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Literatura académica sobre el tema "TreeNet"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 1 de febrero de 2022

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Artículos de revistas sobre el tema "TreeNet"

Changpetch, Pannapa. "Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset". Symmetry 13, n.º 2 (8 de febrero de 2021): 287. http://dx.doi.org/10.3390/sym13020287.

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A model-building framework is proposed that combines two data mining techniques, TreeNet and association rules analysis (ASA) with multinomial logit model building. TreeNet provides plots that play a key role in transforming quantitative variables into better forms for the model fit, whereas ASA is important in finding interactions (low- and high-order) among variables. With the implementation of TreeNet and ASA, new variables and interactions are generated, which serve as candidate predictors in building an optimal multinomial logit model. A real-life example in the context of health care is used to illustrate the major role of these newly generated variables and interactions in advancing multinomial logit modeling to a new level of performance. This method has an explanatory and predictive ability that cannot be achieved using existing methods.

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Lawry, D., G. M. Moore y D. Peacock. "The TREENET Avenues of Honour Project 1915-2015". Acta Horticulturae, n.º 1108 (febrero de 2016): 31–40. http://dx.doi.org/10.17660/actahortic.2016.1108.4.

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Abdi, Omid. "Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis". Sensors 19, n.º 18 (14 de septiembre de 2019): 3965. http://dx.doi.org/10.3390/s19183965.

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Despite increasing the number of studies for mapping remote sensing insect-induced forest infestations, applying novel approaches for mapping and identifying its triggers are still developing. This study was accomplished to test the performance of Geographic Object-Based Image Analysis (GEOBIA) TreeNet for discerning insect-infested forests induced by defoliators from healthy forests using Landsat 8 OLI and ancillary data in the broadleaved mixed Hyrcanian forests. Moreover, it has studied mutual associations between the intensity of forest defoliation and the severity of forest fires under TerraClimate-derived climate hazards by analyzing panel data models within the TreeNet-derived insect-infested forest objects. The TreeNet optimal performance was obtained after building 333 trees with a sensitivity of 93.7% for detecting insect-infested objects with the contribution of the top 22 influential variables from 95 input object features. Accordingly, top image-derived features were the mean of the second principal component (PC2), the mean of the red channel derived from the gray-level co-occurrence matrix (GLCM), and the mean values of the normalized difference water index (NDWI) and the global environment monitoring index (GEMI). However, tree species type has been considered as the second rank for discriminating forest-infested objects from non-forest-infested objects. The panel data models using random effects indicated that the intensity of maximum temperatures of the current and previous years, the drought and soil-moisture deficiency of the current year, and the severity of forest fires of the previous year could significantly trigger the insect outbreaks. However, maximum temperatures were the only significant triggers of forest fires. This research proposes testing the combination of object features of Landsat 8 OLI with other data for monitoring near-real-time defoliation and pathogens in forests.

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Changpetch, Pannapa. "Gambling Consumers in Thailand". Asian Social Science 13, n.º 5 (19 de abril de 2017): 136. http://dx.doi.org/10.5539/ass.v13n5p136.

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This paper presents a study of household gambling consumption in Thailand in 2011. We investigate the nonlinear relationships between this behavior and household alcohol expenditure, household gambling expenditure, and demographic factors. We use Treenet to analyze datasets drawn from a socio-economic survey of 42,083 Thai households conducted in 2011. The results show that the five most significant variables in order of importance for predicting the likelihood of household gambling consumption are household income, household region, work status of the household head, religion of the household head, and age of the household head. In summary, the Treenet results suggest that the likelihood of gambling consumption was higher for households with an income of more than 25,000 Bahts per year, a location in the North, a Buddhist head of household, a head with active work status, a head between 35 and 55 years old, with household expenditure spent on alcohol consumed at home of more than 500 Bahts, with household expenditure spent on tobacco of more than 100 Bahts, and a head of household with less education.

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Padmaja, B., V. V. Rama Prasad y K. V. N. Sunitha. "TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data". Indonesian Journal of Electrical Engineering and Computer Science 3, n.º 2 (1 de agosto de 2016): 446. http://dx.doi.org/10.11591/ijeecs.v3.i2.pp446-452.

