The Internet of Things (IoT) makes it possible to connect objects or things to the internet, with the purpose of collecting data and controlling processes or machines remotely. IoT enables the physical world to be integrated into the digital world in order to optimize time, save costs and facilitate human labor. An IoT system comprises a rich ecosystem of elements that make up its value chain, which includes a computational and communication architecture or model, components and technologies. IoT has evolved rapidly, producing an exuberant scientific literature. This document presents the state of the art of IoT, with updated sources, that guides the reader, who is entering the world of IoT, to have a starting point for future research. In addition to the review of IoT architectures, components of the IoT ecosystem, computational paradigms and, security and governance aspects. Our main contribution is focused on the analysis of the Middleware layer in IoT architectures, oriented to the storage and processing of data.

Leaf diseases, such as Black Sigatoka and Cordana, represent a growing threat to banana crops in Ecuador. These diseases spread rapidly, impacting both leaf and fruit quality. Early detection is crucial for effective control measures. Recently, deep learning has proven to be a powerful tool in agriculture, enabling more accurate analysis and identification of crop diseases. This study applied the CRISP-DM methodology, consisting of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. A dataset of 900 banana leaf images was collected—300 of Black Sigatoka, 300 of Cordana, and 300 of healthy leaves. Three pre-trained models (EfficientNetB0, ResNet50, and VGG19) were trained on this dataset. To improve performance, data augmentation techniques were applied using TensorFlow Keras’s ImageDataGenerator class, expanding the dataset to 9000 images. Due to the high computational demands of ResNet50 and VGG19, training was performed with EfficientNetB0. The models—EfficientNetB0, ResNet50, and VGG19—demonstrated the ability to identify leaf diseases in bananas, with accuracies of 88.33%, 88.90%, and 87.22%, respectively. The data augmentation increased the performance of EfficientNetB0 to 87.83%, but did not significantly improve its accuracy. These findings highlight the value of deep learning techniques for early disease detection in banana crops, enhancing diagnostic accuracy and efficiency.

This work describes the process of multi-category classification of mammograms by using Convolutional Neural Networks (CNN), starting from the acquisition and preparation of the dataset, then the architecture and training of the Inception-v3 model from Google, until obtaining and analyzing our results. We employed a total of 1000 mammograms of the Digital Database for Screening Mammography (DDSM) supported by the University of South Florida, divided in 5 classes according to BI-RADS categories. The optimization of our results is through a grid search method by combining the values of the following parameters: batch size, learning rate, optimizer, loss function and number of layers to be frozen in the Inception-v3 model. The validation metrics of our algorithm performance, given by the parameter combinations, are based mainly on a loss function and accuracy. The best performance result generates an accuracy of 97% and a loss function value of 0.02, respectively.