Food waste is a global concern and is the focus of this research. Currently, no method in the state of the art classifies multiple fruits and vegetables and their level of ripening. The objective of the study is to design and develop an intelligent system based on deep learning techniques to classify between types of fruits and vegetables, and also to evaluate the level of ripeness of some of them. The system consists of two models using the MobileNet V2 architecture. One algorithm is for the classification of 32 classes of fruits and vegetables, and another is for the determination of the ripeness of 6 classes of them. The overall intelligent system is the union of the two models, predicting first the class of fruit or vegetable and then its ripeness. The fruits and vegetables classification model achieved 97.86% accuracy, 98% precision, 98% recall, and 98% F1-score, while the ripeness assessment model achieved 100% accuracy, 98% precision, 99% recall, and 99% F1-score. According to the results, the proposed system is able to classify between types of fruits and vegetables and evaluate their ripeness. To achieve the best performance indicators, it is necessary to obtain the appropriate hyperparameters for the artificial intelligence models, in addition to having an extensive database with well-defined classes.

Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

Infrared thermography (IRT) has become an interesting alternative for performing condition assessments of different types of induction motor (IM)-based equipment when it operates under harsh conditions. The reported results from state-of-the-art articles that have analyzed thermal images do not consider (1): the presence of more than one fault, and (2) the inevitable noise-corruption the images suffer. Bearing in mind these reasons, this paper presents a convolutional neural network (CNN)-based methodology that is specifically designed to deal with noise-corrupted images for detecting the failures that have the highest incidence rate: bearing and broken bar failures; moreover, rotor misalignment failure is also considered, as it can cause a further increase in electricity consumption. The presented results show that the proposal is effective in detecting healthy and failure states, as well as identifying the failure nature, as a 95% accuracy is achieved. These results allow considering the proposal as an interesting alternative for using IRT images obtained in hostile environments.

Knee injury is a common health problem that affects both people who practice sports and those who do not do it. The high prevalence of knee injuries produces a considerable impact on the health-related life quality of patients. For this reason, it is essential to develop procedures for an early diagnosis, allowing patients to receive timely treatment for preventing and correcting knee injuries. In this regard, this paper presents, as main contribution, a methodology based on infrared thermography (IT) and convolutional neural networks (CNNs) to automatically differentiate between a healthy knee and an injured knee, being an alternative tool to help medical specialists. In general, the methodology consists of three steps: (1) database generation, (2) image processing, and (3) design and validation of a CNN for automatically identifying a patient with an injured knee. In the image-processing stage, grayscale images, equalized images, and thermal images are obtained as inputs for the CNN, where 98.72% of accuracy is obtained by the proposed method. To test its robustness, different infrared images with changes in rotation angle and different brightness levels (i.e., possible conditions at the time of imaging) are used, obtaining 97.44% accuracy. These results demonstrate the effectiveness and robustness of the proposal for differentiating between a patient with a healthy knee and an injured knee, having the advantages of using a fast, low-cost, innocuous, and non-invasive technology.

Accurate quality detection of fruits and vegetables is crucial for optimizing harvest timing, minimizing post-harvest losses, and reducing waste. This research aims to integrate remote-sensing and deep learning (DL) technologies to develop and evaluate object detection models employing a novel DL approach for precision agriculture through automated quality detection in fruits and vegetables. To achieve this, twelve state-of-the-art object detection models from the MMDetection framework were trained by utilizing a custom-created and annotated dataset that comprises 1535 images and 39 classes of fruits and vegetables categorized into unripe, ripe, and overripe qualities. To evaluate the performance of each model, metrics like loss, mean Average Precision (mAP), receiver operating characteristic (ROC) curve, area under the curve (AUC), and confusion matrix were employed. The results determined that the Detection Transformer with Improved Denoising Anchor Boxes (DINO) and Dynamic Denoising Query (DDQ) models outperformed the others, achieving a mAP of 0.65 and a loss of 1.8 and 1.9, respectively. These metrics demonstrate their ability to distinguish the quality of fruits and vegetables accurately. These findings highlight the potential of DL models for real-world agricultural applications, as they facilitate timely quality assessment and contribute to the development of intelligent solutions.

Stress is a factor that affects many people today and is responsible for many of the causes of poor quality of life. For this reason, it is necessary to be able to determine whether a person is stressed or not. Therefore, it is necessary to develop tools that are non-invasive, innocuous, and easy to use. This paper describes a methodology for classifying stress in humans by automatically detecting facial regions of interest in thermal images using machine learning during a short Trier Social Stress Test. Five regions of interest, namely the nose, right cheek, left cheek, forehead, and chin, are automatically detected. The temperature of each of these regions is then extracted and used as input to a classifier, specifically a Support Vector Machine, which outputs three states: baseline, stressed, and relaxed. The proposal was developed and tested on thermal images of 25 participants who were subjected to a stress-inducing protocol followed by relaxation techniques. After testing the developed methodology, an accuracy of 95.4% and an error rate of 4.5% were obtained. The methodology proposed in this study allows the automatic classification of a person’s stress state based on a thermal image of the face. This represents an innovative tool applicable to specialists. Furthermore, due to its robustness, it is also suitable for online applications.