The class imbalance problem has been a hot topic in the machine learning community in recent years. Nowadays, in the time of big data and deep learning, this problem remains in force. Much work has been performed to deal to the class imbalance problem, the random sampling methods (over and under sampling) being the most widely employed approaches. Moreover, sophisticated sampling methods have been developed, including the Synthetic Minority Over-sampling Technique (SMOTE), and also they have been combined with cleaning techniques such as Editing Nearest Neighbor or Tomek’s Links (SMOTE+ENN and SMOTE+TL, respectively). In the big data context, it is noticeable that the class imbalance problem has been addressed by adaptation of traditional techniques, relatively ignoring intelligent approaches. Thus, the capabilities and possibilities of heuristic sampling methods on deep learning neural networks in big data domain are analyzed in this work, and the cleaning strategies are particularly analyzed. This study is developed on big data, multi-class imbalanced datasets obtained from hyper-spectral remote sensing images. The effectiveness of a hybrid approach on these datasets is analyzed, in which the dataset is cleaned by SMOTE followed by the training of an Artificial Neural Network (ANN) with those data, while the neural network output noise is processed with ENN to eliminate output noise; after that, the ANN is trained again with the resultant dataset. Obtained results suggest that best classification outcome is achieved when the cleaning strategies are applied on an ANN output instead of input feature space only. Consequently, the need to consider the classifier’s nature when the classical class imbalance approaches are adapted in deep learning and big data scenarios is clear.

This work introduces two datasets: FTIR-Plastics-C4 (Fourier Transform Infrared Spectroscopy, in plastics, at a wavenumber spectral resolution of 4 cm⁻¹) and FTIR-Plastics-C8 (Fourier Transform Infrared Spectroscopy, in plastics, at a wavenumber spectral resolution of 8 cm⁻¹), each comprising 3,000 spectra corresponding to the most used synthetic polymers worldwide. The main contribution of this work lies in the selection and FTIR characterization of the six polymers commonly used in everyday life and industry, namely Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS). FTIR-Plastics-C4 consists of 3,000 spectra obtained with a configuration of 32 scans and a resolution of 4 cm⁻¹, covering a range from 4000 to 400 cm⁻¹. The FTIR-Plastics-C8 dataset also contains 3,000 spectra obtained with 32 scans and a resolution of 8 cm⁻¹ within the same range. A cleaning stage was applied to the FTIR-Plastics datasets, removing the header containing 19 lines and a footer with 34 lines from the original file. Additionally, a standardization process assigns 15 lines in the files to highlight information regarding the equipment used (based on the information provided by a Jasco spectrophotometer, model FT/IR-6700 PRO 4x, used for polymer characterization). The final dataset is in tabular .csv file format. The dataset is available on an open repository, and its application was designed to identify microplastics extracted from the environment and enable comparisons between commercial polymers.

This study examines the potential of machine learning (ML) and deep learning (DL) techniques for classifying microplastics using Fourier-transform infrared (FTIR) spectroscopy. Six commonly used industrial plastics (PET, HDPE, PVC, LDPE, PP, and PS) were analyzed. A significant contribution of this research is the use of broader and more varied spectral ranges than those typically reported in the state of the art. Furthermore, the impact of different normalization techniques (Min-Max, Max-Abs, Sum of Squares, and Z-Score) on classification accuracy was evaluated. The study assessed the performance of ML algorithms, such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB), random forest (RF), and artificial neural networks architectures (including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs)). Models were trained and validated using the FTIR-PLASTIC-c4 dataset with a 10-fold cross-validation approach to ensure robustness. The results showed that Z-score normalization significantly improved stability and generalization across most models, with CNN, MLP, and RF achieving near-perfect values in accuracy, precision, recall, and F1-score. In contrast, the sum of squares normalization was less effective, particularly for CNNs, due to its sensitivity to scale and data distribution. Notably, naive Bayes consistently underperformed because of its limitations in analyzing complex spectral data. The findings highlight the effectiveness of FTIR spectra with broad and variable ranges for the automated classification of microplastics using ML techniques, along with appropriate normalization methods.

The problem of gender-based violence in Mexico has been increased considerably. Many social associations and governmental institutions have addressed this problem in different ways. In the context of computer science, some effort has been developed to deal with this problem through the use of machine learning approaches to strengthen the strategic decision making. In this work, a deep learning neural network application to identify gender-based violence on Twitter messages is presented. A total of 1,857,450 messages (generated in Mexico) were downloaded from Twitter: 61,604 of them were manually tagged by human volunteers as negative, positive or neutral messages, to serve as training and test data sets. Results presented in this paper show the effectiveness of deep neural network (about 80% of the area under the receiver operating characteristic) in detection of gender violence on Twitter messages. The main contribution of this investigation is that the data set was minimally pre-processed (as a difference versus most state-of-the-art approaches). Thus, the original messages were converted into a numerical vector in accordance to the frequency of word’s appearance and only adverbs, conjunctions and prepositions were deleted (which occur very frequently in text and we think that these words do not contribute to discriminatory messages on Twitter). Finally, this work contributes to dealing with gender violence in Mexico, which is an issue that needs to be faced immediately.

Precipitation hardening aluminum alloys are used in many industries due to their excellent mechanical properties, including good weldability. During a welding process, the tensile strength of the joint is critical to appropriately exploit the original properties of the material. The welding processes are still under study, and gas metal arc welding (GMAW) in pulsed metal-transfer configuration is one of the best choices to join these alloys. In this study, the welding of 6061 aluminum alloy by pulsed GMAW was performed under two heat treatment conditions and by using two filler metals, namely: ER 4043 (AlSi₅) and ER 4553 (AlMg₅Cr). A solubilization heat treatment T4 was used to dissolve the precipitates of β”- phase into the aluminum matrix from the original T6 heat treatment, leading in the formation of β-phase precipitates instead, which contributes to higher mechanical resistance. As a result, the T4 heat treatment improves the quality of the weld joint and increases the tensile strength in comparison to the T6 condition. The filler metal also plays an important role, and our results indicate that the use of ER 4043 produces stronger joints than ER 4553, but only under specific processing conditions, which include a moderate heat net flux. The latter is explained because Mg, Si and Cu are reported as precursors of the production of β”- phase due to heat input from the welding process and the redistribution of both: β” and β precipitates, causes a ductile intergranular fracture near the heat affected zone of the weld joint.