Academic literature on the topic 'Scoring drivers'
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Journal articles on the topic "Scoring drivers"
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Zuraida, Rida. "Fatigue Risk of Long-Distance Driver as the Impact of the Duration of Work." ComTech: Computer, Mathematics and Engineering Applications 6, no. 3 (2015): 319. http://dx.doi.org/10.21512/comtech.v6i3.2207.
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Studies on the driver's fatigue, must focus on at least two things: the time-of-day that affect by circadian factors, and time-on-task. This paper discusses the risk level of driver fatigue, which generally have to drive in a long duration or more than 4 hours. The risk of fatigue was assessed using Fatigue Likelihood Scoring (FLS) by Transport Canada. Based on interviews with 24 inter-city bus drivers, 18 of the 24 drivers have a very high risk of fatigue that characterized by FLS scores greater than 20, while the rest have a high risk driver that characterized by FLS value greater than 10. A high risk of chronic fatigue that experienced by most of drivers caused by working hours which is more than 36 hours in a week, the duration of the shift of greater than 8 hours a day, lack of time off, the amount hours of driving at night, and the amount of time off.
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Garegnani, Giovanni Maria, Emilia Piera Merlotti, and Angeloantonio Russo. "Scoring Firms’ Codes of Ethics: An Explorative Study of Quality Drivers." Journal of Business Ethics 126, no. 4 (2013): 541–57. http://dx.doi.org/10.1007/s10551-013-1968-8.
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Perry, Anthony R. "Type a Behavior Pattern and Motor Vehicle Drivers' Behavior." Perceptual and Motor Skills 63, no. 2 (1986): 875–78. http://dx.doi.org/10.2466/pms.1986.63.2.875.
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2 major components of the Type A coronary-prone behavior pattern are said to be a chronic sense of time urgency and impatience. The present study was done to determine whether these characteristics are associated with the driving performance of Type A individuals. 38 women and 32 men completed the Jenkins Activity Survey and a questionnaire concerning their driving. Those subjects exhibiting more Type A behavior tended to be more impatient, reported being involved in more accidents, and received more tickets for driving violations than those scoring lower on the Type A scale.
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Muguro, Joseph K., Pringgo Widyo Laksono, Yuta Sasatake, Kojiro Matsushita, and Minoru Sasaki. "User Monitoring in Autonomous Driving System Using Gamified Task: A Case for VR/AR In-Car Gaming." Multimodal Technologies and Interaction 5, no. 8 (2021): 40. http://dx.doi.org/10.3390/mti5080040.
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Background: As Automated Driving Systems (ADS) technology gets assimilated into the market, the driver’s obligation will be changed to a supervisory role. A key point to consider is the driver’s engagement in the secondary task to maintain the driver/user in the control loop. This paper aims to monitor driver engagement with a game and identify any impacts the task has on hazard recognition. Methods: We designed a driving simulation using Unity3D and incorporated three tasks: No-task, AR-Video, and AR-Game tasks. The driver engaged in an AR object interception game while monitoring the road for threatening road scenarios. Results: There was a significant difference in the tasks (F(2,33) = 4.34, p = 0.0213), identifying the game-task as significant with respect to reaction time and ideal for the present investigation. Game scoring followed three profiles/phases: learning, saturation, and decline profile. From the profiles, it is possible to quantify/infer drivers’ engagement with the game task. Conclusion: The paper proposes alternative monitoring that has utility, i.e., entertaining the user. Further experiments with AR-Games focusing on the real-world car environment will be performed to confirm the performance following the recommendations derived from the current test.
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Viswanathan, P. K., and S. K. Shanthi. "Modelling Credit Default in Microfinance—An Indian Case Study." Journal of Emerging Market Finance 16, no. 3 (2017): 246–58. http://dx.doi.org/10.1177/0972652717722084.