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Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction features and social surveys for finding human stress behavior. For this, we gathered mobile phone call logs data set containing 111444 voice calls of 131 adult members of a living community for a period of more than 5 months. And we identified that top 5 social network measures like hierarchy, density, farness, reachability and eigenvector of individuals have profound influence on individuals stress levels in a social network. If an ego lies in the shortest path of all other alters then the ego receives more information and hence is more stressed. In this paper, we have used TreeNet machine learning algorithm for its speed and immune to outliers. We have tested our results with another Random Forest classifier as well and yet, we found TreeNet to be more efficient. This research can be of vital importance to economists, professionals, analysts, and policy makers.

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Padmaja, B., V. V. Rama Prasad y K. V. N. "TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data". Indonesian Journal of Electrical Engineering and Computer Science 4, n.º 1 (1 de octubre de 2016): 148. http://dx.doi.org/10.11591/ijeecs.v4.i1.pp148-154.

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<p>Human behavior is essentially social and humans start their daily routines by interacting with others. There are many forms of social interactions and we have used mobile phone based social interaction features and social surveys for finding human stress behavior. For this, we gathered mobile phone call logs data set containing 111444 voice calls of 131 adult members of a living community for a period of more than 5 months. And we identified that top 5 social network measures like hierarchy, density, farness, reachability and eigenvector of individuals have profound influence on individuals stress levels in a social network. If an ego lies in the shortest path of all other alters then the ego receives more information and hence is more stressed. In this paper, we have used TreeNet machine learning algorithm for its speed and immune to outliers. We have tested our results with another Random Forest classifier as well and yet, we found TreeNet to be more efficient. This research can be of vital importance to economists, professionals, analysts, and policy makers.<em></em></p>

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Steinberg, Dan, Nicholas C. Cardell, John Ries y Mykhaylyo Golovnya. "Using TreeNet to Cross-sell Home Loans to Credit Card Holders". International Journal of Data Warehousing and Mining 4, n.º 2 (abril de 2008): 32–45. http://dx.doi.org/10.4018/jdwm.2008040105.

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Mohanty, Ramakanta, V. Ravi y M. R. Patra. "Application of Machine Learning Techniques to Predict Software Reliability". International Journal of Applied Evolutionary Computation 1, n.º 3 (julio de 2010): 70–86. http://dx.doi.org/10.4018/jaec.2010070104.

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In this paper, the authors employed machine learning techniques, specifically, Back propagation trained neural network (BPNN), Group method of data handling (GMDH), Counter propagation neural network (CPNN), Dynamic evolving neuro–fuzzy inference system (DENFIS), Genetic Programming (GP), TreeNet, statistical multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), to accurately forecast software reliability. Their effectiveness is demonstrated on three datasets taken from literature, where performance is compared in terms of normalized root mean square error (NRMSE) obtained in the test set. From rigorous experiments conducted, it was observed that GP outperformed all techniques in all datasets, with GMDH coming a close second.

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Tang, Yanchao, Tong Zhao, Nian Huang, Wanfu Lin, Zhiying Luo y Changquan Ling. "Identification of Traditional Chinese Medicine Constitutions and Physiological Indexes Risk Factors in Metabolic Syndrome: A Data Mining Approach". Evidence-Based Complementary and Alternative Medicine 2019 (3 de febrero de 2019): 1–10. http://dx.doi.org/10.1155/2019/1686205.

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Objective. In order to find the predictive indexes for metabolic syndrome (MS), a data mining method was used to identify significant physiological indexes and traditional Chinese medicine (TCM) constitutions. Methods. The annual health check-up data including physical examination data; biochemical tests and Constitution in Chinese Medicine Questionnaire (CCMQ) measurement data from 2014 to 2016 were screened according to the inclusion and exclusion criteria. A predictive matrix was established by the longitudinal data of three consecutive years. TreeNet machine learning algorithm was applied to build prediction model to uncover the dependence relationship between physiological indexes, TCM constitutions, and MS. Results. By model testing, the overall accuracy rate for prediction model by TreeNet was 73.23%. Top 12.31% individuals in test group (n=325) that have higher probability of having MS covered 23.68% MS patients, showing 0.92 times more risk of having MS than the general population. Importance of ranked top 15 was listed in descending order . The top 5 variables of great importance in MS prediction were TBIL difference between 2014 and 2015 (D_TBIL), TBIL in 2014 (TBIL 2014), LDL-C difference between 2014 and 2015 (D_LDL-C), CCMQ scores for balanced constitution in 2015 (balanced constitution 2015), and TCH in 2015 (TCH 2015). When D_TBIL was between 0 and 2, TBIL 2014 was between 10 and 15, D_LDL-C was above 19, balanced constitution 2015 was below 60, or TCH 2015 was above 5.7, the incidence of MS was higher. Furthermore, there were interactions between balanced constitution 2015 score and TBIL 2014 or D_LDL-C in MS prediction. Conclusion. Balanced constitution, TBIL, LDL-C, and TCH level can act as predictors for MS. The combination of TCM constitution and physiological indexes can give early warning to MS.

