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A Abu Kaliya movie
This paper proposes a new strategy to realize the English movie resource information mining. The main idea of this paper is to build an English film and television resource information mining model by combining fuzzy neural network algorithms and dynamic data stream classification technology. Firstly, we use dynamic data stream classification technology to preprocess and screen English film and television resource information. Secondly, we use fuzzy neural network algorithm to conduct data mining on related film and television resource information. The experimental simulation test results verify the superior performance of the English film and television resource information mining model established in this paper. This model can help people find the resources and information they need.
The contributions of this paper can be summarized follows:(1)This paper proposes a new scheme which can realize resource and information mining. This paper combines dynamic data stream classification technology and fuzzy neural network algorithm, which can help to preprocess and screen English film and television resource information and conduct data mining on related film and television resource information, respectively.(2)We use this new scheme in English movie resource information mining, which is a difficult problem. Generally speaking, massive English film and television resources will provide people with richer content, but at the same time, it has become more and more difficult for people to find the resources and information they need. With this new scheme, we can solve this problem effectively.
For example, you can start from the movie plot that the user has watched, extract keywords to generate a user portrait, and then further match and push the movie. For example, currently Dougan only opens a part of the interface, and many external data interfaces are not free, or directly not open to the outside world. Even if the API is open to the outside world, there are strict controls on the frequency and number of access data.
Building a high-quality film and television information network is an important foundation for subsequent mining and analysis. This chapter will introduce how to construct a network by obtaining film and television data from data sources. Firstly, we introduce the composition of film and television data and the mode of film and television information network. Then, it introduces how to extract the entities used to construct the information network, the relationships between entities, and attribute information from the obtained film and television data, including the extraction of plot keywords and the alias labeling of entities. Finally, we will introduce how to effectively store the film and television information network to make subsequent analysis work more efficient. In this experiment, the movie data were analyzed in a cluster of five multinodes to measure the overall performance of the recommendation system.
This article uses an open dataset about movie ratings provided by Group Lens. Movie Lens is an experimental dataset specifically aimed at researching related technologies. Because of the reliability and authenticity of the data, this article uses the Movie Lens dataset for various experiments. Group Lens currently provides datasets of different sizes, with thousands of users and ratings and even hundreds of thousands of movie ratings. In this paper, several thousands to tens of thousands of datasets of different numbers are used to carry out related experiments and analysis, because the movie dataset provided by Group Lens is already a standard with a very standardized format.
Figure 7 gives the comparison of data mining quality of English film resources mining and reconstruction of different detection method. Similarly, there are four methods considered here, which are FDNDCA proposed in this paper, IGC proposed in [44], AAAR proposed in [45], and NEUMN proposed in [46]. These four methods correspond to detection method 1, detection method 2, detection method 3, and detection method 4, respectively. As can be seen from Figure 7, when the amount of data is relatively small, in the stand-alone mode and the cluster architecture environment, the recommended accuracy is not much different. But from 200 data to 700 data, the quality has a little obvious difference, but it is still not very large, and even the recommendation effect of the two can be considered the same to a certain extent. However, from 300 to 700, the fluctuations and changes in the value of the cluster mode are relatively stable so that there is not much change in the accuracy of the recommendation. Because in practical applications, as the amount of processed data continues to increase, its recommendation accuracy and recommendation effect will decrease, because the movies rated by users are very sparse.
Although the data can be obtained directly based on the open API, its open permissions are quite restricted. There are many ways to practice this idea. For example, you can start from the movie plot that the user has watched, extract keywords to generate a user portrait, and then further match and push the movie. For example, currently Dougan only opens a part of the interface, and many external data interfaces are not free, or directly not open to the outside world. Even if the API is open to the outside world, there are strict controls on the frequency and number of access data. With the help of the concept of heterogeneous information network, the paper organizes film and television data into a heterogeneous information network containing multiple types of nodes and multiple relationships between nodes and uses network representation learning and text representation learning algorithms to effectively deal with the key information in the film data said. In the work of this paper, the information contained in the video data is organized through a heterogeneous information network, and the key information in it is effectively expressed through network representation learning and text representation learning algorithms.
On this basis, this paper proposes a set of query-driven mining and analysis solutions, which can efficiently complete a variety of different analysis tasks. First, we use dynamic data stream classification technology to preprocess and screen English film and television resource information. Secondly, we use a fuzzy neural network algorithm to conduct data mining on related film and television resource information. The experimental simulation test results verify the superior performance of the English film and television resource information mining model established in this paper. This model can help people find the resources and information they need. In addition, a query-driven mining analysis framework is proposed, which can efficiently complete a variety of different analysis tasks. Based on the abovementioned research, we designed and implemented a Dougan film and television data analysis prototype system, which can effectively discover the important information hidden in the film and television data and can serve a variety of analysis scenarios. Looking to the future, there are many areas worthy of further improvement in the work of this article. The film and television data obtained in this article are not enough in terms of comprehensiveness and volume of information. This also limits the depth and breadth of analysis to a certain extent. In fact, in addition to obtaining relevant data from professional movie websites such as Dougan movies, a lot of movie information can also be obtained from social networking sites and news-related websites, which can be used as a supplement to the former. In addition, it is worth trying to enrich the amount of information in the data by introducing an external knowledge base.
As for the proposed scheme, it can help to realize English movie resource information mining, and the experimental results show the effectiveness of this method. A large number of studies and experiments have proved that this method can show good performance when the amount of data is not large and there is a certain relationship between the data. When the amount of data is very large or the relationship between data is small, the performance of this method will be poor. So, the future research direction is to improve the performance of the algorithm in the case of large amount of data and small relationship between data.
Neki Aur Badi Song Lyrics from movie Abu Kaliya. Neki Aur Badi song sung by Nitin Mukesh. Neki Aur Badi Song Composed by . Neki Aur Badi Song Lyrics was Penned by Aish Kanwal. Abu Kaliya movie cast in the lead role actor and actress. Abu Kaliya movie released on 1990 2ff7e9595c
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