Os sistemas automáticos são normalmente empregados na indústria com o objetivo de otimizar a produção. Na agro-indústria, estes sistemas são usados com o mesmo propósito, sendo que dentre estes sistemas é possível destacar os que empregam a visão computacional, pois esta tem sido usada para inspeção de lavouras, colheita mecanizada, guiagem de veículos e robôs entre outras aplicações. No presente trabalho, técnicas de visão computacional foram utilizadas para segmentar e classificar elementos presentes em imagens obtidas de pomares de laranjeiras. Uma arquitetura modular foi utilizada na qual a imagem é segmentada automaticamente e, posteriormente, os segmentos são classificados. Nesta arquitetura, o algoritmo de segmentação e o classificador podem ser alterados sem prejudicar a flexibilidade do sistema implementado. Foram realizados experimentos com um banco de imagens composto por 658 imagens. Estas imagens foram obtidas sob diferentes condições de iluminação durante o período que as frutas estavam maduras. Estes experimentos foram realizados para avaliar, no contexto da arquitetura desenvolvida, o algoritmo de segmentação JSEG, vetores de características derivados dos espaços de cores RGB e HSV, além de três tipos de classificadores: bayesiano, classificador ingênuo de Bayes e classificador baseado no perceptron multicamadas. Finalmente, foram construídos os mapas de classes. As funções de distribuição de probabilidades foram estimadas com o algoritmo de Figueiredo-Jain. Dos resultados obtidos, deve-se destacar que o algoritmo de segmentação mostrou-se adequado aos propósitos deste trabalho e o classificador bayesiano mostrou-se mais prático que o classificador baseado no perceptron multicamadas. Por fim, a arquitetura mostrou-se adequada para o reconhecimento de cenas obtidas em pomares de laranjeiras. / Automation systems are usually used in the industry to optimize the production. In the agroindustry, these systems are used with the same intentions. Among them are systems that use computer vision for inspection, mechanized harvest, vehicles and robots guidance and other applications. Because of this, in the present work, techniques of computer vision were used to segment and classify elements in the images from oranges orchards. A modular architecture was used. The image are automatically segmented and, then the segments are classified. In this architecture, the segmentation algorithm and the classifier can be modified without loss of flexibility. The experiments were carried out with 658 images. These images were acquired under different illumination conditions during the period that the fruits are mature. These experiments were carried out to evaluate, in the context of developed architecture, the segmentation algorithm JSEG, characteristics vectors derived from the colors spaces RGB and HSV and three classifiers: Bayes\'s classifier, Bayes\'s naive classifier and multilayer perceptron classifier. Finally, the class maps were constructed. The Figueiredo-Jain algorithm was used to estimate the probability distribution functions. The results show that the segmentation algorithm is adequate to this work and the Bayes classifier is more practical that the multilayer perceptron classifier. Finally, the architecture is adequate for recognition of images acquired in orange orchards.

Promoter classification is the task of separating promoter from non promoter sequences. Determining promoter regions where the transcription initiation takes place is important for several reasons such as improving genome annotation and defining transcription start sites. In this study, various promoter prediction methods called ProK-means, ProSVM, and 3S1C are proposed. In ProSVM and ProK-means algorithms, structural features of DNA sequences are used to distinguish promoters from non promoters. Obtained results are compared with ProSOM which is an existing promoter prediction method. It is shown that ProSVM is able to achieve greater recall rate compared to ProSOM results. Another promoter prediction methods proposed in this study is 3S1C. The difference of the proposed technique from existing methods is using signal, similarity, structure, and context features of DNA sequences in an integrated way and a hierarchical manner. In addition to current methods related to promoter classification, the similarity feature, which compares the promoter regions between human and other species, is added to the proposed system. We show that the similarity feature improves the accuracy. To classify core promoter regions, firstly, signal, similarity, structure, and context features are extracted and then, these features are classified separately by using Support Vector Machines. Finally, output predictions are combined using multilayer perceptron. The result of 3S1C algorithm is very promising.

