Convolutional neural networks (CNNs) have shown outstanding image classification performance, having been successfully applied in several real‐world applications. However, there are performance limitations encountered in CNNs and few works have pointed out these limitations across the literature. Therefore, this survey aims to provide a comprehensive explanation of both the importance and performance limitations of CNNs on image classification tasks. In this paper, we start with a brief CNN background and follow the main CNN architectures along with their contributions and benchmark datasets. We propose a classification of the CNNs performance limitations, which are grouped into four categories: labeled datasets, translation invariance, adversarial attacks, and spatial relationship. In addition, we describe some approaches that are currently being developed to overcome these performance limitations. Finally, we introduce specific ongoing research to address these performance limitations through capsule networks (CapsNets) and CNNs combined with CapsNets.

Organizations are in the constant pursuit of reliable strategies for increasing customer satisfaction. One of these strategies refers to the implementation of the ISO 9001 standard as a quality management system to ensure that products and services meet or surpass customer expectations. However, successful ISO 9001-based systems are possible only if companies count on a series of critical success factors that guarantee successful ISO 9001 implementation. This research aims at developing and validating a survey instrument to evaluate the degree of compliance with ISO 9001 critical success factors among manufacturing organizations. The research is conducted in Mexican manufacturing companies. The survey is designed by considering the quality management principles of the ISO 9001:2015 standard as critical success factors. We conducted an exploratory and a confirmatory factor analysis to identify the structure of the survey and confirm its validity. Our findings confirm that the survey instrument comprises four constructs, three of them associated with the quality principles of the ISO 9001 standard, and one associated with the benefits of said standard. In conclusion, the proposed survey is a valid, useful tool to assess ISO 9001 implementation in manufacturing companies and similar organizations.

Vegetation Indices (VIs) represent a useful method for extracting vegetation information from satellite images. Erosion models like the Revised Universal Soil Loss Equation (RUSLE), employ VIs as an input to determine the RUSLE soil Cover factor (C). From the standpoint of soil conservation planning, the C factor is one of the most important RUSLE parameters because it measures the combined effect of all interrelated cover and management variables. Despite its importance, the results are generally incomplete because most indices recognize healthy or green vegetation, but not senescent, dry or dead vegetation, which can also be an important contributor to C. The aim of this research is to propose a novel approach for calculating new VIs that are better correlated with C, using field and satellite information. The approach followed by this research is to state the generation of new VIs in terms of a computer optimization problem and then applying a machine learning technique, named Genetic Programming (GP), which builds new indices by iteratively recombining a set of numerical operators and spectral channels until the best composite operator is found. Experimental results illustrate the efficiency and reliability of this approach to estimate the C factor and the erosion rates for two watersheds in Baja California, Mexico, and Zaragoza, Spain. The synthetic indices calculated using this methodology produce better approximation to the C factor from field data, when compared with state-of-the-art indices, like NDVI and EVI.