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Muchira, James M., Philimon N. Gona, Suzanne Leveille y Laura L. Hayman. "A Review of Methodological Approaches for Developing Diagnostic Algorithms for Diabetes Screening". Journal of Nursing Measurement 27, n.º 3 (1 de diciembre de 2019): 433–57. http://dx.doi.org/10.1891/1061-3749.27.3.433.

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Background and PurposeDiagnostic algorithms are invaluable tools for screening diabetes. This review aimed to evaluate and identify the most robust methodological approaches for developing diagnostic algorithms for screening diabetes.MethodsFollowing a literature search, methodological quality of algorithm development studies was evaluated using the TRIPOD guidelines (Collins, Reitsma, Altman, & Moons, 2015).ResultsMethods used for developing the algorithms included logistic regression models, classification and regression trees, Random Forest and TreeNet, Artificial Neural Networks, and Naïve Bayes. Methodological issues for algorithm development studies were related to handling of missing values, reporting recruitment methods, categorization of continuous variables, and statistical controls.ConclusionsMost studies exhibited critical methodological flaws and poor adherence to reporting standards. Diabetes screening algorithms can easily be availed electronically and utilized by nurses at minimal cost even in underserved areas.

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Tesis sobre el tema "TreeNet"

Wickert, Claudia. "Breeding white storks in former East Prussia : comparing predicted relative occurrences across scales and time using a stochastic gradient boosting method (TreeNet), GIS and public data". Master's thesis, Universität Potsdam, 2007. http://opus.kobv.de/ubp/volltexte/2007/1353/.

