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ESN load forecasting model has high stability, and is able to learn fast and not easy to fall into local optimum, compared with standard recurrent neural network. In the process of constructing the typical ESN model, the choice of parameters is always empirical or random. The forecasting performance of ESN was analyzed on the basis of its key parameters. While the dynamic reserve pool has black box...
For processing purposes of silver colloidal suspensions in view of specific applications, this study evaluates the suitability of using alginate/lignosulfonate mixtures as an efficient dispersion matrix for the silver nanoparticles. The rheological behavior of the in situ obtained silver nanoparticle suspensions was investigated by rotational measurements performed using cone-plate geometry, considering...
In this paper we propose five Neural Network models for forecasting public transit. These models are evaluated in terms of accuracy and robustness. The research has two major objectives: to identify the best performing machine learning model in predicting bus travel time and to establish a set of methods in order to obtain a detailed dataset (a variety of practical input values) which will further...
Forecasting visitor arrivals is of great importance since it is an indicator of the tourism demand and can serve as the reference for the government policies about tourism and the business strategies of tourism industries. So an accurate forecasting model must be developed. There are two machine learning algorithms, including support vector machine and neural network, which are include in the comparison...
Monitoring the presence of occupants in a room in a timely manner is a fundamental step for effective building management. Environmental sensor networks have the advantages of high cost-efficiency and non-intrusiveness on privacy and are very suitable for room occupancy detection. Nonlinear discriminative models, e.g., support vector machine and neural networks, have shown good detection performance...
Efficient spectrum sensing can be realized by predicting the future idle times of primary users' activity in a cognitive radio network. In dynamic spectrum access, based on a reliable prediction scheme, a secondary user chooses a channel with the longest idle time for data transmission. In this paper, four supervised machine learning techniques, two from ANN, i.e. Multilayer Perceptron & Recurrent...
Gait analysis applications are not only limited to medical, rehabilitation and sports, but it can also play a decisive role in security and surveillance as a behavioral biometric factor. Gait recognition is non-invasive and doesn't need any cooperation from subject in case of video surveillance. This paper presents a framework for human recognition based on gait without using markers or sensors using...
This research aimed at integrating data from remote sensing resources and machine learning for developing a forecasting model of successful royal rainmaking operation in the upper north provinces of Thailand. The Support Vector Machine (SVM), neuron network method, and decision tree (C4.5) were used for data integration and forecast modeling. The data were collected between 1 January 2012 to 31 December...
Electricity price, consumption, and demand forecasting has been a topic of research interest for a long time. The proliferation of smart meters has created new opportunities in energy prediction. This paper investigates energy cost forecasting in the context of entertainment event-organizing venues, which poses significant difficulty due to fluctuations in energy demand and wholesale electricity prices...
Wind power prediction is of great significance to the safe and stable operation of the power system. The key factor of wind power prediction is the selection of prediction model. This paper chooses support vector machine (SVM) as the wind power prediction model and applies an improved grid search method to optimize the parameters of C and g in SVM model. The model is able to predict the real-time...
This paper presents an approach combining machine learning (ML), cross-validation methods and cellular automata (CA) model for simulating land use changes in Luxembourg and the areas adjacent to its borders. Throughout this article, we emphasize the interest in using ML methods as a base of CA model transition rule. The proposed approach shows promising results for prediction of land use changes over...
Hemorrhagic shock is the cause of one third of deaths resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physicians to treat patients successfully. The objective of this study was to select an optimal survival prediction model using physiological parameters from rats during our hemorrhagic experiment. These physiological parameters were used for the training...
This This study aims to investigate the robustness of prediction model by comparing artificial neural networks (ANNs), and support vector machine (SVMs) model. The study employs ten years monthly data of six types of macroeconomic variables as independent variables and the average rate of return of one-month time deposit of Indonesian Islamic banks (RR) as dependent variable. Finally, the performance...
Linear regression and classification techniques are very common in statistical data analysis but they are often able to extract from data only linear models, which can be a limitation in real data context. Aim of this study is to build an innovative procedure to overcome this defect. Initially, a multiple linear regression analysis using the best-subset algorithm was performed to determine the variables...
The Brazilian electric sector reform specifying that the remuneration of distribution utilities must be through the management of their systems increased the necessity of control and management of load flows through the connection points between their systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds...
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