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Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each...
Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software...
Software effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. Several estimation techniques have been developed in this regard and it is difficult to determine which model gives more accurate estimation on which dataset. Among all proposed methods, the Radial Basis Function Neural (RBFN) networks models have presented promising results in software...
In this paper we examine efficacy of occlusion-free appearance learning for part based model. Appearance modeling with less accurate appearance data is problematic because it adversely affects entire learning process. We evaluate the effectiveness of excluding occluded body parts to be modeled for better appearance modeling process. To meet this end, We employ a simple but effective occlusion detection...
Human tracking across multiple cameras is highly demanded for large scale video surveillance. To successfully track human across multiple uncalibrated cameras that have no overlapping field of views, a system to train more reliable camera link models is proposed in this paper. We employ a novel approach of combining multiple camera links and building bidirectional transition time distribution in the...
This paper presents a new approach for shortterm load forecasting using the participatory learning paradigm. Participatory learning paradigm is a new training procedure that follows the human learning mechanism adopting an acceptance mechanism to determine which observation is used based upon its compatibility with the current beliefs. Here, participatory learning is used to train a class of hybrid...
Data centers as a cost-effective infrastructure for hosting Cloud and Grid applications incur tremendous energy cost and C02 emissions in terms of power distribution and cooling. One of the effective approaches for saving energy in a cluster environment is workload consolidation. However, it is challenging to address this schedule problem as it requires the understanding of various cost factors. One...
In the multibiometric systems, various matcher/modality scores are fused together to provide better performance than the individual matcher scores. In [1] the authors have proposed a likelihood ratio test (LRT) based fusion technique for the biometric verification task that outperformed several other classifiers. They model the genuine and the imposter densities by the finite Gaussian mixture models...
This study presents a methodology for on-line identification of the nonlinear reaction process in presence of measurement noise and uncertainty, for accurate simulation of this process, a link between HYSYS (chemical software) and MATLAB was generated by the author. In this link HYSYS simulates the continuous stirred tank reactor (CSTR) and MATLAB performs the data acquisition algorithm. The chemical...
In this work, a method for the automatic estimation of a threshold that allows the user of an OCR system to define an expected error rate is presented. When the OCR output is post-processed using a language model, a probability, a reliability index (or a “transformation cost”) is usually obtained, reflecting the likelihood (or its inverse) that the string of OCR hypotheses belongs to the model. Using...
Many methods have been employed to study Land Use Change (LUC) in different areas, including some new algorithms from Artificial Intelligence (AI) field, such as Case-Based Reasoning (CBR), Artificial Neural Network (ANN), Bayesian Network (BN) and Support Vector Machine (SVM). Applications of some new methods have indicated both advantages and limitations. This paper presents a comparison between...
Financial crises forewarning has important practical significance both for the investors and for the lenders. This paper uses the financial forewarning models, including the Logistic Regressive model and SVM model, to verify the feasibility of the short-term forecast for the financial situation of enterprises. And the paper also gives comparisons between these two models. The results of the study...
In this paper, we propose a novel artificial neural network ensemble rainfall forecasting model based K-nearest neighbor (K-nn) nonparametric estimation of regression. In this model, original data set are partitioned into some different training subsets via Bagging technology. Then using different ANNs algorithms and different network architecture generate diverse individual neural network ensemble...
In this paper, we propose a novel semisupervised Gaussian regression approach for the estimation of biophysical parameters from remote sensing data with limited training samples. During the learning phase, unlabeled samples are exploited to inflate the training set. The estimation of the targets associated with these samples is carried out by solving an optimization problem formulated within a genetic...
To help clinicians diagnose Heart failure (HF) at the early stage, this study proposes a scoring model based on support vector machine (SVM). Missing data in clinic are imputed by employing Bayesian principal component analysis. According to the evaluation of cardiac dysfunction, samples are classified into three groups: the healthy group (without cardiac dysfunction), the HF-prone group (in asymptomatic...
Most previous facial expression analysis works only focused on expression recognition. In this paper, we propose a novel framework of facial expression analysis based on the ranking model. Different from previous works, it not only can do facial expression recognition, but also can estimate the intensity of facial expression, which is very important to further understand human emotion. Although it...
Support Vector Machines for Regression (SVR) proved to perform well. However, they are not preferred in image analysis due to a high number of needed support vectors (SV) and consequently long processing times. We present a method for simplifying the original SVR regression function up to a user-specified degree of accepted performance decrease. We show results for two regression problems: modelling...
Reliable cost estimation is crucial to the planning process of a wastewater treatment plant (WWTP). Among the developed methods in literatures, not only the assumption of linearity but the existence of a great deal of uncertainty limits the actual application. In this paper, cost estimation of WWTPs in Taiwan region using BP neural network (NN) was investigated. The correlations between cost related...
In this paper performances of automatic speech recognition systems which use vocal tract length normalization (VTN) are presented. Beside standard procedure for VTN coefficient estimation several variants based on robust statistic methods are introduced. All systems which use VTN performed better than referent systems, while the best performance was achieved by the system in which the VTN coefficient...
In this paper, a novel artificial neural network ensemble rainfall forecasting model is proposed for rainfall forecasting based on K-nearest neighbor nonparametric estimation of regression. In this model, original data set are partitioned into some different training subsets via Bagging technology. Then different ANN algorithms and different network architecture generate diverse individual neural...
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