Pub Date : 2019-10-14DOI: 10.1142/S2424922X19500074
Yiqi Bao, V. Cancho, D. Dey, F. Louzada, A. K. Suzuki
An authentic way for assessing the goodness of a model is to estimate its predictive capability. In this paper, we propose the D-measure, which measures the goodness of a model by comparing how close its predictions are from the observed data based on the survival function. The proposed D-measure can be used for all kinds of survival data in the presence of censoring. It can also be used to compare cure rate models, even in the presence of random effects or frailties. The advantages of the D-measure are verified via simulation, in which it is compared to the deviance information criterion, which is a widely used Bayesian model comparison criterion. The D-measure is illustrated in two real data sets.
{"title":"D-Measure: A Bayesian Model Selection Criterion for Survival Data","authors":"Yiqi Bao, V. Cancho, D. Dey, F. Louzada, A. K. Suzuki","doi":"10.1142/S2424922X19500074","DOIUrl":"https://doi.org/10.1142/S2424922X19500074","url":null,"abstract":"An authentic way for assessing the goodness of a model is to estimate its predictive capability. In this paper, we propose the D-measure, which measures the goodness of a model by comparing how close its predictions are from the observed data based on the survival function. The proposed D-measure can be used for all kinds of survival data in the presence of censoring. It can also be used to compare cure rate models, even in the presence of random effects or frailties. The advantages of the D-measure are verified via simulation, in which it is compared to the deviance information criterion, which is a widely used Bayesian model comparison criterion. The D-measure is illustrated in two real data sets.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"16 1","pages":"1950007:1-1950007:18"},"PeriodicalIF":0.6,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82072613","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}
Pub Date : 2019-10-14DOI: 10.1142/s2424922x19500086
Carlos Galvão Pinheiro Júnior, Marcus Fraga Vieira, C. Amorim, G. Bourhis, A. Andrade
Assistive technology allows motor-impaired people to overcome limitations. Several myoelectric interfaces have been developed, however, there is no reported study employing information at a motor unit (MU) level for controlling purposes. Thus, we developed a facial myoelectric interface operating at the level of MU for controlling a computer screen cursor. Data were collected from 11 able-bodied and 1 tetraplegic subjects. Different from traditional approaches, there was no significant difference ([Formula: see text]) in learning with respect to the level of difficulty, occurring evenly and faster. Information at MU level opens new possibilities for the development of fine control myoelectric interfaces.
{"title":"Facial Muscular Human-Computer Interface at a Motor Unit Level","authors":"Carlos Galvão Pinheiro Júnior, Marcus Fraga Vieira, C. Amorim, G. Bourhis, A. Andrade","doi":"10.1142/s2424922x19500086","DOIUrl":"https://doi.org/10.1142/s2424922x19500086","url":null,"abstract":"Assistive technology allows motor-impaired people to overcome limitations. Several myoelectric interfaces have been developed, however, there is no reported study employing information at a motor unit (MU) level for controlling purposes. Thus, we developed a facial myoelectric interface operating at the level of MU for controlling a computer screen cursor. Data were collected from 11 able-bodied and 1 tetraplegic subjects. Different from traditional approaches, there was no significant difference ([Formula: see text]) in learning with respect to the level of difficulty, occurring evenly and faster. Information at MU level opens new possibilities for the development of fine control myoelectric interfaces.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"67 1","pages":"1950008:1-1950008:23"},"PeriodicalIF":0.6,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86431912","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}
Pub Date : 2019-10-14DOI: 10.1142/S2424922X19500050
Yiqi Bao, V. Cancho, F. Louzada, A. K. Suzuki
In this work, we proposed the semi-parametric cure rate models with independent and dependent spatial frailties. These models extend the proportional odds cure models and allow for spatial correlations by including spatial frailty for the interval censored data setting. Moreover, since these cure models are obtained by considering the occurrence of an event of interest is caused by the presence of any nonobserved risks, we also study the complementary cure model, that is, the cure models are obtained by assuming the occurrence of an event of interest is caused when all of the nonobserved risks are activated. The MCMC method is used in a Bayesian approach for inferential purposes. We conduct an influence diagnostic through the diagnostic measures in order to detect possible influential or extreme observations that can cause distortions on the results of the analysis. Finally, the proposed models are applied to the analysis of a real data set.
