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1.
São Paulo; s.n; 2024. 86 p.
Thesis in Portuguese | LILACS | ID: biblio-1580144

ABSTRACT

A proteção de áreas verdes urbanas enfrenta desafios complexos, especialmente quando comparada a emergências sociais como saúde, moradia e educação. No entanto, mecanismos econômicos como os Créditos Voluntários de Biodiversidade (CVB) emergem como soluções viáveis e sustentáveis para a manutenção dessas áreas. A criação de CVBs tem avançado rapidamente, com abordagens inovadoras sendo implementadas globalmente, incluindo a proteção de áreas nos Andes na Argentina, a valoração da biodiversidade oceânica e a conservação de espécies ameaçadas como a onça-pintada no Brasil. Tecnologias acessíveis, como Sistemas de Informação Geográfica (GIS), Inteligência Artificial (IA) e Machine Learning, mostram-se eficazes na identificação e monitoramento de espécies, qualidade de habitats e mudanças na vegetação ao longo do tempo. Esses sistemas oferecem uma base sólida para a criação de indicadores que suportam a gestão de áreas verdes urbanas. Nosso estudo demonstrou que os índices de vegetação NDVI e GNDVI possuem alta correlação com a biodiversidade real, especialmente em períodos de maior disponibilidade hídrica, como a primavera e o verão em São Paulo. Métodos visuais simples, como fotografias no solo feitas com dispositivos móveis e imagens aéreas de drones, complementam tecnologias mais avançadas, oferecendo formas acessíveis de monitorar a densidade de espécies invasoras e a saúde da vegetação. Elas fornecem um registro visual-temporal valioso para demonstrar melhorias na saúde do ecossistema e o sucesso de estratégias de controle de espécies invasoras. O método de Aprendizado semi-supervisionado para contagem de objetos (palmeiras e troncos) com anotações mínimas mostrou-se eficaz na obtenção de contagens robustas e confiáveis em ambientes com oclusão significativa. A estrutura semis-supervisionada, que combina dados anotados e pseudo-rotulados, melhora o desempenho do modelo e minimiza a necessidade de anotações manuais extensivas. O conjunto de dados gerado, composto por imagens ao nível do solo e anotações correspondentes, é uma contribuição valiosa para estudos adicionais em estimativa de biodiversidade. Apesar dos desafios, os experimentos demonstraram a eficácia desses métodos na avaliação da saúde da vegetação e na contagem de palmeiras invasoras, sugerindo que podem ser ferramentas úteis na criação de CVBs voltados para a conservação de áreas verdes urbanas. Essas abordagens simples, baseadas em monitoramento e coleta de dados remoto e de baixo custo, são uma contribuição importante e útil para auxiliar na priorização, conservação e gestão de áreas verdes, especialmente em municípios brasileiros que frequentemente carecem de estrutura adequada para decisões relacionadas às áreas verdes urbanas.


The protection of urban green areas faces complex challenges, especially when compared to social emergencies such as health, housing, and education. However, economic mechanisms like Voluntary Biodiversity Credits (VBCs) emerge as viable and sustainable solutions for maintaining these areas. The development of VBCs has progressed rapidly, with innovative approaches being implemented globally, including the protection of areas in the Andes in Argentina, the valuation of oceanic biodiversity, and the conservation of threatened species such as the jaguar in Brazil. Accessible technologies, such as Geographic Information Systems (GIS), Artificial Intelligence (AI), and Machine Learning, have proven effective in identifying and monitoring species, habitat quality, and vegetation changes over time. These systems provide a robust foundation for the creation of indicators that support the management of urban green areas. Our study demonstrated that the NDVI and GNDVI vegetation indices are highly correlated with actual biodiversity, particularly during periods of greater water availability, such as spring and summer in São Paulo. Simple visual methods, such as ground-level photographs taken with mobile devices and aerial images captured by drones, complement more advanced technologies, offering accessible ways to monitor invasive species density and vegetation health. These methods provide a valuable visual-temporal record to showcase ecosystem health improvements and the success of invasive species control strategies. The semi-supervised learning method for object counting (palm trees and trunks) with minimal annotations proved effective in obtaining robust and reliable counts in environments with significant occlusion. The semi-supervised structure, which combines annotated and pseudo-labeled data, enhances model performance and minimizes the need for extensive manual annotations. The dataset generated, composed of ground-level images and corresponding annotations, represents a valuable contribution to further studies on biodiversity estimation. Despite the challenges, the experiments demonstrated the effectiveness of these methods in assessing vegetation health and counting invasive palm trees, suggesting they can serve as useful tools in developing VBCs for urban green area conservation. These simple approaches, based on remote, low-cost monitoring and data collection, are a significant and practical contribution to assisting with the prioritization, conservation, and management of green areas - particularly in Brazilian municipalities, which often lack adequate structures for decision-making regarding urban green spaces.


