. For this, the two-step skin lesion diagnostic procedure of the dermatologists is followed. Using a deep learning model, the skin lesion is first classified as melanocytic or non-melanocytic and then malignant types are detected using other deep learning models. The performance evaluations show that melanocytic and non-melanocytic skin lesions are detected with the highest accuracy. They also show that melanocytic malignant skin lesions can be classified with a higher accuracy than non-melanocytic malignant skin lesions.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"11 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":"124778137","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: 35
Comparative Analysis of Different ANN Methods for the Noise Wave Temperature Extraction 不同人工神经网络噪声波温度提取方法的比较分析
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586987
Vladica Đorđević, Z. Marinković, O. Pronić-Rančić, V. Markovic
{"title":"Comparative Analysis of Different ANN Methods for the Noise Wave Temperature Extraction","authors":"Vladica Đorđević, Z. Marinković, O. Pronić-Rančić, V. Markovic","doi":"10.1109/NEUREL.2018.8586987","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586987","url":null,"abstract":"The noise wave temperatures are extracted from the measured transistor noise parameters usually using time-demanding optimization procedures in microwave circuit simulators. For more efficient extraction of these temperatures, we developed four different extraction methods based on artificial neural networks. The developed extraction methods are compared in terms of accuracy, complexity and effectiveness in the case of GaAs HEMT device.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"4 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":"123766304","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
Vehicle Fine-grained Recognition Based on Convolutional Neural Networks for Real-world Applications 基于卷积神经网络的车辆细粒度识别在现实世界中的应用
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587012
Jakub Špaňhel, Jakub Sochor, A. Makarov
{"title":"Vehicle Fine-grained Recognition Based on Convolutional Neural Networks for Real-world Applications","authors":"Jakub Špaňhel, Jakub Sochor, A. Makarov","doi":"10.1109/NEUREL.2018.8587012","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587012","url":null,"abstract":"We explore the implementation of vehicle fine-grained type and color recognition based on neural networks in a real-world application. We suggest changes to the previously published method with respect to capabilities of low-powered devices, such as Nvidia Jetson. Experimental evaluation shows that the accuracy of MobileNet net slightly decreases compared to ResNet-50 from 89.55% to 86.13% while inference is 2.4× faster on Jetson.","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":"123419615","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
Analysis of Color Space Quantization in Split-Brain Autoencoder for Remote Sensing Image Classification 用于遥感图像分类的裂脑自编码器色彩空间量化分析
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587001
Vladan Stojnić, V. Risojevic
{"title":"Analysis of Color Space Quantization in Split-Brain Autoencoder for Remote Sensing Image Classification","authors":"Vladan Stojnić, V. Risojevic","doi":"10.1109/NEUREL.2018.8587001","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587001","url":null,"abstract":"This paper investigates the importance of different parameters of split-brain autoencoder to performance of learned image representations for remote sensing scene classification. We investigate the usage of LAB color space as well as color space created using PCA applied to RGB pixel values. We show that these two spaces give almost equal results, with slight favor towards the LAB color space. We also investigate choices of different quantization methods of color targets and number of quantization bins. We have found that using k-means clustering for quantization works slightly better than using uniform quantization. We also show that even when using really small number of bins it is possible to get only slightly worse results.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"39 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":"129284718","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: 5
Real-time Algorithms for Facial Emotion Recognition: A Comparison of Different Approaches 面部情绪识别的实时算法:不同方法的比较
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587011
Aneta Kartali, Miloš Roglić, M. Barjaktarović, M. Đurić-Jovičić, M. Janković