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Credit score models have been successfully applied in a traditional credit card industry and by mortgage firms to determine defaulting customer from the non-defaulting customer. In the light of growing competition in the microfinance industry, over-indebtedness and other factors, the industry has come under increased regulatory supervision. Our study provides evidence from a large microfinance institutions (MFI) in India, and we have applied both the credit scoring method and neural network (NN) method and compared the results. In this article, we demonstrate the capability of credit scoring models for an Indian-based microfinance firm in terms of predicting default probability as well the relative importance of each of its associated drivers. A logistic regression model and NN have been used as the predictive analytic tools for sifting the key drivers of default.
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Gelinas, Isabelle, Barbara Mazer, Yu-Ting Chen, et al. "Evaluating Older Drivers in Their Everyday Driving Environments." Innovation in Aging 4, Supplement_1 (2020): 731. http://dx.doi.org/10.1093/geroni/igaa057.2597.
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Abstract Developing tools that accurately detect at-risk driving behaviors is a public-health priority. There is a need for a measure that accurately assesses older drivers’ level of competence on familiar roadways. The objective of this presentation is to describe the development of the procedures and scoring of a new approach, the Electronic Driving Observation Schedule (eDOS), to observe everyday driving in the community. The eDOS was used to record and compare the driving environment and performance of older drivers and low-risk younger drivers during their everyday driving. Older (n=160, >74y) and younger (n=60, 35-64y) drivers completed a 20-30-minute drive from their home to destinations of their choice. Older drivers drove on simpler routes with fewer intersections and lane changes. Both groups made few driving errors, which were mostly low-risk. Younger drivers tended to demonstrate poor driving habits (not signaling, speeding, poor lane position) and compliance with road rules. Part of a symposium sponsored by Transportation and Aging Interest Group.
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Parganas, Petros, Roman Liasko, and Christos Anagnostopoulos. "Scoring goals in multiple fields." Sport, Business and Management: An International Journal 7, no. 2 (2017): 197–215. http://dx.doi.org/10.1108/sbm-11-2016-0072.
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Purpose Professional football clubs currently strive for a number of concurrent goals, ranging from on-field success to profit maximization to fan expansion and engagement. The purpose of this paper, theoretically informed by the social penetration theory, is to analyze the economics behind such goals and examine the association between team performance, commercial success, and social media followers in professional team sports. Design/methodology/approach A data set relating to 20 European professional football clubs that combines financial (revenues and costs), sporting, and digital-reach measures for three consecutive football seasons (2013/2014 to 2015/2016) was used. In addition, to elaborate on this data in terms of a descriptive study, the study constructs a range of correlation statistical tests and linear modeling techniques to obtain quantitative results. Findings The results indicate that all the three main sources of club revenues (match-day, commercial/sponsorship, and broadcasting) are positive drivers for Facebook followers. Staff investments (staff costs) are also positively related to Facebook followers, albeit to a lesser extent, while higher-ranked clubs seem to follow a constant approach in terms of their revenues and cost structure. Originality/value This study seeks to bridge the communication and sport economic research, providing evidence that Facebook followers are part of the cyclical phenomenon of team revenues and team performance. In doing so, it initiates a debate on the relationship between the digital expansion of a football club and its sports and financial indicators.
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Turko, Andy J., Giulia S. Rossi, and Patricia A. Wright. "More than Breathing Air: Evolutionary Drivers and Physiological Implications of an Amphibious Lifestyle in Fishes." Physiology 36, no. 5 (2021): 307–14. http://dx.doi.org/10.1152/physiol.00012.2021.
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Amphibious and aquatic air-breathing fishes both exchange respiratory gasses with the atmosphere, but these fishes differ in physiology, ecology, and possibly evolutionary origins. We introduce a scoring system to characterize interspecific variation in amphibiousness and use this system to highlight important unanswered questions about the evolutionary physiology of amphibious fishes.
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Oku, Y., A. Fukushima, J. Dorey, and M. Toumi. "Health Technology Assessment: Drivers of Additional Benefit Scoring and the Impact on Orphan Drug Pricing in France." Value in Health 21 (September 2018): S112. http://dx.doi.org/10.1016/j.jval.2018.07.851.
