Learning and Nonlinear Models最新文献_第6页

Clustering students based on grammatical errors for on-line education 基于语法错误的在线教学学生聚类

Learning and Nonlinear Models Pub Date : 2018-06-30 DOI: 10.21528/lnlm-vol16-no1-art2

M. Macedo, Elliackin M. N. Figueiredo, Fabiana Soares, H. Siqueira, A. M. A. Maciel, A. Gokhale, C. Bastos-Filho

{"title":"Clustering students based on grammatical errors for on-line education","authors":"M. Macedo, Elliackin M. N. Figueiredo, Fabiana Soares, H. Siqueira, A. M. A. Maciel, A. Gokhale, C. Bastos-Filho","doi":"10.21528/lnlm-vol16-no1-art2","DOIUrl":"https://doi.org/10.21528/lnlm-vol16-no1-art2","url":null,"abstract":"Learning Management System (LMS) is an educational solution created for people who need flexibility regarding time and place. The problem of this kind of tool primarily concerns the difficulty in identifying which students have learned the content correctly. This paper aims to analyze the performance of a group of distance learning students regarding grammar errors in two different terms of an undergraduate course. Our hypothesis relies on the existence of different characteristics that emerge from subgroups of students with similar difficulties. This division can help tutors in educational platforms to develop specific recommendations tasks for each group of students. A previous work applied the well-known K-means algorithm to cluster the groups, but in that paper, we fixed the number of clusters. Therefore, we carried out a methodology to find the best number of clusters to be used in K-means for this problem. Moreover, we also applied the Fuzzy C-means to tackle the clustering problem and analyzed the results obtained by both algorithms using the well-known metrics in the literature (Gap Statistic and Davies-Bouldin) to assess the quality of the obtained groups. The experimental results showed that Fuzzy C-means approach outperforms the K-means algorithm. Moreover, the application of the Spearman Correlation on each group expose several differences, relations and similarities between groups and inside each one.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122805892","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: 4

Caos em tempo discreto: introdução, exemplos e representação estatística 离散时间中的混沌:介绍、例子和统计表示

Learning and Nonlinear Models Pub Date : 2018-06-30 DOI: 10.21528/LNLM-VOL16-NO1-ART1

Marcio Eisencraft

引用次数: 0

Álgebra multilinear aplicada ao reconhecimento facial 多元线性代数在人脸识别中的应用

Learning and Nonlinear Models Pub Date : 2018-06-30 DOI: 10.21528/lnlm-vol16-no1-art3

Emanuel D.R. Sena, André Almeida

引用次数: 0

The lower bound error as an auxiliary technique to select the integration step-size in the simulation of chaotic systems 在混沌系统仿真中，下界误差作为选择积分步长的辅助技术

Learning and Nonlinear Models Pub Date : 2018-06-30 DOI: 10.21528/LNLM-VOL16-NO1-ART4

W. R. L. Júnior, S. Martins, E. Nepomuceno

引用次数: 0

Máquinas de vetores-suporte: uma revisão 支持向量机:综述

Learning and Nonlinear Models Pub Date : 2017-06-30 DOI: 10.21528/lnlm-vol15-no1-art2

Ajalmar R. da Rocha Neto

引用次数: 2

Wi-Fi Device Identification in Crowd Counting Using Machine Learning Methods 使用机器学习方法进行人群计数中的Wi-Fi设备识别

Learning and Nonlinear Models Pub Date : 2017-06-30 DOI: 10.21528/lnlm-vol15-no1-art4