Pagrindinis darbo tikslas – atlikti kelių tiesioginio sklidimo neuroninių tinklų sistemų lyginamąją analizę siekiant įvertinti jų funkcionalumą. Šiame darbe apžvelgiama: biologinio ir dirbtinio neuronų modeliai, neuroninių tinklų klasifikacija pagal jungimo konstrukciją (tiesioginio sklidimo ir rekurentiniai neuroniniai tinklai), dirbtinių neuroninių tinklų mokymo strategijos (mokymas su mokytoju, mokymas be mokytojo, hibridinis mokymas). Analizuojami pagrindiniai tiesioginio sklidimo neuroninių tinklų metodai: vienasluoksnis perceptronas, daugiasluoksnis perceptronas realizuotas „klaidos skleidimo atgal” algoritmu, radialinių bazinių funkcijų neuroninis tinklas. Buvo nagrinėjama 14 skirtingų tiesioginio sklidimo neuroninių tinklų sistemos. Programos buvo suklasifikuotos pagal kainą, tiesioginio sklidimo neuroninių tinklo mokymo metodų taikymą, galimybę vartotojui keisti parametrus prieš apmokant tinklą ir techninį programos įvertinimą. Programos buvo įvertintos dešimtbalėje vertinimo sistemoje pagal mokymo metodų įvairumą, parametrų keitimo galimybes, programos stabilumą, kokybę, bei kainos ir kokybės santykį. Aukščiausiu balu įvertinta „Matlab” programa (10 balų), o prasčiausiai – „Sharky NN” (2 balai). Detalesnei analizei pasirinktos keturios programos („Matlab“, „DTREG“, „PathFinder“, „Cortex“), kurios buvo įvertintos aukščiausiais balais, galėjo apmokyti tiesioginio sklidimo neuroninį tinklą daugiasluoksnio perceptrono metodu ir bent dvi radialinių bazinių funkcijų... [toliau žr. visą tekstą] / The main aim – to perform a comparative analysis of several feedforward neural system networks in order to identify its functionality. The work presents both: biological and artificial neural models, also classification of neural networks, according to connections’ construction (of feedforward and recurrent neural networks), studying strategies of artificial neural networks (with a trainer, without a trainer, hybrid). The main methods of feedforward neural networks: one-layer perceptron, multilayer perceptron, implemented upon “error feedback” algorithm, also a neural network of radial base functions have been considered. The work has included 14 different feedforward neural system networks, classified according its price, application of study methods of feedforward neural networks, also a customer’s possibility to change parameters before paying for the network and a technical evaluation of a program. The programs have been evaluated from 1 point to 10 points according to the following: variety of training systems, possibility to change parameters, stability, quality and ratio of price and quality. The highest evaluation has been awarded to “Matlab” (10 points), the lowest – to “Sharky NN” (2 points). Four programs (”Matlab“, “DTREG“, “PathFinder“,”Cortex“) have been selected for a detail analysis. The best evaluated programs have been able to train feedforward neural networks using multilayer perceptron method, also at least two radial base function networks. “Matlab“ and... [to full text]

Les systèmes de traduction statistique à base de segments traduisent les phrases un segment à la fois, en plusieurs étapes. À chaque étape, ces systèmes ne considèrent que très peu d’informations pour choisir la traduction d’un segment. Les scores du dictionnaire de segments bilingues sont calculés sans égard aux contextes dans lesquels ils sont utilisés et les modèles de langue ne considèrent que les quelques mots entourant le segment traduit.Dans cette thèse, nous proposons un nouveau modèle considérant la phrase en entier lors de la sélection de chaque mot cible. Notre modèle d’intégration du contexte se différentie des précédents par l’utilisation d’un ppc (perceptron à plusieurs couches). Une propriété intéressante des ppc est leur couche cachée, qui propose une représentation alternative à celle offerte par les mots pour encoder les phrases à traduire. Une évaluation superficielle de cette représentation alter- native nous a montré qu’elle est capable de regrouper certaines phrases sources similaires même si elles étaient formulées différemment. Nous avons d’abord comparé avantageusement les prédictions de nos ppc à celles d’ibm1, un modèle couramment utilisé en traduction. Nous avons ensuite intégré nos ppc à notre système de traduction statistique de l’anglais vers le français. Nos ppc ont amélioré les traductions de notre système de base et d’un deuxième système de référence auquel était intégré IBM1. / Phrase-based statistical machine translation systems translate source sentences one phrase at a time, conditioning the choice of each phrase on very little information. Bilingual phrase table scores are computed regardless of the context in which the phrases are used and language models only look at few words surrounding the target phrases. In this thesis, we propose a novel model to predict words that should appear in a translation given the source sentence as a whole. Our model differs from previous works by its use of mlp (multilayer perceptrons). Our interest in mlp lies in their hidden layer that encodes source sentences in a representation that is only loosely tied to words. We observed that this hidden layer was able to cluster some sentences having similar translations even if they were formulated differently. In a first set of experiments, we compared favorably our mlp to ibm1, a well known model in statistical machine translation. In a second set of experiments, we embedded our ppc in our English to French statistical machine translation system. Our MLP improved translations quality over our baseline system and a second system embedding an IBM1 model.

