Estudo e análise de Redes Neurais Convolucionais Profundas na identificação de doenças em plantas por imagens

Tese (doutorado) — Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2022.Rede Neurais Convolucionais (CNNs), demonstram um potencial para tarefas relacionadas à Visão Computacional. A característica de maior destaque das CNNs é sua capacidade de explorar a correlação espacial ou temporal nos dados. Assim, várias melhorias na metodologia e arquitetura de aprendizagem das redes foram realizadas para tornar as CNNs escaláveis para problemas grandes, heterogêneos, complexos e multiclasses. A agricultura delimita um escopo de problemas desafiadores, que carecem de tecnologias para proporcionar maior incremento na produção agrícola, principalmente em relação ao enfrentamento de doenças. As doenças de plantas são consideradas um dos principais fatores que influenciam a produção de alimentos, e a sua identificação é primordialmente realizada por técnicas manuais ou por microscopia, oque aumenta o tempo de diagnóstico e as possibilidades de erro. Soluções automatizadas de identificação de doenças de plantas, usando imagens e aprendizado de máquina, em especial as CNNs, têm proporcionado avanços significativos. Entretanto, a maioria das abordagens possui baixa capacidade de classificação, tendo como agravante as infestações simultâneas por diferentes patógenos e as confusões sintomáticas causadas por fatores abióticos. Assim, o objetivo deste trabalho é analisar e avaliar as arquiteturas CNNs, explorando potencialidades e prospectando novos arranjos de arquitetura para classificar doenças de plantas e identificar patógenos. A abordagem fez uso de uma estratégia de customização, na qual redes operativas independentes ou blocos convolucionais são integradas em um único modelo para capturar um conjunto mais variado de características. A NEMANeté um resultado relevante desta abordagem de customização de CNNs para classificação de fitonematoides em imagens microscópicas. O mo-delo alcançou a melhor taxa de acurácia atingindo 99,35%, possibilitando melhorias gerais de precisão superiores a 6,83% e 4,1%, para treinamento com inicialização dos pesos e para transferência de aprendizagem, em comparação com outras arquiteturas avaliadas. Os resultados demonstraram que a customização de arquiteturas CNNs é uma abordagem promissora para o aumento de ganhos em termo de acurácia, convergência das redes e tamanho dos modelos.Convolutional Neural Networks (CNNs) demonstrate a potential for computer vision tasks.The most prominent feature of CNNs is their ability to explore spatial or temporal correlationin the data. Thus, several improvements in the methodology and architecture of learning of thenetworks were made to make the CNNs scalable for large, heterogeneous, complex, and multi-class problems. Agriculture delimits a scope of challenging problems, which lack technologiesto increase agricultural production, especially about coping with diseases. Plant diseases areconsidered one of the main factors that influence food production, and their identification is pri-marily performed by manual techniques or microscopy, which increases the time of diagnosisand the possibility of errors. Using imaging and machine learning, especially CNNs, automatedplant disease identification solutions have provided significant advances. However, most appro-aches have low classification capacity, with simultaneous infestations by different pathogensand symptomatic confusion caused by abiotic factors as an aggravating factor. Thus, this workaims to analyze and evaluate CNN architectures, exploring potentialities and prospecting newarchitectural arrangements to classify plant diseases and identify pathogens. The approach useda customization strategy, in which independent operative networks or convolutional blocks areintegrated into a single model to capture a more varied set of characteristics. TheNEMANetis arelevant result of this CNN customization approach for the classification of phytonematodes inmicroscopic images. The model achieved the best accuracy rate reaching 99.35%, enabling ove-rall accuracy improvements greater than 6.83% and 4.1%, for weight initialization training andlearning transfer, compared to other evaluated architectures. The results showed that the custo-mization of CNN architectures is a promising approach to increase gains in terms of accuracy,the convergence of networks, and the size of the model

