Academic literature on the topic 'Classification analysi'
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Journal articles on the topic "Classification analysi"
Himmelreich, Nastassja, Rosa Navarrete, Lourdes Ruiz Desviat, Santiago Ramon, Belen Perez, and Nenad Blau. "PATHOGENICITY CLASSIFICATION OF PHENYLALANINE HYDROXYLASE () MISSENSE VARIANTS USING ACMG/AMP/ACGS RECOMMENDATIONS, VARIANT EFFECT PREDICTORS (VEP) AND 3D ANALYSI." Molecular Genetics and Metabolism 138, no. 3 (March 2023): 107422. http://dx.doi.org/10.1016/j.ymgme.2023.107422.
Full textWillatt, D. J., M. S. McCormick, R. P. Morton, and P. M. Stell. "Staging of Maxillary Cancer." Annals of Otology, Rhinology & Laryngology 96, no. 2 (March 1987): 137–41. http://dx.doi.org/10.1177/000348948709600201.
Full textDwivedi, Somya, Harsh Patel, and Shweta Sharma. "Movie Reviews Classification Using Sentiment Analysis." Indian Journal of Science and Technology 12, no. 41 (November 20, 2019): 1–6. http://dx.doi.org/10.17485/ijst/2019/v12i41/145554.
Full textSupe, Liāna, and Ingūna Jurgelāne-Kaldava. "Classification of higher education institutions: qualitative content analysis." Pedagoģija: teorija un prakse : zinātnisko rakstu krājums = Pedagogy: Theory and Practice : collection of scientific articles, no. IX (April 6, 2020): 87–99. http://dx.doi.org/10.37384/ptp.2020.09.087.
Full textDi Lauro, Salvatore, Mustafa R. Kadhim, David G. Charteris, and J. Carlos Pastor. "Classifications for Proliferative Vitreoretinopathy (PVR): An Analysis of Their Use in Publications over the Last 15 Years." Journal of Ophthalmology 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/7807596.
Full textGehlsen, Gale M., and Joan Karpuk. "Analysis of the NWAA Swimming Classification System." Adapted Physical Activity Quarterly 9, no. 2 (April 1992): 141–47. http://dx.doi.org/10.1123/apaq.9.2.141.
Full textKhan, Nida Zafar, and Prof S. R. Yadav. "Analysis of Text Classification Algorithms: A Review." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 579–81. http://dx.doi.org/10.31142/ijtsrd21448.
Full textRoberts, Kevin C., John B. Lindsay, and Aaron A. Berg. "An Analysis of Ground-Point Classifiers for Terrestrial LiDAR." Remote Sensing 11, no. 16 (August 16, 2019): 1915. http://dx.doi.org/10.3390/rs11161915.
Full textPaula Neto, Antonio Candido de, Douglas Hideki Ikeuti, Augusto Braga dos Santos, Rui Dos Santos Barroco, Bruno Rodrigues de Miranda, and Rafael Da Rocha Macedo. "Concordance analysis of adult ankle fracture classifications." Scientific Journal of the Foot & Ankle 13, no. 1 (March 31, 2019): 10–14. http://dx.doi.org/10.30795/scijfootankle.2019.v13.885.
Full textHassan, Awring Falah. "Analysis of BBC News by Applying Classification Algorithms." Journal of Advanced Research in Dynamical and Control Systems 12, no. 1 (February 13, 2020): 148–52. http://dx.doi.org/10.5373/jardcs/v12i1/20201023.
Full textDissertations / Theses on the topic "Classification analysi"
Marchetti, A. "Automatic classification of galaxy spectra in large redshift surveys." Doctoral thesis, Università degli Studi di Milano, 2014. http://hdl.handle.net/2434/243304.
Full textROMELLI, KATIA. "Discourse, society and mental disorders: deconstructing DSM over time through critical and lacanian discourse analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/83278.
Full textBonneau, Jean-Christophe. "La classification des contrats : essai d'une analyse systémique des classifications du Code civil." Grenoble, 2010. http://www.theses.fr/2010GREND017.
