@Article{cmc.2023.030924,
AUTHOR = {Ahmed Abdu Alattab, Mohammed Eid Ibrahim, Reyazur Rashid Irshad, Anwar Ali Yahya, Amin A. Al-Awady},
TITLE = {Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {74},
YEAR = {2023},
NUMBER = {2},
PAGES = {2397--2413},
URL = {http://www.techscience.com/cmc/v74n2/50219},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2023.030924}
}