Segurança do paciente assistido na atenção primária

Quality has become a factor of great significance, directing institutions to national and international markets, requiring organizational success and development. The expression “patient safety” refers to the reduction, to an acceptable minimum level, of the risk of unnecessary harm associated with health care. It can be considered a relatively new area of knowledge, related to the sphere of management and quality, which gained momentum from the 2000s onwards. The Health Units have been growing gradually over the decades, being annually accredited by the National Accreditation Organization (ONA), meeting the goals established for a safe practice for the patient. The present work has the final objective of demonstrating the safety goals standardized by the ONA, exposing the adequate practice to the patient assisted in primary care units. This is a qualitative literature review study, with a temporal cut in the last ten years, addressing the proposed theme. The scientific databases PubMed, SciELO, LILACS and Google Scholar were consulted, also considering in this work, citations and references within the articles used that precede the proposed time frame. Thus, to thoroughly demonstrate these safety items in this study, it was exposed from this conception of what a Primary Health Care service is to the safety goals applied within health units in Brazil.A qualidade se tornou um fator de grande significância, encaminhando instituições para os mercados nacionais e internacionais, requerendo êxito organizacional e desenvolvimento. A expressão “segurança do paciente” refere-se à redução, a um nível mínimo aceitável, do risco de dano desnecessário associado ao cuidado de saúde. Pode ser considerada uma área relativamente nova do conhecimento, afeita à esfera da gestão e da qualidade, que ganhou impulso a partir da década de 2000 . As Unidades de Saúde vêm crescendo gradativamente ao longo das décadas, sendo anualmente acreditadas pela Organização Nacional de Acreditação (ONA), atendendo as metas estabelecidas para uma prática segura ao paciente. O presente trabalho tem por objetivo final de demonstrar as metas segurança padronizadas pela ONA, expondo a prática adequada ao paciente assistido em unidades de atenção primária. Trata-se de um estudo de revisão bibliográfica do tipo qualitativa, com recorte temporal nos últimos dez anos, abordando a temática proposta. Foram consultadas as bases de dados cientificas PubMed, SciELO, LILACS e Google Acadêmico, considerando também neste trabalho, as citações e referencias dentro dos artigos utilizados que antecedem o recorte temporal proposto. Desta forma, para demonstrar minuciosamente estes itens de segurança neste estudo, foi exposto deste a concepção do que é um atendimento em Atenção Primária de Saúde até as metas de segurança aplicadas dentro das unidades de saúde no Brasil

VALUATION SEGUNDO DAMODARAN E A VANTAGEM COMPETITIVA DAS EMPRESAS DA ZONA FRANCA DE MANAUS DE ACORDO COM BUFFETT

O Polo Industrial de Manaus – PIM é o principal modelo de desenvolvimento socioeconômico da Amazônia Ocidental. Então, é necessário apontar se as empresas instaladas no PIM geram valor e expressam vantagem competitiva. Diante disso, o presente trabalho analisou o desempenho de duas empresas do Polo Industrial de Manaus (PIM), a Moto Honda da Amazonia e NCR Brasil, quanto à geração de valor e considerando os conceitos de vantagem competitiva segundo Warren Buffett (WB). Especificamente, objetivou identificar nas demonstrações contábeis das empresas os fatores de vantagem competitiva segundo WB e estimar o valor das empresas, conforme as projeções de dividendos segundo Gordon (1962). Os resultados obtidos demonstraram que a Honda não apresentou Valuation positivo, devido à elevada necessidade de investimento em giro. Já, a NCR Brasil expressou elevado potencial de gerar valor. Porém nenhuma das empresas inscreveu os itens de vantagem competitiva conforme os critérios de investimentos de Warren Buffett

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Third molar development is used for dental age estimation when all the other teeth are fully mature. In most medicolegal facilities, dental age estimation is an operator-dependent procedure. During the examination of unaccompanied and undocumented minors, this procedure may lead to binary decisions around age thresholds of legal interest, namely the ages of 14, 16 and 18 years. This study aimed to test the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development. The sample consisted of 11,640 panoramic radiographs (9680 used for training and 1960 used for validation) of males (n = 5400) and females (n = 6240) between 6 and 22.9 years. Computer-based image annotation was performed with V7 software (V7labs, London, UK). The region of interest was the mandibular left third molar (T38) outlined with a semi-automated contour. DenseNet121 was the Convolutional Neural Network (CNN) of choice and was used with Transfer Learning. After Receiver-operating characteristic curves, the area under the curve (AUC) was 0.87 and 0.86 to classify males and females below and above the age of 14, respectively. For the age threshold of 16, the AUC values were 0.88 (males) and 0.83 (females), while for the age of 18, AUC were 0.94 (males) and 0.83 (females). Specificity rates were always between 0.80 and 0.92. Artificial intelligence was able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy.</p

