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Relevant bibliographies by topics / Classification risk

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Academic literature on the topic 'Classification risk'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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Journal articles on the topic "Classification risk"

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Franke, Warren D. "RISK CLASSIFICATION." ACSM's Health & Fitness Journal 17, no. 4 (2013): 16–22. http://dx.doi.org/10.1249/fit.0b013e318296db01.

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Glaser, Daniel. "Classification for Risk." Crime and Justice 9 (January 1987): 249–91. http://dx.doi.org/10.1086/449137.

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Mirjalil, Iskandarov Abdurasul. "AGRICULTURAL RISK INSURANCE." European International Journal of Multidisciplinary Research and Management Studies 02, no. 09 (2022): 56–57. http://dx.doi.org/10.55640/eijmrms-02-09-12.

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Agriculture is a sector facing many risks. Agricultural risks have become the object of many scientific studies as a result of their direct impact on the food security of the state and thereby the standard of living of the population. Agricultural risks have their own characteristics. Also, the classification of agricultural risks is specific to industry risks. The article considers two main principles of risk classification and proposes a classification model based on the principle of grouping.

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Ogunsanmi, O. E., O. A. Salako, and O. M. Ajayi. "Risk Classification Model for Design and Build Projects." Journal of Engineering, Project, and Production Management 1, no. 1 (2011): 46–60. http://dx.doi.org/10.32738/jeppm.201107.0006.

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Wade, Lee M. "High-Risk Pursuit Classification." Criminal Justice Policy Review 26, no. 3 (2014): 278–92. http://dx.doi.org/10.1177/0887403413516000.

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García, Jesús Enrique, Verónica Andrea González-López, Hugo Helito da Silva, and Thainá Soares Silva. "Risk of fraud classification." 4open 3 (2020): 9. http://dx.doi.org/10.1051/fopen/2020010.

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In this article, we define consumers’ profiles of electricity who commit fraud. We also compare these profiles with users’ profiles not classified as fraudsters in order to determine which of these clients should receive an inspection. We present a statistically consistent method to classify clients/users as fraudsters or not, according to the profiles of previously identified fraudsters. We show that it is possible to use several characteristics to inspect the classification of fraud; those aspects are represented by the coding performed in the observed series of clients/users. In this way, s

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Tajiri, Takuya, Kosuke Mima, Toru Beppu, et al. "RAS-Beppu Classification: A New Recurrence Risk Classification System Incorporating the Beppu Score and RAS Status for Colorectal Liver Metastases." Cancers 17, no. 4 (2025): 640. https://doi.org/10.3390/cancers17040640.

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Background: Preoperative recurrence risk stratification for colorectal liver metastases (CRLM) undergoing hepatectomy is essential when designing a treatment strategy. We developed a Beppu classification system consisting of three risk groups and found that the RAS mutation increased risk in low- and moderate-risk patients. Methods: A total of 173 patients undergoing initial hepatectomy for CRLM between 2004 and 2020 were analyzed. Disease-free survival (DFS) and overall survival (OS) were assessed. Patients in the low- and moderate-risk groups of the Beppu classification with RAS mutations we

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Awad Hegazy, Abdelmonem. "Myocardial Infarction: Risk Factors, Pathophysiology, Classification, Assessment and Management." Cardiology Research and Reports 4, no. 5 (2022): 01–11. http://dx.doi.org/10.31579/2692-9759/056.

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Myocardial infarction is a serious health problem affecting populations worldwide. Proper and prompt diagnosis and management is critical to achieving good prognosis and avoiding or reducing morbidity and mortality. It is essential for clinicians to be aware of the risks, diagnosis and treatment. In this review we aim to update and discuss knowledge of myocardial infarction with respect to risk factors, pathophysiology, types or classification, clinical manifestations, diagnosis and treatment in a concise and simplified manner in order to define a simple comprehensive guide for clinicians. The

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AC*, Ferrazza, Maluf JU, and Talamini E. "Treatment of Slaughter Sub products: High Health Risk Classification." Open Access Journal of Waste Management & Xenobiotics 7, no. 4 (2024): 1–7. http://dx.doi.org/10.23880/oajwx-16000200.