Subject(s)
Artificial Intelligence , Geographic Information Systems , Biodiversity , Green Areas , Supervised Machine Learning
2.
Article in Chinese | WPRIM | ID: wpr-940944

ABSTRACT

OBJECTIVE@#To predict the trends for fine-scale spread of Oncomelania hupensis based on supervised machine learning models in Shanghai Municipality, so as to provide insights into precision O. hupensis snail control.@*METHODS@#Based on 2016 O. hupensis snail survey data in Shanghai Municipality and climatic, geographical, vegetation and socioeconomic data relating to O. hupensis snail distribution, seven supervised machine learning models were created to predict the risk of snail spread in Shanghai, including decision tree, random forest, generalized boosted model, support vector machine, naive Bayes, k-nearest neighbor and C5.0. The performance of seven models for predicting snail spread was evaluated with the area under the receiver operating characteristic curve (AUC), F1-score and accuracy, and optimal models were selected to identify the environmental variables affecting snail spread and predict the areas at risk of snail spread in Shanghai Municipality.@*RESULTS@#Seven supervised machine learning models were successfully created to predict the risk of snail spread in Shanghai Municipality, and random forest (AUC = 0.901, F1-score = 0.840, ACC = 0.797) and generalized boosted model (AUC= 0.889, F1-score = 0.869, ACC = 0.835) showed higher predictive performance than other models. Random forest analysis showed that the three most important climatic variables contributing to snail spread in Shanghai included aridity (11.87%), ≥ 0 °C annual accumulated temperature (10.19%), moisture index (10.18%) and average annual precipitation (9.86%), the two most important vegetation variables included the vegetation index of the first quarter (8.30%) and vegetation index of the second quarter (7.69%). Snails were more likely to spread at aridity of < 0.87, ≥ 0 °C annual accumulated temperature of 5 550 to 5 675 °C, moisture index of > 39% and average annual precipitation of > 1 180 mm, and with the vegetation index of the first quarter of > 0.4 and the vegetation index of the first quarter of > 0.6. According to the water resource developments and township administrative maps, the areas at risk of snail spread were mainly predicted in 10 townships/subdistricts, covering the Xipian, Dongpian and Tainan sections of southern Shanghai.@*CONCLUSIONS@#Supervised machine learning models are effective to predict the risk of fine-scale O. hupensis snail spread and identify the environmental determinants relating to snail spread. The areas at risk of O. hupensis snail spread are mainly located in southwestern Songjiang District, northwestern Jinshan District and southeastern Qingpu District of Shanghai Municipality.


Subject(s)
Animals , Bayes Theorem , China/epidemiology , Ecosystem , Gastropoda , Supervised Machine Learning
3.
Rev. Assoc. Med. Bras. (1992, Impr.) ; Rev. Assoc. Med. Bras. (1992, Impr.);65(12): 1438-1441, Dec. 2019. graf
Article in English | LILACS | ID: biblio-1057097

ABSTRACT

SUMMARY Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enabling cost-effectiveness, and reducing readmission and mortality rates. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.