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Third molar development is used for dental age estimation when all the other teeth are fully mature. In most medicolegal facilities, dental age estimation is an operator-dependent procedure. During the examination of unaccompanied and undocumented minors, this procedure may lead to binary decisions around age thresholds of legal interest, namely the ages of 14, 16 and 18 years. This study aimed to test the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development. The sample consisted of 11,640 panoramic radiographs (9680 used for training and 1960 used for validation) of males (n = 5400) and females (n = 6240) between 6 and 22.9 years. Computer-based image annotation was performed with V7 software (V7labs, London, UK). The region of interest was the mandibular left third molar (T38) outlined with a semi-automated contour. DenseNet121 was the Convolutional Neural Network (CNN) of choice and was used with Transfer Learning. After Receiver-operating characteristic curves, the area under the curve (AUC) was 0.87 and 0.86 to classify males and females below and above the age of 14, respectively. For the age threshold of 16, the AUC values were 0.88 (males) and 0.83 (females), while for the age of 18, AUC were 0.94 (males) and 0.83 (females). Specificity rates were always between 0.80 and 0.92. Artificial intelligence was able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy.</p

Characterization of biochemical behavior of sorghum (Sorghum bicolor [Moench.]) under saline stress conditions using multivariate analysis

The aim of this research was to characterize the biochemical behavior of sorghum plants under saline stress using multivariate statistical analysis methods for efficient management of Sorghum bicolor [Moench.]). The experimental design was completely randomized design composed of three saline concentrations (0, 1.5 and 2.0 M) in 10 replications. In the multivariate analysis (hierarchical method), there were distinct and sub-groups in the sorghum plant treatments. Group 1 consisted of the root parts and under this group there were two subgroups: 1.5 to 2.0 concentration (Group 1) and 2 concentration (Group 2). The increase of NaCl concentration in the roots and leaves has inverse correlation with decrease of nitrate reductase, amino acids, protein and starch. The amounts of amino acids, carbohydrates, sucrose and proline in the roots and carbohydrates, sucrose and proline in the leaves of sorghum plants are reliable biological indicators of saline stress conditions in the soil. The nitrate compound differed (p ≤ 0.05) in the sorghum plant roots; it had an average value of 0.04 μmol kg-1 of nitrate in the control treatment dry matter. The nitrate average was between 0.04 and 0.06 μmol kg-1, but without statistical difference for all concentrations.Key words: Multivariate statistics, salt concentration, proline, carbohydrate

Uma abordagem de teste estrutural de uma transformações M2T baseada em hipergrafos

Context: MDD (Model-Driven Development) is a software development paradigm in which the main artefacts are models, from which source code or other artefacts are generated. Even though MDD allows different views of how to decompose a problem and how to design a software to solve it, this paradigm introduces new challenges related to the input models, transformations and output artefacts. Problem Statement: Thus, software testing is a fundamental activity to reveal defects and improve confidence in the software products developed in this context. Several techniques and testing criteria have been proposed and investigated. Among them, functional testing has been extensively explored primarily in the M2M (Model-to-Model) transformations, while structural testing for M2T (Model-to-Text) transformations still poses challenges and lacks appropriate approaches. Objective: This work aims to to present a proposal for the structural testing of M2T transformations through the characterisation of input models as complex data, templates and output artefacts involved in this process. Method: The proposed approach was organised in five phases. Its strategy proposes that the complex data (grammars and metamodels) are represented by directed hypergraphs, allowing that a combinatorial-based traversal algorithm creates subsets of the input models that will be used as test cases for the M2T transformations. In this perspective, we carried out two exploratory studies with the specific purpose of feasibility analysis of the proposed approach. Results and Conclusion: The evaluation of results from the exploratory studies, through the analysis of some testing coverage criteria, demonstrated the relevance and feasibility of the approach for characterizing complex data for M2T transformations testing. Moreover, structuring the testing strategy in phases enables the revision and adjustment of activities, in addition to assisting the replication of the approach within different applications that make use of the MDD paradigm.Não recebi financiamentoContexto: O MDD (Model-Driven Development ou Desenvolvimento Dirigido por Modelos) e um paradigma de desenvolvimento de software em que os principais artefatos são os modelos, a partir dos quais o código ou outros artefatos são gerados. Esse paradigma, embora possibilite diferentes visões de como decompor um problema e projetar um software para soluciona-lo, introduz novos desafios, qualificados pela complexidade dos modelos de entrada, as transformações e os artefatos de saída. Definição do Problema: Dessa forma, o teste de software e uma atividade fundamental para revelar defeitos e aumentar a confiança nos produtos de software desenvolvidos nesse contexto. Diversas técnicas e critérios de teste vem sendo propostos e investigados. Entre eles, o teste funcional tem sido bastante explorado primordialmente nas transformações M2M (Model-to-Model ou Modelo para Modelo), enquanto que o teste estrutural em transformações M2T (Model-to-Text ou Modelo para Texto) ainda possui alguns desafios e carência de novas abordagens. Objetivos: O objetivo deste trabalho e apresentar uma proposta para o teste estrutural de transformações M2T, por meio da caracterização dos dados complexos dos modelos de entrada, templates e artefatos de saída envolvidos neste processo. Metodologia: A abordagem proposta foi organizada em cinco fases e sua estratégia propõe que os dados complexos (gramáticas e metamodelos) sejam representados por meio de hipergrafos direcionados, permitindo que um algoritmo de percurso em hipergrafos, usando combinatória, crie subconjuntos dos modelos de entrada que serão utilizados como casos de teste para as transformações M2T. Nesta perspectiva, realizou-se dois estudos exploratórios com propósito específico da analise de viabilidade quanto a abordagem proposta. Resultados: A avaliação dos estudos exploratórios proporcionou, por meio da analise dos critérios de cobertura aplicados, um conjunto de dados que demonstram a relevância e viabilidade da abordagem quanto a caracterização de dados complexos para os testes em transformações M2T. A segmentação das estratégias em fases possibilita a revisão e adequação das atividades do processo, além de auxiliar na replicabilidade da abordagem em diferentes aplicações que fazem uso do paradigma MDD

