A New Cascade-Correlation Growing Deep Learning Neural Network Algorithm

Optimizing Neural Networks for Chemical Reaction Prediction: Insights from Methylene Blue Reduction Reactions

This paper offers a thorough investigation of hyperparameter tuning for neural network architectures using datasets encompassing various combinations of Methylene Blue (MB) Reduction by Ascorbic Acid (AA) reactions with different solvents and concentrations. The aim is to predict coefficients of decay plots for MB absorbance, shedding light on the complex dynamics of chemical reactions. Our findings reveal that the optimal model, determined through our investigation, consists of five hidden layers, each with sixteen neurons and employing the Swish activation function. This model yields an NMSE of 0.05, 0.03, and 0.04 for predicting the coefficients A, B, and C, respectively, in the exponential decay equation A + B · e-x/C. These findings contribute to the realm of drug design based on machine learning, providing valuable insights into optimizing chemical reaction predictions.

Computer-aided classification of successional stage in subtropical Atlantic Forest: a proposal based on fuzzy artificial intelligence

STATEMENT OF PROBLEM

Due to the continuous variability of the forest regeneration process, patterns of indicator variables with membership in more than one successional stage may occur, making the classification of such stages a challenging and complex task.

PURPOSE

This study aims at presenting a comparative analysis of artificial intelligence methods as an alternative for computer-aided classification of successional stages in subtropical Atlantic Forest. As a research hypothesis, the authors consider that a fuzzy inference system should provide the best performance due to its ability to deal with uncertainties inherent to complex processes.

MATERIAL AND METHODS

The analyses were carried out using a database of the forest inventory of Santa Catarina, Southern Brazil. The data are composed of 177 sampling units of subtropical Atlantic Forest (mixed ombrophilous forest), characterized according to eighth indicator variables verified from the field by experts. This database was employed to train several machine learning methods under a tenfold cross-validation process. The overall accuracy (θ) and kappa coefficient were used to compare the performance between FIS and neural networks, classifier committees and support vector machine. Then, to verify if the classification by the FIS differed from the one performed by experts, the Kappa index and a statistical significance analysis by Pearson's [Formula: see text] test were determined. The hypotheses were verified with two-way tests at a significance level (α) 0.05, for a test power (1-β) 0.8 and minimum expected effect size between medium (ρ = 0.3).

RESULTS

Statistical significance tests confirmed the hypothesis that FIS achieved the highest performance, with θ = 98.3% and a kappa value equal to 0.93 (almost perfect agreement) and showed no significant difference ([Formula: see text] = 0.047, p = 0.976) in comparison with the classification by experts.

CONCLUSIONS

The use of FIS represents a promising alternative as a tool applicable for computer-aided classification of successional stages in subtropical Atlantic Forest.

PRACTICAL IMPLICATIONS

The results and conclusions should substantially impact the guidelines and decision-making process for deforestation authorizations and applicable compensation measures, which are based on the forest succession stage.