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  • Abstract
  • Abstract A corpus of nearly 6500 syllables and their component segments were analysed to formulate a model of segment and syllable duration for Australian English. (assta.org)
  • 1 Time Series Data Mining in Rainfall Forecasting Using Artificial Neural Network Prince Gupta 1, Satanand Mishra 2, S.K.Pandey 3 1,3 VNS Group, RGPV, Bhopal, 2 CSIR-AMPRI, BHOPAL Abstract: Rainfall is very important parameter in hydrological model. (docplayer.net)
  • nonlinear
  • In recent years, a variety of artificial intelligence (AI) techniques, such as neural networks and fuzzy inference systems, have been used in modeling of complex nonlinear water treatment processes. (springer.com)
  • Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. (hindawi.com)
  • We propose a new model that is robust against nonlinear dependencies in feature spaces and their underlying datasets: the Neural Multi-Task Logistic Regression (N-MTLR). (medium.com)
  • We've found that the N-MTLR model performs about the same as CoxPH and traditional MTLR models when the data set does not seem to contain any obvious nonlinear dependencies. (medium.com)
  • Where the N-MTLR model really shines, though, is in its outperformance against the MTLR and CoxPH models nonlinear dependencies are present in the feature space. (medium.com)
  • In short, the N-MTLR model provides a great solution to survival modeling when you're all but guaranteed that your feature space exhibits nonlinear behaviour (e.g. some of your features take on a power law distribution). (medium.com)
  • Nonlinear feature distributions are quite common in seller behaviour, so having the N-MTLR model has allowed us to better model churn and conversion events with our sellers. (medium.com)
  • mathematical models
  • However, the compensation techniques such as they are practised imply the need for measuring, under typical operating and environmental conditions, all the components of the error, before combining them through mathematical models for then generating the data necessary to make the correction in real time [ 9 , 10 ]. (hindawi.com)
  • neurons
  • CONCLUSION We have presented in this paper a new measure allowing explicit neuron selection in connectionnist feed-forward models using scalar or euclidean arti cial neurons. (psu.edu)
  • When working with neural networks it is essential to reduce the amount of neurons in the hidden layer, which also reduces the number of weight coefficients of the neural network as a whole. (hindawi.com)
  • The results show that different feature datasets and different number of neurons of hidden layer of neural network yield different activity recognition accuracy. (springer.com)
  • We then present a network model based on neurons with these properties that learns time-based sequences. (frontiersin.org)
  • data
  • Uncertainty of data, fuzzy membership functions, and multilayer perceptrons. (springer.com)
  • Using data-mining techniques, we modeled recent (1980-1999) distributions and identified climate-soil interactions as main controlling factors. (pnas.org)
  • We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans. (pnas.org)
  • Multilayer Perceptrons are often used to model complex relationships between sets of data. (hindawi.com)
  • The sub-models are developed individually on the basis of each clustered data set. (springer.com)
  • Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression (SAGE), this paper presents a predictive methodology comprising several in silico models for detecting key complex features and relationships encoded in the data, which may be useful to distinguish genes in terms of their functional roles. (biomedcentral.com)
  • To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, three supervised classification models were compared. (biomedcentral.com)
  • This feature makes these models very attractive in applications where there is very little, or incomplete, understanding of the problem, but where sufficient data is available for network training. (springer.com)
  • Initially, the new model is tested using Spanish bank and company stocks, to ascertain its ability of handling exogenous data with minimal preprocessing. (springer.com)
  • Discharge and rainfall data are took as input parameter for ANN model to predict rainfall time series. (docplayer.net)
  • Data preprocessing and model s sensitivity analysis were executed. (docplayer.net)
  • For rainfall-runoff modelling, Data mining techniques have been widely used. (docplayer.net)
  • Classification networks, consisting of preprocessing layers combined with well-known classification networks, are well suited for medical data analysis. (diva-portal.org)
  • It describes how to work with the preprocessing perceptron from the moment when a data file is provided to the moment when a new decision support system is built. (diva-portal.org)