Full textThe classification of contracts as it is stated in the civil Code articles 1102 onwards structurally distinguishes itself from modern classifications having been added to it. Looking thoroughly at the matter of a global approach of classification, the classifications of the civil Code, separated from a legal regime which does not in fact depend on them and on notions which are foreign to it, such as the concept of “cause”, were considered in their connections of logic and complementarity. The existence of the chains of classifications, a new classification resulting from the coherent assembly of the various classifications provided for the civil Code, were brought to light thanks to a study aiming at understanding how these classifications are bound and harmonized. The features of the classification of contracts were then deducted from the very structure of the classifications of the civil Code combined in chains. These have for feature to reveal what constitutes the essence of the contract, by allowing to distinguish it from certain figures which try to assimilate to it but nevertheless distinguish themselves from it since the capacity of a legal object to become integrated into the chains of classifications is perceived as conditional on the contractual qualification itself. Considered as a preferred criterion of the definition of the contract, which can give rise to projects aiming at the elaboration of a body of European contract laws, the chains of classifications were then conceptualised in their connections with the variety of the named contracts. The chains of classifications absorb these contracts as well as their legal regime which can, consequently, be transposed into the unnamed contracts. Allowing a renewal of the groupings generally perceived, the chains of classifications bring a new light to the process of qualification of the contract. They contribute to specify the domain of the modification of the contract, and finally supply a foundation for the direct contractual action which is applied to the chains of contracts
Llobell, Fabien. "Classification de tableaux de données, applications en analyse sensorielle." Thesis, Nantes, Ecole nationale vétérinaire, 2020. http://www.theses.fr/2020ONIR143F.
Full textMultiblock datasets are more and more frequent in several areas of application. This is particularly the case in sensory evaluation where several tests lead to multiblock datasets, each dataset being related to a subject (judge, consumer, ...). The statistical analysis of this type of data has raised an increasing interest over the last thirty years. However, the clustering of multiblock datasets has received little attention, even though there is an important need for this type of data.In this context, a method called CLUSTATIS devoted to the cluster analysis of datasets is proposed. At the heart of this approach is the STATIS method, which is a multiblock datasets analysis strategy. Several extensions of the CLUSTATIS clustering method are presented. In particular, the case of data from the so-called "Check-All-That-Apply" (CATA) task is considered. An ad-hoc clustering method called CLUSCATA is discussed.In order to improve the homogeneity of clusters from both CLUSTATIS and CLUSCATA, an option to add an additional cluster, called "K+1", is introduced. The purpose of this additional cluster is to collect datasets identified as atypical.The choice of the number of clusters is discussed, ans solutions are proposed. Applications in sensory analysis as well as simulation studies highlight the relevance of the clustering approach.Implementations in the XLSTAT software and in the R environment are presented
Platon, Ludovic. "Algorithms for ab initio identification and classification of ncRNAs." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLE003/document.
Full textThe non-coding RNA (ncRNA) identification helps to improve our comprehension of biology. We know the biological functions for a majority of ncRNA classes. But, we don't know all the classes of ncRNAs. Besides, the identification of ncRNAs using computational methods is not a trivial task. The relevant features for each class of ncRNAs rely on multiple heterogeneous sources of data (sequences, secondary structure, interaction with other biological components, etc.). During this thesis, we developed methods relying on Self-Organizing Maps (SOM).The SOM is used to analyze and represent the ncRNAs by a map of clusters where the topology of the data is preserved.We proposed a new SOM version called MSSOM which can handle multiple sources of data composed of numerical data or complex data (represented by kernels). MSSOM combines data sources by using a SOM for each source and learns the weights of each source at the cluster level.We also proposed a supervised variant of SOM with rejection called SLSOM. SLSOM is able to identify and classify the known classes using multi layer perceptron and the output of a SOM.The rejection options associated to the output layer allow to reject the unreliable prediction and to identify the potential new classes.These methods lead to the development of bioinformatic tools.We applied a variant of SLSOM to the discrimination of coding and non-coding RNAs. This method called IRSOM has been evaluated on a wide range of species coming from different reigns (plants, animals, bacteria and fungi).By using a simple set of sequence features, we showed that IRSOM is able to separate the coding and non-coding RNAs efficiently.With the SOM visualization and the rejection option, we also highlighted and analyzed some ambiguous RNAs on the human. The second one is called CRSOM.CRSOM classify ncRNAs into sub classes by integrating two data sources which are the sequence k-mer frequencies and a Gaussian kernel using the edit distance. We show that CRSOM give comparable results with the reference tool (nRC) without reject and better results with the rejection option
Neovius, Sofia. "René Descartes’ Foundations of Analytic Geometry and Classification of Curves." Thesis, Uppsala universitet, Algebra och geometri, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-202147.