Submitted by Lara Oliveira (lara@ufersa.edu.br) on 2017-09-08T22:30:39Z No. of bitstreams: 1 AldemárioAS_DISSERT.pdf: 18856416 bytes, checksum: dcd37bbe9d111ef051c4d27c3481a41f (MD5) / Approved for entry into archive by Vanessa Christiane (referencia@ufersa.edu.br) on 2017-09-11T16:27:51Z (GMT) No. of bitstreams: 1 AldemárioAS_DISSERT.pdf: 18856416 bytes, checksum: dcd37bbe9d111ef051c4d27c3481a41f (MD5) / Approved for entry into archive by Vanessa Christiane (referencia@ufersa.edu.br) on 2017-09-11T16:28:25Z (GMT) No. of bitstreams: 1 AldemárioAS_DISSERT.pdf: 18856416 bytes, checksum: dcd37bbe9d111ef051c4d27c3481a41f (MD5) / Made available in DSpace on 2017-09-11T16:29:16Z (GMT). No. of bitstreams: 1 AldemárioAS_DISSERT.pdf: 18856416 bytes, checksum: dcd37bbe9d111ef051c4d27c3481a41f (MD5) Previous issue date: 2017-08-18 / The training of Multilayer Perceptron Neural Network (MLPNN) done by exact algorithm to find the maximum accuracy is NP-hard. Thus, we use the algorithm Back-Propagation who needs a starting point (weights and bias initials) to compute the training of the MLPNN. This research has developed and implemented a heuristic algorithm HeCI - Heuristic to Calculate Weights and Bias Initials - to compute the data to train the MLPNN and return the starting point for the Back-Propagation. HeCI uses Principal Component Analysis, Least Square Method, Probability Density Function of the Normal Gaussian Distribution, two strategic configurations, and partially controls the number of MLPNN training epochs. Experimentally, HeCI was used with Back-Propagation in MLPNN training to recognize patterns and solve data classification problems. Six case studies with datasets between Health, Business and Botany were used in the experiments. The methodology of this research uses Deductive analysis by the Experimental method with Quantitative approach and hypothesis tests: Test of Fridman with post Teste of Tukey HSD Post-hoc and Wilcoxon Test-M W. The results of accuracy have increased significantly improving attested by evaluation of tests of hypotheses, inferringstatistical robustness of the result motivated by HeCI / O treinamento de Rede Neural Perceptron Multicamadas (RNPM) feito por algoritmo exato para encontrar a máxima acurácia é NP-Difícil. Sendo assim, usa-se o algoritmo "Back-Propagation" que necessita de um ponto de partida (pesos e bias iniciais) para computar o treinamento da RNPM. Esta pesquisa desenvolveu e aplicou um algoritmo heurístico HeCI - Heurística para Calcular Pesos e Bias Iniciais - para computar os dados de treinamento da RNPM e retornar o ponto de partida para o "Back-Propagation". A HeCI usa Análise de Componentes Principais, Método dos Mínimos Quadrados, Função de Densidade de Probabilidade da Normal Distribuição Gaussiana, duas configurações estratégicas e controla parcialmente o número de épocas de treinamento da RNPM. Experimentalmente, a RNPM foi treinada usando "Back-Propagation" com HeCI, para reconhecer padrões e resolver problemas de classificação de dados. Seis estudos de caso com "datasets" entre as áreas de Saúde, Negócio e Botânica foram usados nos experimentos. A metodologia desta pesquisa usa análise Dedutiva pelo método Experimental com abordagem Quantitativa e testes de hipóteses: Teste de Fridman com Pós Teste de Tukey HSD Post-hoc e Teste de Wilcoxon-M-W. Os resultados de acurácia incrementaram melhoria significativa atestada pela avaliação dos testes de hipóteses, inferindo estatisticamente robustez de resultado motivado pela HeCI / 2017-09-08

Made available in DSpace on 2014-12-17T14:55:03Z (GMT). No. of bitstreams: 1 OscarGF.pdf: 1901439 bytes, checksum: f8f1a37dca7a69d726f7a9453cbf0a98 (MD5) Previous issue date: 2004-03-05 / This work develops a robustness analysis with respect to the modeling errors, being applied to the strategies of indirect control using Artificial Neural Networks - ANN s, belong to the multilayer feedforward perceptron class with on-line training based on gradient method (backpropagation). The presented schemes are called Indirect Hybrid Control and Indirect Neural Control. They are presented two Robustness Theorems, being one for each proposed indirect control scheme, which allow the computation of the maximum steady-state control error that will occur due to the modeling error what is caused by the neural identifier, either for the closed loop configuration having a conventional controller - Indirect Hybrid Control, or for the closed loop configuration having a neural controller - Indirect Neural Control. Considering that the robustness analysis is restrict only to the steady-state plant behavior, this work also includes a stability analysis transcription that is suitable for multilayer perceptron class of ANN s trained with backpropagation algorithm, to assure the convergence and stability of the used neural