Relevant bibliographies by topics / Profiling

Academic literature on the topic 'Profiling'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 July 2024

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Journal articles on the topic "Profiling"

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McCarthy, Nicola. "Profiling the profilins." Nature Reviews Cancer 13, no. 1 (December 21, 2012): 8. http://dx.doi.org/10.1038/nrc3435.

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NDW. "Profiling the profiling." Journal of the Forensic Science Society 32, no. 4 (October 1992): 365–66. http://dx.doi.org/10.1016/s0015-7368(92)73094-1.

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Muschinski, Andreas, Volker Lehmann, Lutz Justen, and Gerd Teschke. "Advanced radar wind profiling." Meteorologische Zeitschrift 14, no. 5 (October 10, 2005): 609–25. http://dx.doi.org/10.1127/0941-2948/2005/0067.

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Georgiev, Venelin. "Profiling Human Roles in Cybercrime." Information & Security: An International Journal 43, no. 2 (2019): 145–60. http://dx.doi.org/10.11610/isij.4313.

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Goldwyn, Robert M. "Profiling." Plastic and Reconstructive Surgery 111, no. 5 (April 2003): 1756. http://dx.doi.org/10.1097/01.prs.0000053551.45063.3e.

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Fernandez, Katya C., Cheri A. Levinson, and Thomas L. Rodebaugh. "Profiling." Social Psychological and Personality Science 3, no. 6 (January 19, 2012): 706–13. http://dx.doi.org/10.1177/1948550611434967.

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Vebi Natasya, Salsabila, and Rolly Maulana Awangga. "PROFILING MAHASISWA DAN ALUMNI MENGGUNAKAN METODE DECISION TREE." JATI (Jurnal Mahasiswa Teknik Informatika) 7, no. 2 (September 8, 2023): 1359–63. http://dx.doi.org/10.36040/jati.v7i2.6824.

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Profilling mahasiswa dan alumni merupakan sebuah studi yang bertujuan untuk mengidentifikasi karakteristik individu yang memasuki dan keluar dari sebuah institusi pendidikan. Melalui analisis data yang dikumpulkan, studi ini dapat memberikan informasi yang berharga bagi institusi untuk meningkatkan program-program yang ditawarkan dan membantu mahasiswa serta alumni untuk merencanakan karir mereka. Literature Riview ini akan membeirkan sebuah tinjauan sistematis mengenai profiling mahasiswa dan alumni. Pada penelitian ini digunakan artikel-artikel yang diterbitkan dari tahun 2016-2023 dalam melakukan literature riview. Yang mana artikel tersebut didapat diberbagai database perpustakaan digital seperti IEE Explore, Science Direct, Google Scholar dan masih banyak lagi. Tujuan utama pada penelitian ini yaitu untuk mengetahui atau mencari tren dalam topik yang nantinya akan dilakukan penelitian lebih dalam. Pada penelitian ini menggunakan metode Systematic Literature Review. Hasil dari penelitian Ssystematic Literature Review ini adalah metode yang paling banyak digunakan untuk profiling adalah K-Means, Decision Tree menjadi metode dengan akurasi yang cukup tinggi dalam melakukan profiling, algoritma Decision tree yang paling banyak digunakan untuk profiling adalah C4.5.
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Lobon-Iglesias, María-Jesús, Arnault Tauziede-Espariat, Mamy Andrianteranagna, Zhiyan Han, Julien Masliah-Planchon, and Franck Bourdeaut. "ATRT-27. COST-EFFECTIVE ASSAYS TO SUBGROUP ATRT IN THE DAILY ROUTINE." Neuro-Oncology 22, Supplement_3 (December 1, 2020): iii281. http://dx.doi.org/10.1093/neuonc/noaa222.026.

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Abstract Three atypical teratoid rhabdoid tumors (ATRT) molecular subgroups with different bio-clinical characteristics have been reported (TYR, SHH and MYC). Molecular subgrouping relies on either methylation profiling (reference methods), or expression profiling. However, the cost-effectiveness of such pangenomic screening is questionable. This work aims to study the reliability of alternative techniques for subgroup classification in the daily routine. Illumina EPIC-arrays were performed on 46 samples. Among those cases, expression profiling were analysed by RNAseq (n=30). We designed a 26-gene panel to assess expression profiling using the Nanostring technology; this was applied to 35 tumors. Immunohistochemistry (IHC) was used for 20 samples; it relied on the expression of MITF, TYR, OTX2 and MYC. We first assessed the concordance between DNA methylation and RNAseq based profilings; then, between RNAseq and Nanostring and, finally, between methylation profiling and Nanostring or IHC, the two rapidest and cheapest tools. The concordance between the two expression-based profiling was 19/21. EPIC-arrays and RNAseq or Nanostring were concordant in 26/30 and 30/35 samples, respectively. The concordance was perfect for methylation-defined MYC subtype. Finally, 17/20 tumor samples were classified in the same subgroup by EPIC-arrays and IHC; the 3/20 misclassified tumors were SHH by methylation but consistently MYC by IHC, Nanostring and RNAseq. There was 90–100% of concordance for TYR subgroup (all techniques). We have designed a gene panel-based expression signature that shows promising concordance with RNAseq and methylation profiling. Nanostring assay and IHC well predict ATRT subgroup classification for MYC and TYR subclass, but less so for methylation-defined SHH ones.
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Yoon, Ung-Han, Yeon-Ki Kim, Chang-Kug Kim, Jang-Ho Hahn, Dong-Hern Kim, Tae-Ho Lee, Gang-Seob Lee, Soo-Chul Park, and Baek-Hie Nahm. "Current status on expression profiling using rice microarray." Journal of Plant Biotechnology 37, no. 2 (June 30, 2010): 144–52. http://dx.doi.org/10.5010/jpb.2010.37.3.144.