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Brazil is one of the largest producers and exporters of food in the world, but faces significant challenges in agricultural production, especially in relation to dependence on imported fertilizers. By-products of animal slaughter, condemned by the Health Inspection System (SIF) during the slaughter process, are classified as Animal By-products (ABPs) with a high health risk (Cat1). Cat1 ABPs cannot be used for human food or animal feed, even after being treated by rendering. This biomass is rich in nutrients but presents an imminent risk of pathogenicity. Heat treatments at high temperatures (

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Elaldi, Roxane, Emmanuel Chamorey, Renaud Schiappa, et al. "Comparative Performance of Four Staging Classifications to Select «High-Risk» Head and Neck Cutaneous Squamous Cell Carcinomas." Journal of Clinical Medicine 12, no. 12 (2023): 3929. http://dx.doi.org/10.3390/jcm12123929.

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Background: Many classifications exist to select patients with “high-risk” head and neck cutaneous squamous cell carcinoma (HNCSCC). Objective: To compare the performance of the Brigham and Women’s Hospital (BWH) classification with the performance of the American Joint Committee on Cancer 8th Edition (AJCC8), the Union for International Cancer Control 8th Edition (UICC8), and the National Comprehensive Cancer Network (NCCN) classifications. Methods: In this single-center retrospective study, HNCSCC resected in a tertiary care center were classified as “low-risk” or “high-risk” tumors accordin

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Dissertations / Theses on the topic "Classification risk"

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Mendoza, Ivan. "Robustness in risk classification." Thesis, Connect to online version, 2008. http://proquest.umi.com/pqdweb?did=1675128161&sid=1&Fmt=2&clientId=10306&RQT=309&VName=PQD.

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Omidvar, Ali. "Classification of risk mitigation strategies in construction projects." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2704.

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This thesis proposes a classification system of risk mitigation strategies based on literature search and industry interviews. Following that, a list of generic properties was generated to describe individual strategies. In parallel, populating the properties of a large number of identified strategies was attempted. The practical implications are discussed mainly focusing on knowledge management for risk mitigation strategies.

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Kübler, Bernhard Christian. "Risk classification by means of clustering." Frankfurt, M. Berlin Bern Bruxelles New York, NY Oxford Wien Lang, 2009. http://d-nb.info/998737291/04.

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Åkerberg, Frida. "Risk Ranking Reversals and Classification Ranking Reversals in Risk Matrices." Thesis, Högskolan i Gävle, Avdelningen för datavetenskap och samhällsbyggnad, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-37112.

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The risk matrix may appear to be a simple and effective tool that is commonly used to provide an overview of risk. Previous research indicates that risk matrices contain many weaknesses which may impact the outcome of risk assessment. This thesis gives examples of strengths and weaknesses of risk matrices with focus on several types of risk ranking reversals, specifically classification ranking reversals. The constructor and the user of a risk matrix should be aware of the implications that risk ranking reversals may cause, and that these cannot be easily mitigated.

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Wang, Xiujun. "Bank risk classification and optimal regulatory choice." Thesis, Montana State University, 1991. http://etd.lib.montana.edu/etd/1991/wang/WangX1991.pdf.

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A theory of bank regulation is formed in this study by choosing an optimal classification scheme so as to minimize specific costs with assumed fixed regulatory instruments and relative costs. The likelihood of failure of a financial institution can be estimated using the financial data of that institution. Previous research studies have attempted to predict the probability of failure by using one year of data or lagged data. In these studies, a bank on a failure trajectory was counted as a nonfailure until it actually failed. The estimation was biased in favor of nonfailures, meaning that a fa

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Hao, Mingjie. "Insurance loss coverage under restricted risk classification." Thesis, University of Kent, 2017. https://kar.kent.ac.uk/62465/.