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Amat, Oriol, Raffaele Manini, and Marcos Antón Renart. "Credit concession through credit scoring: Analysis and application proposal." Intangible Capital 13, no. 1 (2017): 51. http://dx.doi.org/10.3926/ic.903.
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Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests. Design/methodology/approach: The empirical study has the objective of answering two questions: (1) Which ratios better discriminate the companies based on their being solvent or insolvent? and (2) What is the relative importance of these ratios? To do this, several statistical techniques with a multifactorial focus have been used (Multivariate Analysis of Variance, Linear Discriminant Analysis, Logit and Probit Models). Several samples of companies have been used in order to obtain and to test the model. Findings: Through the application of several statistical techniques, the credit scoring model has been proved to be effective in discriminating between good and bad creditors. Research limitations: This study focuses on manufacturing, commercial and services companies of all sizes in Spain; Therefore, the conclusions may differ for other geographical locations.Practical implications: Because credit is one of the main drivers of growth, a solid credit scoring model can help financial institutions assessing to whom to grant credit and to whom not to grant credit.Social implications: Because of the growing importance of credit for our society and the fear of granting it due to the latest financial turmoil, a solid credit scoring model can strengthen the trust toward the financial institutions assessment’s. Originality/value: There is already a stream of literature related to credit scoring. However, this paper focuses on Spanish firms and proves the results of our model based on real data. The application of the model to detect the probability of default in loans is original.
Dissertations / Theses on the topic "Scoring drivers"
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Matějková, Jitka. "Bodový systém a statistika nehodovosti v silniční dopravě ČR." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-19227.
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This thesis gives an overview of the principles of a points system for evaluating the drivers (point system) and statistics of accidents in road transport of the Czech Republic.. The operating principle of the points system is supplemented by graphical analysis of drivers who have received some penalty point and also overview of European countries with similar systems. The thesis contains the results of public research on the topic "point system". At the conclusion of this thesis is an analysis of time series of basic indicators of accidents on the roads of the Czech Republic supplemented by analysis of the impact point system for accident and its consequences on people's lives.
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Miraftabzadeh, Seyed Ali. "Real-Time Adaptive Data-Driven Perception for Anomaly Priority Scoring at Scale." Thesis, The University of Texas at San Antonio, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10686275.
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<p> With the aim of ultimately contributing to humanitarian response to disasters and violent events, detecting anomalies in daily human life is a crucial requirement for developing secured smart-home and smart-communities in the context of smart cities. In early security systems, it was necessary for a team of security experts to analyze a vast amount of surveillance data from a network of cameras, for instance to pick out patterns of human behavior identified as potential harmful threats. Now, however, in the big data era, online excavation and interpretation of streamed zettabyte data requires two automated technologies: (1) intelligent models—to extract suspicious patterns and discover latent anomalies; and, (2) agile systems—to take real-time action based on decision- making processes. As such, the two primary contributions of this dissertation are: (1) developing accurate intelligent models that perform much like human precision, and (2) proposing sub-systems of smart city infrastructure that intimately incorporate these intelligent models. </p><p> For this dissertation, pattern recognition models with applications in real-time video analysis were developed based on four computer vision tasks: (1) identity recognition, (2) object detection, (3) gesture recognition, and (4) action recognition. Applications of these models include, but are not limited to, recognition of: suspicious identities, active threats (life-threatening events i.e. bomb threats, civil unrest, criminal activity, earthquakes, evacuations, fires, hazardous materials), and suspicious packages. To perform these tasks, the intent is to have the models emulate the processes that take place within a human brain, i.e. with a close resemblance to human neuro processing, albeit in high-powered computational machines. Deep learning, the state-of-the-art concept in artificial intelligence, was the developmental basis for the proposed cognitive models. </p><p> In order to run efficiently, these computationally intensive models rely on the use of and co- ordination between high-throughput, high-performance, and many-task (parallel-task) computing- enabled machines that have a high level of computing performance compared to general-purpose computers. This variety of computational resources are served at scale in a cloud system, which serves as a central location with the core building blocks needed for compute, storage and network- ing. Nevertheless, anomaly detection and then taking real-time actions demands a faster processing speed than what is possible when communicating with cloud networks. It requires the use of phys- ical infrastructure that is closer to the edge, near the source of the data (end-device); otherwise, when the data is centrally processed and stored, there is too much bandwidth required. This edge- computing approach helps reduce latency for critical applications, lower dependence on the cloud, and better manage the massive deluge of data being generated. In addition, security and privacy can also be improved with edge computing by keeping sensitive data within the end-device. In this dissertation, these distributed and decentralized deep learning systems aimed at enabling smart city applications—spread throughout the end-device, edge, and cloud—are designed following the requirements of smart city infrastructure.</p><p>
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Englund, Björn. "Key aspects to consider when designing an IT-tool based on scoring rubrics to support formative assessment: an exploratory design-driven study." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192367.