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In dieser Arbeit wurden verschiedene GIS-basierte Habitatmodelle für den Weißstorch (Ciconia ciconia) im Gebiet der ehemaligen deutschen Provinz Ostpreußen (ca. Gebiet der russischen Exklave Kaliningrad und der polnischen Woiwodschaft Ermland-Masuren) erstellt. Zur Charakterisierung der Beziehung zwischen dem Weißstorch und der Beschaffenheit seiner Umwelt wurden verschiedene historische Datensätze über den Bestand des Weißstorches in den 1930er Jahren sowie ausgewählte Variablen zur Habitat-Beschreibung genutzt. Die Aufbereitung und Modellierung der verwendeten Datensätze erfolgte mit Hilfe eines geographischen Informationssystems (ArcGIS) und einer statistisch-mathematischen Methode aus den Bereichen „Machine Learning“ und „Data-Mining“ (TreeNet, Salford Systems Ltd.). Unter Verwendung der historischen Habitat-Parameter sowie der Daten zum Vorkommen des Weißstorches wurden quantitative Modelle auf zwei Maßstabs-Ebenen erstellt: (i) auf Punktskala unter Verwendung eines Rasters mit einer Zellgröße von 1 km und (ii) auf Verwaltungs-Kreisebene basierend auf der Gliederung der Provinz Ostpreußen in ihre Landkreise. Die Auswertung der erstellten Modelle zeigt, dass das Vorkommen von Storchennestern im ehemaligen Ostpreußen, unter Berücksichtigung der hier verwendeten Variablen, maßgeblich durch die Variablen ‚forest’, ‚settlement area’, ‚pasture land’ und ‚coastline’ bestimmt wird. Folglich lässt sich davon ausgehen, dass eine gute Nahrungsverfügbarkeit, wie der Weißstorch sie auf Wiesen und Weiden findet, sowie die Nähe zu menschlichen Siedlungen ausschlaggebend für die Nistplatzwahl des Weißstorches in Ostpreußen sind. Geschlossene Waldgebiete zeigen sich in den Modellen als Standorte für Horste des Weißstorches ungeeignet. Der starke Einfluss der Variable ‚coastline’ lässt sich höchstwahrscheinlich durch die starke naturräumliche Gliederung Ostpreußens parallel zur Küstenlinie erklären. In einem zweiten Schritt konnte unter Verwendung der in dieser Arbeit erstellten Modelle auf beiden Skalen Vorhersagen für den Zeitraum 1981-1993 getroffen werden. Dabei wurde auf dem Punktmaßstab eine Abnahme an potentiellem Bruthabitat vorhergesagt. Im Gegensatz dazu steigt die vorhergesagte Weißstorchdichte unter Verwendung des Modells auf Verwaltungs-Kreisebene. Der Unterschied zwischen beiden Vorhersagen beruht vermutlich auf der Verwendung unterschiedlicher Skalen und von zum Teil voneinander verschiedenen erklärenden Variablen. Weiterführende Untersuchungen sind notwendig, um diesen Sachverhalt zu klären. Des Weiteren konnten die Modellvorhersagen für den Zeitraum 1981-1993 mit den vorliegenden Bestandserfassungen aus dieser Zeit deskriptiv verglichen werden. Es zeigt sich hierbei, dass die hier vorhergesagten Bestandszahlen höher sind als die in den Zählungen ermittelten. Die hier erstellten Modelle beschreiben somit vielmehr die Kapazität des Habitats. Andere Faktoren, die die Größe der Weißstorch-Population bestimmen, wie z.B. Bruterfolg oder Mortalität sollten in zukünftige Untersuchungen mit einbezogen werden. Es wurde ein möglicher Ansatz aufgezeigt, wie man mit den hier vorgestellten Methoden und unter Verwendung historischer Daten wertvolle Habitatmodelle erstellen sowie die Auswirkung von Landnutzungsänderungen auf den Weißstorch beurteilen kann. Die hier erstellten Modelle sind als erste Grundlage zu sehen und lassen sich mit Hilfe weitere Daten hinsichtlich Habitatstruktur und mit exakteren räumlich expliziten Angaben zu Neststandorten des Weißstorches weiter verfeinern. In einem weiteren Schritt sollte außerdem ein Habitatmodell für die heutige Zeit erstellt werden. Dadurch wäre ein besserer Vergleich möglich hinsichtlich erdenklicher Auswirkungen von Änderungen der Landnutzung und relevanten Umweltbedingungen auf den Weißstorch im Gebiet des ehemaligen Ostpreußens sowie in seinem gesamten Verbreitungsgebiet.
Different habitat models were created for the White Stork (Ciconia ciconia) in the region of the former German province of East Prussia (equals app. the current Russian oblast Kaliningrad and the Polish voivodship Warmia-Masuria). Different historical data sets describing the occurrence of the White Stork in the 1930s, as well as selected variables for the description of landscape and habitat, were employed. The processing and modeling of the applied data sets was done with a geographical information system (ArcGIS) and a statistical modeling approach that comes from the disciplines of machine-learning and data mining (TreeNet by Salford Systems Ltd.). Applying historical habitat descriptors, as well as data on the occurrence of the White Stork, models on two different scales were created: (i) a point scale model applying a raster with a cell size of 1 km2 and (ii) an administrative district scale model based on the organization of the former province of East Prussia. The evaluation of the created models show that the occurrence of White Stork nesting grounds in the former East Prussia for most parts is defined by the variables ‘forest’, ‘settlement area’, ‘pasture land’ and ‘proximity to coastline’. From this set of variables it can be assumed that a good food supply and nesting opportunities are provided to the White Stork in pasture and meadows as well as in the proximity to human settlements. These could be seen as crucial factors for the choice of nesting White Stork in East Prussia. Dense forest areas appear to be unsuited as nesting grounds of White Storks. The high influence of the variable ‘coastline’ is most likely explained by the specific landscape composition of East Prussia parallel to the coastline and is to be seen as a proximal factor for explaining the distribution of breeding White Storks. In a second step, predictions for the period of 1981 to 1993 could be made applying both scales of the models created in this study. In doing so, a decline of potential nesting habitat was predicted on the point scale. In contrast, the predicted White Stork occurrence increases when applying the model of the administrative district scale. The difference between both predictions is to be seen in the application of different scales (density versus suitability as breeding ground) and partly dissimilar explanatory variables. More studies are needed to investigate this phenomenon. The model predictions for the period 1981 to 1993 could be compared to the available inventories of that period. It shows that the figures predicted here were higher than the figures established by the census. This means that the models created here show rather a capacity of the habitat (potential niche). Other factors affecting the population size e.g. breeding success or mortality have to be investigated further. A feasible approach on how to generate possible habitat models was shown employing the methods presented here and applying historical data as well as assessing the effects of changes in land use on the White Stork. The models present the first of their kind, and could be improved by means of further data regarding the structure of the habitat and more exact spatially explicit information on the location of the nesting sites of the White Stork. In a further step, a habitat model of the present times should be created. This would allow for a more precise comparison regarding the findings from the changes of land use and relevant conditions of the environment on the White Stork in the region of former East Prussia, e.g. in the light of coming landscape changes brought by the European Union (EU).