{"title":"Real-time Algorithms for Facial Emotion Recognition: A Comparison of Different Approaches","authors":"Aneta Kartali, Miloš Roglić, M. Barjaktarović, M. Đurić-Jovičić, M. Janković","doi":"10.1109/NEUREL.2018.8587011","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587011","url":null,"abstract":"Emotion recognition has application in various fields such as medicine (rehabilitation, therapy, counseling, etc.), e-learning, entertainment, emotion monitoring, marketing, law. Different algorithms for emotion recognition include feature extraction and classification based on physiological signals, facial expressions, body movements. In this paper, we present a comparison of five different approaches for real-time emotion recognition of four basic emotions (happiness, sadness, anger and fear) from facial images. We have compared three deep-learning approaches based on convolutional neural networks (CNN) and two conventional approaches for classification of Histogram of Oriented Gradients (HOG) features: 1) AlexNet CNN, 2) commercial Affdex CNN solution, 3) custom made FER-CNN, 4) Support Vector Machine (SVM) of HOG features, 5) Multilayer Perceptron (MLP) artificial neural network of HOG features. The result of real-time testing of five different algorithms on the group of eight volunteers is presented.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"4 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":"126452911","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: 27
Collection and Sentiment Analysis of Twitter Data on the Political Atmosphere 推特政治氛围数据的收集与情感分析
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586980
Merima Čišija, E. Žunić, Dženana Đonko
{"title":"Collection and Sentiment Analysis of Twitter Data on the Political Atmosphere","authors":"Merima Čišija, E. Žunić, Dženana Đonko","doi":"10.1109/NEUREL.2018.8586980","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586980","url":null,"abstract":"The past decade was marked, among other things, by the rapid growth of social networks. These networks collect personal data about their users - their photographs, interests, friends, locations, website visits, clicks, status updates and much more. A large number of users and a big collection of various data collected about the users make social media networks an abundant source of data that can be analyzed and used for targeted marketing, social phenomena analysis, generating different statistics and so on. In this paper we will use the potential of the tool RapidMiner in order to collect data from the social media network Twitter using the AYLIEN extension, preparing the data and applying sentiment analysis, which will give insight into the general atmosphere surrounding the actions of the current USA president Donald Trump","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"36 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":"121552908","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
Transthoracic Bioimpedance Measurements for Heart Failure Detection 经胸生物阻抗测量检测心力衰竭
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587015
N. Reljin
{"title":"Transthoracic Bioimpedance Measurements for Heart Failure Detection","authors":"N. Reljin","doi":"10.1109/NEUREL.2018.8587015","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587015","url":null,"abstract":"Heart failure (HF) is affecting more than 40 million people worldwide, and is one of the most common causes of hospitalization among the elderly. As such, it imposes high demands on health care systems. HF symptoms are severe, and include shortness of breath, chest discomfort, swelling in lower extremities, and generalized fatigue. Since these symptoms are dominated by shortness of breath from pulmonary edema, the detection of HF is performed by measuring fluid accumulations in lungs. To date several methods have been used for heart failure detection, but the predictive accuracy of such methods is marginal, often made in non-timely manner, and expensive. Therefore, there is a need for a reliable, noninvasive, easy to use, inexpensive, and accurate device. To this end, transthoracic bioimpedance (TBI) has risen as such approach, with promising results in detection of intrathoracic volume retention. The theory behind TBI measurements of fluid accumulations in the lungs will be presented, as well as the results obtained on healthy participants and HF patients in controlled and clinical settings respectively.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"68 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":"130741706","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
Deep Gaussian processes: Theory and applications 深度高斯过程:理论与应用
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586994
P. Djurić
{"title":"Deep Gaussian processes: Theory and applications","authors":"P. Djurić","doi":"10.1109/NEUREL.2018.8586994","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586994","url":null,"abstract":"Gaussian processes are an infinite-dimensional generalization of multivariate normal distributions. They provide a principled approach to learning with kernel machines and they have found wide applications in many fields. More recently, with the advance of deep learning, the concept of deep Gaussian processes has emerged. Deep Gaussian processes have improved capacity for prediction and classification over standard Gaussian processes and models based on them preserve the features of allowing for full Bayesian treatment and for applications when the amount of available data is limited. The theory of recent progress in deep Gaussian processes will be presented and selected applications to problems in medicine will be provided.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"75 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":"128377923","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信