{"title":"Semi-Parametric Cure Rate Proportional Odds Models with Spatial Frailties for Interval-Censored Data","authors":"Yiqi Bao, V. Cancho, F. Louzada, A. K. Suzuki","doi":"10.1142/S2424922X19500050","DOIUrl":"https://doi.org/10.1142/S2424922X19500050","url":null,"abstract":"In this work, we proposed the semi-parametric cure rate models with independent and dependent spatial frailties. These models extend the proportional odds cure models and allow for spatial correlations by including spatial frailty for the interval censored data setting. Moreover, since these cure models are obtained by considering the occurrence of an event of interest is caused by the presence of any nonobserved risks, we also study the complementary cure model, that is, the cure models are obtained by assuming the occurrence of an event of interest is caused when all of the nonobserved risks are activated. The MCMC method is used in a Bayesian approach for inferential purposes. We conduct an influence diagnostic through the diagnostic measures in order to detect possible influential or extreme observations that can cause distortions on the results of the analysis. Finally, the proposed models are applied to the analysis of a real data set.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"68 1","pages":"1950005:1-1950005:32"},"PeriodicalIF":0.6,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84093848","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}
Pub Date : 2019-07-02DOI: 10.1142/S2424922X19500037
F. Prataviera, G. Cordeiro, E. Ortega, A. K. Suzuki
In several applications, the distribution of the data is frequently unimodal, asymmetric or bimodal. The regression models commonly used for applications to data with real support are the normal, skew normal, beta normal and gamma normal, among others. We define a new regression model based on the odd log-logistic geometric normal distribution for modeling asymmetric or bimodal data with support in [Formula: see text], which generalizes some known regression models including the widely known heteroscedastic linear regression. We adopt the maximum likelihood method for estimating the model parameters and define diagnostic measures to detect influential observations. For some parameter settings, sample sizes and different systematic structures, various simulations are performed to verify the adequacy of the estimators of the model parameters. The empirical distribution of the quantile residuals is investigated and compared with the standard normal distribution. We prove empirically the usefulness of the proposed models by means of three applications to real data.
{"title":"The Odd Log-Logistic Geometric Normal Regression Model with Applications","authors":"F. Prataviera, G. Cordeiro, E. Ortega, A. K. Suzuki","doi":"10.1142/S2424922X19500037","DOIUrl":"https://doi.org/10.1142/S2424922X19500037","url":null,"abstract":"In several applications, the distribution of the data is frequently unimodal, asymmetric or bimodal. The regression models commonly used for applications to data with real support are the normal, skew normal, beta normal and gamma normal, among others. We define a new regression model based on the odd log-logistic geometric normal distribution for modeling asymmetric or bimodal data with support in [Formula: see text], which generalizes some known regression models including the widely known heteroscedastic linear regression. We adopt the maximum likelihood method for estimating the model parameters and define diagnostic measures to detect influential observations. For some parameter settings, sample sizes and different systematic structures, various simulations are performed to verify the adequacy of the estimators of the model parameters. The empirical distribution of the quantile residuals is investigated and compared with the standard normal distribution. We prove empirically the usefulness of the proposed models by means of three applications to real data.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"121 1","pages":"1950003:1-1950003:25"},"PeriodicalIF":0.6,"publicationDate":"2019-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78168226","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}
Pub Date : 2019-07-02DOI: 10.1142/S2424922X19500013
S. Shen
Global climate observations from ground stations require an evaluation of the effectiveness of a station network, which is often an assessment of the geometric distribution of [Formula: see text] points on a sphere. The representativeness of the Angell–Korshover 63-station network (AK-network) is assessed in this paper. It is shown that AK-network can effectively sample the January global average temperature data of the NCEP/NCAR Reanalysis from 1948 to 2015 when estimating inter-decadal variations, but it has large uncertainties for estimating linear trends. This paper describes a method for the assessment, and also includes an iterative numerical algorithm used to search for the locations of 63 uniformly distributed stations, named U63. The results of AK-63 and U63 are compared. The Appendix explains a problem of searching for the optimal distribution of [Formula: see text] points on a unit sphere in three-dimensional space under the condition of the maximum sum of the mutual distances among the points. The core R code for finding U63 is included. The R code can generate various interesting configurations for different [Formula: see text], among which one is particularly surprising: The configuration of 20 points is not a dodecahedron although the configurations for [Formula: see text], and 12 are tetrahedron, octahedron, cube, and icosahedron, respectively.