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Why this thesis is needed. This thesis is motivated by the falling school results of Swedish 15-year-olds, a lack of IT tools in Swedish schools and a call for turning the theory on formative assessment into practice. Previous research that is used in the thesis. This thesis mainly builds on the research done by John Hattie which is presented in his book Visible Learning from 2009, Wiliam & Thompson's research on effective formative assessment from 2007, Pachler et al. research on formative e-assessment from 2010 and Panadero & Jonsson's research on scoring rubrics from 2013. Research question. What key aspects should be taken into consideration when designing and implementing an IT tool based on scoring rubrics which aims to support formative assessment according to the key strategies for effective formative assessment as identified by Wiliam & Thompson (2007)? Method. To answer this question I chose a qualitative approach of parallel paper prototyping where I iteratively exposed the design and my ideas during focus groups to teachers, a headmaster, employees at The Swedish National Agency for Education and high school students, followed by an evaluation of the key topics that surfaced during the focus groups. Results. The results consist of eight key aspects to take into consideration when designing and implementing such a tool. Discussion and future research. Finally I discuss the implications of my findings and present directions for future research which include the construction of the tool, further investigation of the key aspects identified in this study, identification of additional key aspects and more.<br>Varför denna avhandling behövs. Denna avhandling motiveras av de fallande skolresultaten hos svenska 15-åringar, en brist på IT-verktyg i svenska skolor och ett rop efter att vända forskning inom formativ bedömning till praktik. Tidigare forskning som används. Denna avhandling bygger i huvudsak på forskningen av John Hattie som presenteras i hans bok Visible Learning från 2009, William & Thompsons forskning på effektiv formativ bedömning från 2007, Pachler et al. forskning på formativ e-bedömning från 2010 och Panadero & Jonssons forskning på betygsmatriser från 2013. Forskningsfråga. Vilka nyckelaspekter ska tas i åtanke vid utveckling av ett IT-verktyg baserat på betygsmatriser som stödjer formativ bedömning enligt de fem nyckelstrategierna för effektiv formativ bedömning som formulerats av Wiliam & Thompson (2007)? Metod. För att besvara denna fråga valdes en kvalitativ metod där parallell pappersprototypning användes under ett antal iterationer av fokusgrupper under vilka designen och tidigare diskussionpunkter diskuterades med lärare, en rektor, anställda vid Skolverket samt gymnasiestudenter. Fokusgrupperna följdes upp med en utvärdering av de största diskussionpunkterna som dök upp. Resultat. Resultaten består av åtta nyckelaspekter att ha i åtanke vid utveckling av ett sådant verktyg. Diskussion och framtida forskning. Slutligen diskuteras implikationerna av resultaten och direktioner för framtida forskning framförs. Dessa direktioner inkluderar utveckling av verktyget, vidare utredning av de nyckelaspekter som hittats i denna avhandling, identifikation av ytterligare nyckelaspekter och mer.
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OlLIVEIRA, NETO Rosalvo Ferreira de. "COMOVI: um framework para transformação de dados em aplicações de credit behavior scoring baseado no desenvolvimento dirigido por modelos." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/17330.