RESUMO A inteligência artificial (IA) é um campo da ciência da computação que tem como objetivo imitar os processos de pensamento humano. Técnicas de IA têm sido aplicadas na medicina cardiovascular para explorar novos genótipos e fenótipos em doenças existentes, melhorar a qualidade do atendimento ao paciente, possibilitar custo-efetividade e reduzir taxas de readmissão e mortalidade. Existe um grande potencial da IA na medicina cardiovascular; no entanto, a ignorância dos desafios pode ofuscar seu impacto clínico. Esse artigo fornece a aplicação da IA no atendimento clínico cardiovascular e discute seu papel potencial na facilitação da medicina cardiovascular de precisão.


Subject(s)
Humans , Artificial Intelligence/trends , Cardiovascular Diseases/diagnosis , Algorithms , Precision Medicine/trends , Supervised Machine Learning/trends , Unsupervised Machine Learning , Big Data
4.
Article in English | WPRIM | ID: wpr-763946

ABSTRACT

OBJECTIVES: Human motion analysis can be applied to the diagnosis of musculoskeletal diseases, rehabilitation therapies, fall detection, and estimation of energy expenditure. To analyze human motion with micro-Doppler signatures measured by radar, a deep learning algorithm is one of the most effective approaches. Because deep learning requires a large data set, the high cost involved in measuring large amounts of human data is an intrinsic problem. The objective of this study is to augment human motion micro-Doppler data employing generative adversarial networks (GANs) to improve the accuracy of human motion classification. METHODS: To test data augmentation provided by GANs, authentic data for 7 human activities were collected using micro-Doppler radar. Each motion yielded 144 data samples. Software including GPU driver, CUDA library, cuDNN library, and Anaconda were installed to train the GANs. Keras-GPU, SciPy, Pillow, OpenCV, Matplotlib, and Git were used to create an Anaconda environment. The data produced by GANs were saved every 300 epochs, and the training was stopped at 3,000 epochs. The images generated from each epoch were evaluated, and the best images were selected. RESULTS: Each data set of the micro-Doppler signatures, consisting of 144 data samples, was augmented to produce 1,472 synthesized spectrograms of 64 × 64. Using the augmented spectrograms, the deep neural network was trained, increasing the accuracy of human motion classification. CONCLUSIONS: Data augmentation to increase the amount of training data was successfully conducted through the use of GANs. Thus, augmented micro-Doppler data can contribute to improving the accuracy of human motion recognition.


Subject(s)
Humans , Boidae , Classification , Dataset , Diagnosis , Energy Metabolism , Human Activities , Learning , Motion Perception , Musculoskeletal Diseases , Rehabilitation , Supervised Machine Learning
5.
Article in English | WPRIM | ID: wpr-766052

ABSTRACT

PURPOSE: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). METHODS: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. RESULTS: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%–91.2%) for premolars and 73.4% (95% CI, 59.9%–84.0%) for molars. CONCLUSIONS: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.


Subject(s)
Area Under Curve , Artificial Intelligence , Bicuspid , Boidae , Dataset , Diagnosis , Learning , Machine Learning , Methods , Molar , Periodontal Diseases , ROC Curve , Sensitivity and Specificity , Supervised Machine Learning , Tooth , Weights and Measures
6.
Article in English | WPRIM | ID: wpr-717659

ABSTRACT

OBJECTIVES: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. METHODS: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. RESULTS: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. CONCLUSIONS: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.


Subject(s)
Arousal , Classification , Dataset , Delivery of Health Care , Electrocardiography , Learning , Machine Learning , Memory, Short-Term , Methods , Peripheral Nervous System , Supervised Machine Learning
7.
Motriz (Online) ; 24(2): e1018173, 2018. tab, graf
Article in English | LILACS | ID: biblio-955133

ABSTRACT

Abstract AIMS To analyze if different resistance training (machine vs free weight) would have different acute hypotensive effect in active and normotensive individuals. METHODS Fifteen male volunteers (39.2±2.5 years) performed two different resistance exercise protocols, one on machinery and one with free weights for the similar muscle groups and volume (sets x repetitions). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured at rest and during 60 minutes after the sessions. RESULTS There was no interaction between (treatments vs time) for SBP (F 70, 84 = 0.9445, p = 0.595) and DBP (F 70, 84 = 0.5743, p = 0.991). However, significant differences for time for SBP (F 14, 84 = 7.058, p = 0.001) and DBP (F 14, 84 = 3.296, p = 0.001), and treatment for SBP (F 5, 6 = 291.1, p = 0.001) and DBP (F 5, 6 = 13.29, p = 0.003) were varified. CONCLUSION Our findings indicate that both resistance training (machine or free weight) induce a similar hypotensive response for SBP in normotensive men.