{"title":"A Representativeness Assessment of the Angell-Korshover 63-Station Network Sampling Based on Reanalysis Temperature Data","authors":"S. Shen","doi":"10.1142/S2424922X19500013","DOIUrl":"https://doi.org/10.1142/S2424922X19500013","url":null,"abstract":"Global climate observations from ground stations require an evaluation of the effectiveness of a station network, which is often an assessment of the geometric distribution of [Formula: see text] points on a sphere. The representativeness of the Angell–Korshover 63-station network (AK-network) is assessed in this paper. It is shown that AK-network can effectively sample the January global average temperature data of the NCEP/NCAR Reanalysis from 1948 to 2015 when estimating inter-decadal variations, but it has large uncertainties for estimating linear trends. This paper describes a method for the assessment, and also includes an iterative numerical algorithm used to search for the locations of 63 uniformly distributed stations, named U63. The results of AK-63 and U63 are compared. The Appendix explains a problem of searching for the optimal distribution of [Formula: see text] points on a unit sphere in three-dimensional space under the condition of the maximum sum of the mutual distances among the points. The core R code for finding U63 is included. The R code can generate various interesting configurations for different [Formula: see text], among which one is particularly surprising: The configuration of 20 points is not a dodecahedron although the configurations for [Formula: see text], and 12 are tetrahedron, octahedron, cube, and icosahedron, respectively.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"38 4 1","pages":"1950001:1-1950001:12"},"PeriodicalIF":0.6,"publicationDate":"2019-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89604786","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}
Pub Date : 2019-07-02DOI: 10.1142/S2424922X19500049
Fengyi Zhang, Z. Liao, Hongping Hu
The stock market is a complex, evolving, and nonlinear dynamic system. Forecasting stock prices has been regarded as one of the most challenging applications of modern time series forecasting. This paper proposes a novel multi-input Hamacher-ANFIS (adaptive network-based fuzzy inference system based on Hamacher operator) ensemble model to forecast stock prices in China’s stock market and achieve good prediction performance. We selected five stocks with the largest total market capitalization from the Shanghai and Shenzhen Stock Exchanges, measured their historical volatility over the same time period, and weighed the performance of each stock forecasting model based on the above volatility. Then, the experiment was repeated 100 times for each data set, and we calculated the comprehensive [Formula: see text] of the testing set according to the weight that we obtained earlier. The statistical test of the experimental results shows that: (1) In terms of comprehensive [Formula: see text] of the stock price, the multi-input Hamacher-ANFIS model is superior to other conventional models; (2) when compared with the nonensemble forecasting strategy, the ensemble strategy of the Hamacher-ANFIS model has significant advantages.
{"title":"Application of Multi-Input Hamacher-ANFIS Ensemble Model on Stock Price Forecast","authors":"Fengyi Zhang, Z. Liao, Hongping Hu","doi":"10.1142/S2424922X19500049","DOIUrl":"https://doi.org/10.1142/S2424922X19500049","url":null,"abstract":"The stock market is a complex, evolving, and nonlinear dynamic system. Forecasting stock prices has been regarded as one of the most challenging applications of modern time series forecasting. This paper proposes a novel multi-input Hamacher-ANFIS (adaptive network-based fuzzy inference system based on Hamacher operator) ensemble model to forecast stock prices in China’s stock market and achieve good prediction performance. We selected five stocks with the largest total market capitalization from the Shanghai and Shenzhen Stock Exchanges, measured their historical volatility over the same time period, and weighed the performance of each stock forecasting model based on the above volatility. Then, the experiment was repeated 100 times for each data set, and we calculated the comprehensive [Formula: see text] of the testing set according to the weight that we obtained earlier. The statistical test of the experimental results shows that: (1) In terms of comprehensive [Formula: see text] of the stock price, the multi-input Hamacher-ANFIS model is superior to other conventional models; (2) when compared with the nonensemble forecasting strategy, the ensemble strategy of the Hamacher-ANFIS model has significant advantages.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"16 1","pages":"1950004:1-1950004:15"},"PeriodicalIF":0.6,"publicationDate":"2019-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75993805","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}
Pub Date : 2019-04-01DOI: 10.1142/S2424922X19500025
R. Alguliyev, R. Aliguliyev, F. Abdullayeva
Recently, data collected from social media enable to analyze social events and make predictions about real events, based on the analysis of sentiments and opinions of users. Most cyber-attacks are carried out by hackers on the basis of discussions on social media. This paper proposes the method that predicts DDoS attacks occurrence by finding relevant texts in social media. To perform high-precision classification of texts to positive and negative classes, the CNN model with 13 layers and improved LSTM method are used. In order to predict the occurrence of the DDoS attacks in the next day, the negative and positive sentiments in social networking texts are used. To evaluate the efficiency of the proposed method experiments were conducted on Twitter data. The proposed method achieved a recall, precision, [Formula: see text]-measure, training loss, training accuracy, testing loss, and test accuracy of 0.85, 0.89, 0.87, 0.09, 0.78, 0.13, and 0.77, respectively.
