We propose a solution to the menu planning problem adapting the bacterial foraging-based optimization algorithm. We design a mathematical model which satisfies the nutritional needs of individuals while enforcing the “Laws of Nutrition”, a set of postulates used by virtually all nutrition scientists in Latin America. This model corresponds to a constrained numerical optimization problem. We design a menu generator software prototype to set up a custom menu for 15 users with different characteristics. Results obtained by our proposal were satisfactory from an expert's point of view.

An Isolated Microgrid (IMG) is an electrical distribution network combined with modern information technologies aiming at reducing costs and pollution to the environment. In this article, we implement the Bacterial Foraging Optimization Algorithm (BFOA) to optimize an IMG model, which includes renewable energy sources, such as wind and solar, as well as a conventional generation unit based on diesel fuel. Two novel versions of the BFOA were implemented and tested: Two-Swim Modified BFOA (TS-MBFOA), and Normalized TS-MBFOA (NTS-MBFOA). In a first experiment, the TS-MBFOA parameters were calibrated through a set of 87 independent runs. In a second experiment, 30 independent runs of both TS-MBFOA and NTS-MBFOA were conducted to compare their performance on minimizing the IMG using the best parameter tuning. Results showed that TS-MBFOA obtained better numerical solutions compared to NTS-MBFOA and LSHADE-CV, an Evolutionary Algorithm, found in the literature. However, the best solution found by NTS-MBFOA is better from a mechatronic point of view because it favors the lifetime of the IMG, resulting in economic savings in the long term.

Automatic image description, also known as image captioning, aims to describe the elements included in an image and their relationships. This task involves two research fields: computer vision and natural language processing; thus, it has received much attention in computer science. In this review paper, we follow the Kitchenham review methodology to present the most relevant approaches to image description methodologies based on deep learning. We focused on works using convolutional neural networks (CNN) to extract the characteristics of images and recurrent neural networks (RNN) for automatic sentence generation. As a result, 53 research articles using the encoder-decoder approach were selected, focusing only on supervised learning. The main contributions of this systematic review are: (i) to describe the most relevant image description papers implementing an encoder-decoder approach from 2014 to 2022 and (ii) to determine the main architectures, datasets, and metrics that have been applied to image description.

Guillain-Barré Syndrome (GBS) is a neurological disorder affecting people of any age and sex, mainly damaging the peripheral nervous system. GBS is divided into several subtypes, in which only four are the most common, demanding different treatments. Identifying the subtype is an expensive and time-consuming task. Early GBS detection is crucial to save the patient’s life and not aggravate the disease. This work aims to provide a primary screening tool for GBS subtypes fast and efficiently without complementary invasive methods, based only on clinical variables prospected in consultation, taken from clinical history, and based on risk factors. We conducted experiments with four classifiers with different approaches, five different filters for feature selection, six wrappers, and One versus All (OvA) classification. For the experiments, we used a data set that includes 129 records of Mexican patients and 26 clinical representative variables. Random Forest filter obtained the best results in each classifier for the diagnosis of the four subtypes, in the same way, this filter with the SVM classifier achieved the best result (0.6840). OvA with SVM classifier reached a balanced accuracy of 0.8884 for the Miller-Fisher (MF) subtype.

Timetabling problem is a complex task that is performed by a number of institutions worldwide, which has been usually addressed as an optimization problem where every approach considers the particular constraints of each institution under consideration. In this paper, we describe, model, and propose a solution to the timetabling problem at the División Académica de Ciencias y Tecnologías de la Información of the Universidad Juárez Autónoma de Tabasco (UJAT), México. We modeled the specific constraints of this problem instance using the Object Constraint Language (OCL) of the Unified Modeling Language (UML), and we validated the model while using the state-of-the-art tool USE: UML-based Specification Environment. The solution strategy tackles the problem in two stages: (1) ACA: academic assignments, i.e., assign lectures to professors and (2) TTP: the timetabling process. We developed a Tabu Search customization named Tabu Search with Probabilistic Aspiration Criterion (TS-PAC) in order to solve the timetabling problem, and we developed a software prototype to test our proposal. Two feasible timetables for two different semesters were obtained according to the modeled constraints.

Guillain–Barré Syndrome (GBS) is an unusual disorder where the body’s immune system affects the peripheral nervous system. GBS has four main subtypes, whose treatments vary among them. Severe cases of GBS can be fatal. This work aimed to investigate whether balancing an original GBS dataset improves the predictive models created in a previous study. purpleBalancing a dataset is to pursue symmetry in the number of instances of each of the classes.The dataset includes 129 records of Mexican patients diagnosed with some subtype of GBS. We created 10 binary datasets from the original dataset. Then, we balanced these datasets using four different methods to undersample the majority class and one method to oversample the minority class. Finally, we used three classifiers with different approaches to creating predictive models. The results show that balancing the original dataset improves the previous predictive models. The goal of the predictive models is to identify the GBS subtypes applying Machine Learning algorithms. It is expected that specialists may use the model to have a complementary diagnostic using a reduced set of relevant features. Early identification of the subtype will allow starting with the appropriate treatment for patient recovery. This is a contribution to exploring the performance of balancing techniques with real data.