• 대한약침학회지
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• 제25권3호
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• pp.149-185
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• 2022

Objectives: The Barak Valley is well known for its rich diversity of medicinal plants. Ethnomedicinal practices are prominent among Barak Valley's major and minor ethnic groups. This systemic review focuses on traditionally used medicinal plants found in the Barak Valley as reported in different ethnobotanical surveys. Methods: We searched various databases, including PubMed, Web of Science, and Google Scholar, to find ethnomedicinal surveys conducted in the Barak Valley. The search was performed using different terms, including ethnomedicinal survey, folk medicine, indigenous knowledge, and Barak Valley. Potential articles were identified following the exclusion and inclusion criteria. Results: A total of eight ethnobotanical surveys were included in this study. We identified a total of 216 plant species belonging to 167 genera and 87 families, which are widely used by the ethnic communities who live in the rural areas of Barak Valley for the treatment of various diseases and ailments. Conclusion: Folk medicine is the result of decades of accumulated knowledge and practices by people who live in rural communities based on their needs and provides an important source of information to assist the search for new pharmaceuticals. Therefore, available information on traditional medicinal plants needs to be explored scientifically to find effective and alternative treatments for different diseases.

• Computers and Concrete
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• 제34권3호
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• pp.279-296
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• 2024

In this study, ensemble machine learning (ML) models are employed to estimate the hardened properties of Self-Compacting Geopolymer Concrete (SCGC). The input variables affecting model development include the content of the SCGC such as the binder material, the age of the specimen, and the ratio of alkaline solution. On the other hand, the output parameters examined includes compressive strength, flexural strength, and split tensile strength. The ensemble machine learning models are trained and validated using a database comprising 396 records compiled from 132 unique mix trials performed in the laboratory. Diverse machine learning techniques, notably K-nearest neighbours (KNN), Random Forest, and Extreme Gradient Boosting (XGBoost), have been employed to construct the models coupled with Bayesian optimisation (BO) for the purpose of hyperparameter tuning. Furthermore, the application of nested cross-validation has been employed in order to mitigate the risk of overfitting. The findings of this study reveal that the BO-XGBoost hybrid model confirms better predictive accuracy in comparison to other models. The R² values for compressive strength, flexural strength, and split tensile strength are 0.9974, 0.9978, and 0.9937, respectively. Additionally, the BO-XGBoost hybrid model exhibits the lowest RMSE values of 0.8712, 0.0773, and 0.0799 for compressive strength, flexural strength, and split tensile strength, respectively. Furthermore, a SHAP dependency analysis was conducted to ascertain the significance of each parameter. It is observed from this study that GGBS, Flyash, and the age of specimens exhibit a substantial level of influence when predicting the strengths of geopolymers.