Query-based information retrieval system adopting whale manhattan optimization-based deep belief neural network

• Published in: Multimedia tools and applications Vol. 84; no. 12; pp. 10587 - 10607
• Main Author: Dahiya, Deepak
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2025; Springer Nature B.V
• Subjects: Accuracy; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Information retrieval; Multimedia Information Systems; Neural networks; Optimization; Queries; Relevance; Special Purpose and Application-Based Systems; Unstructured data; User requirements; Improved artificial flora; Dimension reduction; Information retrieval; Feature selection; Whale manhattan optimization; Deep belief neural network
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18783-y

The Information Retrieval system aims to discover relevant documents and display them as query responses. However, the ever-changing nature of user queries poses a substantial research problem in defining the necessary data to respond accurately. The Major intention for this study is for enhance the retrieval of relevant information in response to user queries. The aim to develop an advanced IR system that adapts to changing user requirements. By introducing WMO_DBN, we seek to improve the efficiency and accuracy of information retrieval, catering to both general and specific user searches. The proposed methodology comprises three important steps: pre-processing, feature choice, and categorization. Initially, unstructured data subject to pre-processing to transform it into a structured format. Subsequently, relevant features are selected to optimize the retrieval process. The final step involves the utilization of WMO_DBN, a novel deep learning model designed for information retrieval based on the query data. Additionally, similarity calculation is employed to improve the effectiveness for the network training model. The investigational evaluation for the suggested model was conducted, and its performance is measured regarding the metrics of recall, precision, accuracy, and F1 score, the present discourse concerns their significance within the academic realm. The results prove the superiority of WMO_DBN in retrieving relevant information compared to traditional approaches. This research introduces novel method for addressing the challenges in information retrieval with the integration of WMO_DBN. By applying pre-processing, feature selection, and a deep belief neural network, the proposed system achieves more accurate and efficient retrieval of relevant information. The study contributes to the advancement of information retrieval systems and emphasizes the importance of adapting to users' evolving search queries. The success of WMO_DBN in retrieving relevant information highlights its potential for enhancing information retrieval processes in various applications.