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Journal articles on the topic 'Classification risk'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

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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2

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

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3

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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4

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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5

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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6

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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7

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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10

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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11

D.E., Narbekov. "On Some Methods Of Economic Risk Management: Essence And Classifications." American Journal of Management and Economics Innovations 3, no. 06 (2021): 146–56. http://dx.doi.org/10.37547/tajmei/volume03issue06-22.

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The article notes that economic risk is one of the most multifaceted phenomena, the nature and components of which are not subject to a simple and unambiguous interpretation. But it should be noted that the classification of risks of this type, taking into account the many factors containing its signs is a rather voluminous problem due to the lack of a generally accepted systemic classification. The author argues that the above classification of methods for managing economic risks makes it possible to determine the characteristics used in their classification in the management process as a who

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12

Sviyazova, Tatyana. "Risk Management in Quality Management System: Economic Content and Classification of Risks." Moscow University Economics Bulletin 2017, no. 6 (2017): 143–67. http://dx.doi.org/10.38050/01300105201768.

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The paper aims to study the nature of risk management in the context of quality management system. Through historical and logical methods of research, we analyze the stages of formation and development of ideas about risk and its impact to economic activity. We consider different definitions of risks that were formulated by economists and presented in international standards, and analyze key moments which characterize the economic content of risk. Based on the considered approaches we propose the author’s definition of risk. Further, attention is paid to different risk classifications, and we

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13

Hachich, Waldemar C. "Requiem for risk classification matrices." Soils and Rocks 43, no. 3 (2020): 497–501. http://dx.doi.org/10.28927/sr.433497.

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14

Belykh, V. I., and S. G. Polkovnikova. "SIGNS BACKBONE RISK CLASSIFICATION COMPANIES." Science of the Person: Humanitarian Researches 2, no. 24 (2016): 177–83. http://dx.doi.org/10.17238/issn1998-5320.2016.24.177.

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15

Lord, Blair, J. D. Cummins, B. D. Smith, R. N. Vance, and J. L. VanDerhei. "Risk Classification in Life Insurance." Journal of Risk and Insurance 55, no. 3 (1988): 579. http://dx.doi.org/10.2307/253271.

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16

Hoy, Michael. "Risk Classification and Social Welfare." Geneva Papers on Risk and Insurance - Issues and Practice 31, no. 2 (2006): 245–69. http://dx.doi.org/10.1057/palgrave.gpp.2510079.

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17

Schwarze, Reimund. "Risk Classification and Public Policy." Geneva Papers on Risk and Insurance - Issues and Practice 31, no. 2 (2006): 187–89. http://dx.doi.org/10.1057/palgrave.gpp.2510084.

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18

Bartlett, Peter L., Michael I. Jordan, and Jon D. McAuliffe. "Convexity, Classification, and Risk Bounds." Journal of the American Statistical Association 101, no. 473 (2006): 138–56. http://dx.doi.org/10.1198/016214505000000907.

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19

Bloch, Daniel A., Richard A. Olshen, and Michael G. Walker. "Risk Estimation for Classification Trees." Journal of Computational and Graphical Statistics 11, no. 2 (2002): 263–88. http://dx.doi.org/10.1198/106186002760180509.

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20

Horgby, Per-Johan. "Risk Classification by Fuzzy Inference." Geneva Papers on Risk and Insurance Theory 23, no. 1 (1998): 63–82. http://dx.doi.org/10.1023/a:1008682014796.

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21

Monahan, John, Henry J. Steadman, Paul S. Appelbaum, et al. "The Classification of Violence Risk." FOCUS 17, no. 4 (2019): 429. http://dx.doi.org/10.1176/appi.focus.17404.

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22

Fonseca, Rafael, and Jorge Monge. "Myeloma: Classification and Risk Assessment." Seminars in Oncology 40, no. 5 (2013): 554–66. http://dx.doi.org/10.1053/j.seminoncol.2013.07.002.