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Previous issue date: 2015-12-11<br>CAPEs<br>A etapa de pré-processamento em um projeto de descoberta do conhecimento é custosa,
em geral, consome cerca de 50 a 80% do tempo total de um projeto. É nesta etapa que um
banco de dados relacional é transformado para aplicação de um algoritmo de mineração de
dados. A transformação dos dados nesta etapa é uma tarefa complexa, uma vez que exige uma
forte integração entre projetistas de banco de dados e especialistas do domínio da aplicação. Os
frameworks que buscam sistematizar a etapa de transformação dos dados encontrados na literatura
apresentam limitações significativas quando aplicados a soluções comportamentais, como Credit
Behavior Scoring. Estas soluções visam a auxiliar as instituições financeiras a decidirem sobre
a concessão de crédito aos consumidores com base no risco das solicitações. Este trabalho
propõe um framework baseado no Desenvolvimento Dirigido por Modelos para sistematizar
esta etapa em soluções de Credit Behavior Scoring. Ele é composto por um meta-modelo que
mapeia os conceitos do domínio e um conjunto de regras de transformações. As três principais
contribuições do framework proposto são: 1) aumentar o poder discriminatório da solução,
através da construção de novas variáveis que maximizam o conteúdo estatístico da informação
do domínio; 2) reduzir o tempo da transformação dos dados através da geração automática de
código e 3) permitir que profissionais e pesquisadores de Inteligência Artificial e Estatística
realizem a transformação dos dados sem o auxílio de especialistas de Banco de Dados. Para
validar o framework proposto, dois estudos comparativos foram realizados. Primeiro, um estudo
comparando o desempenho entre os principais frameworks existentes na literatura e o framework
proposto foi realizado em duas bases de dados. Uma base de dados de um conhecido benchmark
de uma competição internacional organizada pela PKDD, e outra obtida de uma das maiores
empresas de varejo do Brasil, que possui seu próprio cartão de crédito. Os frameworks RelAggs
e Validação de Múltiplas Visões Baseado em Correção foram escolhidos como representantes
das abordagens proposicional e mineração de dados relacional, respectivamente. A comparação
foi realizada através do processo de validação cruzada estratificada, para definir os intervalos de
confiança para a avaliação de desempenho. Os resultados mostram que o framework proposto
proporciona um desempenho equivalente ou superior aos principais framework existentes, medido
pela área sob a curva ROC, utilizando uma rede neural MultiLayer Perceptron, K vizinho mais
próximos e Random Forest como classificadores, com um nível de confiança de 95%. O segundo
estudo verificou a redução de tempo proporcionada pelo framework durante a transformação dos
dados. Para isso, sete times compostos por estudantes de uma universidade brasileira mensuraram
o tempo desta atividade com e sem o framework proposto. O teste pareado Wilcoxon Signed-Rank
mostrou que o framework proposto reduz o tempo de transformação com um nível de confiança
de 95%.<br>The pre-processing stage in knowledge discovery projects is costly, generally taking
between 50 and 80% of total project time. It is in this stage that data in a relational database are
transformed for applying a data mining technique. This stage is a complex task that demands
from database designers a strong interaction with experts who have a broad knowledge about
the application domain. The frameworks that aim to systemize the data transformation stage
have significant limitations when applied to behavior solutions such as the Credit Behavior
Scoring solutions. Their goal is help financial institutions to decide whether to grant credit to
consumers based on the credit risk of their requests. This work proposes a framework based on
the Model Driven Development to systemize this stage in Credit Behavioral Scoring solutions.