Full textFazeli, Goldisse. "Classification and discriminant analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ47800.pdf.
Full textde, Roos Dolf. "Spectral analysis classification sonars." Thesis, University of Canterbury. Electrical Engineering, 1986. http://hdl.handle.net/10092/5575.
Full textLee, Lily 1971. "Gait analysis for classification." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8116.
Full textIncludes bibliographical references (p. 121-124).
This thesis describes a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple localized image features such as moments extracted from orthogonal view video silhouettes of human walking motion. A suite of time-integration methods, spanning a range of coarseness of time aggregation and modeling of feature distributions, are applied to these image features to create a suite of gait sequence representations. Despite their simplicity, the resulting feature vectors contain enough information to perform well on human identification and gender classification tasks. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times, and under varying lighting environments. Each of the integration methods are investigated for their advantages and disadvantages. An improved gait representation is built based on our experiences with the initial set of gait representations. In addition, we show gender classification results using our gait appearance features, the effect of our heuristic feature selection method, and the significance of individual features.
by Lily Lee.
Ph.D.
Duong, Minh Duc <1992>. "Classification by pairwise coupling." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16806.
Full textBooks on the topic "Classification analysi"
Mirkin, B. G. Mathematical classification and clustering. Dordrecht: Kluwer Academic Publishers, 1996.
Find full textPhipps, Arabie, Hubert Lawrence J. 1944-, and Soete Geert de, eds. Clustering and classification. Singapore: World Scientific, 1996.
Find full textC, Gower J., ed. Ordination and classification. Montréal, Québec, Canada: Les Presses de l'Université de Montréal, 1986.
Find full textJajuga, Krzysztof, Krzysztof Najman, and Marek Walesiak, eds. Data Analysis and Classification. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75190-6.
Full textJajuga, Krzysztof, Jacek Batóg, and Marek Walesiak, eds. Classification and Data Analysis. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52348-0.
Full textBook chapters on the topic "Classification analysi"
Soille, Pierre. "Classification." In Morphological Image Analysis, 255–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03939-7_10.
Full textSoille, Pierre. "Classification." In Morphological Image Analysis, 293–315. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05088-0_10.
Full textNiemann, Heinrich. "Classification." In Pattern Analysis and Understanding, 151–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-74899-8_4.
Full textL. Jockers, Matthew, and Rosamond Thalken. "Classification." In Text Analysis with R, 195–210. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39643-5_16.
Full textCesare, Silvio, and Yang Xiang. "Dynamic Analysis." In Software Similarity and Classification, 51–56. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2909-7_6.
Full textGöker, Markus. "What can genome analysis offer for bacteria?" In Trends in the systematics of bacteria and fungi, 255–81. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789244984.0255.
Full textDigby, P. G. N., and R. A. Kempton. "Classification." In Multivariate Analysis of Ecological Communities, 124–49. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3133-6_5.
Full textDigby, P. G. N., and R. A. Kempton. "Classification." In Multivariate Analysis of Ecological Communities, 124–49. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3135-0_5.
Full textCauston, D. R. "Classification." In An Introduction to Vegetation Analysis, 98–144. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-011-7981-2_6.
Full textCauston, D. R. "Classification." In An Introduction to Vegetation Analysis, 98–144. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-011-9737-3_6.
Full textConference papers on the topic "Classification analysi"
Cheng, Qianwei, AKM Mahbubur Rahman, Anis Sarker, Abu Bakar Siddik Nayem, Ovi Paul, Amin Ahsan Ali, M. Ashraful Amin, Ryosuke Shibasaki, and Moinul Zaber. "Deep-learning Coupled with Novel Classification Method to Classify the Urban Environment of the Developing World." In 8th International Conference on Artificial Intelligence and Applications (AIAP 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110103.