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Jemberie, Wossenseged Birhane. "Diet, Emission and Diabetes : A treelet transform pattern analysis on Västerbotten Intervention Program". Thesis, Umeå universitet, Epidemiologi och global hälsa, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-152624.

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Objective: Researches which studied the relation of dietary greenhouse gas emissions with health outcomes are few, inconsistent and most of them are modelling studies which have not investigated empiric dietary emission patterns. In this study, we employ a posteriori data dimension reduction method, treelet transform, to identify dietary and diet related emission patterns concurrently. We aim to evaluate if these patterns are correlated, if they areassociated with diabetes and if emission patterns can be used as a proxy for dietary patterns for assessment of association with diabetes. Design: Food items from dietary questionnaire were aggregated to 34 food groups. GHGE was estimated by linking food intakes with life cycle assessment data on emission. Dietary and emission patterns were identified by employing treelet transform on food intake and corresponding greenhouse gas emission data. Multivariate logistic regression was performed to investigate associations between quintiles of dietary patterns and diabetes. Adjusted mean values of emission estimates were obtained for the identified dietary patterns. Adjusted proportions of diabetes across quintiles of emission patterns were computed. Setting: Västerbotten Intervention Program Subjects: women (n 38,118); men (n 36,042) between the age of 35 and 65 years Results: Four dietary and four corresponding emission patterns in women, five dietary and five corresponding emission patterns in men were identified. Moderate to strong correlations were observed between dietary and corresponding emission patterns. Prudent dietary pattern (PP) in women was inversely associated with dysglycemia [ORQ5 vs. Q1 = 0.82 (95% CI 0.69—0.97, Ptrend =0.003)]. PP in women was also inversely associated with diabetes [ORQ5 vs.Q1 = 0.37 (95% CI 0.17—0.78, Ptrend = 0.002)]. However, adherence to this dietary pattern was associated with higher dietary emission. Finally, none of the corresponding emission patterns, were associated with adjusted proportions of either dysglycemia or diabetes. Conclusion: Treelet transform produces correlated dietary and emission patterns which are sparse and easily interpretable. However, some differences in loading structures between dietary and emission patterns result in different conclusion regarding the association with diabetes, rendering the usage of emission patterns as proxies of dietary patterns inappropriate. Results from our study also show that healthy dietary patterns do not necessarily reduce greenhouse gas emission.

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Rohden, Jens-Uwe von. "Die Gewässernamen im Einzugsgebiet der Treene : ein Beitrag zur Ortsnamenforschung in Schleswig-Holstein /". Neumünster : K. Wachholtz, 1989. http://catalogue.bnf.fr/ark:/12148/cb35566869s.

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Förster, Jana [Verfasser], Heiner [Akademischer Betreuer] Boeing y Reinhard [Akademischer Betreuer] Busse. "Treelet transform for untargeted metabolomics data : treelet transform generates serum metabolite and lipid components that are correlated to anthropometry and intestinal microbiota in a cross-sectional EPIC-Potsdam sub-study / Jana Förster. Gutachter: Heiner Boeing ; Reinhard Busse. Betreuer: Heiner Boeing". Berlin : Technische Universität Berlin, 2014. http://d-nb.info/1067386602/34.

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Abreu, Rafaela Novaes de. "Crescimento de brotações após poda de árvores em vias públicas na cidade de Piracicaba, SP". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11150/tde-08032018-105703/.

