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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

An integrated mathematical cognitive system employing swarm computing for convergence of artificial intelligence and knowledge representation

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pp. 1919–1930Vol. 47Issue 5-BMay 2026DOI: 10.47974/JIOS-2284XML
Received:
01 Apr 2025
Published Online:
23 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2284
Pages:
1919–1930

Abstract

This research introduces an enhanced LSTM framework utilizing PSO for forecasting the opening value of the NSE index. This study introduces the LSTM and augmented PSO-LSTM framework for stock price prediction, utilizing the notion of time series. The enhanced PSO-LSTM technique utilizing PSO to enhance the weights of Long Short Term Memory framework, thereby enhancing prediction accuracy. PSO is employed to adjust weights of the LSTM framework, thereby diminishing forecasting error. Following the preliminary processing of past stock data, which encompasses starting price, closing cost, highest cost, lowest rate, as well as regular volume, we develop the LSTM utilizing time series derived from this past dataset. Ultimately, we implement the suggested LSTM to forecast opening value of NSE index. The PSO optimization algorithm, applied to the LSTM model through empirical study, efficiently identifies the best neural network weights, minimizes the loss function, facilitates speedy fitting, and yields more accurate predictions.

Keywords

Subject Classifications

26A3335C0539B72

References

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