The telecommunication and Ethernet trafic prediction problem is studied. Network traffic prediction is an important problem of telecommunication and Ethernet congestion control and network management. In order to improve network traffic prediction accuracy, a network traffic hybrid prediction model was proposed by using the advantages of grey model and Elman neural network, grey model and Elman neural network predictive values were independently obtained, the different weight coefficients of two prediction models were given. In terms of weight coefficients optimization, an improved harmony search algorithm with better convergence speed and accuracy was proposed, the optimal weight coefficients of network traffic hybrid prediction model were determined through this algorithm, two prediction models results were multiplied by the weight coefficients to obtain the final prediction value. The network traffic sample data from an actual telecommunication network was collected as simulation object. The simulation results verified that the proposed network traffic hybrid prediction model based on improved harmony search algorithm has higher prediction accuracy.
We present an approach for probabilistic contour prediction within the framework of an object tracking system. We combine level-set methods for image segmentation with optical flow estimations based on probability distribution functions (pdfs) calculated at each image position. Unlike most recent level-set methods that consider exclusively the sign of the level-set function to determine an object and its background, we introduce a novel interpretation of the value of the level-set function that reflects the confidence in the contour. To this end, in a sequence of consecutive images, the contour of an object is transformed according to the optical flow estimation and used as the initial object hypothesis in the following image. The values of the initial level-set function are set according to the optical flow pdfs and thus provide an opportunity to incorporate the uncertainties of the optical flow estimation in the object contour prediction.
As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction performance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.
Objective: The anxiety of Alzheimer's disease (AD) contributes significantly to decreased quality of life, increased morbidity, higher levels of caregiver distress, and the decision to institutionalize a patient. However, the incidence of anxiety in AD patients hasn't been discussed. In this study, artificial neural networks were used to predict the incidence of anxiety inAD patients.
Methods: A large randomized controlled clinical trial was analyzed in this study, which involved AD patients and caregivers from 6 different sites in the United States. The incidence of anxiety in AD patients was predicted by backpropagation artificial neural networks with one and hidden layers. After cross validation, the Predictive Accuracy (PA) of the models was measured to select the best structure of artificial neural networks.
Results: Among all models for predicting the incidence of anxiety in AD patients, the artificial neural network with respectively 6 and 3 neurons in the first and second hidden layers achieved the highest predictive accuracy of 85.56%.
Conclusions: The incidence of anxiety in AD patients can be predicted by an accuracy of over 80%. When used for anxiety prediction, neural networks with two hidden layers perform better than those with one hidden layer. These findings will benefit the prevention and early intervention of anxiety in Alzheimer's patients.
The soil engineer needs to be able to readily identify difficult or problematic soils and to determine the amount of settlement that may occur. This paper deals with the assessment and identification of three types of difficult soils: collapsible soils, swelling soils, and liquefiable soils. In the first instance, the study investigates the effect of some soil properties on wetting-induced collapse strain and the swelling potential of soils. Also, two new methods for predicting soil collapse and swelling potential are developed. The proposed relationships correlate between collapse strain and swelling potential and some soil parameters which are believed to govern soil collapse and swelling. Validation of these two relationships with some data reported in literature is also examined. Furthermore, the paper describes the different steps suggested in a new procedure for soil liquefaction assessment. The procedure was presented in the form of an evaluation guide. In addition, a relationship was suggested for computing the potential for liquefaction. An application of the proposed procedure to a practical case is included in order to validate and illustrate the different steps to be followed in the suggested evaluation procedure.
Accurate prediction of bus arrival time is of great significance to improve passenger satisfaction and bus attraction. This paper presents the prediction model of bus arrival time based on support vector machine with genetic algorithm (GA-SVM). The character of the time period, the length of road, the weather, the bus speed and the rate of road usage are adopted as input vectors in Support Vector Machine (SVM), and the genetic algorithm search algorithm is combined to find the best parameters. Finally, the data from Bus No.249 in Shenyang, china are used to check the model. The experimental results show that the forecasting model is superior to the traditional SVM model and the Artificial Neural Network (ANN) model in terms of the same data, and is of higher accuracy, which verified the feasibility of the model to predict the bus arrival time.
Introduction: The dataset of 826 patients who were suspected of the prostate cancer was examined. The best single marker and the combination of markers which could predict the prostate cancer in very early stage of the disease were looked for. Methods: For combination of markers the logistic regression, the multilayer perceptron neural network and the k-nearest neighbour method were used. 10 models for each method were developed on the training data set and the predictive accuracy verified on the test data set. Results and conclusions: The ROCs for the models were constructed and AUCs were estimated. All three examined methods have given comparable results. The medians of estimates of AUCs were 0.775, which were larger than AUC of the best single marker.
Prediction of reservoir level fluctuation is important in the operation, design, and security of dams. In this paper, Artificial Neural Networks (ANN) is used for modeling. In such modeling approaches, it is possible to determine dam reservoir level and water balance (budget) by taking the monthly average precipitation and needed parameters into consideration. The basic data are available for over 29 years at the Tahtakőprű Dam in the southeast Mediterranean region of Turkey. As a sub-approach of ANN, a multi layer perceptron (MLP) is used. Bayesian regularization back-propagation training algorithm is employed for optimization of the network. MLP results are compared with the results of conventional multiple linear regression (MLR) and autoregressive (AR) models. The comparison shows that the ANN model provides better performance than the mentioned models in reservoir level estimation.
Chronic smoking can cause imbalance in endocrine homeostasis and impairment of fertility in both sexes. The male reproductive system is more resilient, still the literature provides conflicting results about the influence of smoking on the steroid hormone levels. The data about smoking cessation are limited; there has not yet been a study primarily focused on changes in steroids levels. In our study, we analyzed levels of testosterone, dehydroepiandrosterone (DHEA), dehydroepiandrosterone sulphate (DHEAS), cortisol and sex hormone-binding globulin (SHBG) in male smokers and during smoking cessation. Monitored analytes were determined by RIA. The free testosterone index was calculated. Basal samples of men successful and unsuccessful in smoking cessation did not differ and monitored hormones could hardly predict success of smoking cessation. After one year without smoking, a significant BMI increase and SHBG decrease in former smokers was observed. The decrease in total testosterone was non-significant. Changes in SHBG and testosterone did not correlate with BMI, presumably due to the direct effect of smoking cessation., H. Hruškovičová, ... [et al.]., and Obsahuje seznam literatury
Noisy time series are typical results of observations or technical measurements. Noise reduction and signál structure saving are contradictory but useful aims. Non-linear time series processing is a way for non-gaussian noise suppression. Many valued algebras enriched by square root are able to realize the operators close to the weighted averages. Fuzzy data processing based on Łukasiewicz algebra [3] with square root satisfies the Lipschitz condition and causes constrained sensitivity of the mapping. The paper presents a fuzzy neural network based on Modus Ponens [1] with fuzzy logic function [6] preprocessing in the hidden layer. AU the fuzzy algorithms were realized in the Matlab systém and in C++. The fuzzy processing is applied to prediction of sunspot numbers. The systematic approach based on filter selection is combined with weight optimization.