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Gotowe bibliografie tematyczne / Crowd risk prediction

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Gotowa bibliografia na temat „Crowd risk prediction”

Autor: Grafiati

Data publikacji: 6 września 2023

Data aktualizacji: 7 września 2023

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Artykuły w czasopismach na temat "Crowd risk prediction"

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Harihara Subramanian, Gayathri, and Ashish Verma. "Crowd risk prediction in a spiritually motivated crowd." Safety Science 155 (November 2022): 105877. http://dx.doi.org/10.1016/j.ssci.2022.105877.

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Lee, Ris S. C., and Roger L. Hughes. "Prediction of human crowd pressures." Accident Analysis & Prevention 38, no. 4 (2006): 712–22. http://dx.doi.org/10.1016/j.aap.2006.01.001.

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Fu, Runshan, Yan Huang, and Param Vir Singh. "Crowds, Lending, Machine, and Bias." Information Systems Research 32, no. 1 (2021): 72–92. http://dx.doi.org/10.1287/isre.2020.0990.

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Can machines outperform crowds in financial lending decisions? Using data from a crowd-lending platform, we show that, compared with portfolios created by crowds, a reasonably sophisticated machine can construct financial portfolios that provide better returns while controlling for risk. Further, we find that the machine-created portfolios benefit not only the lenders, but also the borrowers. Borrowers receive loans at a much lower interest rate as the machine can weed out the riskiest loans better than the crowds. We also find suggestive evidence of algorithmic bias in machine decisions. We f

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Zhang, Meihua, Yuan Yao, and Kefan Xie. "Prediction and Diversion Mechanisms for Crowd Management Based on Risk Rating." Engineering 09, no. 05 (2017): 377–87. http://dx.doi.org/10.4236/eng.2017.95021.

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Zhao, Hui, Runran Miao, Fei Lin, and Guoan Zhao. "Risk Score for Prediction of Acute Kidney Injury in Patients with Acute ST-Segment Elevation Myocardial Infarction." Disease Markers 2022 (December 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/7493690.

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Background. Acute kidney injury (AKI) is an important comorbidity of ST-Segment Elevation Myocardial Infarction (STEMI) and worsens the prognosis. The purpose of this study was to investigate the relationship between clinical data, test results, surgical findings, and AKI in STEMI patients and to develop a simple, practical model for predicting the risk of AKI. Method. Prognostic prediction research with clinical risk score development was conducted. The data used for model development was derived from the database of the Henan Province Cardiovascular Disease Clinical Data and Sample Resource

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Xu, Xiaojun, Sen Xiong, Yifeng Huang, and Rong Qin. "Prediction of Epidemic Transmission Path and Risk Management Method in Urban Subway." Mathematical Problems in Engineering 2022 (May 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/7555251.

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With the development of COVID-19, the epidemic prevention requirements of city subway system have become stricter. This study studies the transmission path of epidemic disease in city subway system. Using FLUENT software and AnyLogic software, the simulation models of subway platform ventilation structure and crowd behavior mode in subway system are constructed, respectively, and SEIR (vulnerable exposed affected recovered) is used as the general infection model of epidemic disease. According to the actual situation, the parameters such as shoulder width, flow, and moving speed of crowd are de

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Kondofersky, Ivan, Michael Laimighofer, Christoph Kurz, et al. "Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration." F1000Research 5 (November 16, 2016): 2671. http://dx.doi.org/10.12688/f1000research.8680.1.

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In today's information age, the necessary means exist for clinical risk prediction to capitalize on a multitude of data sources, increasing the potential for greater accuracy and improved patient care. Towards this objective, the Prostate Cancer DREAM Challenge posted comprehensive information from three clinical trials recording survival for patients with metastatic castration-resistant prostate cancer treated with first-line docetaxel. A subset of an independent clinical trial was used for interim evaluation of model submissions, providing critical feedback to participating teams for tailori

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Li, Zhihong, Shiyao Qiu, Xiaoyu Wang, and Li Zhao. "Modeling and Simulation of Crowd Pre-Evacuation Decision-Making in Complex Traffic Environments." International Journal of Environmental Research and Public Health 19, no. 24 (2022): 16664. http://dx.doi.org/10.3390/ijerph192416664.

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Human movements in complex traffic environments have been successfully simulated by various models. It is crucial to improve crowd safety and urban resilience. However, few studies focus on reproducing human behavior and predicting escape reaction time in the initial judgement stage in complex traffic environments. In this paper, a pedestrian pre-evacuation decision-making model considering pedestrian heterogeneity is proposed for complex environments. Firstly, the model takes different obvious factors into account, including cognition, information, experience, habits, stress, and decision-mak

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Seyednasrollah, Fatemeh, Devin C. Koestler, Tao Wang, 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, no. 1 (November 2017): 1–15. http://dx.doi.org/10.1200/cci.17.00018.