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Tsai, Alexander C. "Prescriber Profiling." Annals of Internal Medicine 148, no. 1 (January 1, 2008): 81. http://dx.doi.org/10.7326/0003-4819-148-1-200801010-00012.

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Dissertations / Theses on the topic "Profiling"

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Bouhana, Noémie. "Profiling arson." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615799.

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Faur, Andrei. "Memory Profiling Techniques." Thesis, Linköpings universitet, Programvara och system, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79598.

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Memory profiling is an important technique which aids program optimization and can even help tracking down bugs. The main problem with the current memory profiling techniques and tools is that they slow down the target software considerably therefore making them inadequate for mainline integration. Ideally, the user would be able to monitor memory consumption without having to worry about the rest of the software being affected in any way. This thesis provides a comparison of existing techniques and tools along with the description of a memory profiler implementation which tries to provide a balance between the information it is able to retrieve and the influence it has on the target software.
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Lai, Ka-ying, and 黎家盈. "Profiling internet pirates." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46606920.

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SPAHIU, BLERINA. "Profiling Linked Data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/151645.

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Nonostante l'elevato numero di dati pubblicati come LD, il loro utilizzo non ha ancora mostrato il loro potenziale per l’assenza di comprensione dei metadati. I consumatori di dati hanno bisogno di ottenere informazioni dai dataset in modo veloce e concentrato per poter decidere se sono utili per il loro problema oppure no. Le tecniche di profilazione dei dati offrono una soluzione efficace a questo problema in quanto sono utilizzati per generare metadati e statistiche che descrivono il contenuto dei dataset. Questa tesi presenta una ricerca, che affronta i problemi legati alla profilazione Linked Data. Nonostante il termine profilazione dei dati è usato in modo generico per diverse informazioni che descrivono i dataset, in questa tesi noi andiamo a ricoprire tre aspetti della profilazione; topic-based, schema-based e linkage-based. Il profilo proposto in questa tesi è fondamentale per il processo decisionale ed è la base dei requisiti che portano verso la comprensione dei dataset. In questa tesi presentiamo un approccio per classificare automaticamente insiemi di dati in una delle categorie utilizzate nel mondo dei LD. Inoltre, indaghiamo il problema della profilazione multi-topic. Per la profilazione schema-based proponiamo un approccio riassuntivo schema-based, che fornisce una panoramica sui rapporti nei dati. I nostri riassunti sono concisi e chiari sufficientemente per riassumere l'intero dataset. Inoltre, essi rivelano problemi di qualità e possono aiutare gli utenti nei compiti di formulazione dei query. Molti dataset nel LD cloud contengono informazioni simili per la stessa entità. Al fine di sfruttare appieno il suo potenziale LD bisogna far vedere questa informazione in modo esplicito. Profiling Linkage fornisce informazioni sul numero di entità equivalenti tra i dataset e rivela possibili errori.Le tecniche di profiling sviluppate durante questo lavoro sono automatiche e possono essere applicate a differenti insiemi di dati indipendentemente dal dominio.
Recently, the increasing diffusion of Linked Data (LD) as a standard way to publish and structure data on the Web has received a growing attention from researchers and data publishers. LD adoption is reflected in different domains such as government, media, life science, etc., building a powerful Web available to anyone. Despite the high number of datasets published as LD, their usage is still not exploited as they lack comprehensive metadata. Data consumers need to obtain information about datasets content in a fast and summarized form to decide if they are useful for their use case at hand or not. Data profiling techniques offer an efficient solution to this problem as they are used to generate metadata and statistics that describe the content of the dataset. Existing profiling techniques do no cover a wide range of use cases. Many challenges due to the heterogeneity nature of Linked Data are still to overcome. This thesis presents the doctoral research which tackles the problems related to Profiling Linked Data. Even though the term of data profiling is the umbrella term for diverse descriptive information that describes a dataset, in this thesis we cover three aspects of profiling; topic-based, schema-based and linkage-based. The profile provided in this thesis is fundamental for the decision-making process and is the basic requirement towards the dataset understanding. In this thesis we present an approach to automatically classify datasets in one of the topical categories used in the LD cloud. Moreover, we investigate the problem of multi-topic profiling. For the schema-based profiling we propose a schema-based summarization approach, that provides an overview about the relations in the data. Our summaries are concise and informative enough to summarize the whole dataset. Moreover, they reveal quality issues and can help users in the query formulation tasks. Many datasets in the LD cloud contain similar information for the same entity. In order to fully exploit its potential LD should made this information explicit. Linkage profiling provides information about the number of equivalent entities between datasets and reveal possible errors. The techniques of profiling developed during this work are automatic and can be applied to different datasets independently of the domain.
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Bach, Daniel Andreas. "Profiling and Optimizing a Seismic Application on Modern Architectures : Profiling for performance." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9805.