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Insurers hope to make profit through pooling policies from a large number of individuals. Unless the risk in question is similar for all potential customers, an insurer is exposed to the possibility of adverse selection by attracting only high-risk individuals. To counter this, insurers have traditionally employed underwriting principles, identifying suitable risk factors to subdivide their potential customers into homogeneous risk groups, based on which risk-related premiums can be charged. In reality, however, insurers may not have all the information reflecting individuals' risks due to inf

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MORGADO, WILSON LINS. "APPLYING RISK CLASSIFICATION METHOD IN CAR INSURANCE MARKET." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5932@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>A estimação do risco em seguros de automóveis representa um difícil problema de regressão. As dificuldades vão desde a utilização de um grande número de variáveis discretas como explicativas, até a distribuição particular dos ruídos e uma quantidade expressiva de categorias com valores nulos e valores discrepantes. Supondo que os problemas de estimação estejam relacionados com a classificação do risco adotada pelo mercado, este trabalho propõe um método de classificação alternativo. O método desenvolvido foi baseado na técn

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Sikorska, Małgorzata, and P. G. Pererva. "Classification of corporative banks." Thesis, NTU "KhPI", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/36484.

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Witting, Katarina. "Classification, comorbidity, heredity, and risk factors of female sexual dysfunctions /." Abo, Finland : Abo Akademi University, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017633778&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Pörn, Sebastian, and Arvid Rönnblom. "Assesing counterparty risk classification using transition matrices : Comparing models' predictive ability." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136667.

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An important part when managing credit risk is to assess the probability of default of different counterparties. Increases and decreases in such probabil- ities are central components in the assessment, and this is where transition matrices become useful. These matrices are commonly used tools when as- sessing counterparty credit risk, and contain the probability of default, as well as the probability to migrate between different predefined rating classifica- tions. These rating classifications are used to reflect the risk taken towards different counterparties. Therefore, it is important for

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Books on the topic "Classification risk"

1

National Research Council (U.S.). Committee on Risk-Based Criteria for Non-RCRA Hazardous Waste., ed. Risk-based waste classification in California. National Academy Press, 1999.

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Gentry, James A. A commercial loan risk classification system. College of Commerce and Business Administration, Bureau of Economic and Business Research, University of Illinois Urbana-Champaign, 1989.

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National Council on Radiation Protection and Measurements., ed. Risk-based classification of radioactive and hazardous chemical wastes. National Council on Radiation Protection & Measurements, 2002.

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Washington (State). Dept. of Labor and Industries., ed. Common work-related injuries in trucking: Risk classification 1102. State of Washington, Dept. of Labor and Industries, Data Analysis Section, 1992.

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Clarke, Stevens H. Probationer recidivism in North Carolina: Measurement and classification of risk. Institute of Government, University of North Carolina at Chapel Hill, 1988.

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M, Denuit, ed. Actuarial modelling of claim counts: Risk classification, credibility, and bonus-malus systems. Wiley, 2007.

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IESNA Photobiology Committee., Illuminating Engineering Society of North America., and American National Standards Institute, eds. Recommended practice for photobiological safety for lamps-- risk group classification and labeling. Illuminating Engineering Society of North America, 2007.

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IESNA Photobiology Committee., ed. Recommended practice for photobiological safety for lamps--risk group classification and labeling. Illuminating Engineering Society of North America, 1996.

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Wierman, T. E. Common-cause failure database and analysis system: Event data collection, classification, and coding. U.S. Nuclear Regulatory Commission, Office of Nuclear Regulatory Research, 2007.

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Austin, James. Classification of high-risk and special management prisoners: A national assessment of current practices. U.S. Dept. of Justice, National Institute of Corrections, 2004.

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Book chapters on the topic "Classification risk"

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Finazzi, Guido. "Risk Classification." In Myeloproliferative Neoplasms. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24989-1_7.

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Kikuchi, Hirotoshi, Hiroyuki Konno, and Hiroya Takeuchi. "Risk Classification." In Gastrointestinal Stromal Tumor. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3206-7_5.

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Marques de Sá, Joaquim P., Luís M. A. Silva, Jorge M. F. Santos, and Luís A. Alexandre. "Continuous Risk Functionals." In Minimum Error Entropy Classification. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29029-9_2.

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Denuit, Michel, Donatien Hainaut, and Julien Trufin. "Insurance Risk Classification." In Springer Actuarial. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25820-7_1.

