Preprocessing, term selection, term weighting, sentence weighting, and sentence selection are the main issues in generating extractive summaries of text sentences. Although many outstanding related works only are focused in the last step, they show sophisticated features in each one. In order to determine the relevance of the sentences (sentence selection step) many sentence features have been proposed in this task (in fact, these features are related to all the steps). Recently, some good related works have coincided in the same features but they present different ways for weighting these features. In this paper, a method to optimize the combination of previous relevant features in each step based on a genetic algorithm is presented. The proposed method not only outperforms previous related works in two standard document collections, but also shows the relevance of these features to this problem.

The fitness function is a type of objective function that quantifies the optimality of a solution; the correct formulation of this function is relevant, in evolutionary-based ATS systems, because it must indicate the quality of the summaries. Several unsupervised evolutionary methods for the automatic text summarization (ATS) task proposed in current standards require authors to manually construct an objective function that guides the algorithms to create good-quality summaries. In this sense, it is necessary to test each fitness function created to measure its performance; however, this process is time consuming and only a few functions are analyzed. This study proposes the automatic generation of heuristic functions, through genetic programming (GP), to be applied in the ATS task. Therefore, our proposed method for ATS provides an automatically generated fitness function for cluster-based unsupervised approaches. The results of this study, using two standard collections, demonstrate to automatically obtain an orientation function that leads to good quality abstracts.

Confused drug names are a common cause of medication errors, and are related to look-alike and sound-alike drug names. For the problem of identifying confused drug name pairs, individual similarity measures are used between the drug names. In the state-of-art, a logistic regression with the standard learning algorithm has been used to combine individual similarity measures. However, only three similarity measures have been combined but the results of previous research do not outperform with a statistical significance to any individual measure. In addition, the problem of potential confused drug names pairs presents a high unbalanced distribution of dataset that it is a hard problem to supervised machine learning models. In this paper, an improved combined logistic regression measure based on 21 individual measures is presented with the standard learning algorithm. Also, we present an evolutionary learning method for a combined logistic regression measure that allows to learn an unbalanced dataset. According to the experimentation with a gold standard dataset, our proposed combined measures outperform previous research with a statistical significance to identify pairs of confused drug names. In addition, the rankings of individual and combined similarity measures are presented.

Accurate identification of plastic polymers is essential for effective recycling, quality control, and environmental monitoring. This study assesses how spectral derivative preprocessing affects the classification of six common plastic polymers: Polyethylene Terephthalate (PET), Polyvinyl Chloride (PVC), Polypropylene (PP), Polystyrene (PS), and both High- and Low-Density Polyethylene (HDPE and LDPE), based on Fourier Transform Infrared (FTIR) spectroscopy data acquired at a resolution of 8 ${cm}^{-1}$. Using Savitzky–Golay derivatives (orders 0, 1, and 2), five machine learning algorithms, namely Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Linear Discriminant Analysis (LDA), Support Vector Classifier (SVC), and Random Forest (RF), were tested within a strict framework involving stratified repeated cross-validation and a final hold-out test set to evaluate generalization. The first spectral derivative notably improved the model performance, especially for MLP and SVC, and increased the stability of the ET, LDA, and RF classifiers. The combination of the first derivative with the ET model provided the best results, achieving a mean F1-score of 0.99995 (±0.00033) in cross-validation and perfect classification (1.0 in Accuracy, F1-score, Cohen’s Kappa, and Matthews Correlation Coefficient) on the independent test set. LDA also performed very well, underscoring the near-linear separability of spectral data after derivative transformation. These results demonstrate the value of derivative-based preprocessing and confirm a robust method for creating high-precision, interpretable, and transferable machine learning models for automated plastic polymer identification.