Academic literature on the topic 'TreeNet'
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Journal articles on the topic "TreeNet"
Changpetch, Pannapa. "Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset." Symmetry 13, no. 2 (February 8, 2021): 287. http://dx.doi.org/10.3390/sym13020287.
Full textLawry, D., G. M. Moore, and D. Peacock. "The TREENET Avenues of Honour Project 1915-2015." Acta Horticulturae, no. 1108 (February 2016): 31–40. http://dx.doi.org/10.17660/actahortic.2016.1108.4.
Full textAbdi, 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, no. 18 (September 14, 2019): 3965. http://dx.doi.org/10.3390/s19183965.
Full textChangpetch, Pannapa. "Gambling Consumers in Thailand." Asian Social Science 13, no. 5 (April 19, 2017): 136. http://dx.doi.org/10.5539/ass.v13n5p136.
Full textPadmaja, B., V. V. Rama Prasad, and K. V. N. Sunitha. "TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 2 (August 1, 2016): 446. http://dx.doi.org/10.11591/ijeecs.v3.i2.pp446-452.
Full textPadmaja, B., V. V. Rama Prasad, and K. V. N. "TreeNet Analysis of Human Stress Behavior using Socio-Mobile Data." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 1 (October 1, 2016): 148. http://dx.doi.org/10.11591/ijeecs.v4.i1.pp148-154.
Full textSteinberg, Dan, Nicholas C. Cardell, John Ries, and Mykhaylyo Golovnya. "Using TreeNet to Cross-sell Home Loans to Credit Card Holders." International Journal of Data Warehousing and Mining 4, no. 2 (April 2008): 32–45. http://dx.doi.org/10.4018/jdwm.2008040105.
Full textMohanty, Ramakanta, V. Ravi, and M. R. Patra. "Application of Machine Learning Techniques to Predict Software Reliability." International Journal of Applied Evolutionary Computation 1, no. 3 (July 2010): 70–86. http://dx.doi.org/10.4018/jaec.2010070104.
Full textTang, Yanchao, Tong Zhao, Nian Huang, Wanfu Lin, Zhiying Luo, and 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 (February 3, 2019): 1–10. http://dx.doi.org/10.1155/2019/1686205.
Full textMuchira, James M., Philimon N. Gona, Suzanne Leveille, and Laura L. Hayman. "A Review of Methodological Approaches for Developing Diagnostic Algorithms for Diabetes Screening." Journal of Nursing Measurement 27, no. 3 (December 1, 2019): 433–57. http://dx.doi.org/10.1891/1061-3749.27.3.433.
Full textDissertations / Theses on the topic "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/.
Full textDifferent 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).
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.
Full textRohden, 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.
Full textFörster, Jana [Verfasser], Heiner [Akademischer Betreuer] Boeing, and 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.
Full textAbreu, 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/.
Full textThe 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.
Treese, Julian [Verfasser], and 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.
Full textTreede, Martina [Verfasser], and 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.
Full textHeid, Conrad [Verfasser], and 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.
Full textKristoffersson, 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.
Full textTreese, Julia Sophie [Verfasser], Ingo [Akademischer Betreuer] Nolte, and 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.
Full textBooks on the topic "TreeNet"
M, Lindskold Jane, ed. Treecat wars: A Star Kingdom novel. Riverdale, NY: Baen, 2013.
Find full textRohden, Jens-Uwe von. Die Gewässernamen im Einzugsgebiet der Treene: Ein Beitrag zur Ortsnamenforschung in Schleswig-Holstein. Neumünster: K. Wachholtz, 1989.
Find full textHenriksen, Levi. Bare mjuke pakker under treet: Fortellinger. [Oslo]: Gyldendal, 2005.
Find full textTreese, Steven A. Our family: A genealogy of the Treese and Noble families. Placentia, CA: S.A. Treese, 1988.
Find full textHollingstedt an der Treene: Ein Flusshafen der Wikingerzeit und des Mittelalters für den Transitverkehr zwischen Nord- und Ostsee. Neumünster: Wachholtz, 2012.
Find full textYoung, Robert, 1954 June 1-, ed. Treen for the table: Wooden objects relating to eating and drinking. Woodbridge: Antique Collector's Club, 1998.
