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Web services evolve over time to fix bugs or update and add new features. However, the design of the Web service's interface may become more complex when aggregating many unrelated operations in terms of context and functionalities. A possible solution is to refactor the Web services interface into different modules that help the user quickly identifying relevant operations. The most challenging issue...
Electrical load forecasting is essential in the field of power systems to enhance the operation and economical utilization In this paper, a combined approaches of artificial neural networks (ANN) with particle-swarm-optimization (PSO) and genetic algorithm optimization (GA) for short and mid-term load forecasting is developed. The model identifies the relationship among load, temperature and humidity...
Power generation from wind generators is always associated with some intermittency due to wind speed and other weather parameters variation, and accurate short-term forecasts are essential for their efficient and effective operation. This can well support transmission and distribution system operators and schedulers to enhance the power network control and management in the smart grid context. This...
Rainfall is a vital phenomenon that contributes in the success of sugar industry season. The ability to determine the amount of precipitation in sugarcane areas enhances the profitability of the season. Different types of climate indices and attributes are usually applied to model rainfall forecasting systems. In this paper, we present a novel genetic algorithm based feature selection approach to...
In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index, hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are...
The world is gradually moving towards being a smart planet. Intelligent Transportation Systems are a key constituent of any smart planet solution. Forecasting Traffic flow is one of the aspects of Intelligent Transportation Systems. A hybrid model comprising of genetic algorithm and time delay neural network has been used here to predict short term traffic flow. Three new input parameters; dew point,...
This paper presents a method of optimizing the elements of a hierarchy of fuzzy-rule-based systems (FRBSs). It is a hybridization of a genetic algorithm (GA) and the cross-entropy (CE) method, which is here called GACE. It is used to predict congestion in a 9-km-long stretch of the I5 freeway in California, with time horizons of 5, 15, and 30 min. A comparative study of different levels of hybridization...
This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of...
Machine based systems can't keep up with the task of organizing the data in an up-to-date manner unless and until the data acquired is being planned or scheduled and managed in an appropriate manner. Today's datasets start as small chunk of information and grow exponentially over a period of time. Once the size is extremely large it becomes difficult to make decisions and to predict consistently and...
A new and robust hybrid model is presented here for the purpose of forecasting currency exchange rate. Initially forecasts are obtained from three different models: linear-trend model, autoregressive moving average model as well as from artificial neural network. Because of its non-linear features, results obtained from artificial neural network outperform rest of the two linear models. With the goal...
Yarn quality prediction plays an important role in modern textile production management. Due to the nonlinearity and non-stationarity of yarn quality indicator series, the accuracy of the commonly used conventional methods, including regression analyses and artificial neural networks (ANN), has been limited. A prediction model based on support vector regression (SVR) is proposed in this paper to solve...
The wind turbine power curve (WTPC) shows the relationship between the wind speed and power output of the turbine. Power curves, which are provided by the manufacturers, are mainly used in planning, forecasting, performance monitoring and control of the wind turbines. Hence an accurate WTPC model is very important in predictive control and monitoring. This paper presents comparative analysis of various...
Soil saturated hydraulic conductivity (Ks) is one of the key parameters as a main input for many water transport models in environmental studies. Direct measuring of this parameter is laborious, time consuming and expensive. So indirect prediction techniques such as Fuzzy c-mean (FCM) clustering and Genetic Algorithm was used to predict Ks parameter from other easily available metadata. FCM algorithm...
Management and pricing of electricity in power system is largely influenced by Short-Term Load Forecasting (STLF). This paper presents a hybrid algorithm, where Radial Basis Function Neural Network (RBFNN) is optimized using Genetic Algorithm (GA) for STLF, with load and day-type as input parameters. Since, conventional training methods, viz., principle component analysis and least square method,...
In order to improve the forecasting accuracy for clean energy consumption with inherently high complexity, a hybrid learning paradigm integrating genetic algorithm (GA) and least squares support vector regression (LSSVR), i.e., GA-LSSVR model, is formulated in this study. In this learning paradigm, LSSVR, as a powerful artificial intelligence tool, is employed to forecast clean energy consumption,...
Artificial Neural Networks (ANN) have been widely used in time series forecasting problem. However, a more promising approach is the combination of ANN with other intelligent techniques, such as genetic algorithms, evolutionary strategies, etc, where these evolutionary algorithms have the objective of train and adjust all parameter of the ANN. In the evolutionary process is necessary define a fitness...
The wide-spread integration of renewable energies in modern power systems is a vital pre-requisite to transform the global energy system towards sustainability. The very obstacle that prevents these sources from spreading is its intermittent nature which results in a fluctuating generated power profile and that considerably affects the ability of these supplies to satisfy the required demand independently...
Prediction of dengue outbreak becomes crucial in Malaysia because this infectious disease remains one of the main health issues in the country. Malaysia has a good surveillance system but there have been insufficient findings on suitable model to predict future outbreaks. While there are previous studies on dengue prediction models in Malaysia, unfortunately some of these models still have constraints...
An experimental mixture design coupled with data analysis by means of genetic algorithm-artificial neural network (GA-ANN) was applied to optimize the fermentation medium of Cordyceps gunnii Mycelia for enhancing the yields of the intracellular polysaccharide. With the yield rate of intracellular polysaccharide as index, a sequential statistical strategy was investigated during this optimization process,...
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