Automatic resource scaling is one advantage of Cloud systems. Cloud systems are able to scale the number of physical machines depending on user requests. Therefore, accurate request prediction brings a great improvement in Cloud systems’ performance. If we can make accurate requests prediction, the appropriate number of physical machines that can accommodate predicted amount of requests can be ...

numerous studies yet have been carried out on downscaling of the large-scale climate data usingboth dynamical and statistical methods to investigate the hydrological and meteorological impacts of climatechange on different parts of the world. this study was also conducted to investigate the capability of feedforwardneural network with error back-propagation algorithm to downscale the provincial...

parameter estimation of the nonlinear muskingum model is a highly nonlinear optimization problem. although various techniques have been applied to optimize the coefficients of the nonlinear muskingum flood routing models, but an efficient method for this purpose in the calibration process is still lacking. the accuracy of artificial bee colony (abc) algorithm is investigated in this paper to op...

We use referential translation machines (RTMs) for predicting the semantic similarity of text in both STS Core and Cross-lingual STS. RTMs pioneer a language independent approach to all similarity tasks and remove the need to access any task or domain specific information or resource. RTMs become 14th out of 26 submissions in Cross-lingual STS. We also present rankings of various prediction tas...

if both reference station (rs) and navigational device in differential global positioning system (dgps) receive signals from the same satellite, rs position components error (rpce) can be used to compensate for navigational device error. this research used hybrid method for rpce prediction which was collected by a low-cost gps receiver. it is a combination of genetic algorithm (ga) computing an...

Construction projects are often subject to scheduling errors caused by uncertainty and systematic planning fallacies. This paper evaluates the prediction accuracy of CatBoost expert intuition predict duration public construction in predesign phase. The authors use a dataset city New York (USA) with 367 projects. Both compared performance indicator Mean Absolute Error (MAE) absolute preferrence....

Reliable prediction of groundwater depth fluctuations has been an important component in sustainable water resources management. In this study, a data-driven prediction model combining discrete wavelet transform (DWT) preprocess and support vector machine (SVM) was proposed for groundwater depth forecasting. Regular artificial neural networks (ANN), regular SVM, and wavelet preprocessed artific...

Investors and scholars pay continuous attention to the stock market, as each day, many investors attempt to use different methods to predict stock price trends. However, as stock price is affected by economy, politics, domestic and foreign situations, emergency, human factor, and other unknown factors, it is difficult to establish an accurate prediction model. This study used a back-propagation...

Key Results: Cases are often indiscriminatingly added to case bases. This potentially results in poor solutions proposed by ‘misleading’ cases. Research to-date has resulted in development of a prototype that can potentially filter ‘misleading’ cases in case bases to avoid delivery of poor solutions. The prototype was tested in a pilot study, in which absolute residuals (error in prediction) we...