The rise of global warming and climate change poses a potential threat to the agricultural industry. In response to this challenge, this research introduces an intelligent integrated system for recommending future crops using climate forecasting and crop recommendation models. The goal is to improve efficiency and productivity within the Cuban sector. By employing a weighted classifier with the Relative Model Accuracy Equation as the weight distribution, the classification models were improved, achieving 99.8% for each performance metric (precision, recall, and f1 score) evaluated over the test set. The main contribution of this study is an integrated intelligent system that leverages supervised machine learning techniques to predict the optimal crop for a given soil in a specific state of Cuba during a particular year among 22 possible crops. The system is built as a python module to allow its integration into future software solutions and is released under the MIT open-source license.

Artificial Intelligence has enabled scientists to rapidly sift through and analyze massive collections of images, helping to identify objects worthy of closer study such as supernovae, pulsars, and quasars, and allowing us to classify stars, label galaxies, and other astronomical systems. Stellar classification serves as a cornerstone of astronomy, providing a framework for understanding and characterizing the diversity of celestial objects. This classification is based on the spectral properties. AI algorithms, specifically machine learning and deep learning models, have been designed to learn from data and make predictions or classifications based on patterns and trends. These models of spectral and stellar classification are trained on large datasets of known celestial objects, enabling them to recognize and categorize new objects based on their spectral signatures. Here we present a novel Fine Tunned Deep Convolutional Neural Network of 1D Separable Convolutional blocks for stellar classification based on spectral characteristics using SSDS-17 data captured by the Sloan Digital Sky Survey, where the class imbalance is assessed using the SMOTE balancing technique. The results obtained during the performance evaluation confirmed the reliability of the proposed architecture of StellarNet in multi-class stellar classification, obtaining remarkable values of around 97% and 99% for accuracy and AUC score respectively. The proposed StellarNet architecture can be applied to enable real-time classification of captured spectral characteristic data, facilitating automation of the task and providing labeled data for study and future research.

This study addresses the escalating problem of financial fraud, with a particular focus on credit card fraud, a phenomenon that has skyrocketed due to the increasing prevalence of online transactions. The research aims to strengthen anti-money laundering (AML) systems, thereby improving the detection and prevention of fraudulent transactions. For this study, a Dense Neural Network (DNN) has been developed to predict fraudulent transactions with high accuracy. The model is based on deep learning, and given the highly unbalanced nature of the dataset, balancing techniques were employed to mitigate the bias towards the minority class and improve performance. The DNN model demonstrated robust performance, generalizability, and reliability, achieving over 99% accuracy across training, validation, and test sets. This indicates the model's potential as a powerful tool in the ongoing fight against financial fraud. The results of this study could have significant implications for the financial sector, corporations, and governments, contributing to safer and more secure financial transactions.

Food freshness classification is a growing concern in the food industry, mainly to protect consumer health and prevent illness and poisoning from consuming spoiled food. Intending to take a significant step towards improving food safety and quality control measures in the industry, this study presents two models based on deep learning for the classification of fruit and vegetable freshness: a robust model and an efficient model. Models' performance evaluation shows remarkable results; in terms of accuracy, the robust model and the efficient model achieve 97.6% and 94.0% respectively, while in terms of AUC score, both models achieve more than 99%, with the difference in inference time between each model over 844 images being 14 seconds.