Fuzzy inference systems for predicting the mass yield in extractions of chia cake extract

Johann, Gracielle; Santos, Casi Santos dos; Montanher, Paula Fernandes; Oliveira, Rafael de Novaes; Carniel, Anderson Chaves

doi:10.1016/j.simpa.2021.100145

Cited by 6 publications

(3 citation statements)

References 13 publications

(17 reference statements)

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“…However, Knowledge Graphs have difficulty representing knowledge and inferring output labels on medical symptom datasets with some characteristics such as amplitude and phase term, uncertain or incomplete input information. Some applications of Picture Fuzzy Set in disease diagnosis [15][16][17][18] or fuzzy techniques based on Fuzzy Inference System, such as Fuzzy Inference System [19][20][21][22][23][24][25][26][27], Complex Fuzzy Inference System [28][29][30], and Mamdani Complex Fuzzy Inference System [31,32] have overcome the limitations mentioned in Knowledge Graph models. These techniques can represent knowledge for datasets containing ambiguous and unclear information, but these models cannot find output labels for new samples that are not in the Fuzzy Rules Base.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Knowledge Graph Pairs-based Model in extreme cases for Hepatitis Sign Diagnosis

Khanh,

Long,

Hai

2023

Preprint

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Recently, machine learning models based on fuzzy inference systems have effectively solved problems in medical diagnosis and received much attention from researchers around the world. For example, Fuzzy Inference System (FIS), Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R), Mamdani Complex Fuzzy Inference System with Fuzzy Knowledge Graph (M-CFIS-FKG), and Fuzzy Knowledge Graph Pairs (FKG-Pairs) have been proposed. These machine-learning models have successfully solved most of the disease diagnosis problems based on complex symtom datasets with some characteristics such as amplitude and phase term, uncertain or incomplete input information. However, these models have revealed limitations when dealing with decision-making or disease diagnosis problems in extreme cases (where training datasets are too small or input datasets are large). To overcome this challenge, the FKG-Extreme model, based on FKG-Pairs combined with Q-learning techniques, has been proposed. In order to promote the practical application of the theoretical model FKG-Extreme, a model for Hepatitis Sign Diagnosis has been proposed in this paper. A simulation application of the proposed model has been built, and experimental results have been carried out based on the Liver dataset collected from the UCI machine learning repository, with results in terms of accuracy 13.3% higher than the FKG-Pairs model. Furthermore, some measures (Precision, Recall, and F1-Score) have also been used to evaluate performance and demonstrate that the proposed model has high reliability.

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Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Knowledge Graph Pairs-based Model in extreme cases for Hepatitis Sign Diagnosis

Khanh,

Long,

Hai

2023

Preprint

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“…Also, multiple LM and ANFIS were employed in evaluation of migration modeling of phthalate from non‐alcoholic beer bottles 19 and oxidative stability of virgin olive oil 20 . Moreover, different configurations of FIS and ANFIS are used to estimate the mass yield of chia cake extract 21,22 …”

Section: Introductionmentioning

confidence: 99%

“…20 Moreover, different configurations of FIS and ANFIS are used to estimate the mass yield of chia cake extract. 21,22 However, to the best of the authors' knowledge, a detailed analysis of an alternative bleaching process of yerba mate leaves using water immersion and its influence on AA is still missing in the literature. Further, LM, RSM, FIS, and ANFIS are not explored in this context.…”

Section: Introductionmentioning

confidence: 99%

Effects of an alternative bleaching method for yerba mate leaves on total phenolic content: evaluation and prediction with soft‐computing approaches

et al. 2022

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BACKGROUND The effects of an alternative bleaching method on the total phenolic content (TPC) and antioxidant activity (AA) of yerba mate extracts were evaluated. Traditional bleaching (‘zapeco’) was compared with submerging the leaves in water followed by a hot air oven‐drying process. Seven different approaches, i.e. linear model (LM), response surface model (RSM), Mamdani, Larsen, adaptive neuro‐fuzzy inference system (ANFIS) with the product (Prod) and the minimum (Min) operators, and ANFIS with automatically membership functions (Auto), were employed to compare the TPC of yerba mate extracts based on drying temperature and AA assays. RESULTS The results showed that if leaves were bleached followed by drying at higher temperatures, we obtained higher AA and TPC values. For submerging bleaching treatment, RSM model delivered the best accuracy measures with a mean absolute error (MAE), average absolute percentage error (MAPE), and mean squared error (MSE) of 0.128, 0.006, and 0.028, respectively. The ANFIS Auto model was the best for traditional bleaching treatment, with MAE, MAPE, and MSE of 0.490, 0.013, and 0.612, respectively. CONCLUSION The results suggest a second‐order linear relation between drying temperature, AA assays to TPC, and a high level of relation complexity of drying temperature, AA assays, and TPC. The evaluated soft‐computing approaches have the excellent ability to estimate TPC from bleached leaves. © 2022 Society of Chemical Industry.

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A novel fuzzy knowledge graph pairs approach in decision making

Long

Hai

Tuấn

et al. 2022

Multimed Tools Appl

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Fuzzy inference systems for predicting the mass yield in extractions of chia cake extract

Cited by 6 publications

References 13 publications

A Novel Fuzzy Knowledge Graph Pairs-based Model in extreme cases for Hepatitis Sign Diagnosis

A Novel Fuzzy Knowledge Graph Pairs-based Model in extreme cases for Hepatitis Sign Diagnosis

Effects of an alternative bleaching method for yerba mate leaves on total phenolic content: evaluation and prediction with soft‐computing approaches

A novel fuzzy knowledge graph pairs approach in decision making

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