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Purpose Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. Patients and Methods The comparator arms of four phase III clinical trials in first-line mCRPC were collected, annotated, and compiled, with a total of 2,070 patients. Early discontinuation was defined as treatment stoppage within 3 months as a result of adverse treatment effects; 10% of pat

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Shiga, Motoki. "Two-step feature selection for predicting survival time of patients with metastatic castrate resistant prostate cancer." F1000Research 5 (November 16, 2016): 2678. http://dx.doi.org/10.12688/f1000research.8201.1.

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Metastatic castrate resistant prostate cancer (mCRPC) is the major cause of death in prostate cancer patients. Even though some options for treatment of mCRPC have been developed, the most effective therapies remain unclear. Thus finding key patient clinical variables related with mCRPC is an important issue for understanding the disease progression mechanism of mCRPC and clinical decision making for these patients. The Prostate Cancer DREAM Challenge is a crowd-based competition to tackle this essential challenge using new large clinical datasets. This paper proposes an effective procedure fo

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Rozprawy doktorskie na temat "Crowd risk prediction"

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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.

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College students often affiliate with similar peers, forming identity-based peer crowds. Research has shown that affiliations with certain peer crowds is associated with risky behaviors, thus derailing college success. This study examined whether college peer crowd affiliations predicted risky and prosocial behaviors. Participants were 527 students at a public university in the Midwest (aged 18 - 26). Hierarchical multiple regression analyses showed that Counterculture and Athletic/Social affiliations positively predicted risky behaviors. Arts/Ethnic and Scholastic affiliations positively pre

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Cabarle, 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.

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Business Administration/Accounting<br>D.B.A.<br>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

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Gayathri, Harihara. "Macroscopic crowd flow and risk modelling in mass religious gathering." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5630.

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Understanding the principles and applications of crowd dynamics in mass gatherings is very important, specifically with respect to crowd risk analysis and crowd safety. Historical trends from India and other countries suggest that the crowd crushes in mass gatherings, especially in religious events, frequently occur, highlighting the importance of studying crowd behaviour more scientifically. This is required to support appropriate and timely crowd management principles in planning crowd control measures and providing early warning systems at mass gatherings. Hitherto, the researchers have stu

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Książki na temat "Crowd risk prediction"

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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. 5th ed. R.B. Abernethy, 2006.

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Kulak, 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.

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Presented reasearch aimed to develop and analyse the suitability of the CART models for prediction of the extent and probability of occurrence of damage to outer soil layers caused by timber harvesting performed under varied conditions. Having employed these models, the author identified certain methods of logging works and conditions, under which they should be performed to minimise the risk of damaging forest soils. The analyses presented in this work covered the condition of soils upon completion of logging works, which was investigated in 48 stands located in central and south-eastern Pola

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Części książek na temat "Crowd risk prediction"

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Baranovskiy, Nikolay Viktorovich. "Predicting Forest Fire Numbers Using Deterministic-Probabilistic Approach." In Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1867-0.ch004.

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The annual task of forecasting forest fire danger is becoming increasingly relevant, especially in the context of global warming. The forecast of surface fires is most important, as more than 80% of all vegetation fires are surface fires. Practically all crown fires develop from surface fires. This chapter discusses the deterministic-probabilistic method for predicting the number of forest fires in a controlled forest area. This methodology is based on the assumption that the number of registered and projected forest fires is related to the probability of their occurrence. The influence of forest fire retrospective data on the predicted number of forest fires for some sites of the Timiryazevskiy forestry of the Tomsk region was studied. This chapter presents the results of a comparative analysis of forecast data and statistics.

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Anderson, Raymond A. "Business Credit." In Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.003.0004.

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This chapter covers modelling of business-credit risk, whether retail or wholesale. (1) Risk 101—i) data sources—variations by firm or loan size (financial statements, traded securities prices, environmental assessments); ii) assessment tools—rating agency grades, business-report scores, public and private firm, hazard, portfolio, and exposure models; iii) rating grades—internal and external (Moody’s, Standard and Poor (S&P), Fitch; S&P provided further insights); iv) small and medium enterprises (SME) lending—including reviewing principals in the personal capacities. (2) Financial-ratio scoring—i) pioneers—including Altman’s Z score and Moody’s commercially successful RiskCalc; ii) predictive ratios—that have appeared; iii) agency usage—for the development of public- and private-firm models; iv) Moody’s RiskCalc—basics and results when first launched; v) non-financial factors—those typically considered, and how objectivity can be improved. (3) Forward-looking data—most provided by human judgment, even the ‘wisdom of the crowd’ inherent in market prices. Rating transitions and functional versus reduced-form models are also used.

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Tay, Yen Pei, Vasaki Ponnusamy, and Lam Hong Lee. "Big Data in Telecommunications." In Big Data. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9840-6.ch036.