Full textKrylov, Alexey. "EUPHEMIA AS A LANGUAGE PHENOMENON: WORD-FORMATIVE ASPECT." In ЯЗЫК. КУЛЬТУРА. ПЕРЕВОД = LANGUAGE. CULTURE. TRANSLATION. Science and Innovation Center Publishing House, 2019. http://dx.doi.org/10.12731/lct.2019.18.
Full textDmitrieva, Elena, and Elena Terekhova. "Semantic matching of State Rubricator of Sci-tech Information and OECD headings." In Sixth World Professional Forum "The Book. Culture. Education. Innovations". Russian National Public Library for Science and Technology, 2021. http://dx.doi.org/10.33186/978-5-85638-236-4-2021-60-76.
Full textPercival, Will J., and Coryn A. L. Bailer-Jones. "Statistical Analysis of Galaxy Redshift Surveys." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059038.
Full textFliri, J., D. Martínez-Delgado, M. Jurić, and Coryn A. L. Bailer-Jones. "SDSS analysis of Galactic stellar streams." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059051.
Full textGorguluarslan, Recep M., and Seung-Kyum Choi. "Predicting Reliability of Structural Systems Using Classification Method." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13323.
Full textHeasley, J. N., and Coryn A. L. Bailer-Jones. "The Pan-STARRS Data Processing and Science Analysis Software Systems." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059075.
Full textKurdyukov, V. N., and T. V. Lebedeva. "ANALYSIS OF MEASURES TO REDUCE ECOLOGICAL AND ECONOMIC DAMAGE FROM AUTOMOBILE TRANSPORT." In STATE AND DEVELOPMENT PROSPECTS OF AGRIBUSINESS. DSTU-PRINT, 2020. http://dx.doi.org/10.23947/interagro.2020.1.643-646.
Full text"Statistical Analysis of the Human EEG during RF Exposure from Mobile Phones: An Alternative Method to Analysis of the EEG in Frequency Bands." In The First International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001197301670174.
Full textIgasheva, Anastasiia Sergeevna. "LINGUISTIC PECULIARITIES OF PUN, ITS TYPOLOGY AND CLASSIFICATION." In Сollection of articles. Publishing house Sreda, 2019. http://dx.doi.org/10.31483/r-32974.
Full textReports on the topic "Classification analysi"
Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41960.
Full textD. W. Gwyn. QA CLASSIFICATION ANALYSIS OF GROUND SUPPORT SYSTEMS. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/891530.
Full textJimenez, Luis O., Miguel Velez, and Shawn Hunt. Unsupervised Classification System for Hyperspectral Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, May 2001. http://dx.doi.org/10.21236/ada398803.
Full textSantos, Eunice E. Social Networks Analysis: Classification, Evaluation, and Methodologies. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada567185.
Full textSukittanon, Somsak, Les E. Atlas, James W. Pitton, and Jack McLaughlin. Non-Stationary Signal Classification Using Joint Frequency Analysis. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada436792.
Full textHOBART, R. L. B-Cell waste classification sampling and analysis plan. Office of Scientific and Technical Information (OSTI), September 1999. http://dx.doi.org/10.2172/797998.
Full textSopher, Ariana M., Sally A. Shoop, Jesse Jr M. Stanley, and Brian T. Tracy. Image Analysis and Classification Based on Soil Strength. Fort Belvoir, VA: Defense Technical Information Center, August 2016. http://dx.doi.org/10.21236/ad1014532.
Full textMcLaughlin, Jack, Scott Philips, and James Pitton. Perceptually-Driven Signal Analysis for Acoustic Event Classification. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada476810.
Full textRamm-Granberg, Tynan, F. Rocchio, Catharine Copass, Rachel Brunner, and Eric Nelsen. Revised vegetation classification for Mount Rainier, North Cascades, and Olympic national parks: Project summary report. National Park Service, February 2021. http://dx.doi.org/10.36967/nrr-2284511.
Full textHan, Euihong, and George Karypis. Centroid-Based Document Classification Algorithms: Analysis & Experimental Results. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada439538.
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