Results: implementation of the collaborative filtering recommendation and design a set of experimental results of experimental process, gives the index prediction of the RMS error and the experimental time and the quantity of data. Methods: network data experiment of machine learning algorithm based on Hadoop platform. One of the strengths of machine learning is the efficient identification of patterns in data that enable classification. We welcome novel applications of machine learning and data mining in areas of electrical engineering, such as antennas, communications, controls, devices, hardware design, power and energy, sensor systems, and signal processing. Other Scientific Applications 6. Three approaches included the classification of avian species into structural successional stages, life forms, or their sociological associations. This report provides an overview of machine learning and data analysis with explanation of the advantages and disadvantages of different methods. However, the prediction model coefficient of determination (R²) using GMDH method was ranging from 0.964 to 0.981 and 0.934 to 0.974 for training and validation, respectively. A predictive model was developed using both regression and group method of data handling (GMDH) techniques; 70% of the 33 WAG pilot projects data were used as validation, whereas remaining 30% of data set were used for validation. Finance,2017(03):263-264. 392 062202, https://doi.org/10.1088/1757-899X/392/6/062202. Access scientific knowledge from anywhere. Water alternating gas (WAG) injection process is a proven EOR technology that has been successfully deployed in many fields around the globe. Environments[J].Natural sciences journal of harbin normal univ. Machine learning (ML) is the study of computer algorithms that improve automatically through experience. Applications, such as processing vehicle accident data to predict crash severity for a given location (Li et al., 2008), processing textual data to identify key messages in accident investigation reports (Marucci-Wellman et al., 2017a, Marucci-Wellman et al., 2017b) are some of the studies published in recent risk and safety focused journals. response to quantitative habitat variables. The basic model of machine learning, 2.3 The development stage of machine learning, In the first stage, in the 1950s the main method, transmission signal of the threshold logic unit.In, study concept-oriented learning, which is symbolic learning. But there are several key distinctions between these two areas. This research was performed for a purpose of developing a predictive model for WAG incremental recovery factor based on integrated approach that involves reservoir simulation and data mining. Apply machine learning methods to data mining domain can be more helpful to extract useful knowledge for problems with changing conditions. By continuing to use this site you agree to our use of cookies. University,2007,24(2):21-24. Learning source. We list a few of them below. Reservoir model parameters assessed in this research study were horizontal and vertical permeabilities, fluids properties, WAG injection scheme, fluids mobility, trapped gas saturation, reservoir pressure, residual oil saturation to gas, and injected gas volume. application of machine learning in big data. The characteristics of data mining as a cross discipline. WAG pilot projects demonstrate that WAG incremental recovery factor typically ranges from 5 to 10% of original oil in place, though up to 20% has been observed in some fields. Both data mining and machine learning fall under the aegis of Data Science, which makes sense since they both use data. Volume 392, The goal often is provided by the fact of making a student grow and learn in various facets using advanced scientific knowledge and here data mining comes majorly into play by ensuring that the right quality of knowledge and decision making con… With the explosive growth of the industry data, more and more attention is paid to big data. However, due to the volume, complex and fast-changing characteristics of big data, traditional machine learning algorithms for small data are not applicable. It presents machine learning techniques to dissolve it. Despite its proven success, WAG application growth has been very slow. Here is the list of areas where data mining is widely used − 1. However, some misleading high-level patterns could be included in the mined set. By exploiting a taxonomy, patterns are usually extracted at any level of abstraction. Each MGI, denoted as X@?E, represents a frequent generalized itemset X and its set E of low-level frequent descendants for which the correlation type is in contrast to the one of X. All rights reserved. Application of machine learning algorithms in data mining.