Today, IT technologies and the internet are become an essential factors in human life's. The demands level of the users are growing extensively, they need more generic software and hardware services, also they need, security in their processes and data transfers, dynamic accessibility, swift processing and also saving more money in their works. In this paper, we want to present new method for estimating and predicting the next user demands, and provide those services before direct request of the users. Our algorithm is based on Kalman filtering, and it used background usages of the users along with their suggestions in CRM section to be able to predict their next needs. By providing the next needs of the users, before their request, we can improve the level of customer satisfaction and also earn more money via suggesting the new software and applications.

Sedimentation of heavy fractions of oil such as asphaltene is the main concern in different parts of petroleum industries like production and transportation. Due to this fact, the inhibition of asphaltene precipitation becomes one of the great interests in the petroleum industry. In the present investigation, multi-layer perceptron artificial neural network (MLP-ANN) was developed to estimate asphaltene precipitation reduction as a function of concentration and kind of inhibitors and oil properties. To this end, a total number of 75 data points were extracted from reliable source for training and validation of predicting algorithm. The outputs of MLP-ANN were compared with experimental data graphically and statistically, the determined coefficients of determination (R²) for training and testing are 0.996522 and 0.995239 respectively. These comparisons expressed the high quality of this algorithm in the prediction of asphaltene precipitation reduction. so the MLP-ANN can be used as a powerful machine for estimation of different processes in petroleum industries.