  • The model uses roughly 100,000 records extracted from a SQL database using a custom tool they've developed internally and then divided into 70% Training, 20% Cross Validation and 10% Testing data sets. (neurosolutions.com)
  • Then it uses distributed computing, advanced neural networks, and artificial intelligence (AI) to model your data. (neurosolutions.com)
  • We first built semiempirical models using Artificial Neural Networks and all water uptake data, as individual input. (hindawi.com)
  • Comparison of the final model results with measured data of the Severn River showed that the model has good performance. (springer.com)
  • Lastly, we're very happy to open-source this research to the greater data science community as we believe that more research collaboration results in better models, best practices, and ultimately products for our users. (medium.com)
  • fuzzy
  • Fuzzy kernel perceptron. (springer.com)
  • Support vector learning mechanism for fuzzy rule-based modeling: A new approach. (springer.com)
  • The paper proposes a technique based on fuzzy clustering and self-organized maps to disseminate multi-lingual web content for more effective information access. (springer.com)
  • In the introductory part of the thesis, an introduction to feed-forward neural networks and fuzzy logic is given as a background to work with the preprocessing perceptron. (diva-portal.org)
  • predict
  • Therefore, it is necessary to develop decision support models that are able to predict the treated water quality and the required coagulant dosage. (springer.com)
  • In general, a process model is used to predict the treated water quality by process inputs, such as raw water quality and coagulant dosage. (springer.com)
  • On the contrary, an inverse process model is used to predict coagulant dosage using the treated water quality and the raw water quality. (springer.com)
  • Therefore, the inverse process model can be used to predict directly the optimal coagulant dosage based on the raw water quality and the desired treated water quality. (springer.com)
  • 1999 ) developed a full-scale ANN process model to predict clarifier effluent color at the Rossdale WTP in Edmonton, Alberta, Canada. (springer.com)
  • On the basis of these evaluation parameter results, it is found that multilayer perceptron (MLP) network predict more accurate than other traditional models. (docplayer.net)
  • At Square, we've experimented with survival analysis models to predict a variety of outcomes: from Seller churn and product conversions, to loan defaults on Capital. (medium.com)
  • results
  • Results 1 25 of 25eBay determines this price through a machine learned model of the product's sale pricesTONY MOLY DELIGHT TONY TINT LIP STAIN 2 COLOURS *UK SELLER* KOREAN COSMETICSLiquid Tony Moly Cherry Pink Lip Tint Stain Magic Lip PlumperFrom MalaysiaTony MolyFilter Applied. (indianewstoday.co.in)
  • Results reveal that the sub-models constructed by a hybrid k -means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). (springer.com)
  • Our experimental results show that we achieve an accuracy of 99% with the winner method and 96% with the threshold method, respectively, for recognizing multi-user activities. (springer.com)
  • We present results which demonstrate the practical effectiveness of the model in improving the performance of a system for segregating speech from other acoustic sources. (assta.org)
  • The paper describes the best results as coming from ensembles generated from a number of predicted output values of the models. (springer.com)
  • But, its quantitative applications require chemometric models for the results interpretation. (infowine.com)
  • Results concerning the preprocessing layer and its connection to multivariate decision limits are included. (diva-portal.org)
  • performance
  • Table3 reports the performance in terms of action error rate (AER), equivalent to the word error rate in continuous ASR, for both multi-layer HMM and the single-layer HMM methods, tested on the M4 corpus. (psu.edu)
  • however, the issue of different feature selection methods and different classification models as they relate to classification performance has not been addressed. (biomedcentral.com)
  • The high performance and modelling ability of a Neural Network has enabled it to be used extensively in most domains, specifically in medical domain. (springer.com)
  • Inspite of its excellent modelling performance, NN acts as a black - box because of its inability to provide a simple interpretation of the model. (springer.com)
  • Mean Absolute error (MAE), Mean Squared error (MSE) and correlation coefficient (CC) are used to evaluate the model performance. (docplayer.net)
  • A performance comparison have been done between Support vector regression and multilayer feed-forward neural network models with respect to their forecasting capabilities. (docplayer.net)
  • We analyzed sensitivity, specificity and area under the ROC curve (AUC-value) to compare the performance of various modelling techniques by 10 × 10 cross-validation. (biomedcentral.com)