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Chen, John Tuhao, Lincy Y. Chen, and Clement Lee. "Minimum Risk Classification." In Statistical Prediction and Machine Learning. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9780429318689-7.

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Banks, Erik. "Classification of Risk." In The Credit Risk of Financial Instruments. Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1007/978-1-349-13247-8_4.

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Wassertheil-Smoller, Sylvia, and Jordan Smoller. "Risk Prediction and Risk Classification." In Biostatistics and Epidemiology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2134-8_9.

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Wassertheil-Smoller, Sylvia, and Jordan Smoller. "Risk Prediction and Risk Classification." In Biostatistics and Epidemiology. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-53043-2_9.

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Chaudhuri, Arindam, and Soumya K. Ghosh. "Need for Risk Classification." In Bankruptcy Prediction through Soft Computing based Deep Learning Technique. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6683-2_5.

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Trzpiot, Grażyna. "Gini Regression in the Capital Investment Risk Assessment—Sensitivity Risk Measures in Portfolio Analysis." In Data Analysis and Classification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75190-6_7.

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Conference papers on the topic "Classification risk"

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Apriani, Ani, Iwan Tri Riyadi Yanto, Aldo Erianda, and Rahmat Hidayat. "Soft Set Classification for Landslide Risk Level." In 2024 Beyond Technology Summit on Informatics International Conference (BTS-I2C). IEEE, 2024. https://doi.org/10.1109/bts-i2c63534.2024.10942131.

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Sun, Yujie, Chuyi Fan, and Qun Chen. "Transfer Learning for Text Classification via Model Risk Analysis." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.160.

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Al-Ghazi, Muhammad Kent, Ryan Bertrand, Muhammad Dzul Qarrnayn Destra, Alexander Agung Santoso Gunawan, and Karli Eka Setiawan. "Classification of Health Insurance Fraud Risk with Machine Learning." In 2024 International Conference on Information Technology Research and Innovation (ICITRI). IEEE, 2024. http://dx.doi.org/10.1109/icitri62858.2024.10699052.

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Ghozzi, Sirine Ben, Mohamed Aymen Ben HajKacem, and Nadia Essoussi. "Explainable Ensemble Machine Learning Method for Credit Risk Classification." In 2024 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 2024. http://dx.doi.org/10.1109/inista62901.2024.10683830.

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Zhao, Xinni, Jinze Song, Ruiye Shi, Rongbing Jia, and Wenjie Jiang. "MobileViT-Based Dog Breed Classification and AI Risk Assessment." In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025. https://doi.org/10.1109/iscait64916.2025.11010498.

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Fathulin, A. A., N. A. Fathulina, and S. N. Basova. "THE METHODOLOGICAL VALUE OF THE CLASSIFICATION IN THE COMPANY FINANCIAL RISK MANAGEMENT SYSTEM." In New forms of production and entrepreneurship in the coordinates of neo-industrial development of the economy. PD of KSUEL, 2020. http://dx.doi.org/10.38161/978-5-7823-0731-8-2020-045-051.

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The complexity of understanding the nature of risks, as well as the diversity of their types and manifestations, including financial risks, requires the use of a methodological approach to their classification. Classification of financial risks is of particular importance in the company's activities in order to effectively manage them. The article analyzes the concepts of "risk" and "uncertainty", and provides risk classifications for various reasons. It is concluded that it is possible to control and manage risks through comprehensive accounting and, accordingly, prevention of various types o

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Zachariadis, P., H. N. Psaraftis, and C. A. Kontovas. "Risk Based Rulemaking And Design - Proceed With Caution." In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.08.

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Bień, Jan, and Małgorzata Gładysz-Bień. "Multi-level Classification of Bridge Defects in Asset Management." In IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management. International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/guimaraes.2019.1100.

