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Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

Ahmed Abdu Alattab1,*, Mohammed Eid Ibrahim1, Reyazur Rashid Irshad1, Anwar Ali Yahya2, Amin A. Al-Awady3

1 Department of Computer Science, College of Science and Arts, Sharurah, Najran University, Najran, Saudi Arabia
2 Department of Computer Science, College of Computer Science & Information Systems, Najran University, Najran, Saudi Arabia
3 Computer Skills Department, Deanship of Preparatory Year, Najran University, Najran, Saudi Arabia

* Corresponding Author: Ahmed Abdu Alattab. Email: email

Computers, Materials & Continua 2023, 74(2), 2397-2413.


This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.


Cite This Article

A. A. Alattab, M. E. Ibrahim, R. R. Irshad, A. A. Yahya and A. A. Al-Awady, "Fuzzy-hlstm (hierarchical long short-term memory) for agricultural based information mining," Computers, Materials & Continua, vol. 74, no.2, pp. 2397–2413, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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