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A principal função da arborização viária, nos últimos 30 anos, passou de apenas estética para promotora de serviços ambientais, mas essas árvores estão submetidas a diversos fatores estressantes, como a poluição do ar, a baixa disponibilidade de água, solo compactado, podas severas, e área de crescimento limitada. As redes aéreas de distribuição de energia são um dos elementos que mais interferem no manejo das árvores viárias, pois o conflito, geralmente, já está estabelecido e as concessionárias de energia optam por podas drásticas, que podem comprometer a sanidade da árvore e estimular brotações epicórmicas que crescem rapidamente e vão de encontro com as fiações. Posto isso, o presente estudo teve como objetivo determinar a taxa de crescimento de brotações epicórmicas após poda executada pela Companhia Paulista de Força e Luz (CPFL) em indivíduos das espécies tipuana (Tipuana tipu (Benth.) Kuntze), ipê-roxo (Tabebuia heptaphylla (Vell.) Mart.), e chapéu-de-sol (Terminalia catappa L.), na cidade de Piracicaba/SP e relacionar esta taxa com as características da árvore e do meio. Para isso, foi realizado levantamento em campo de variáveis como a espécie, diâmetro à altura do peito (DAP), índice de área foliar (IAF), tipo de calçada, tipo de solo, fotos do canteiro e dos brotos ao lado de uma baliza para posterior medição digital, que teve sua confiabilidade testada e aprovada nesta pesquisa. A análise foi executada com todas as espécies juntas, para cada espécie, e por classe de DAP dentro de cada espécie. O índice de crescimento médio diário para a tipuana foi de 0,360 cm; para o ipê-roxo, 0,298 cm; e para chapéu-de-sol, 0,404 cm. Houve diferença significativa entre a taxa de crescimento da espécie chapéu-de-sol e ipê-roxo, indicando que a espécie e o grupo sucessional a qual pertencem influenciam no ritmo de crescimento. Não foi constatada influência do tipo de calçada e nem do tipo de solo. O IAF também não influenciou no crescimento, a não ser em ipês com DAP entre 50 e 60 cm. A dimensão do canteiro não influenciou o crescimento dos brotos de chapéu-de-sol, mas influenciou positivamente tanto as tipuanas com 60 a 70 cm de DAP, que foram as árvores com o maior ritmo de crescimento da espécie, como os ipês com DAP entre 30 e 40 cm que ainda estão vigorosos e suas raízes continuam explorando o solo. Os estudos com essa temática são poucos e fatores como clima, época de poda e histórico de manejo da área podem exercer influência na resposta da árvore à poda. Portanto, é sugerido maior tempo de estudo com os mesmos indivíduos e a inclusão de mais árvores.
The main function of the street trees, in the last 30 years, had been switched from aesthetic to the promotion of environmental services. However, this trees are submitted to a lot of stressful factors like limited growth aerea, air pollution, low water availability, compacted soil and drastic pruning. Aerial power networks distribution are the elements that most interfere with the management of street trees, because the conflict is usually already established and the energy concessionaires opt for drastic pruning, which can compromise tree sanity and stimulate epicormic shoots, that grows quickly toward the aerial networks. Therefore, the present study had the objective of determining the growth rate of epicormic shoots after it having been pruned by CPFL for the species Tipuana tipu (Benth.) Kuntze; Tabebuia heptaphylla (Vell.) Mart.; and Terminalia catappa L. in the city of Piracicaba, São Paulo, Brazil, and relate this rate to the tree and environment characteristics. For that, a field survey was made of variables such as the species, diameter at breast height (DBH), leaf area index (LAI), type of pavement, type of soil, and photos of the treebed and shoots next to a beacon for later digital measurement, which had its reliability tested and approved in this study. The analysis was performed with all species together, for each species separately, and by dbh class within each species. The average daily growth rate for Tipuana tipu was 0.360 cm; for Tabebuia heptaphylla was 0.298 cm; and for Terminalia catappa was 0.404 cm. There was a significant difference between the growth rate of Terminalia catappa and Tabebuia heptaphylla, indicating that the species and its successional group influence the growth rhythm. There was no influence of the type of sidewalk or of the type of soil in the growth rate. The LAI also did not influence any growth, just for Tabebuia Heptaphylla with DBH between 50 and 60 cm. The treebed area did not influenced the Terminalia catappa shoots growth, but it had influenced positively the Tipuana tipu with 60 to 70 cm of DBH, wich were the trees with the greater growth rate of the species, as much as the Tabebuia heptaphylla with DBH between 30 and 40 cm, which are at a fairly vigorous age and their roots continue to explore the soil. There are a few studies with this theme and factors such as climate, pruning season and area management history can influence tree response to pruning. Therefore, longer study time with the same individuals and more trees included is suggested.

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Treese, Julian [Verfasser] y Dieter [Akademischer Betreuer] Bathen. "Selektivität von Probemolekülen bei der Adsorption an Aktivkohlen aus der flüssigen Phase / Julian Treese ; Betreuer: Dieter Bathen". Duisburg, 2017. http://d-nb.info/1142113647/34.

