Abstract
Data have multiplied at an exponential rate in the age of the Internet. Large amounts of data can be combined at this science hotspot. Making sense of big data has become increasingly difficult due to its volume, velocity, precision, and variety (sometimes referred to as heterogeneity). Many data sources are employed to create data heterogeneity. Big data fusion has both advantages and disadvantages when it comes to integrating data from a variety of sources. The focus of this work is on large data fusion using deep learning approaches to combine datasets from a variety of different sources. It is also possible to combine data from many sources. People are increasingly turning to the Internet and web-based services to meet their daily demands. Storage media can hold data in a variety of formats. Managing the vast volume of data is quite tough for an organization (referred to as “big data”). These data are rationally combined and incorporated into the system. Data fusion will be the subject of this paper. The process of collecting data and making judgments based on that data has become much more challenging as a result of technological advancements. The heterogeneity of data is made possible by the great volume, precision, and, most critically, variety of big data. A wide range of data sources can both help and hinder big-data converging. This study was created to introduce several methods and techniques for semantically merging huge datasets.
Highlights
Is paper proposes an effective and reasonable feature fusion strategy in order to extract complementary information from different feature layers in order to increase the model’s flexibility and scalability. e combination of a deep information fusion model and a neural network for inference, deep learning, training, and learning is based on multisource fusion mode for heterogeneous data input, fusion of multisource sharing function for feature extraction of high-level target features
IntelliJ IDEA and Python3.6 are used in the programming environment
We may conclude that incorporating the Bayesian personalized ranking (BPR) framework into our model is a sensible decision. ese findings show that including social networks in the recommendation process may improve its accuracy
Summary
E combination of a deep information fusion model and a neural network for inference, deep learning, training, and learning is based on multisource fusion mode for heterogeneous data input, fusion of multisource sharing function for feature extraction of high-level target features. A simple and generalized deep learning framework is constructed by first extracting the target’s multivalued features from the data source. To extract the advanced function of a selected object, the deep learning model is used in conjunction with a feature transformation algorithm and a feature selection method. Research begins with the development of a hierarchical structure, which should be built using a deep learning model To put it all together, the framework is made up of feature learning modules with multiple levels and classification modules at the end. A simple deep learning approach used for feature extraction, feature selection, and feature classification must be able to generalize well enough to begin building a model [1–6]
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