Literatura académica sobre el tema "Crowd risk prediction"
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Artículos de revistas sobre el tema "Crowd risk prediction"
Harihara Subramanian, Gayathri y Ashish Verma. "Crowd risk prediction in a spiritually motivated crowd". Safety Science 155 (noviembre de 2022): 105877. http://dx.doi.org/10.1016/j.ssci.2022.105877.
Texto completoLee, Ris S. C. y Roger L. Hughes. "Prediction of human crowd pressures". Accident Analysis & Prevention 38, n.º 4 (julio de 2006): 712–22. http://dx.doi.org/10.1016/j.aap.2006.01.001.
Texto completoFu, Runshan, Yan Huang y Param Vir Singh. "Crowds, Lending, Machine, and Bias". Information Systems Research 32, n.º 1 (1 de marzo de 2021): 72–92. http://dx.doi.org/10.1287/isre.2020.0990.
Texto completoZhang, Meihua, Yuan Yao y Kefan Xie. "Prediction and Diversion Mechanisms for Crowd Management Based on Risk Rating". Engineering 09, n.º 05 (2017): 377–87. http://dx.doi.org/10.4236/eng.2017.95021.
Texto completoZhao, Hui, Runran Miao, Fei Lin y Guoan Zhao. "Risk Score for Prediction of Acute Kidney Injury in Patients with Acute ST-Segment Elevation Myocardial Infarction". Disease Markers 2022 (20 de diciembre de 2022): 1–7. http://dx.doi.org/10.1155/2022/7493690.
Texto completoXu, Xiaojun, Sen Xiong, Yifeng Huang y Rong Qin. "Prediction of Epidemic Transmission Path and Risk Management Method in Urban Subway". Mathematical Problems in Engineering 2022 (31 de mayo de 2022): 1–9. http://dx.doi.org/10.1155/2022/7555251.
Texto completoKondofersky, Ivan, Michael Laimighofer, Christoph Kurz, Norbert Krautenbacher, Julia F. Söllner, Philip Dargatz, Hagen Scherb, Donna P. Ankerst y Christiane Fuchs. "Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration". F1000Research 5 (16 de noviembre de 2016): 2671. http://dx.doi.org/10.12688/f1000research.8680.1.
Texto completoLi, Zhihong, Shiyao Qiu, Xiaoyu Wang y Li Zhao. "Modeling and Simulation of Crowd Pre-Evacuation Decision-Making in Complex Traffic Environments". International Journal of Environmental Research and Public Health 19, n.º 24 (12 de diciembre de 2022): 16664. http://dx.doi.org/10.3390/ijerph192416664.
Texto completoSeyednasrollah, Fatemeh, Devin C. Koestler, Tao Wang, Stephen R. Piccolo, Roberto Vega, Russell Greiner, Christiane Fuchs et al. "A DREAM Challenge to Build Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration-Resistant Prostate Cancer". JCO Clinical Cancer Informatics, n.º 1 (noviembre de 2017): 1–15. http://dx.doi.org/10.1200/cci.17.00018.
Texto completoShiga, Motoki. "Two-step feature selection for predicting survival time of patients with metastatic castrate resistant prostate cancer". F1000Research 5 (16 de noviembre de 2016): 2678. http://dx.doi.org/10.12688/f1000research.8201.1.
Texto completoTesis sobre el tema "Crowd risk prediction"
Chinopfukutwa, Vimbayi Sandra. "Peer Crowd Affiliations as Predictiors of Prosocial and Risky Behaviors Among College Students". Thesis, North Dakota State University, 2019. https://hdl.handle.net/10365/29460.
Texto completoCabarle, Carla. "PREDICTING THE RISK OF FRAUD IN EQUITY CROWDFUNDING OFFERS AND ASSESSING THE WISDOM OF THE CROWD". Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/541453.
Texto completoD.B.A.