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The meteoric rise of smart devices in dominating worldwide consumer electronics market complemented with data-hungry mobile applications and widely accessible heterogeneous networks e.g. 3G, 4G LTE and Wi-Fi, have elevated Mobile Internet from a ‘nice-to-have' to a mandatory feature on every mobile computing device. This has spurred serious data traffic congestion on mobile networks as a consequence. The nature of mobile network traffic today is more like little Data Tsunami, unpredictable in terms of time and location while pounding the access networks with waves of data streams. This chapter explains how Big Data analytics can be applied to understand the Device-Network-Application (DNA) dimensions in annotating mobile connectivity routine and how Simplify, a seamless network discovery solution developed at Nextwave Technology, can be extended to leverage crowd intelligence in predicting and collaboratively shaping mobile data traffic towards achieving real-time network congestion control. The chapter also presents the Big Data architecture hosted on Google Cloud Platform powering the backbone behind Simplify in realizing its intelligent traffic steering solution.

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Tay, Yen Pei, Vasaki Ponnusamy, and Lam Hong Lee. "Big Data in Telecommunications." In Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8505-5.ch004.

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The meteoric rise of smart devices in dominating worldwide consumer electronics market complemented with data-hungry mobile applications and widely accessible heterogeneous networks e.g. 3G, 4G LTE and Wi-Fi, have elevated Mobile Internet from a ‘nice-to-have' to a mandatory feature on every mobile computing device. This has spurred serious data traffic congestion on mobile networks as a consequence. The nature of mobile network traffic today is more like little Data Tsunami, unpredictable in terms of time and location while pounding the access networks with waves of data streams. This chapter explains how Big Data analytics can be applied to understand the Device-Network-Application (DNA) dimensions in annotating mobile connectivity routine and how Simplify, a seamless network discovery solution developed at Nextwave Technology, can be extended to leverage crowd intelligence in predicting and collaboratively shaping mobile data traffic towards achieving real-time network congestion control. The chapter also presents the Big Data architecture hosted on Google Cloud Platform powering the backbone behind Simplify in realizing its intelligent traffic steering solution.

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Streszczenia konferencji na temat "Crowd risk prediction"

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Li, Hongjian, and Yan Shao. "Investors' Financing Risk Prediction in Crowd-funding Platform." In 2017 7th International Conference on Education and Management (ICEM 2017). Atlantis Press, 2018. http://dx.doi.org/10.2991/icem-17.2018.120.

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Morgan, Jeffrey J., Otto C. Wilson, and Prahlad G. Menon. "The Wisdom of Crowds Approach to Influenza-Rate Forecasting." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86559.

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Influenza is an important public health concern. Influenza leads to the death or hospitalization of thousands of people around the globe every year. However, the flu-season varies every year viz. when it starts, when it peaks, and the severity of the outbreak. Knowing the trajectory of the epidemic outbreak is important for taking appropriate mitigation strategies. Starting with the 2013–2014 flu season, the Influenza Division of the Centers for Disease Control and Prevention (CDC) has held a “Predict the Influenza Season Challenge” to encourage the scientific community to make advances in the

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Teng, Harold Ze Chie, Hongchao Jiang, Xuan Rong Zane Ho, et al. "Predictive Analytics for COVID-19 Social Distancing." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/716.

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The COVID-19 pandemic has disrupted the lives of millions across the globe. In Singapore, promoting safe distancing by managing crowds in public areas have been the cornerstone of containing the community spread of the virus. One of the most important solutions to maintain social distancing is to monitor the crowdedness of indoor and outdoor points of interest. Using Nanyang Technological University (NTU) as a testbed, we develop and deploy a platform that provides live and predicted crowd counts for key locations on campus to help users plan their trips in an informed manner, so as to mitigat

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Sohn, Samuel S., Seonghyeon Moon, Honglu Zhou, et al. "Harnessing Fourier Isovists and Geodesic Interaction for Long-Term Crowd Flow Prediction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/185.

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With the rise in popularity of short-term Human Trajectory Prediction (HTP), Long-Term Crowd Flow Prediction (LTCFP) has been proposed to forecast crowd movement in large and complex environments. However, the input representations, models, and datasets for LTCFP are currently limited. To this end, we propose Fourier Isovists, a novel input representation based on egocentric visibility, which consistently improves all existing models. We also propose GeoInteractNet (GINet), which couples the layers between a multi-scale attention network (M-SCAN) and a convolutional encoder-decoder network (CE

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Pavlovski, Martin, Fang Zhou, Nino Arsov, Ljupco Kocarev, and Zoran Obradovic. "Generalization-Aware Structured Regression towards Balancing Bias and Variance." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/363.

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Attaining the proper balance between underfitting and overfitting is one of the central challenges in machine learning. It has been approached mostly by deriving bounds on generalization risks of learning algorithms. Such bounds are, however, rarely controllable. In this study, a novel bias-variance balancing objective function is introduced in order to improve generalization performance. By utilizing distance correlation, this objective function is able to indirectly control a stability-based upper bound on a model's expected true risk. In addition, the Generalization-Aware Collaborative Ense

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