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23

Shermer, Lauren O’Neill, David M. Bierie, and Amber Stock. "Endogeneity in Prison Risk Classification." International Journal of Offender Therapy and Comparative Criminology 57, no. 10 (2012): 1248–74. http://dx.doi.org/10.1177/0306624x12452404.

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24

Hass, Ulla. "Hazard classification or risk assessment." Toxicology Letters 221 (August 2013): S57. http://dx.doi.org/10.1016/j.toxlet.2013.06.205.

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25

Shaw, M. J., and J. A. Gentry. "Inductive learning for risk classification." IEEE Expert 5, no. 1 (1990): 47–53. http://dx.doi.org/10.1109/64.50856.

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26

Boucher, Jean-Philippe, Michel Denuit, and Montserrat Guillén. "Risk Classification for Claim Counts." North American Actuarial Journal 11, no. 4 (2007): 110–31. http://dx.doi.org/10.1080/10920277.2007.10597487.

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27

Martinez, Matthew, Phillip L. De Leon, and David Keeley. "Bayesian classification of falls risk." Gait & Posture 67 (January 2019): 99–103. http://dx.doi.org/10.1016/j.gaitpost.2018.09.028.

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28

Puppala, Venkata Krishna, Oana Dickinson, and David G. Benditt. "Syncope: Classification and risk stratification." Journal of Cardiology 63, no. 3 (2014): 171–77. http://dx.doi.org/10.1016/j.jjcc.2013.03.019.

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29

Baldo, Aline, Sarah Welby, Chuong Daï Do Thi, Amaya Leunda, Philippe Herman, and Didier Breyer. "Risk Classification of Zoonotic Microorganisms." Applied Biosafety 23, no. 4 (2018): 199–210. http://dx.doi.org/10.1177/1535676018799094.

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30

Leitner, Jesse, and Tupper Hyde. "Modernizing NASA's risk classification system." Acta Astronautica 202 (January 2023): 333–40. http://dx.doi.org/10.1016/j.actaastro.2022.10.043.

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31

Monahan, John, Henry J. Steadman, Paul S. Appelbaum, et al. "The classification of violence risk." Behavioral Sciences & the Law 24, no. 6 (2006): 721–30. http://dx.doi.org/10.1002/bsl.725.

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32

Mahmoud, Gouda M., Seif M. Osman, and Riham S. Hegazy. "Proposed approach for force transducers classification." International Journal of Metrology and Quality Engineering 12 (2021): 3. http://dx.doi.org/10.1051/ijmqe/2021001.

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In accordance with the recent version of ISO 376:2011, the classification of the force transducers is based on the relative errors calculated from the calibration results. This classification approach doesn't take the uncertainty of measurement into consideration. It becomes one of the most important factors that must be utilized when making a classification decision based on of ISO/IEC 17025:2017. In this study a proposed approach for force proving instrument classification was developed. This approach is based on taking into account the calibration results uncertainty of the instruments as a

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33

Potapova, E. V. "Typology of metro structures for the tasks of geotechnical risk classification." Gornye nauki i tekhnologii = Mining Science and Technology (Russia) 6, no. 1 (2021): 52–60. http://dx.doi.org/10.17073/2500-0632-2021-1-52-60.

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Risk management is an important part of a modern enterprise activity. Currently, risk management should be included in the overall quality management system of a construction organization. Geotechnical construction, for instance, construction of metro, should also be carried out taking into account possible risks. Metro construction is connected with a number of uncertainties related to enclosing rock mass conditions, and specific geotechnical risks, which can lead to materialization of hazards with various consequences. Predicting possible hazards at all stages of a metro structure life cycle

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34

Luca, Silvia Ana, Raluca Malina Bungau, Sandra Lazar, Ovidiu Potre, and Bogdan Timar. "To What Extent Does Cardiovascular Risk Classification of Patients with Type 2 Diabetes Differ between European Guidelines from 2023, 2021, and 2019? A Cross-Sectional Study." Medicina 60, no. 2 (2024): 334. http://dx.doi.org/10.3390/medicina60020334.

