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An intelligent framework of heuristic approach-aided optimal gene selection and residual LSTM with MLP for disease prediction in rice crop using gene expression data

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Abstract

The use of gene expression information for disease prediction in the cultivation of rice offers a novel approach to improve agricultural output and health. In order to predict future epidemics, it involves examining how genes are expressed based on different illnesses. Early identification of illness by gene expression might be achieved even before signs appear, allowing for timely treatments to reduce crop loss and stop the propagation of the illness. However, because gene expression information is so complicated and requires advanced quantitative and bioinformatic knowledge, understanding it may be quite difficult. This paper leverages cutting-edge technological solutions by developing a system based on deep learning and machine learning for disease prediction in rice crops using gene expression data. The initial step involves gathering gene expression data from reputable sources to ensure a diverse and comprehensive dataset. Once collected, variable-length gene data are filled by padding to standardize data input formats. Following this, repetitions in gene data are identified using multi-similarity methods, ensuring the uniqueness and relevance of the data for analysis. The obtained data are further used for optimal gene selection using the enhanced red-tailed hawk algorithm (ERTH), which ensures superior performance. Finally, the prediction is performed using an innovative approach named adaptively optimized residual long short-term memory with multilayer perception (AO-RLSTM-MLP) that combines residual long short-term memory (RLSTM) and multilayer perception (MLP). During this phase, the parameters of the AO-RLSTM-MLP are optimally tuned using the same ERTH approach. Experimental analysis is conducted on this deep learning-based model to validate its effectiveness in disease prediction in rice crops using gene expression data. This analysis demonstrates the potential of this robust solution in the field, highlighting its capabilities to predict disease outbreaks effectively and contribute to sustainable agriculture practices.

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No datasets were generated or analysed during the current study.

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Acknowledgements

I would like to express my very great appreciation to the co-authors of this manuscript for their valuable and constructive suggestions during the planning and development of this research work.

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  1. Department of Computer Science and Engineering, National Institute of Technology, Warangal, Telangana, 506004, India

    Vijaya Lakshmi Adluri &  Raju Bhukya

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  1. Vijaya Lakshmi Adluri
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Contributions

Vijaya Lakshmi Adluri and Dr. Raju Bhukya designed the model, computational framework and carried out the implementation. Vijaya Lakshmi Adluri performed the calculations and wrote the manuscript with all the inputs. Vijaya Lakshmi Adluri and Dr. Raju Bhukya discussed the results and contributed to the final manuscript.

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Correspondence to Vijaya Lakshmi Adluri.

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Adluri, V.L., Raju Bhukya An intelligent framework of heuristic approach-aided optimal gene selection and residual LSTM with MLP for disease prediction in rice crop using gene expression data. SIViP 19, 307 (2025). https://doi.org/10.1007/s11760-025-03859-5

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  • DOI: https://doi.org/10.1007/s11760-025-03859-5

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