systems. By other side, the boundness of the initial transient behavior is assured by the assumption that the plant is BIBO (Bounded Input, Bounded Output) stable. The Robustness Theorems were tested on the proposed indirect control strategies, while applied to regulation control of simulated examples using nonlinear plants, and its results are presented / Este trabalho utiliza as Redes Neurais Multicamadas - RNM s, totalmente com treinamento em tempo real (on-line), no desenvolvimento de duas estrat?gias de controle indireto. Os esquemas propostos denominam-se Controle H?brido Indireto e Controle Neural Indireto. Todo o treinamento dos neurodispositivos - o identificador da planta e o controlador, quando presentes na malha de controle indireto, ? realizado com um m?nimo de atraso computacional, de modo a contemplar o controle de plantas com pequenos per?odos de amostragem. S?o apresentados Teoremas de Estabilidade para garantia da converg?ncia dos dispositivos neurais, assim como foram feitas considera??es para adequar o m?todo de acelera??o da converg?ncia h-adaptativo utilizado ?s condi??es de estabilidade. Para cada esquema de controle indireto foi desenvolvido um teorema que permite calcular o m?ximo erro permanente (steady-state error) que poder? ocorrer em fun??o da toler?ncia previamente especificada para converg?ncia dos dispositivos neurais usados na malha de controle, desde que a estabilidade seja garantida. Estes teoremas foram denominados de Teoremas da Robustez e constituem a principal contribui??o deste trabalho. As condi??es de estabilidade e robustez foram testadas para as estrat?gias de Controle H?brido Indireto e de Controle Neural Indireto, sendo apresentados os resultados obtidos na simula??o computacional do controle de regula??o de plantas n?o-lineares, BIBO (Bounded Input, Bounded Output) est?veis

Made available in DSpace on 2014-06-12T16:01:05Z (GMT). No. of bitstreams: 2 arquivo7154_1.pdf: 1370997 bytes, checksum: 1580b7b5979343826e4d0a3b88b57dac (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2005 / Esta dissertação propõe modificações na metodologia yamazaki para a otimização simultânea de arquiteturas e pesos de redes Multilayer Perceptron (MLP). O objetivo principal é propô-las em conjunto com as respectivas validações, visando tornar mais eficiente o processo de otimização. A base para o algoritmo híbrido de otimização são os algoritmos simulated annealing, tabu search e a metodologia yamazaki. As modificações são realizadas nos critérios de implementação tais como mecanismo de geração de vizinhança, esquema de esfriamento e função de custo. Um dos pontos principais desta dissertação é a criação de um novo mecanismo de geração de vizinhança visando aumentar o espaço de busca. O esquema de esfriamento é de grande importância na convergência do algoritmo. O custo de cada solução é medido como média ponderada entre o erro de classificação para o conjunto de treinamento e a porcentagem de conexões utilizadas pela rede. As bases de dados utilizadas nos experimentos são: classificação de odores provenientes de três safras de um mesmo vinho e classificação de gases. A fundamentação estatística para as conclusões observadas através dos resultados obtidos é realizada usando teste de hipóteses. Foi realizado um estudo do tempo de execução separando as fases de otimização global da fase de refinamento local. Concluiu-se que com o novo mecanismo de geração de vizinhança fez desnecessário o uso do backpropagation obtendo assim um alto ganho em tempo de execução. O algoritmo híbrido de otimização apresentou, para ambas as bases de dados, o menor valor da média do erro de classificação do conjunto de teste e o menor valor da quantidade de conexões. Além disso, o tempo de execução foi reduzido em média 46.72%

Abstract The amount of information available has increased due to the development of our modern digital society. This has caused an information overflow, meaning that there is lot of data available but the meaningful information or knowledge is hidden inside the overwhelming data smog. Nevertheless, the large amount of data together with the increased capabilities of computers provides a great opportunity to learn the behaviour of different kinds of phenomena at a more detailed level. The quality of life, well-being and a healthy living environment, for example, are fields where new information services can assist the creation of proactive decisions to avoid environmental problems caused by industrial activity, traffic, or extraordinary weather conditions. The combination of data coming from different sources such as public registers, companies’ operational information systems, online sensors and process monitoring systems provides a fruitful basis for creating new valuable information for citizens, decision makers or other end users. The aim of this thesis