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In this thesis, we discuss several profilers and use selected ones to optimize a seismic application for StatoilHydro, Norway's main oil company. Paralellization techniques are also discussed. The application scans multiple traces of seismic data and removes unwanted multiples(noise). Seismic applications are central in simulations aiding geophysicists in finding oil reservoirs. The motivation for selecting this particular application, Adafil, is that it needs to be faster to be useful in practice. Abstract Our work gives several useful general hints for how to parallelize and optimize such applications for modern architectures. Abstract The application is profiled using several tools, singeling out three hotspots. This thesis will show that this application has some L2 cache misses. which can be avoided with prefetching. The work also shows that specific parts of the code, among others one containing a convolution algorithm, can benefit greatly by using FFT to lower complexity from O(n^2) to O(n log n) for these parts, and by leveraging the adaptive implementations of FFTW leads to a significant speedup of the application.

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Reppel, Hans Alexander. "Consumer managed profiling : exploring the potential for marketing profiling managed by consumers." Thesis, University of Birmingham, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509336.

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Kelling, Jeffrey, and Guido Juckeland. "Profiling of RT-PICLS Code." Helmholtz-Zentrum Dresden-Rossendorf, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:d120-qucosa-224404.

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It was observed, that the RT-PICLS code ran by FWKT on the hypnos cluster was producing an unusual amount of system load, according to Ganglia metrics. Since this may point to an IO-problem in the code, this code was analyzed more closely.
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Becker, Carl Martin. "Profiling of rough terrain." Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-11262009-171410/.

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Vogel, Martin Joseph. "Proteomic profiling following cryopreservation." Diss., Online access via UMI:, 2004. http://wwwlib.umi.com/dissertations/fullcit/1424168.

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Björklund, Wictor. "Personalized pricing through profiling." Thesis, Stockholms universitet, Juridiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-153198.

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Books on the topic "Profiling"

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Gillham, Bruce. Profiling. Newcastle upon Tyne: University of Northumbria at Newcastle, 1993.

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Fiona, Wilson. Profiling. Glasgow: Jordanhill College, 1990.

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Fiona, Wilson. Profiling. Glasgow: Jordanhill College, 1990.

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Fletcher, Paul, Martin J. Ball, and David Crystal, eds. Profiling Grammar. Bristol, Blue Ridge Summit: Multilingual Matters, 2016. http://dx.doi.org/10.21832/9781783094875.

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Labunskyy, Vyacheslav M., ed. Ribosome Profiling. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1150-0.

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Rani, Sweta, ed. MicroRNA Profiling. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6524-3.

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Espina, Virginia, ed. Molecular Profiling. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6990-6.

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Posch, Anton, ed. Proteomic Profiling. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2550-6.

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Kocsis, Richard N., ed. Criminal Profiling. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-60327-146-2.

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Grootveld, Martin, ed. Metabolic Profiling. Cambridge: Royal Society of Chemistry, 2014. http://dx.doi.org/10.1039/9781849735162.

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Book chapters on the topic "Profiling"

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Schäufele, Fabia. "Profiling." In Profiling zwischen sozialer Praxis und technischer Prägung, 7–36. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-16969-5_2.

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Abramowicz, Witold, Paweł Kalczyński, and Krzysztof Węcel. "Profiling." In Filtering the Web to Feed Data Warehouses, 155–77. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0137-6_6.

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Patterson, Wayne, and Cynthia E. Winston-Proctor. "Profiling." In Behavioral Cybersecurity, 35–44. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2020. http://dx.doi.org/10.1201/9781003052029-5.

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Leasure, Bruce, David J. Kuck, Sergei Gorlatch, Murray Cole, Gregory R. Watson, Alain Darte, David Padua, et al. "Profiling." In Encyclopedia of Parallel Computing, 1637. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2038.

