Open Access

ARTICLE

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

Ahmed Abdu Alattab^1,*, 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:

Computers, Materials & Continua 2023, 74(2), 2397-2413. https://doi.org/10.32604/cmc.2023.030924

Received 06 April 2022; Accepted 26 May 2022; Issue published 31 October 2022

Abstract

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.

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Keywords

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