Neural Network World最新文献_第10页

MODELLING SMART ROAD TRAFFIC CONGESTION CONTROL SYSTEM USING MACHINE LEARNING TECHNIQUES 利用机器学习技术对智能道路交通拥堵控制系统进行建模

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/NNW.2019.29.008

A. Ata, Muhammad Adnan Khan, Sagheer Abbas, Gulzar Ahmad, A. Fatima

{"title":"MODELLING SMART ROAD TRAFFIC CONGESTION CONTROL SYSTEM USING MACHINE LEARNING TECHNIQUES","authors":"A. Ata, Muhammad Adnan Khan, Sagheer Abbas, Gulzar Ahmad, A. Fatima","doi":"10.14311/NNW.2019.29.008","DOIUrl":"https://doi.org/10.14311/NNW.2019.29.008","url":null,"abstract":": By the dramatic growth of the population in cities requires the traf-ﬁc systems to be designed eﬃciently and sustainably by taking full advantage of modern-day technology. Dynamic traﬃc ﬂow is a signiﬁcant issue which brings about a block of traﬃc movement. Thus, for tackling this issue, this paper aims to provide a mechanism to predict the traﬃc congestion with the help of Artiﬁcial Neural Networks (ANN) which shall control or minimize the blockage and result in the smoothening of road traﬃc. Proposed Modeling Smart Road Traﬃc Congestion Control using Artiﬁcial Back Propagation Neural Networks (MSR2C-ABPNN) for road traﬃc increase transparency, availability and eﬃciency in services oﬀered to the citizens. In this paper, the prediction of congestion is operationalized by using the algorithm of backpropagation to train the neural network. The proposed system aims to provide a solution that will increase the comfort level of travellers to make intelligent and better transportation decision, and the neural network is a plausible approach to ﬁnd traﬃc situations. Proposed MSR2C-ABPNN with Time series gives attractive results concerning MSE as compared to the ﬁtting approach.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"126 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67121796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 54

IMPROVED ANTLION OPTIMIZER ALGORITHM AND ITS PERFORMANCE ON NEURO FUZZY INFERENCE SYSTEM 改进的蚁群优化算法及其在神经模糊推理系统中的性能

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/nnw.2019.29.016

Haydar Kiliç, Uğur Yüzgeç, C. Karakuzu

{"title":"IMPROVED ANTLION OPTIMIZER ALGORITHM AND ITS PERFORMANCE ON NEURO FUZZY INFERENCE SYSTEM","authors":"Haydar Kiliç, Uğur Yüzgeç, C. Karakuzu","doi":"10.14311/nnw.2019.29.016","DOIUrl":"https://doi.org/10.14311/nnw.2019.29.016","url":null,"abstract":"Antlion optimizer algorithm (ALO) is inspired by hunting strategy of antlions. In this study, an improved antlion optimization algorithm is proposed for training parameters of adaptive neuro fuzzy inference system (ANFIS). In the standard ALO algorithm, the greatest deficiency is its long running time during optimization process. The random walking model of ants, the selection procedure and boundary checking mechanism have been developed to speed up standard ALO algorithm. To evaluate the performance of the improved antlion optimization algorithm (IALO), it has been tested on dynamic system modelling problems. ANFIS’s parameters has been optimized by IALO algorithm to model five dynamic systems. ANFIS training procedure has been performed with 30 independent runs. Each training has been started with the random initial parameters of ANFIS and performance metrics have been obtained at the end of training. The results show that the IALO algorithm is able to provide competitive results in terms of mean, best, worst, standard deviation, training time metrics. According to the training time result, the proposed IALO algorithm has better performance than standard ALO algorithm and the average training time has been reduced to approximately 80 %.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

DO EINSTEIN'S EQUATIONS DESCRIBE REALITY WELL? 爱因斯坦的方程能很好地描述现实吗?

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/nnw.2019.29.017

M. Křížek

引用次数: 1

DEEP LEARNING FOR STOCK MARKET TRADING: A SUPERIOR TRADING STRATEGY? 股票市场交易的深度学习:一种优越的交易策略?

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/NNW.2019.29.011

D. Fister, Johnathan Mun, Vita Jagrič, Timotej Jagrič

引用次数: 13

GENETIC ALGORITHM FOR THE CONTINUOUS LOCATION-ROUTING PROBLEM 连续定位路由问题的遗传算法

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/nnw.2019.29.012

Alena Rybičková, D. Mocková, D. Teichmann

引用次数: 11

Editorial: How can artificial systems rise in a tool for mind? 社论:人工系统如何成为一种思维工具?

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/nnw.2019.29.023

P. Bouchner, M. Novák, Z. Votruba

引用次数: 3

SOM IN HILBERT SPACE 希尔伯特空间中的Som

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/NNW.2019.29.002

Jakub Snor, Jaromir Kukal, Quang Van Tran

引用次数: 0

ECG CLASSIFICATION USING HIGHER ORDER SPECTRAL ESTIMATION AND DEEP LEARNING TECHNIQUES 使用高阶谱估计和深度学习技术的心电分类

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/nnw.2019.29.014

Hiam Alquran, Ali Mohammad Alqudah, Isam Abu-Qasmieh, Alaa Al-Badarneh, S. Almashaqbeh

{"title":"ECG CLASSIFICATION USING HIGHER ORDER SPECTRAL ESTIMATION AND DEEP LEARNING TECHNIQUES","authors":"Hiam Alquran, Ali Mohammad Alqudah, Isam Abu-Qasmieh, Alaa Al-Badarneh, S. Almashaqbeh","doi":"10.14311/nnw.2019.29.014","DOIUrl":"https://doi.org/10.14311/nnw.2019.29.014","url":null,"abstract":"Electrocardiogram (ECG) is one of the most important and effective tools in clinical routine to assess the cardiac arrhythmias. In this research higherorder spectral estimations, bispectrum and third-order cumulants, are evaluated, saved, and pre-trained using convolutional neural networks (CNN) algorithm. CNN is transferred in this study to carry out automatic ECG arrhythmia diagnostics after employing the higher-order spectral algorithms. Transfer learning strategies are applied on pre-trained convolutional neural network, namely AlexNet and GoogleNet, to carry out the final classification. Five different arrhythmias of ECG waveform are chosen from the MIT-BIH arrhythmia database to evaluate the proposed approach. The main contribution of this study is to utilize the pre-trained convolutional neural networks with a combination of higher-order spectral estimations of arrhythmias ECG signal to implement a reliable and applicable deep learning classification technique. The Highest average accuracy obtained is 97.8 % when using third cumulants and GoogleNet. As is evident from these results, the proposed approach is an efficient automatic cardiac arrhythmia classification method and provides a reliable recognition system based on well-established CNN architectures instead of training a deep CNN from scratch.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67123276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 40

FUZZY LOGIC MODEL OF IRRADIATED AGGREGATES 辐照聚集体的模糊逻辑模型

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/nnw.2019.29.001

M. Vaitová, P. Stemberk, T. Rosseel

引用次数: 4

CLASSIFICATION BASED ON MISSING FEATURES IN DEEP CONVOLUTIONAL NEURAL NETWORKS 基于缺失特征的深度卷积神经网络分类

IF 0.8 4区计算机科学

Neural Network World Pub Date : 2019-01-01 DOI: 10.14311/nnw.2019.29.0015

Nemanja Milošević, M. Rackovic

引用次数: 8