In designing porous scaffolds, permeability is essential to consider as a function of cell migration and bone tissue regeneration. Good permeability has been achieved by mimicking the complexity of natural cancellous bone. In this study, a porous scaffold was developed according to the morphological indices of cancellous bone (porosity, specific surface area, thickness, and tortuosity). The computational fluid dynamics method analyzes the fluid flow through the scaffold. The permeability values of natural cancellous bone and three types of scaffolds (cubic, octahedron pillar, and Schoen’s gyroid) were compared. The results showed that the permeability of the Negative Schwarz Primitive (NSP) scaffold model was similar to that of natural cancellous bone, which was in the range of 2.0 × 10⁻¹¹ m² to 4.0 × 10⁻¹⁰ m². In addition, it was observed that the tortuosity parameter significantly affected the scaffold’s permeability and shear stress values. The tortuosity value of the NSP scaffold was in the range of 1.5–2.8. Therefore, tortuosity can be manipulated by changing the curvature of the surface scaffold radius to obtain a superior bone tissue engineering construction supporting cell migration and tissue regeneration. This parameter should be considered when making new scaffolds, such as our NSP. Such efforts will produce a scaffold architecturally and functionally close to the natural cancellous bone, as demonstrated in this study.

The Air Quality Index (AQI) dataset contains information on measurements of pollutants and ambient air quality conditions at certain location that can be used to predict air quality. Unfortunately, this dataset often has many missing observations and imbalanced classes. Both of these problems can affect the performance of the prediction model. In particular, predictions for the minority class are very important because inaccurate predictions can be fatal or cause big losses. Moreover, the missing data may lead to biased results. This paper proposes the single imputation of the median and the multiple imputations of the $k$-Nearest Neighbor (KNN) regressor to handle missing values of less than or equal to 10% and more than 10%, respectively. At the same time, the SMOTE-Tomek Links address the imbalanced class. These proposed approaches to handle both issues are then used to assess the air quality prediction of the India AQI dataset using Naive Bayes (NB), KNN, and C4.5. The five treatments show that the proposed method of the Median-KNN regressor-SMOTE-Tomek Links is able to improve the performance of the India air quality prediction model. In other words, the proposed method succeeds in overcoming the problems of missing values and class imbalance.

Statistical machine learning has developed into integral components of contemporary scientific methodology. This integration provides automated procedures for predicting phenomena, case diagnosis, or object identification based on previous observations, uncovering patterns underlying data, and providing insights into the problem. Identification of corn plant diseases and pests using it has become popular recently. Corn (Zea mays L) is one of the essential carbohydrate-producing foodstuffs besides wheat and rice. Corn plants are sensitive to pests and diseases, resulting in a decrease in the quantity and quality of the production. Eradicate pests and diseases according to their type is a solution to overcome the problem of disease in corn plants. This research aims to identify corn plant diseases and pests based on the digital image using the Multinomial Naïve Bayes and K-Nearest Neighbor methods. The data used consisted of 761 digital images with six classes of corn plants disease and pest. The investigation shows that the K-Nearest Neighbor method has a better predictive performance than the Multinomial Naïve Bayes (MNB) method. The MNB method with two categories has an accuracy level of 92.72%, a precision level of 79.88%, a recall level of 79.24%, F1-score 78.17%, kappa 72.44%, and AUC 71.91%. Simultaneously, the K-Nearest Neighbor approach with k=3 has an accuracy of 99.54 %, a precision of 88.57%, recall 94.38%, F1-score 93.59%, kappa 94.30%, and AUC 95.45%.

As an agricultural commodity, corn functions as food, animal feed, and industrial raw material. Therefore, diseases and pests pose a major challenge to the production of corn plants. Modeling the classification of corn plant diseases and pests based on digital images is essential for developing an information technology-based early detection system. This plant’s early detection technology is beneficial for lowering farmers’ losses. The detection system based on digital images is also cost-effective. This paper aims to model the classification of corn plant diseases and pests based on digital images by implementing fuzzy discretization. Discretization is an essential technique to improve the knowledge extraction process of continuous-type data. It is also essential in some methods where continuous data must be processed or handled. Fuzzy discretization allows classes to have overlapping intervals so that they can handle information that is vague or unclear. We developed hypotheses and proved that different combinations of membership functions in fuzzy discretization affect classification performance. Empirical assessment using Monte Carlo resampling was carried out to obtain the generalizability of the performance of the best classification model of all proposed models. The best model is determined based on the number of metrics with the highest value and the highest metric on the Fscore and Kappa, a multiclass measure. The combination of digital image data preprocessing and classification methods also affects the performance of the classification model. We hope this work can provide an overview for experts in building early detection systems of corn plant diseases and pests using classification models based on fuzzy discretization.