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

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Abstract

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.

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References

  1. Pramanik PKD, Pal S, Choudhury P (2019) Beyond automation: the cognitive IoT. artificial intelligence brings sense to the internet of Things. In: Cognitive computing for big data systems over IoT 1–37. Springer, Cham

  2. Verma P, Verma A (2020) Accountability of NLP tools in text summarization for Indian languages. J Sci Res 64(1):258–263

  3. Alhoshan M, Altwaijry N (2020) AUSS: An Arabic query-based update-summarization system. J King Saud Univ-Comput Inf Sci 34(6):3732–3743

  4. Abu-Salih B (2021) Domain-specific knowledge graphs: A survey”. J Netw Comput Appl 185:103076

    Article  Google Scholar 

  5. Nguyen Mau T, Inoguchi Y (2020) Locality-sensitive hashing for information retrieval system on multiple GPGPU devices”. Appl Sci 10(7):2539

    Article  Google Scholar 

  6. Munir K, Anjum MS (2018) The use of ontologies for effective knowledge modelling and information retrieval. Appl Comput Inform 14(2):116–126

    Article  Google Scholar 

  7. Banawan K, Ulukus S (2018) The capacity of private information retrieval from coded databases. IEEE Trans Inf Theory 64(3):1945–1956

    Article  MathSciNet  Google Scholar 

  8. Liu J (2021) Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors. Inf Process Manage 58(3):102522

    Article  Google Scholar 

  9. Rahman N, Borah B (2020) Improvement of query-based text summarization using word sense disambiguation. Complex Intell Syst 6(1):75–85

    Article  Google Scholar 

  10. Jiang X, Hu P, Li Y, Yuan C, Masood I, Jelodar H, Rabbani M, Wang Y (2018) A survey of real-time approximate nearest neighbor query over streaming data for fog computing. J Parallel Distrib Comput 116:50–62

    Article  Google Scholar 

  11. Lim S, Bae JH, Eum JH, Lee S, Kim CH, Kwon D, Park BG, Lee JH (2019) Adaptive learning rule for hardware-based deep neural networks using electronic synapse devices. Neural Comput Appl 31(11):8101–8116

    Article  Google Scholar 

  12. Wang X, Wang K, Lian S (2020) A survey on face data augmentation for the training of deep neural networks. Neural Comput Appl 32(19):15503–15531

    Article  Google Scholar 

  13. Priyadarshini I, Sharma R, Bhatt D, Al-Numay M (2023) Human activity recognition in cyber-physical systems using optimized machine learning techniques. Clust Comput 26(4):2199–2215

    Article  Google Scholar 

  14. Oppong SO, Twum F, Hayfron-Acquah JB, Missah YM (2022) A novel computer vision model for medicinal plant identification using log-gabor filters and deep learning algorithms. Comput Intell Neurosci 2022:1189509. https://doi.org/10.1155/2022/1189509

  15. Mahmood A, Khan HU, Khan W (2017) Query based information retrieval and knowledge extraction using Hadith datasets. In: 2017 13th International conference on emerging technologies (ICET), pp 1–6. IEEE

  16. Anand A, Sharma U, Kumar D (2019) Information Retrieval in Computing Model. In: 2019 International conference on intelligent computing and control systems (ICCS), pp 1364–1369. IEEE

  17. Ahmad K, Sahu M, Shrivastava M, Rizvi MA, Jain V (2020) An efficient image retrieval tool: query-based image management system. Int J Inf Technol 12(1):103–111

    Google Scholar 

  18. Bhunia AK, Bhunia AK, Ghose S, Das A, Roy PP, Pal U (2019) A deep one-shot network for query-based logo retrieval. Pattern Recogn 96:106965

    Article  Google Scholar 

  19. Albishre K, Li Y, Xu Y, Huang W (2020) Query-based unsupervised learning for improving social media search. World Wide Web 23(3):1791–1809

    Article  Google Scholar 

  20. Moawad I, Alromima W, Elgohary R (2018) Bi-gram term collocations-based query expansion approach for improving Arabic information retrieval. Arab J Sci Eng 43(12):7705–7718

    Article  Google Scholar 

  21. Dinesha L, and Kumaraswamy S (2018) Enterprise information retrieval using collaborative fusion and active feedback. In: 2019 1st International conference on advanced technologies in intelligent control, environment, computing & communication engineering (ICATIECE), pp 68–74. IEEE

  22. Asim MN, Wasim M, Khan MUG, Mahmood N, Mahmood W (2019) The use of ontology in retrieval: a study on textual, multilingual, and multimedia retrieval. IEEE Access 7:21662–21686

    Article  Google Scholar 

  23. Welde TM, Liao L (2023) Design and development of counting-based visual question answering model using heuristic-based feature selection with deep learning. Artificial Intelligence Review pp 1–30

  24. Zhang X, Song Y (2021) Research on the realization of travel recommendations for different users through deep learning under global information management. J Glob Inf Manag (JGIM) 30(7):1–16

    Google Scholar 

  25. Lokku G, Reddy GH, Prasad MG (2022) Optimized scale-invariant feature transform with local tri-directional patterns for facial expression recognition with deep learning model. Comput J 65(9):2506–2527

    Article  Google Scholar 

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  1. School of Engineering and Computer Science, University of Pittsburgh Johnstown, Johnstown, 15904, United States

    Deepak Dahiya

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  1. Deepak Dahiya
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Author 1: Deepak Dahiya

He participated in the methodology, Conceptualization, Data collection and writing the study.

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Correspondence to Deepak Dahiya.

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Dahiya, D. Query-based information retrieval system adopting whale manhattan optimization-based deep belief neural network. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18783-y

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