It is composed by a meta-model which maps the domain concepts and a set of transformation
rules. This work has three main contributions: 1) improving the discriminant power of data
mining techniques by means of the construction of new input variables, which embed new
knowledge for the technique; 2) reducing the time of data transformation using automatic code
generation and 3) allowing artificial intelligence and statistics modelers to perform the data
transformation without the help of database experts. In order to validate the proposed framework,
two comparative studies were conducted. First, a comparative study of performance between
the main existing frameworks found in literature and the proposed framework applied to two
databases was performed. One database from a known benchmark of an international competition
organized by PKDD, and another one obtained from one of the biggest retail companies from
Brazil, that has its own private label credit card. The RelAggs and Correlation-based Multiple
View Validation frameworks were chosen as representatives of the propositional and relational
data mining approaches, respectively. The comparison was carried out through by a 10-fold
stratified cross-validation process with ten stratified parts in order to define the confidence
intervals. The results show that the proposed framework delivers a performance equivalent or
superior to those of existing frameworks, for the evaluation of performance measured by the area
under the ROC curve, using a Multilayer Perceptron neural network, k-nearest neighbors and
Random Forest as classifiers, with a confidence level of 95%. The second comparative study
verified the reduction of time required for data transformation using the proposed framework.
For this, seven teams composed by students from a Brazilian university measured the runtime of
this stage with and without the proposed framework. The paired Wilcoxon Signed-Rank’s Test
showed that the proposed framework reduces the time of data transformation with a confidence
level of 95%.
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Alexandre, Victor Manuel Mónica Monteiro. "Customer valuation model: applied to a foodservice commodities supplier." Master's thesis, 2011. http://hdl.handle.net/10071/4610.
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In last few years, terms like customer centric organization; customer focused strategies; customer metrics and customer value assessment have became buzzwords in the organizations. Independently from which angle managers are grasping these concepts, a common denominator seems to accede that customers are probably the most valuable assets in a company.
However, most of costumer value assessments are confined to sales revenues analysis.
In this document, several customer value models are assessed, in order to evaluate which can be used as a metric and as a managerial tool, able be applied to market particularities of a foodservice food products supplier.
The author selected CLV model as the base model to assess customer values, adapting it to the presented case study. In addition to this model, it was developed a complementary scoring model to balance qualitatively the adapted CLV outputs.
The two models working together are the proposed solution to assess the case study customers’ value.<br>Nos últimos anos, expressões como: organizações orientadas para o cliente; estratégias centradas no cliente; métricas de rentabilidade e de valorização de clientes têm sido proferidas como alguma frequência no seio das organizações.
Apesar de nem sempre os gestores perceberem estes conceitos na sua plenitude, a grande maioria consegue conceber que os clientes são provavelmente o ativo de maior valor das suas organizações.
Contudo, e apesar dessa consciência de mais-valia, na grande maioria dos casos, os procedimentos de valorização e de medição do valor dos clientes ainda se resumem a análises lineares da margem de vendas.
Neste documento, procura-se apresentar vários modelos de avaliação de clientes, aferindo qual ou quais melhor solucionarão o problema apresentado. Ou seja, avaliar o portfólio de clientes de uma empresa fornecedora de “commodities” alimentares a operar no sector de “foodservice” no sul da Europa.
O autor optou pelo modelo CLV – “Customer Lifetime Value” como modelo base, adaptando-o à especificidade do problema apresentado. Foi ainda desenvolvido um modelo complementar, que através de procedimentos de ponderação acrescenta valorização qualitativa aos resultados do primeiro.
A aplicação em modo de complementaridade dos dois modelos são a solução proposta para o problema do caso apresentado.
Books on the topic "Scoring drivers"
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John, Andrisani, ed. Total shotmaking: The golfer's guide to low scoring. HarperCollins Publishers, 1994.
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Wherry, Frederick F., and Ian Woodward, eds. The Oxford Handbook of Consumption. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190695583.001.0001.
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This book examines the most pressing questions addressed by consumption studies scholars today. The volume counteracts the tendency towards disciplinary myopia as it engages scholars from around the world drawing on sociology, anthropology, psychology, history, consumption studies, and marketing. The volume’s thirty-one chapters are organized around six themes, facilitating cross-disciplinary exploration. The volume covers consumer transactions and credit scoring as important drivers of consumer behaviors, race and ethnicity and consumer inequality, brands and branding, the embeddedness of marketing, consumer culture theory, the sharing economy, ethical consumption, environmental sustainability, and variations in urban scenes where consumption thrives.