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Patterson, Wayne, and Cynthia E. Winston-Proctor. "Profiling." In Behavioral Cybersecurity, 43–50. Boca Raton : Taylor & Francis, CRC Press, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429461484-5.

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Juola, Patrick. "Profiling." In Encyclopedia of Big Data, 774–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_550.

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Juola, Patrick. "Profiling." In Encyclopedia of Big Data, 1–2. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-32001-4_550-1.

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Paclebar, Anne Marie R., Bryan Myers, and Jocelyn Brineman. "Criminal Profiling." In Criminal Profiling, 249–62. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-146-2_11.

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Palermo, George B. "Homicidal Syndromes." In Criminal Profiling, 3–26. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-146-2_1.

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Meyer, Caroline B. "Criminal Profiling as Expert Evidence?" In Criminal Profiling, 207–47. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-146-2_10.

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Conference papers on the topic "Profiling"

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de Roeck, Anne, Dawei Song, and Udo Kruschwitz. "BCS-IRSG Workshop on Corpus Profiling - Index." In BCS-IRSG Workshop on Corpus Profiling. BCS Learning & Development, 2008. http://dx.doi.org/10.14236/ewic/irsg2008.0.

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Clark, Malcolm W., Ian Ruthven, and Patrik O’Brian Holt. "Genre analysis of structured e-mails for corpus profiling." In BCS-IRSG Workshop on Corpus Profiling. BCS Learning & Development, 2008. http://dx.doi.org/10.14236/ewic/irsg2008.1.

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Berninger, V. F., Yunhyong Kim, and Seamus Ross. "Building a document genre corpus: a profile of the KRYS I corpus." In BCS-IRSG Workshop on Corpus Profiling. BCS Learning & Development, 2008. http://dx.doi.org/10.14236/ewic/irsg2008.2.

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Khosmood, Foaad, and Robert A. Levinson. "Automatic Natural Language Style Classification and Transformation." In BCS-IRSG Workshop on Corpus Profiling. BCS Learning & Development, 2008. http://dx.doi.org/10.14236/ewic/irsg2008.3.

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Greenwood, Mark R., and Goran Nenadic. "Lexical Profiling of Existing Web Directories to Support Fine-grained Topic-Focused Web Crawling." In BCS-IRSG Workshop on Corpus Profiling. BCS Learning & Development, 2008. http://dx.doi.org/10.14236/ewic/irsg2008.4.

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Cooke, Neil J., and Lee Gillam. "Distributional Lexical Semantics for Stop Lists." In BCS-IRSG Workshop on Corpus Profiling. BCS Learning & Development, 2008. http://dx.doi.org/10.14236/ewic/irsg2008.5.

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Ball, Thomas, Peter Mataga, and Mooly Sagiv. "Edge profiling versus path profiling." In the 25th ACM SIGPLAN-SIGACT symposium. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/268946.268958.

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Hayman, Jay, and James Steimel. "Thermal Profiling of PIP-II Radio Wave Distribution." In Thermal Profiling of PIP-II Radio Wave Distribution. US DOE, 2021. http://dx.doi.org/10.2172/1825282.

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Hayman, Jay, and James Steimel. "Thermal Profiling of PIP-II Radio Wave Distribution." In Thermal Profiling of PIP-II Radio Wave Distribution. US DOE, 2021. http://dx.doi.org/10.2172/1825282.

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Michaud, Lisa N., and Kathleen F. McCoy. "Error profiling." In the 39th Annual Meeting. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1073012.1073063.

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Reports on the topic "Profiling"

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Han, D., and T. Jones. MPI Profiling. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/15014654.

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Stevens, James F., Richard A. Caralli, and Bradford J. Willke. Information Asset Profiling. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada441305.

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Corley, Michael. Embedded Statistical Profiling. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada470076.

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Li, Ying. Profiling and Optimization. Office of Scientific and Technical Information (OSTI), February 2022. http://dx.doi.org/10.2172/1844115.

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Whisnant, Austin, and Sid Faber. Network Profiling Using Flow. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada609917.

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Malony, Allen. Program Profiling in Cedar,. Fort Belvoir, VA: Defense Technical Information Center, March 1987. http://dx.doi.org/10.21236/ada190883.

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Li, Ying Wai. Basic OpenMP and Profiling. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1618304.

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Sorenson, Sam, and Nathan Little. Atmospheric Profiling Sensing Module. Ames (Iowa): Iowa State University. Library. Digital Press, January 2011. http://dx.doi.org/10.31274/ahac.8131.

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DeGraw, Jason, and Yeonjin Bae. Initial OpenStudio Profiling Results. Office of Scientific and Technical Information (OSTI), September 2023. http://dx.doi.org/10.2172/2204564.

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Verdin, Carlos Torres. Continuous profiling of magnetotelluric fields. Test accounts, May 1991. http://dx.doi.org/10.2172/10106102.

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