{"title":"Deep Learning Method for Prediction of DDoS Attacks on Social Media","authors":"R. Alguliyev, R. Aliguliyev, F. Abdullayeva","doi":"10.1142/S2424922X19500025","DOIUrl":"https://doi.org/10.1142/S2424922X19500025","url":null,"abstract":"Recently, data collected from social media enable to analyze social events and make predictions about real events, based on the analysis of sentiments and opinions of users. Most cyber-attacks are carried out by hackers on the basis of discussions on social media. This paper proposes the method that predicts DDoS attacks occurrence by finding relevant texts in social media. To perform high-precision classification of texts to positive and negative classes, the CNN model with 13 layers and improved LSTM method are used. In order to predict the occurrence of the DDoS attacks in the next day, the negative and positive sentiments in social networking texts are used. To evaluate the efficiency of the proposed method experiments were conducted on Twitter data. The proposed method achieved a recall, precision, [Formula: see text]-measure, training loss, training accuracy, testing loss, and test accuracy of 0.85, 0.89, 0.87, 0.09, 0.78, 0.13, and 0.77, respectively.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"9 1","pages":"1950002:1-1950002:19"},"PeriodicalIF":0.6,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87587299","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}
Pub Date : 2018-12-12DOI: 10.1142/S2424922X19500062
Hedi Xia, Héctor D. Ceniceros
A new method for hierarchical clustering of data points is presented. It combines treelets, a particular multiresolution decomposition of data, with a mapping on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), uses this mapping to go from a hierarchical clustering over attributes (the natural output of treelets) to a hierarchical clustering over data. KT effectively substitutes the correlation coefficient matrix used in treelets with a symmetric and positive semi-definite matrix efficiently constructed from a symmetric and positive semi-definite kernel function. Unlike most clustering methods, which require data sets to be numeric, KT can be applied to more general data and yields a multiresolution sequence of orthonormal bases on the data directly in feature space. The effectiveness and potential of KT in clustering analysis are illustrated with some examples.
{"title":"Kernel Treelets","authors":"Hedi Xia, Héctor D. Ceniceros","doi":"10.1142/S2424922X19500062","DOIUrl":"https://doi.org/10.1142/S2424922X19500062","url":null,"abstract":"A new method for hierarchical clustering of data points is presented. It combines treelets, a particular multiresolution decomposition of data, with a mapping on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), uses this mapping to go from a hierarchical clustering over attributes (the natural output of treelets) to a hierarchical clustering over data. KT effectively substitutes the correlation coefficient matrix used in treelets with a symmetric and positive semi-definite matrix efficiently constructed from a symmetric and positive semi-definite kernel function. Unlike most clustering methods, which require data sets to be numeric, KT can be applied to more general data and yields a multiresolution sequence of orthonormal bases on the data directly in feature space. The effectiveness and potential of KT in clustering analysis are illustrated with some examples.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"75 1","pages":"1950006:1-1950006:16"},"PeriodicalIF":0.6,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86379172","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}
Pub Date : 2018-11-29DOI: 10.1007/978-981-13-3582-2_8
A. Prajapati, A. Chandiok, D. Chaturvedi
{"title":"Semantic Network Based Cognitive, NLP Powered Question Answering System for Teaching Electrical Motor Concepts","authors":"A. Prajapati, A. Chandiok, D. Chaturvedi","doi":"10.1007/978-981-13-3582-2_8","DOIUrl":"https://doi.org/10.1007/978-981-13-3582-2_8","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"50 4 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78414061","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}
Pub Date : 2018-11-29DOI: 10.1007/978-981-13-3582-2_15
P. Maran, P. M. Velumurugan, B. P. D. Batvari
{"title":"Wind Characteristics and Weibull Parameter Analysis to Predict Wind Power Potential Along the South-East Coastline of Tamil Nadu","authors":"P. Maran, P. M. Velumurugan, B. P. D. Batvari","doi":"10.1007/978-981-13-3582-2_15","DOIUrl":"https://doi.org/10.1007/978-981-13-3582-2_15","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"08 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2018-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85948337","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}
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