Eunjin Kim

Chandrashekar Muniyappa, Eujin Kim

Advancements in cloud computing and distributed computing have fostered research activities in Computer science. As a result, researchers have made significant progress in Neural Networks, Evolutionary Computing Algorithms like Genetic, and Differential evolution algorithms. These algorithms are used to develop clustering, recommendation, and question-and-answering systems using various text representation and similarity measurement techniques. In this research paper, Universal Sentence Encoder (USE) is used to capture the semantic similarity of text; And the transfer learning technique is used to apply Genetic Algorithm (GA) and Differential Evolution (DE) algorithms to search and retrieve relevant top N documents based on user query. The proposed approach is applied to the Stanford Question and Answer (SQuAD) Dataset to identify a user query. Finally, through experiments, we prove that text documents can be efficiently represented as sentence embedding vectors using USE to capture the semantic similarity, and by comparing the results of the Manhattan Distance, GA, and DE algorithms we prove that the evolutionary algorithms are good at finding the top N results than the traditional ranking approach.

Chandrashekar Muniyappa, Eunjin Kim

Swarm Intelligence (SI) is gaining a lot of popularity in artificial intelligence, where the natural behavior of animals and insects is observed and translated into computer algorithms called swarm computing to solve real-world problems. Due to their effectiveness, they are applied in solving various computer optimization problems. This survey will review all the latest developments in Searching for documents based on semantic similarity using Swarm Intelligence algorithms and recommend future research directions.

Chandrashekar Muniyappa, Eunjin Kim

Identifying similar documents within extensive volumes of data poses a significant challenge. To tackle this issue, researchers have developed a variety of effective distributed computing techniques. With the advancement of computing power and the rise of big data, deep neural networks and evolutionary computing algorithms such as genetic algorithms and differential evolution algorithms have achieved greater success. This survey will explore the most recent advancements in the search for documents based on their semantic text similarity, focusing on genetic and differential evolutionary computing algorithms.