NemaNet : a convolutional neural network model for identification of soybean nematodes

Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. In soybean crops, annual losses are estimated at 10.6% of the world production. Besides, the identification of these species through microscopic analysis by an expert with taxonomic knowledge is often laborious, time-consuming, and susceptible to failure. From this perspective, robust and automatic approaches are necessary for identifying phytonematodes that are capable of providing correct diagnoses for the classification of species and subsidizing of all control and prevention measures. This work presents a new public data set called NemaDataset containing 3063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop. Additionally, we propose a new Convolutional Neural Network (CNN) model defined as NemaNet and present a comparative assessment with thirteen popular models of CNNs, all of them representing state-of-the art classification and recognition. The general average was calculated for each model, on a from-scratch training; the NemaNet model reached 96.76% accuracy, while the best evaluation fold reached 98.04%. When training with transfer learning was performed, the average accuracy reached 98.82%. The best evaluation fold reached 99.35%, and overall accuracy improvements of over 6.83% and 4.1%, for from-scratch and transfer learning training, respectively, compared to other popular models were achieved

Binary decisions of artificial intelligence to classify third molar development around the legal age thresholds of 14, 16 and 18 years

Abstract Third molar development is used for dental age estimation when all the other teeth are fully mature. In most medicolegal facilities, dental age estimation is an operator-dependent procedure. During the examination of unaccompanied and undocumented minors, this procedure may lead to binary decisions around age thresholds of legal interest, namely the ages of 14, 16 and 18 years. This study aimed to test the performance of artificial intelligence to classify individuals below and above the legal age thresholds of 14, 16 and 18 years using third molar development. The sample consisted of 11,640 panoramic radiographs (9680 used for training and 1960 used for validation) of males (n = 5400) and females (n = 6240) between 6 and 22.9 years. Computer-based image annotation was performed with V7 software (V7labs, London, UK). The region of interest was the mandibular left third molar (T38) outlined with a semi-automated contour. DenseNet121 was the Convolutional Neural Network (CNN) of choice and was used with Transfer Learning. After Receiver-operating characteristic curves, the area under the curve (AUC) was 0.87 and 0.86 to classify males and females below and above the age of 14, respectively. For the age threshold of 16, the AUC values were 0.88 (males) and 0.83 (females), while for the age of 18, AUC were 0.94 (males) and 0.83 (females). Specificity rates were always between 0.80 and 0.92. Artificial intelligence was able to classify male and females below and above the legal age thresholds of 14, 16 and 18 years with high accuracy

Common mental disorders among medical students

OBJECTIVE: Common mental disorders (CMD) have a high impact on interpersonal relationships and quality of life and are potential underlying causes for the development of more serious disorders. Medical students have been indicated as a risk population for the development of CMD. The aim of this study was to determine the frequency of CMD in undergraduate medical students and to identify related factors. METHODS: A cross-sectional study was performed in a sample population of medical students. CMD was identified according to the 20-item Self-Report Questionnaire. RESULTS: Two hundred and twenty-three students completed the questionnaire. The overall prevalence of CMD was 29.6% and its presence was independently associated with sleep disorders, not owning a car, not working and sedentary lifestyle. CONCLUSIONS: These findings indicate a high prevalence of CMD in the sample studied and are important for supporting actions to prevent mental disorders in future doctors and for reflecting on the curricula currently in use in medical schools