is to present the concept of intelligent information services and a methodological background in order to add intelligence using computational methods for the enrichment of multidimensional data. Moreover, novel examples are presented where new significant information is created and then provided for end users. The data refining process used is called data mining and contains methods for data collection, pre-processing, modelling, visualizing and interpreting the results and sharing the new information thus created. Information systems are a base for the creation of information services, meaning that stakeholder groups have access only to information but they do not own the whole information system that contains measurement systems, data collecting, and a technological platform. Intelligence in information services comes from the use of computational intelligent methods in data processing, modelling and visualization. In this thesis the general concept of such services is presented and concretized using five cases that focus on environmental and industrial examples. The results of these case studies show that the combination of different data sources provides fertile ground for developing new information services. The data mining methods used such as clustering and predictive modelling together with effective pre-processing methods have great potential to handle the large amount of multivariate data in this environmental context also. A self-organizing map combined with k-means clustering is useful for creating more detailed information about personal energy use. Predictive modelling using a multilayer perceptron (MLP) is well suited for estimating the number of tourists visiting a leisure centre and to find the correspondence between pulp process characteristics and the chemicals used. These results have many indirect effects on reducing negative concerns regarding our surroundings and maintaining a healthy living environment. The innovative use of stored data is one of the main elements in the creation of future information services. Thus, more emphasis should be placed on the development of data integration and effective data processing methods. Furthermore, it is noted that final end users, such as citizens or decision makers, should be involved in the data refining process at the very first stage. In this way, the approach is truly customer-oriented and the results fulfil the concrete need of specific end users. / Tiivistelmä Informaation määrä on kasvanut merkittävästi tietoyhteiskunnan kehittymisen myötä. Käytössämme onkin huomattava määrä erimuotoista tietoa, josta voimme hyödyntää kuitenkin vain osan. Jatkuvasti mitattavan datan suuri määrä ja sijoittuminen hajalleen asettavat osaltaan haasteita tiedon hyödyntämiselle. Tietoyhteiskunnassa hyvinvointi ja terveellisen elinympäristön säilyminen koetaan aiempaa tärkeämmäksi. Toisaalta yritysten toiminnan tehostaminen ja kestävän kehityksen edistäminen vaativat jatkuvaa parantamista. Informaatioteknologian avulla moniulotteista mittaus- ja rekisteritietoa voidaan hyödyntää esimerkiksi ennakoivaan päätöksentekoon jolla voidaan edistää edellä mainittuja tavoitteita. Tässä työssä on esitetty ympäristöalan älykkäiden informaatiopalveluiden konsepti, jossa oleellista on loppukäyttäjien tarpeiden tunnistaminen ja ongelmien ratkaiseminen jalostetun informaation avulla. Älykkäiden informaatiopalvelujen taustalla on yhtenäinen tiedonlouhintaan perustuva tiedonjalostusprosessi, jossa raakatieto jalostetaan loppukäyttäjille soveltuvaan muotoon. Tiedonjalostusprosessi koostuu datan keräämisestä ja esikäsittelystä, mallintamisesta, tiedon visualisoinnista, tulosten tulkitsemisesta sekä oleellisen tiedon jakamisesta loppukäyttäjäryhmille. Datan käsittelyyn ja analysointiin on käytetty laskennallisesti älykkäitä menetelmiä, josta juontuu työn otsikko; älykkäät informaatiopalvelut. Väitöskirja pohjautuu viiteen artikkeliin, joissa osoitetaan tiedonjalostusprosessin toimivuus erilaisissa tapauksissa ja esitetään esimerkkejä kuhunkin prosessin vaiheeseen soveltuvista laskennallisista menetelmistä. Artikkeleissa on kuvattu matkailualueen kävijämäärien ennakointiin ja kotitalouksien sähköenergian kulutuksen pienentämiseen liittyvät informaatiopalvelut sekä analyysi selluprosessissa käytettävien kemikaalien määrän pienentämiseksi. Näistä saadut kokemukset ja tulokset on yleistetty älykkään informaatiopalvelun konseptiksi. Väitöskirjan toisena tavoitteena on rohkaista organisaatioita hyödyntämään tietovarantoja aiempaa tehokkaammin ja monipuolisemmin sekä rohkaista tarkastelemaan myös oman organisaation ulkopuolelta saatavien tietolähteiden käyttämistä. Toisaalta, uudenlaisten informaatiopalvelujen ja liiketoimintojen kehittämistä tukisi julkisilla varoilla kerättyjen, ja osin yritysten hallussa olevien, tietovarantojen julkaiseminen avoimiksi.