Subject(s)
Humans , Male , Blood Pressure , Resistance Training , Supervised Machine Learning , Anthropometry/instrumentation , Statistics, Nonparametric
8.
São Paulo med. j ; São Paulo med. j;135(3): 234-246, May-June 2017. tab, graf
Article in English | LILACS | ID: biblio-904082

ABSTRACT

ABSTRACT CONTEXT AND OBJECTIVE: Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. DESIGN AND SETTING: Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil. METHODS: After selecting a subset of 27 candidate variables from the literature, models were built and validated in four sequential steps: (i) parameter tuning with tenfold cross-validation, repeated three times; (ii) automatic variable selection using forward selection, a wrapper strategy with four different machine-learning algorithms and tenfold cross-validation (repeated three times), to evaluate each subset of variables; (iii) error estimation of model parameters with tenfold cross-validation, repeated ten times; and (iv) generalization testing on an independent dataset. The models were created with the following machine-learning algorithms: logistic regression, artificial neural network, naïve Bayes, K-nearest neighbor and random forest. RESULTS: The best models were created using artificial neural networks and logistic regression. ­These achieved mean areas under the curve of, respectively, 75.24% and 74.98% in the error estimation step and 74.17% and 74.41% in the generalization testing step. CONCLUSION: Most of the predictive models produced similar results, and demonstrated the feasibility of identifying individuals with highest probability of having undiagnosed diabetes, through easily-obtained clinical data.


RESUMO CONTEXTO E OBJETIVO: Diabetes tipo 2 é uma doença crônica associada a graves complicações de saúde, causando grande impacto na saúde global. O objetivo foi desenvolver e validar modelos preditivos para detectar diabetes não diagnosticada utilizando dados do Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil) e comparar o desempenho de diferentes algoritmos de aprendizagem de máquina. TIPO DE ESTUDO E LOCAL: Comparação de algoritmos de aprendizagem de máquina para o desenvolvimento de modelos preditivos utilizando dados do ELSA-Brasil. MÉTODOS: Após selecionar 27 variáveis candidatas a partir da literatura, modelos foram construídos e validados em 4 etapas sequenciais: (i) afinação de parâmetros com validação cruzada (10-fold cross-validation); (ii) seleção automática de variáveis utilizando seleção progressiva, estratégia "wrapper" com quatro algoritmos de aprendizagem de máquina distintos e validação cruzada para avaliar cada subconjunto de variáveis; (iii) estimação de erros dos parâmetros dos modelos com validação cruzada; e (iv) teste de generalização em um conjunto de dados independente. Os modelos foram criados com os seguintes algoritmos de aprendizagem de máquina: regressão logística, redes neurais artificiais, naïve Bayes, K vizinhos mais próximos e floresta aleatória. RESULTADOS: Os melhores modelos foram criados utilizando redes neurais artificiais e regressão logística alcançando, respectivamente, 75,24% e 74,98% de média de área sob a curva na etapa de estimação de erros e 74,17% e 74,41% na etapa de teste de generalização. CONCLUSÃO: A maioria dos modelos preditivos produziu resultados semelhantes e demonstrou a viabilidade de identificar aqueles com maior probabilidade de ter diabetes não diagnosticada com dados clínicos facilmente obtidos.


Subject(s)
Humans , Male , Female , Adult , Middle Aged , Aged , Algorithms , Diabetes Mellitus, Type 2/diagnosis , Supervised Machine Learning/standards , Computer Simulation/standards , Brazil , Logistic Models , Feasibility Studies , Reproducibility of Results , Bayes Theorem , Sensitivity and Specificity , Neural Networks, Computer
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