Accurate predictions of bus arrival times help passengers arrange their trips easily and flexibly improve travel efficiency. Thus, it is important to manage schedule the buses for efficient deployment ease traffic congestion, which improves service quality public transport system. However, due many variables disturbing scheduled transportation, accurate prediction challenging. For time a bus, t...

Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large issues. Forecasting is related predicting task an upcoming event avoid any circumstances happen current environment. ...

Received () Revised () Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs. In this case, however, gradient based learning ...

The time-lag method, using a gas permeation experiment, is currently the most popular method for determining the membrane properties: diffusivity coefcient and permeability coefcient, and from which the solubility coefcient can be calculated. In this investigation, the impact of systematic, random (noise), resolution and extrapolation errors associated with gas permeatio...