Jean Pierre Jarrier Conti, Tiago B. Nion da Silveira, H. S. Lopes

{"title":"Wi-Fi Device Identification in Crowd Counting Using Machine Learning Methods","authors":"Jean Pierre Jarrier Conti, Tiago B. Nion da Silveira, H. S. Lopes","doi":"10.21528/lnlm-vol15-no1-art4","DOIUrl":"https://doi.org/10.21528/lnlm-vol15-no1-art4","url":null,"abstract":"The increase in the availability of computational resources gave rise to new technologies to estimate the amount of people in a given area. In this context, algorithm-based solutions for crowd counting can be grouped into image-based and non-image based approaches, the latter considering any other feature that is not visual. Currently, due to the popularization of smartphones and mobile devices, several researchers have been using Wi-Fi request packets for crowd counting estimation. Assuming that, on average, each person in a given place carries a Wi-Fi device, the number of unique MAC addresses can be associated with the number of people. However, since the probe may capture all Wi-Fi traffic – which may include broadcast messages from other access points or packets from notebooks and desktop devices – some strategy must be applied in order to identify only personal mobile devices, thus improving the method accuracy. In this work, we trained classifiers to segment mobile from static devices through its Wi-Fi behavior pattern. Therefore, using data collected from different devices and in different environments, we evaluated the proposed methodology by using several machine learning algorithms. Best results were achieved with logistic regression and neural network (MLP). The results of this study suggest the feasibility of the proposed method for crowd counting in high-density Wi-Fi zones.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116449236","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

Ferramentas para análise de sistemas caóticos de tempo discreto: um breve tutorial 分析离散时间混沌系统的工具:一个简短的教程

Learning and Nonlinear Models Pub Date : 2017-06-30 DOI: 10.21528/LNLM-VOL15-NO1-ART1

J. M. Junior

引用次数: 0

Qualitative analysis of deep learning frameworks 深度学习框架的定性分析

Learning and Nonlinear Models Pub Date : 2017-06-30 DOI: 10.21528/lnlm-vol15-no1-art3

M. Gutoski, L. T. Hattori, Nelson Marcelo Romero Aquino, H. C. Senefonte, Manassés Ribeiro, A. Lazzaretti, H. S. Lopes

引用次数: 3

Using Self-Organizing Maps as a Decision-Making Tool for Multiobjective Design in Engineering 用自组织地图作为工程多目标设计的决策工具

Learning and Nonlinear Models Pub Date : 2016-12-05 DOI: 10.21528/LNLM-VOL7-NO2-ART3

F. Campelo, F. Guimarães, J. A. Ramírez

{"title":"Using Self-Organizing Maps as a Decision-Making Tool for Multiobjective Design in Engineering","authors":"F. Campelo, F. Guimarães, J. A. Ramírez","doi":"10.21528/LNLM-VOL7-NO2-ART3","DOIUrl":"https://doi.org/10.21528/LNLM-VOL7-NO2-ART3","url":null,"abstract":"This paper discusses the use of self-organizing maps (SOM) for decision-making in multiobjec- tive design problems. Estimates of the Pareto-optimal solutions for a given problem are mapped onto a two- dimensional grid, where the distance between solutions is a measure of their similarity in the parameter space. By comparing the clustered points and the color grade of those points in the maps, the designer is able to visualize the similarity of the solutions in both the parameter and objective spaces, hence identifying redundant points. With the use of this technique, one is able to work with clusters of solutions instead of individual points, which can simplify the decision-making step. Resumo| Este trabalho discute o uso de mapas auto-organizativos (MAO) como ferramenta de aux lio para tomada de decis~ oes em problemas de projeto multiobjetivo. Estimativas das solu c~ oes Pareto- otimas para um determinado problema s~ ao mapeadas sobre uma malha bidimensional, no qual a dist^ ancia entre as solu c~ oes representa uma medida da similaridade das mesmas. Atrav es de compara c~ oes entre as posi c~ oes e cores dadas a cada ponto neste mapa e poss v el obter uma visualiza c~ ao da semelhan ca destas solu c~ oes, tanto em termos de par^ ametros de projeto quanto de performance nos diversos objetivos do problema, o que possibilita a detec c~ ao de pontos redundantes e o tratamento de conjuntos de solu c~ oes ao inv es de pontos individuais, simplicando assim a etapa de tomada de decis~ oes em um determinado projeto. Palavras-chave| Mapas auto-organizativos, otimiza c~ ao multiobjetivo, tomada de decis~ oes.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132281811","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

Método Dialético de Otimização Usando o Princípio da Máxima Entropia 利用最大熵原理的辩证优化方法

Learning and Nonlinear Models Pub Date : 2016-12-05 DOI: 10.21528/LNLM-vol7-no2-art2

W. P. D. Santos, F. M. Assis

引用次数: 2