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Bion, Julian, and Anna Dennis. ICU admission and discharge criteria. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0020.
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The decision to admit patients to intensive care or discharge them, is a daily task for intensivists, a life-changing event for patients and families, and a major strategic issue for health care systems worldwide. Decisions must often be made rapidly, in conditions of uncertainty, involving substituted judgements about relative risks and benefits, framed by sociocultural factors that are not well characterized. The outcomes are strongly influenced by available resources, staffing, and skills throughout the patient pathway. The decision to admit should be based on the severity of illness, chronic health and physiological reserve, and therapeutic susceptibility, informed by the patient’s wishes. Discharge decisions are equally complex and involve balancing the needs of individual patients against those of society. Scoring systems and guidelines can aid decision making. The process involves collaboration between intensivist, referring team, patient, and family. The provision of futile care is usually driven by family expectations and lack of agreement among the treating team. Discussions involve value judgements. Effective admission and discharge processes will minimize avoidable morbidity, mortality, and readmissions, and maximize family and patient satisfaction, and cost-efficacy. However, reaching the most effective level of practice involves balances and compromises. Experienced clinical judgement remains a key element in defining suitability of individual patients for ICU admission and discharge.
Book chapters on the topic "Scoring drivers"
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Hsieh, Nan-Chen, Lun-Ping Hung, and Chia-Ling Ho. "A Data Driven Ensemble Classifier for Credit Scoring Analysis." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_33.
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Işik, Zerrin, Volkan Atalay, Cevdet Aykanat, and Rengül Çetin-Atalay. "Data and Model Driven Hybrid Approach to Activity Scoring of Cyclic Pathways." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9794-1_18.
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Singh, Iknoor, Deepak P., and Anoop K. "On the Coherence of Fake News Articles." In ECML PKDD 2020 Workshops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65965-3_42.
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AbstractThe generation and spread of fake news within new and online media sources is emerging as a phenomenon of high societal significance. Combating them using data-driven analytics has been attracting much recent scholarly interest. In this computational social science study, we analyze the textual coherence of fake news articles vis-a-vis legitimate ones. We develop three computational formulations of textual coherence drawing upon the state-of-the-art methods in natural language processing and data science. Two real-world datasets from widely different domains which have fake/legitimate article labellings are then analyzed with respect to textual coherence. We observe apparent differences in textual coherence across fake and legitimate news articles, with fake news articles consistently scoring lower on coherence as compared to legitimate news ones. While the relative coherence shortfall of fake news articles as compared to legitimate ones form the main observation from our study, we analyze several aspects of the differences and outline potential avenues of further inquiry.
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Gulati, Ashok, Pallavi Rajkhowa, Ranjana Roy, and Pravesh Sharma. "Performance of Agriculture in Madhya Pradesh." In India Studies in Business and Economics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9335-2_6.
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AbstractMadhya Pradesh emerged as the state with the highest growth rate in agriculture. Long clubbed with the so-called BIMARU group of poor northern, central and eastern states, MP successfully broke ranks to set a scorching pace of growth, which has been unparalleled in the past quarter-century. Understanding the factors that helped to drive this growth and drawing lessons for other states at similar levels of development is the main objective of this chapter. Madhya Pradesh’s agricultural GDP increased at 8.1% per annum during 2005–06 to 2016–17, surpassing even record holder Gujarat’s 6% agricultural growth in the same period. The last three years have been even more spectacular: agricultural GDP increased at 11.8% per annum. Keeping this background in mind, this chapter has used secondary data published by the government to study the composition, sources and drivers of agriculture growth in Madhya Pradesh and the lessons that can be drawn for developing states. Although MP has recorded a significant decline in poverty rates from 53.6% in 2004–05 to 35.7% in 2011–12, there is still much to be done to improve the livelihood of the rural population. Moreover, MP’s per capita income is low, standing at Rs. 51,798 per annum (FY14 at current prices) as compared to the national annual average income of Rs. 74,380. Although it is much better than that of Bihar (Rs. 31,199) and Uttar Pradesh (Rs. 36,250), it remains way below top-performing states like Sikkim (Rs. 176,491), Maharashtra (Rs. 114,392), Haryana (Rs. 133,427) and Gujarat (Rs. 106,831). Therefore, the study also makes policy suggestions to bolster agricultural growth in MP.