Regulation Crowdfunding, enacted in May 2016, is intended to facilitate capital formation in startups and small businesses funded primarily by small investors (Securities and Exchange Commission (SEC), 2016b). This dissertation investigates (1) the risk of fraud in equity crowdfunding offerings and (2) whether investors respond to fraud signals by selecting (rejecting) offers with low (high) fraud risk. Because equity crowdfunding is quite new, no frauds have yet been identified. Therefore, I employ a predictive analytics tool, Benford’s Law, to assess the fraud risk of the offering. I select observable indicators to represent the Fraud Triangle dimensions—incentives, opportunities and rationalization—and test if they predict fraud risk. I also compare offer funding outcomes to my fraud risk assessments to identify if investors’ selections consider fraud risk appropriately. The relaxed auditor assurance and disclosure requirements attracts both honest and dishonest founders, but I find that the risk of fraud is higher in equity crowdfunding offers than in public offerings as reported by other studies. I find that there are several individual fraud indicators and models that explain fraud risk, but these do not predict whether the offer is funded or not (funding outcomes) or the amount that is raised if funded. This dissertation is the first to apply Benford’s Law to equity crowdfunding offers and map fraud attributes to fraud risk and funding outcomes. My dissertation can inform investors, issuers, regulators, intermediaries and practitioners of the high risk of fraud in equity crowdfunding offerings and of several noteworthy fraud indicators.
Temple University--Theses
Gayathri, Harihara. "Macroscopic crowd flow and risk modelling in mass religious gathering". Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5630.
Texto completoLibros sobre el tema "Crowd risk prediction"
The new Weibull handbook: Reliability & statistical analysis for predicting life, safety, risk, support costs, failures, and forecasting warranty claims, substantiation and accelerated testing, using Weibull, Log normal, crow-AMSAA, probit, and Kaplan-Meier models. 5a ed. North Palm Beach, Fla: R.B. Abernethy, 2006.
Buscar texto completoKulak, Dariusz. Wieloaspektowa metoda oceny stanu gleb leśnych po przeprowadzeniu procesów pozyskania drewna. Publishing House of the University of Agriculture in Krakow, 2017. http://dx.doi.org/10.15576/978-83-66602-28-1.
Texto completoCapítulos de libros sobre el tema "Crowd risk prediction"
Baranovskiy, Nikolay Viktorovich. "Predicting Forest Fire Numbers Using Deterministic-Probabilistic Approach". En Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks, 89–100. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1867-0.ch004.
Texto completoAnderson, Raymond A. "Business Credit". En Credit Intelligence & Modelling, 121–58. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.003.0004.
Texto completoTay, Yen Pei, Vasaki Ponnusamy y Lam Hong Lee. "Big Data in Telecommunications". En Big Data, 778–92. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9840-6.ch036.
Texto completoTay, Yen Pei, Vasaki Ponnusamy y Lam Hong Lee. "Big Data in Telecommunications". En Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, 67–81. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8505-5.ch004.
Texto completoActas de conferencias sobre el tema "Crowd risk prediction"
Li, Hongjian y Yan Shao. "Investors' Financing Risk Prediction in Crowd-funding Platform". En 2017 7th International Conference on Education and Management (ICEM 2017). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/icem-17.2018.120.
Texto completoMorgan, Jeffrey J., Otto C. Wilson y Prahlad G. Menon. "The Wisdom of Crowds Approach to Influenza-Rate Forecasting". En ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86559.
Texto completoTeng, Harold Ze Chie, Hongchao Jiang, Xuan Rong Zane Ho, Wei Yang Bryan Lim, Jer Shyuan Ng, Han Yu, Zehui Xiong, Dusit Niyato y Chunyan Miao. "Predictive Analytics for COVID-19 Social Distancing". En Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/716.
Texto completoSohn, Samuel S., Seonghyeon Moon, Honglu Zhou, Mihee Lee, Sejong Yoon, Vladimir Pavlovic y Mubbasir Kapadia. "Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction". En Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/185.
Texto completoPavlovski, Martin, Fang Zhou, Nino Arsov, Ljupco Kocarev y Zoran Obradovic. "Generalization-Aware Structured Regression towards Balancing Bias and Variance". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/363.
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