Effective and Efficient framework for Multimodal Summarization in neural network using optimized genetic algorithm

A Data mining has become one of the widely researched fields off late especially with the increase in technological advancements which have caused the volume of data to be stored and processed for query based applications or decision based processes to increase in great multiplicative measures. Data mining refers to the process of extraction of useful information from a pool of data. Various algorithms have been proposed in the past for the mining process out of which neural based mining algorithms are predominant. Genetic algorithm is an important algorithm ofassociation rule mining. However, there is some issues thatgenetic algorithm easy to lead premastering convergenceand into the plight of local optimum, or convergence toomuch time and consume a large amount of time to search For resolving this issue, the paper improves thealgorithm through adopting an adaptive mutation rate andimproving the methods of individual choice, and theimproved genetic algorithm that applies to the miningassociation rules. The simulating experiments show that the improved genetic algorithm reduces the cost of computing, and improve the efficiency of association rule mining. The proposed algorithm hasbeen tested with iris data set and results obtain indicate acomparatively higher efficiency based on classification and reduction of computation time. The proposed results havebeen compared against conventional techniques like SVM classifier based mining and neural network in its standalone architecture.