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Treede, Martina [Verfasser] y Hermann [Akademischer Betreuer] Reichenspurner. "Retrospektive Datenanalyse zur postoperativen Rekonvaleszenz nach Mitralklappenchirurgie : Vergleich minimalinvasiver und konventioneller OP-Techniken / Martina Treede. Betreuer: Hermann Reichenspurner". Hamburg : Staats- und Universitätsbibliothek Hamburg, 2012. http://d-nb.info/1024355322/34.

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Heid, Conrad [Verfasser] y Rolf-Detlef [Akademischer Betreuer] Treede. "Frühe Gamma-Oszillationen als Marker für umschriebene Nozizeption im Primären Somatosensorischen Kortex / Conrad Heid ; Betreuer: Rolf-Detlef Treede". Heidelberg : Universitätsbibliothek Heidelberg, 2021. http://d-nb.info/1228700753/34.

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Kristoffersson, Anders. "Guds inre och Guds yttre. : Karl Rahner och Jürgen Moltmann om treenig Gud som (o)personlig och (o)berörbar – klassiker eller modernister?" Thesis, Umeå universitet, Institutionen för idé- och samhällsstudier, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-123529.

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Treese, Julia Sophie [Verfasser], Ingo [Akademischer Betreuer] Nolte y Ingo [Gutachter] Nolte. "Untersuchung zum Nutzen und zur Zuverlässigkeit von Patientenbesitzerbefragungen in der Kleintiermedizin / Julia Sophie Treese ; Gutachter: Ingo Nolte ; Betreuer: Ingo Nolte". Hannover : Stiftung Tierärztliche Hochschule Hannover, 2021. http://d-nb.info/1237684943/34.

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Libros sobre el tema "TreeNet"

Bliss, Jill. Treelets. Hong Kong: Blissen press, 2003.

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Brahmabhatt, Prahlad J. Treeje pagale aakash. Baroda: Gujarat Pushtakalay, 1993.

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M, Lindskold Jane, ed. Treecat wars: A Star Kingdom novel. Riverdale, NY: Baen, 2013.

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Rohden, Jens-Uwe von. Die Gewässernamen im Einzugsgebiet der Treene: Ein Beitrag zur Ortsnamenforschung in Schleswig-Holstein. Neumünster: K. Wachholtz, 1989.

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Henriksen, Levi. Bare mjuke pakker under treet: Fortellinger. [Oslo]: Gyldendal, 2005.

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Treese, Steven A. Our family: A genealogy of the Treese and Noble families. Placentia, CA: S.A. Treese, 1988.

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Hollingstedt an der Treene: Ein Flusshafen der Wikingerzeit und des Mittelalters für den Transitverkehr zwischen Nord- und Ostsee. Neumünster: Wachholtz, 2012.

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Young, Robert, 1954 June 1-, ed. Treen for the table: Wooden objects relating to eating and drinking. Woodbridge: Antique Collector's Club, 1998.

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Alt det som lå meg på hjertet: Flere mjuke pakker under treet. [Oslo]: Gyldendal, 2009.

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Mahurin, Shirley J. Greenlawn Cemetery, Cherokee County, Kansas, northwest of Picher, Oklahoma and Treece, Kansas, 1870's-May 2003. [United States]: Maloy Books, 2004.

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Capítulos de libros sobre el tema "TreeNet"

Zhao, Mengliu y Ghassan Hamarneh. "TreeNet: Multi-loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees". En Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 47–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00889-5_6.

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Grenier, Pierre-Anthony, Luc Brun y Didier Villemin. "Treelet Kernel Incorporating Chiral Information". En Graph-Based Representations in Pattern Recognition, 132–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38221-5_14.

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Craig, Erica H., Tim H. Craig y Mark R. Fuller. "Using TreeNet, a Machine Learning Approach to Better Understand Factors that Influence Elevated Blood Lead Levels in Wintering Golden Eagles in the Western United States". En Machine Learning for Ecology and Sustainable Natural Resource Management, 243–60. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96978-7_12.

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Ding, Yuan y Martha Palmer. "Automatic Learning of Parallel Dependency Treelet Pairs". En Natural Language Processing – IJCNLP 2004, 233–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30211-7_25.

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Muscolo, Giovanni Gerardo y Rezia Molfino. "TREEBOT: Tree Recovering Renewable Energy Robot". En Towards Autonomous Robotic Systems, 28–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43645-5_3.