Find full textAlt det som lå meg på hjertet: Flere mjuke pakker under treet. [Oslo]: Gyldendal, 2009.
Find full textMahurin, Shirley J. Greenlawn Cemetery, Cherokee County, Kansas, northwest of Picher, Oklahoma and Treece, Kansas, 1870's-May 2003. [United States]: Maloy Books, 2004.
Find full textBook chapters on the topic "TreeNet"
Zhao, Mengliu, and Ghassan Hamarneh. "TreeNet: Multi-loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees." In 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.
Full textGrenier, Pierre-Anthony, Luc Brun, and Didier Villemin. "Treelet Kernel Incorporating Chiral Information." In 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.
Full textCraig, Erica H., Tim H. Craig, and 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." In 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.
Full textDing, Yuan, and Martha Palmer. "Automatic Learning of Parallel Dependency Treelet Pairs." In 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.
Full textMuscolo, Giovanni Gerardo, and Rezia Molfino. "TREEBOT: Tree Recovering Renewable Energy Robot." In Towards Autonomous Robotic Systems, 28–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43645-5_3.
Full textLam, Tin Lun, and Yangsheng Xu. "A Novel Tree-Climbing Robot: Treebot." In 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.
Full textJitnah, N., and A. Nicholson. "TreeNets: A framework for anytime evaluation of belief networks." In Qualitative and Quantitative Practical Reasoning, 350–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0035634.
Full textBougleux, Sébastien, François-Xavier Dupé, Luc Brun, and Myriam Mokhtari. "Shape Similarity Based on a Treelet Kernel with Edition." In 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.
Full textGanian, Robert, Neha Lodha, Sebastian Ordyniak, and Stefan Szeider. "SAT-Encodings for Treecut Width and Treedepth." In 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.
Full textIgnat, Claudia-Lavinia, and Moira C. Norrie. "Customizable Collaborative Editor Relying on treeOPT Algorithm." In ECSCW 2003, 315–34. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0068-0_17.
Full textConference papers on the topic "TreeNet"
Li, Wei, Yuxiao Qu, Gengjie Chen, Yuzhe Ma, and Bei Yu. "TreeNet." In ASPDAC '21: 26th Asia and South Pacific Design Automation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3394885.3431566.
Full textCheng, Zhou, Chun Yuan, Jiancheng Li, and Haiqin Yang. "TreeNet: Learning Sentence Representations with Unconstrained Tree Structure." In 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.
Full textElish, Mahmoud O., and Karim O. Elish. "Application of TreeNet in Predicting Object-Oriented Software Maintainability: A Comparative Study." In 2009 13th European Conference on Software Maintenance and Reengineering. IEEE, 2009. http://dx.doi.org/10.1109/csmr.2009.57.
Full textLu, Dong, Yanlong Zhai, Jianqing Wu, and Jun Shen. "TreeNet: A Hierarchical Deep Learning Model to Facilitate Edge Intelligence for Resource-Constrained Devices." In 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2021. http://dx.doi.org/10.1109/ccgrid51090.2021.00062.
Full textQuirk, Chris, Arul Menezes, and Colin Cherry. "Dependency treelet translation." In the 43rd Annual Meeting. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1219840.1219874.
Full textMenezes, Arul, Kristina Toutanova, and Chris Quirk. "Microsoft research treelet translation system." In the Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1654650.1654676.
Full textSevon, Petteri, Hannu T. T. Toivonen, and Vesa Ollikainen. "TreeDT." In the seventh ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/502512.502566.
Full textDomingues, Leonardo R., and Helio Pedrini. "Bounding volume hierarchy optimization through agglomerative treelet restructuring." In the 7th Conference. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2790060.2790065.
Full textNarayanan, Shrivathsan, U. Vinoop, M. Satish, and N. G. Yashwanth. "Autonomous tree climbing robot (Treebot)." In 2013 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2013. http://dx.doi.org/10.1109/iccic.2013.6724241.
Full textXiong, Deyi, Qun Liu, and Shouxun Lin. "A dependency treelet string correspondence model for statistical machine translation." In the Second Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2007. http://dx.doi.org/10.3115/1626355.1626361.
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