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<p>In the paper a proposal of the unified multi-level classification of bridge defects declining condition of bridges is presented as one of the most important elements of asset management. General scheme of bridge degradation process is described with classifications of basic mechanisms of bridge degradation as well as stimulators of degradation mechanisms. Criteria of defects classification are proposed taking into account effects of activities of bridge degradation mechanisms. The proposed general conception of classification methodology of bridge defects is addressed to all types of

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Breinholt, C., W. Hensel, A. Perez de Lucas, et al. "Risk-Based Ship And Ship System Design And Their Approval." In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.10.

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NUCERA, ANTONELLA, GIANDOMENICO FOTI, CATERINA CANALE, PIERFABRIZIO PUNTORIERI, and FRANCESCA MINNITI. "COASTAL FLOODING: DAMAGE CLASSIFICATION AND CASE STUDIES IN CALABRIA, ITALY." In RISK ANALYSIS 2018. WIT Press, 2018. http://dx.doi.org/10.2495/risk180081.

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Reports on the topic "Classification risk"

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Stamp, Jason Edwin, and Philip LaRoche Campbell. A classification scheme for risk assessment methods. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/925643.

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Gupta, Maya R., Santosh Srivastava, and Luca Cazzanti. Minimum Expected Risk Estimation for Near-neighbor Classification. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada479632.

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Manski, Charles, John Mullahy, and Atheendar Venkataramani. Prediction with Differential Covariate Classification: Illustrated by Racial/Ethnic Classification in Medical Risk Assessment. National Bureau of Economic Research, 2025. https://doi.org/10.3386/w33350.

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Aiken, Catherine. Classifying AI Systems. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200025.

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This brief explores the development and testing of artificial intelligence system classification frameworks intended to distill AI systems into concise, comparable and policy-relevant dimensions. Comparing more than 1,800 system classifications, it points to several factors that increase the utility of a framework for human classification of AI systems and enable AI system management, risk assessment and governance.

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Li, Yurui. Principal Global Investor Dynamic Risk Premia Performance Tracking Project -Binary Classification Validation and Visualization. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-1153.

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Craig, D. K., J. S. Davis, J. Prowse, and P. W. Hoffman. Toxic chemical hazard classification and risk acceptance guidelines for use in DOE facilities. Revision 2. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/104436.

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Zio, Enrico, and Nicola Pedroni. Uncertainty characterization in risk analysis for decision-making practice. Fondation pour une culture de sécurité industrielle, 2012. http://dx.doi.org/10.57071/155chr.

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This document provides an overview of sources of uncertainty in probabilistic risk analysis. For each phase of the risk analysis process (system modeling, hazard identification, estimation of the probability and consequences of accident sequences, risk evaluation), the authors describe and classify the types of uncertainty that can arise. The document provides: a description of the risk assessment process, as used in hazardous industries such as nuclear power and offshore oil and gas extraction; a classification of sources of uncertainty (both epistemic and aleatory) and a description of techn

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Ulloa-Suarez, Carolina, and Oscar Valencia. Sustaining Compliance with Fiscal Rules: A Future at Risk? Inter-American Development Bank, 2024. https://doi.org/10.18235/0013256.

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Fiscal rules in Latin American and Caribbean (LAC) countries rarely have fixed objectives, exhibiting significant heterogeneity in design and implementation. This document analyzes how different factors influence the ease or difficulty of compliance with these rules and introduces an update of the Compliance dataset, highlighting new trends. The updated dataset shows that fiscal rule compliance in the region is sensitive to economic conditions, with a notable peak in 2022 due to favorable circumstances. However, the seemingly high compliance in 2022 was largely driven by favorable conditions,

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Sapkota, Yadav, Evan Grimes, and Jacob Berkowitz. Potential benefits of subaqueous soil data on Department of Defense installations. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49698.

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Many domestic and international US Department of Defense (DoD) installations are located in coastal areas. Recent advances in the classification and mapping of subaqueous soils, which occur in shallow freshwater and marine environments, has the potential to benefit US military operations in several different ways. This technical note communicates the importance of subaqueous soil classification and describes how subaqueous soil information can inform the management of natural resources, infrastructure and transportation, mitigation of coastal storm risk, protection of the coast from natural th

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Olivier, Jason, and Sally Shoop. Imagery classification for autonomous ground vehicle mobility in cold weather environments. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42425.

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Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispec

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