Given the volatility of the stock market and the multitude of financial variables at play, forecasting the value of stocks can be a challenging task. Nonetheless, such prediction task presents a fascinating problem to solve using machine learning. The stock market can be affected by news events, social media posts, political changes, investor emotions, and the general economy among other factors. Predicting the stock value of a company by simply using financial stock data of its price may be insufficient to give an accurate prediction. Investors often openly express their attitudes towards various stocks on social medial platforms. Hence, combining sentiment analysis from social media and the financial stock value of a company may yield more accurate predictions. This thesis proposes a method to predict the stock market using sentiment analysis and financial stock data. To estimate the sentiment in social media posts, we use an ensemble-based model that leverages Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models. We use an LSTM model for the financial stock prediction. The models are trained on the AAPL, CSCO, IBM, and MSFT stocks, utilizing a combination of the financial stock data and sentiment extracted from social media posts on Twitter between the years 2015-2019. Our experimental results show that the combination of the financial and sentiment information can improve the stock market prediction performance. The proposed solution has achieved a prediction performance of 74.3%.

Orientador: Gisela Ferreira / Resumo: O nitrogênio é um elemento mineral essencial para as plantas. Sua deficiência em fases iniciais do desenvolvimento pode gerar alterações fisiológicas e morfológicas que reduzem o crescimento e conflui na não expressão total do potencial genético vegetal. As técnicas mais difundidas para a quantificação do N nas plantas demandam tempo, são destrutivas e liberam compostos tóxicos para o ambiente. A NIRS (Near-Infrared Spectroscopy - Espectroscopia no Infravermelho Próximo), se apresenta como uma técnica alternativa, sendo indireta, mas instantânea, não destrutiva e que não utiliza reagentes químicos, mas necessita de calibração, que pode ser feita por métodos estatísticos e computacionais. Para mudas que são produzidas em viveiros, como as de Annona emarginata (Schltdl.) H. Rainer, é essencial manter o monitoramento de N, de forma rápida e não danosa, para garantir a qualidade e vigor das mudas. Desta forma, este trabalho visou detectar alterações na caracterização espectral foliar de A. emarginata em função do fornecimento de concentrações de nitrogênio e classificar as mudas em função dos níveis de nitrogênio, com base na caracterização espectral, utilizando algoritmos de aprendizado de máquinas e análise estatística multivariada. As mudas de A. emarginata (240) foram mantidas em sistema de hidroponia, com alterações na concentração de nitrogênio: 0 mg.L-1 de N (T1); 52,5 mg.L-1 de N (T2); 105 mg.L-1 de N (T3) e 210 mg.L-1 de N (T4), com 60 repetições (mudas) para cada tratam... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Nitrogen is an essential mineral element for plants. Its deficiency in early stages of development can lead to physiological and morphological changes that reduce growth and result in the total non-expression of plant genetic potential. The most widespread techniques for quantifying N in plants are time consuming, destructive and release toxic compounds into the environment. Near-Infrared Spectroscopy (NIRS) is an alternative technique, being indirect, but instantaneous, non-destructive and does not use chemical reagents, but needs calibration, which can be done by statistical and computational methods. For seedlings that are produced in nurseries, such as those of Annona emarginata (Schltdl.) H. Rainer, it is essential to keep N monitoring fast and harmless to ensure seedling quality. Thus, this work aimed to detect changes in the leaf spectral characterization of A. emarginata as a function of nitrogen concentration supply and to classify seedlings as a function of nitrogen levels, based on spectral characterization, using machine learning algorithms and multivariate statistical analysis. 240 A. emarginata seedlings were maintained in a hydroponic system, with modifications in nitrogen concentration: 0 mg.L-1 of N (T1); 52.5 mg.L-1 of N (T2); 105 mg.L-1 N (T3) and 210 mg.L-1 N (T4), with 60 replications (seedlings) for each treatment. After 45 days in these solutions, three leaves of each plant were collected, photographed and their spectral characterizations was measured. ... (Complete abstract click electronic access below) / Mestre