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Background and Objectives: Type 2 Diabetes (T2DM) is intricately associated with an increased cardiovascular (CV) risk, highlighting the imperative for tailored intervention in the prevention and management of CV diseases. To assess the CV risk and subsequent interventions in patients with diabetes, the European Society of Cardiology (ESC) has been consistently developing and updating specific guidelines for risk assessment and patient management since 2019. The 2023 risk classification method has significantly changed, introducing a novel probability-based assessment through the implementatio

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35

Rodrigues, João Antunes, Alexandre Martins, Mateus Mendes, José Torres Farinha, Ricardo J. G. Mateus, and Antonio J. Marques Cardoso. "Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning." Energies 15, no. 24 (2022): 9387. http://dx.doi.org/10.3390/en15249387.

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Monitoring the condition of industrial equipment is fundamental to avoid failures and maximize uptime. The present work used supervised and unsupervised learning methods to create models for predicting the condition of an industrial machine. The main objective was to determine when the asset was either in its nominal operation or working outside this zone, thus being at risk of failure or sub-optimal operation. The results showed that it is possible to classify the machine state using artificial neural networks. K-means clustering and PCA methods showed that three states, chosen through the El

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36

Lun, Venus Y. H., Kee hung Lai, and Markus Vejvar. "Risk in port logistics: risk classification and mitigation framework." International Journal of Shipping and Transport Logistics 12, no. 6 (2020): 576. http://dx.doi.org/10.1504/ijstl.2020.10032893.

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37

Lai, Kee hung, Markus Vejvar, and Venus Y. H. Lun. "Risk in port logistics: risk classification and mitigation framework." International Journal of Shipping and Transport Logistics 12, no. 6 (2020): 576. http://dx.doi.org/10.1504/ijstl.2020.111117.

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38

Lauria Pantano, A., E. Maddaloni, S. I. Briganti, et al. "Differences between ATA, AACE/ACE/AME and ACR TI-RADS ultrasound classifications performance in identifying cytological high-risk thyroid nodules." European Journal of Endocrinology 178, no. 6 (2018): 595–603. http://dx.doi.org/10.1530/eje-18-0083.

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Objective Thyroid ultrasound is crucial for clinical decision in the management of thyroid nodules. In this study, we aimed to estimate and compare the performance of ATA, AACE/ACE/AME and ACR TI-RADS ultrasound classifications in discriminating nodules with high-risk cytology. Design Cross-sectional study. Methods 1077 thyroid nodules undergoing fine-needle aspiration were classified according to ATA, AACE/ACE/AME and ACR TI-RADS ultrasound classifications by an automated algorithm. Odds ratios (ORs) and receiver operating characteristic (ROC) curves for high-risk cytology categories (TIR3b,

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39

Vyacheslavova, Olga F. "Classification of Risks of High-Technology Production." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 1284–92. http://dx.doi.org/10.47059/revistageintec.v11i2.1756.

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In the modern economic system, the concept of developing high-technology production shapes the competitiveness and outlook of national economic development. The specifics of high-technology production imply considerable risks emerging across the lifecycle, which requires risk management. The paper explores the aspects of businesses engaged in high-technology production and the risks involved. A classification of risks is proposed for high-technology production in accordance with the requirements set in standard GOST R ISO 9001-2015.

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40

Ulucan, Aydin, and Kazim Baris Atici. "A MULTIPLE CRITERIA SORTING METHODOLOGY WITH MULTIPLE CLASSIFI CATION CRITERIA AND AN APPLICATION TO COUNTRY RISK EVALUATION." Technological and Economic Development of Economy 19, no. 1 (2013): 93–124. http://dx.doi.org/10.3846/20294913.2012.763070.

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In this paper, we propose an extension of the standard UTADIS methodology, an approach that originates from multicriteria decision aid (MCDA) for sorting problems, such that it can handle more than one classification criteria simultaneously which possibly involves different predefined classes for alternatives. Moreover, we test the classification ability of the standard UTADIS methodology using the out-of-classification criterion approach, a new variant of the studies comprising out-of-time and out-of-sample testing methodologies. Results obtained in out-of-classification criterion testing are

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41

Rudzinski, Erin R., James R. Anderson, Douglas S. Hawkins, Stephen X. Skapek, David M. Parham, and Lisa A. Teot. "The World Health Organization Classification of Skeletal Muscle Tumors in Pediatric Rhabdomyosarcoma: A Report From the Children's Oncology Group." Archives of Pathology & Laboratory Medicine 139, no. 10 (2015): 1281–87. http://dx.doi.org/10.5858/arpa.2014-0475-oa.

