TY  - EJOU
AU  - Alattab, Ahmed Abdu 
AU  - Ibrahim, Mohammed Eid 
AU  - Irshad, Reyazur Rashid 
AU  - Yahya, Anwar Ali 
AU  - Al-Awady, Amin A. 

TI  - Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining
T2  - Computers, Materials \& Continua

PY  - 2023
VL  - 74
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Machine learning; agriculture; IoT; HLSTM; fuzzy rules

DO  - 10.32604/cmc.2023.030924