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Lam, Tin Lun y Yangsheng Xu. "A Novel Tree-Climbing Robot: Treebot". En Springer Tracts in Advanced Robotics, 23–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28311-6_4.

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Jitnah, N. y A. Nicholson. "TreeNets: A framework for anytime evaluation of belief networks". En Qualitative and Quantitative Practical Reasoning, 350–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0035634.

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Bougleux, Sébastien, François-Xavier Dupé, Luc Brun y Myriam Mokhtari. "Shape Similarity Based on a Treelet Kernel with Edition". En Lecture Notes in Computer Science, 199–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34166-3_22.

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Ganian, Robert, Neha Lodha, Sebastian Ordyniak y Stefan Szeider. "SAT-Encodings for Treecut Width and Treedepth". En 2019 Proceedings of the Twenty-First Workshop on Algorithm Engineering and Experiments (ALENEX), 117–29. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2019. http://dx.doi.org/10.1137/1.9781611975499.10.

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Ignat, Claudia-Lavinia y Moira C. Norrie. "Customizable Collaborative Editor Relying on treeOPT Algorithm". En ECSCW 2003, 315–34. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0068-0_17.

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Actas de conferencias sobre el tema "TreeNet"

Li, Wei, Yuxiao Qu, Gengjie Chen, Yuzhe Ma y Bei Yu. "TreeNet". En ASPDAC '21: 26th Asia and South Pacific Design Automation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3394885.3431566.

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Cheng, Zhou, Chun Yuan, Jiancheng Li y Haiqin Yang. "TreeNet: Learning Sentence Representations with Unconstrained Tree Structure". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/557.

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Resumen

Recursive neural network (RvNN) has been proved to be an effective and promising tool to learn sentence representations by explicitly exploiting the sentence structure. However, most existing work can only exploit simple tree structure, e.g., binary trees, or ignore the order of nodes, which yields suboptimal performance. In this paper, we proposed a novel neural network, namely TreeNet, to capture sentences structurally over the raw unconstrained constituency trees, where the number of child nodes can be arbitrary. In TreeNet, each node is learning from its left sibling and right child in a bottom-up left-to-right order, thus enabling the net to learn over any tree. Furthermore, multiple soft gates and a memory cell are employed in implementing the TreeNet to determine to what extent it should learn, remember and output, which proves to be a simple and efficient mechanism for semantic synthesis. Moreover, TreeNet significantly suppresses convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) with fewer parameters. It improves the classification accuracy by 2%-5% with 42% of the best CNN’s parameters or 94% of standard LSTM’s. Extensive experiments demonstrate TreeNet achieves the state-of-the-art performance on all four typical text classification tasks.

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Elish, Mahmoud O. y Karim O. Elish. "Application of TreeNet in Predicting Object-Oriented Software Maintainability: A Comparative Study". En 2009 13th European Conference on Software Maintenance and Reengineering. IEEE, 2009. http://dx.doi.org/10.1109/csmr.2009.57.

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Lu, Dong, Yanlong Zhai, Jianqing Wu y Jun Shen. "TreeNet: A Hierarchical Deep Learning Model to Facilitate Edge Intelligence for Resource-Constrained Devices". En 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2021. http://dx.doi.org/10.1109/ccgrid51090.2021.00062.

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Quirk, Chris, Arul Menezes y Colin Cherry. "Dependency treelet translation". En the 43rd Annual Meeting. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1219840.1219874.

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Menezes, Arul, Kristina Toutanova y Chris Quirk. "Microsoft research treelet translation system". En the Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1654650.1654676.

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Sevon, Petteri, Hannu T. T. Toivonen y Vesa Ollikainen. "TreeDT". En the seventh ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/502512.502566.

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Domingues, Leonardo R. y Helio Pedrini. "Bounding volume hierarchy optimization through agglomerative treelet restructuring". En the 7th Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2790060.2790065.

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Narayanan, Shrivathsan, U. Vinoop, M. Satish y N. G. Yashwanth. "Autonomous tree climbing robot (Treebot)". En 2013 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2013. http://dx.doi.org/10.1109/iccic.2013.6724241.

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Xiong, Deyi, Qun Liu y Shouxun Lin. "A dependency treelet string correspondence model for statistical machine translation". En the Second Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2007. http://dx.doi.org/10.3115/1626355.1626361.

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