  • The preprocessing perceptron has the simplicity of a perceptron combined with a performance comparable to the multi-layer perceptron. (diva-portal.org)
  • The model is extended to a generalised preprocessing perceptron and the performance of this new model is compared to the traditional feed-forward neural networks. (diva-portal.org)
  • This research demonstrates that a surrogate modeling effort can reduce the number of polymers that must be synthesized and characterized to identify an appropriate polymer that meets certain performance criteria. (hindawi.com)
  • A Multi Layer Perceptron model showed the best performance and was therefore selected over the other networks. (beds.ac.uk)
  • To validate the model performance in real engineering problems, the pollutant transmission in Severn River has been simulated. (springer.com)
  • ANFIS
  • The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. (springer.com)
  • Therefore, the hybrid model of k -means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously. (springer.com)
  • methods
  • However, we have tried to evaluate and compare the most common classification methods (decision trees, nearest neighbour methods, naive Bayes model and multi-layer perceptrons) according to the general requirements of context-aware systems. (psu.edu)
  • Various machine learning (ML) methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. (frontiersin.org)
  • Three major groups of methods to estimate the discriminance effect of the preprocessing perceptron are described and a discussion of the trade-off between complexity and approximation strength are included. (diva-portal.org)
  • feed-forward
  • Here, the hydraulic hybrid bus model is a feed forward one and has been simulated using a compound driving cycle. (asme.org)
  • neuron
  • We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. (frontiersin.org)
  • numerical
  • Modeling the pollution transmission includes numerical solution of the ADE and estimating the longitudinal dispersion coefficient (LDC). (springer.com)
  • In this paper, a novel approach is proposed for numerical modeling of the pollution transmission in rivers. (springer.com)
  • functions
  • Modeling systems and functions using neural network mechanisms is a relatively new and developing science in computer technologies. (codeproject.com)
  • Traditional survival analysis models such as the Kaplan-Meier (KM) and Cox Proportional Hazard (CoxPH) models both have some very rigid assumptions and limitations - for example, CoxPH models assume that hazard functions are strictly linear combinations of an individual's features, which might not be true given your feature space. (medium.com)
  • Input
  • The very nature of this particular model is that it will force the output to one of nearby values if a variation of input is fed to the network that it is not trained for, thus solving the proximity issue. (codeproject.com)
  • Input for models of perceived emotion consisted of audio features extracted from the music recordings. (frontiersin.org)
  • An integrity of a non-linear model corresponding to a local input space of the input value may be determined. (google.com)
  • sensitivity
  • Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests. (e-epih.org)
  • After being trained, the models are then analyzed using the Sensitivity about the Mean and accuracy of classification. (neurosolutions.com)
  • hybrid
  • Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. (frontiersin.org)
  • 13, 2006, entitled "Dynamic Controller Utilizing a Hybrid Model" in the name of Eugene Boe et al. (google.com)
  • research
  • High visibility for maximum global exposure with open access publishing model rigorous peer review (blind peer-review) of research papers prompt faster publication. (ac.ir)
  • order
  • To investors, the financial crisis warning model can also strengthen the capital market in order to provide guarantees to investors whom may not be aware of the operation status of the companies. (hindawi.com)
  • Therefore, a feasible warning model is able to detect problems of a listed company early in order to prevent significant losses of investors. (hindawi.com)
  • describes
  • The fifth paper describes a multi-lingual web content filtering application. (springer.com)
  • The two models have been designed to estimate the relationship between rainfall and runoff, which describes the most complex phenomenon of hydrological science . (docplayer.net)
  • time
  • In this study an artificial neural network (ANN) based model was developed for rainfall time series forecasting. (docplayer.net)
  • K-means, Dynamic Time Wrapping (DTW), and agglomerative hierarchical clustering are used to cluster and discover the discharge pattern in terms of the modelling. (docplayer.net)
  • given
  • Models of the mechanical, aerodynamic, and acoustic events in the vicinity of the stop consonant are described, and examples of calculations of the airflow and of various components of the radiated sound are given. (assta.org)