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Context Since 1995, the International Classification of Rhabdomyosarcoma has provided prognostically relevant classification for rhabdomyosarcoma (RMS) and allowed risk stratification for children with RMS. The International Classification of Rhabdomyosarcoma includes botryoid and spindle cell RMS as superior-risk groups, embryonal RMS as an intermediate-risk group, and alveolar RMS as an unfavorable-risk group. The 2013 World Health Organization (WHO) classification of skeletal muscle tumors modified the histologic classification of RMS to include sclerosing RMS as a type of spindle cell RMS

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42

Vasilyeva, Yana Aleksandrovna. "Risk Management: Classification and Management Methods." Общество: политика, экономика право, no. 12 (December 18, 2020): 106–10. http://dx.doi.org/10.24158/pep.2020.12.17.

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The paper contains research of existing risk classifi-cations. The new classification based on this re-search is developed. This classification lets to de-velop effective risk management system in an organ-ization. Suggested classification assumes the selec-tion of three risk types (global, context, system) which demand different management approaches. The manageability of global risks is low, it implies a situational approach to management and an influ-ence of the subjective characteristics of the deci-sion-maker. Context risks require more management resources; therefore, various methods of

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43

Baraeva, Yu V., N. A. Kabanova, and N. E. Ryzhenkova. "CLASSIFICATION OF ECONOMIC RISK IN AGRICULTURE." Экономика сельского хозяйства России, no. 6 (June 2020): 44–49. http://dx.doi.org/10.32651/206-44.

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44

Dmitrieva, Miroslava A. "Currency risk: from definition to classification." Russian Journal of Entrepreneurship 16, no. 15 (2015): 2423. http://dx.doi.org/10.18334/rp.16.15.552.

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45

Kovalenko, Z. A., and M. G. Efanov. "Postpancreatectomy hemorrhage: classification, epidemiology, risk factors." Annaly khirurgicheskoy gepatologii = Annals of HPB Surgery 26, no. 3 (2021): 135–41. http://dx.doi.org/10.16931/1995-5464.2021-3-135-141.

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The paper analyzes modern literature data regarding post-resection hemorrhagic complications in surgical pancreatology. The modern classification system, epidemiological data and risk factors for postoperative bleeding are analyzed in detail. Critical analysis of the use of postpancreatectomy hemorrhage classification was carried out.

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46

Thomas, R. Guy. "Some Novel Perspectives on Risk Classification." Geneva Papers on Risk and Insurance - Issues and Practice 32, no. 1 (2007): 105–32. http://dx.doi.org/10.1057/palgrave.gpp.2510118.

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47

Dionne, Georges, and Casey Rothschild. "Economic Effects of Risk Classification Bans." Geneva Risk and Insurance Review 39, no. 2 (2014): 184–221. http://dx.doi.org/10.1057/grir.2014.15.

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48

Matveev, V. V. "CLASSIFICATION OF BUSINESS RISK AGRIBUSINESS SUBJECTS." Vestnik OrelGAU 1, no. 64 (2017): 99–107. http://dx.doi.org/10.15217/issn1990-3618.2017.1.99.

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49

Şensoy, Murat, Lance M. Kaplan, Simon Julier, Maryam Saleki, and Federico Cerutti. "Risk-aware classification via uncertainty quantification." Expert Systems with Applications 265 (March 2025): 125906. https://doi.org/10.1016/j.eswa.2024.125906.

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50

BATES, BETSY. "Malignant Risk-Based SCC Classification Proposed." Skin & Allergy News 38, no. 12 (2007): 14. http://dx.doi.org/10.1016/s0037-6337(07)70925-7.

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