Forthcoming Articles

International Journal of Internet Technology and Secured Transactions

International Journal of Internet Technology and Secured Transactions (IJITST)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Internet Technology and Secured Transactions (One paper in press)

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  • IoT-enhanced precision agriculture: applying additive neural networks for crop yield prediction in the context of climate change and environmental variability   Order a copy of this article
    by Durgadevi Kumar 
    Abstract: Internet of things (IoT) based crop yield prediction analyses sensor data on temperature, soil moisture, and air quality to assess climate change impacts. In this manuscript, IoT-driven crop yield prediction using interpretable neural networks under climate variability (IoT-IGANN-CYP-CCE) is proposed. Initially input data are collected from crop yield prediction dataset. The input data undergoes pre-processing using the bilinear double-order filter (BDOF) for cleaning the collected data then processed by interpretable generalised additive neural networks (IGANN) for yield prediction. To enhance prediction accuracy, the bitterling fish optimisation algorithm (BFOA) is used to optimise IGANNs weight parameters. Implemented in Python, the method is evaluated using metrics such as accuracy, precision, recall, F1-score, ROC, PCC, and execution time. Results show significant performance improvements over existing models achieving up to 22.82% higher accuracy, 32.26% greater precision, and 23.89% greater recall demonstrating the models effectiveness in climate-resilient agricultural forecasting.
    Keywords: agriculture; climate change; crop yield prediction; internet of things; IoT; sensor data; weather variability.
    DOI: 10.1504/IJITST.2025.10074118
     

 

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