Search Results (3)

The growing demand for energy and the environmental impacts of fossil fuels have driven the search for sustainable alternatives such as biodiesel. Castor oil stands out as a promising non-edible feedstock but requires optimization strategies to overcome challenges in its conversion to biodiesel. This study developed a predictive model to determine the optimal parameters for homogeneous alkaline or acid transesterification of castor oil, aiming to maximize fatty acid methyl ester (FAME) yield. A dataset of 406 operating conditions from the literature was used to train and evaluate six models: Multilayer Perceptron with logistic sigmoid activation (MLP-logsig), hyperbolic tangent activation (MLP-tansig), Radial Basis Function network (RBF), hybrid RBF + MLP, Random Forest (RF), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The MLP-tansig achieved the best performance in training, validation, and testing (R > 0.98). However, when combined with a Genetic Algorithm (GA), it generated infeasible parameters. Conversely, the RBF + GA combination yielded results consistent with the literature: molar ratio 19.35:1, alkaline catalyst 1.13% w/w, temperature 50 °C, reaction time 70 min, and stirring speed 548.32 rpm, achieving 100% FAME yield. This approach reduces the need for extensive experimental testing, offering a cost- and time-efficient solution for optimizing biodiesel production. Full article

In this article we present the multivariate approximation of time splitting random functions defined on a box or ${\mathbb{R}}^N,N\in \mathbb{N},$ by neural network operators of quasi-interpolation type. We achieve these approximations by obtaining quantitative-type Jackson inequalities engaging the multivariate modulus of continuity of a related random function or its partial high-order derivatives. We use density functions to define our operators. These derive from the logistic and hyperbolic tangent sigmoid activation functions. Our convergences are both point-wise and uniform. The engaged feed-forward neural networks possess one hidden layer. We finish the article with a great variety of applications. Full article

Landslide susceptibility mapping is well recognized as an essential element in supporting decision-making activities for preventing and mitigating landslide hazards as it provides information regarding locations where landslides are most likely to occur. The main purpose of this study is to produce a landslide susceptibility map of Mt. Umyeon in Korea using an artificial neural network (ANN) involving the factor selection method and various non-linear activation functions. A total of 151 historical landslide events and 20 predisposing factors consisting of Geographic Information System (GIS)-based morphological, hydrological, geological, and land cover datasets were constructed with a resolution of 5 x 5 m. The collected datasets were applied to information gain ratio analysis to confirm the predictive power and multicollinearity diagnosis to ensure the correlation of independence among the landslide predisposing factors. The best 11 predisposing factors that were selected in this study were randomly divided into a 70:30 ratio for training and validation datasets, which were used to produce ANN-based landslide susceptibility models. The ANN model used in this study had a multi-layer perceptron (MLP) structure consisting of an input layer, one hidden layer, and an output layer. In the output layer, the logistic sigmoid function was used to represent the result value within the range of 0 to 1, and six non-linear activation functions were used for the hidden layer. The performance of the landslide susceptibility models was evaluated using the receiver operating characteristic curve, Kappa index, and five statistical indices (sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV)) with the training dataset. In addition, the landslide susceptibility models were validated using the aforementioned measures with the validation dataset and were compared using the Friedman test to check the significant differences among the six developed models. The optimal number of neurons was determined based on the aforementioned performance evaluation and validation results. Overall, the model with the best performance was the MLP model with the logistic sigmoid activation function in the output layer and the hyperbolic tangent sigmoid activation function with five neurons in the hidden layer. The validation results of the best model showed a sensitivity of 82.61%, specificity of 78.26%, accuracy of 80.43%, PPV of 79.17%, NPV of 81.82%, a Kappa index of 0.609, and AUC of 0.879. The results of this study highlight the effectiveness of selecting an optimal MLP model structure for shallow landslide susceptibility mapping using an appropriate predisposing factor section method. Full article