RESUMO
Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red-Green-Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas.
Assuntos
Aprendizado Profundo , Animais , Chile , Benchmarking , Alimentos , IndústriasRESUMO
Obesity is a chronic disease with an increasing impact on the world's population. In this work, we present a method of identifying obesity automatically using text mining techniques and information related to body weight measures and obesity comorbidities. We used a dataset of 3015 de-identified medical records that contain labels for two classification problems. The first classification problem distinguishes between obesity, overweight, normal weight, and underweight. The second classification problem differentiates between obesity types: super obesity, morbid obesity, severe obesity and moderate obesity. We used a Bag of Words approach to represent the records together with unigram and bigram representations of the features. We implemented two approaches: a hierarchical method and a nonhierarchical one. We used Support Vector Machine and Naïve Bayes together with ten-fold cross validation to evaluate and compare performances. Our results indicate that the hierarchical approach does not work as well as the nonhierarchical one. In general, our results show that Support Vector Machine obtains better performances than Naïve Bayes for both classification problems. We also observed that bigram representation improves performance compared with unigram representation.
Assuntos
Inteligência Artificial , Mineração de Dados/métodos , Registros Eletrônicos de Saúde/organização & administração , Obesidade/diagnóstico , Teorema de Bayes , Comorbidade , Humanos , Processamento de Linguagem Natural , Sobrepeso/diagnóstico , Máquina de Vetores de SuporteRESUMO
Plantain starch was esterified with octenylsuccinic anhydride (OSA) at two concentrations (3 and 15% w/w) of OSA. The morphology, granule size distribution, pasting, gelatinization, swelling, and solubility of granules and structural features of the starch polymers were evaluated. Granules of the OSA-modified starches increased in size during cooking more than did the granules of the native starch, and the effect was greater at the higher OSA concentration. Pasting viscosities also increased, but gelatinization and pasting temperatures and enthalpy of gelatinization decreased in the OSA-modified starches. It was concluded that insertion of OS groups effected disorder in the granular structure. Solubility, weight average molar mass, Mw¯, and z-average radius of gyration, RGz, of the amylopectin decreased as the OSA concentration increased, indicating a decrease in molecular size.