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Simm, Jonathan D., Colin R. Thorne, and Jim W. Hall. "12 Driver impact scoring, ranking and uncertainty." In Future flooding and coastal erosion risks. Thomas Telford Publishing, 2007. http://dx.doi.org/10.1680/ffacer.34495.0012.
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Willetts, David. "Driver of Innovation." In A University Education. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198767268.003.0017.
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We saw in Chapter Four that the UK can be proud of the quality of the research conducted in its universities. But we need to be clear what this success is. Success means relentless pressure on academics to produce papers that are going to be assessed by fellow academics as of the highest quality and frequently cited. For most academics today that means getting published in the most prestigious peer-reviewed journals which is what matters for promotion, even though the assessment of their research excellence in the REF is supposed to be independent of the status of the journal in which their work appears. That drives competition in research performance whose logic is as obvious as rewarding a football team for scoring goals. The sure way for a university to move up the rankings and boost its prestige is to promote or hire hot-shot academics with strong publishing records. In science as in football we run the most open and most competitive league in the world—and hope to continue to do so even outside the EU. It has projected our universities high up the rankings, alongside the US. (There is one important difference from football—under the rules of the REF, academics could take their publications with them when they moved. It as if when you buy a star striker you get the goals he scored in the previous season as well. The logic is that it is part of their personal research performance that is being assessed. It has driven up the pay of the academic stars but also provided opportunities to younger post-docs after they get something published. Nicholas Stern’s review of the REF in 2016 proposed that such portability should end.) The high rankings of our research-intensive universities are a real achievement. But that is not the same as having the best national R&D system or contributing to tackling big global challenges or successfully commercializing new technologies or making a region a lively innovation cluster. We might hope that our research strengths contribute to these wider goals. But we may have been forced to make trade-offs to achieve research excellence which can actually make it harder to achieve other objectives.
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McClanahan, Annie. "Credit, Characterization, Personification." In Dead Pledges. Stanford University Press, 2016. http://dx.doi.org/10.11126/stanford/9780804799058.003.0003.
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Chapter 2 addresses the relationship between debt and personhood. Practices for evaluating economic credibility in the late eighteenth century relied on subjective, qualitative, narrative forms of evaluation and thus depended on a realist model of literary character. By the early twenty-first century, however, credit scoring had become objective, quantitative, and data driven. Yet contemporary creditors still import the fictions of personhood stripped from human subjects into the scores themselves. To understand the perduring presence of the person, this chapter considers both characterization and personification. Gary Shytengart’s 2010 novel Super Sad True Love Story attests to the persistence of racial discrimination in “objective” credit scoring, while conceptual art by Cassie Thornton, Occupy Wall Street debtor-portraits, and poetry by Mathew Timmons and Timothy Donnelley register debt as a material and historical force.
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Raydugin, Yuri G. "Overview of Conventional Risk Quantification Methods." In Modern Risk Quantification in Complex Projects. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198844334.003.0004.
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Deterministic (scoring) and probabilistic (Monte Carlo) risk quantification methodologies are overviewed as related to risk assessment steps of the PRM process. A role of various realizations of bias including zealotry in selection of conventional methods is reviewed. It is shown that deterministic (scoring) methods are irreplaceable when developing risk-addressing actions (the first PRM goal), although they are notoriously inaccurate when developing project contingencies (the second PRM goal). These methods can handle risks of impacts on both quantifiable and unquantifiable project objectives and are effective in selection of project alternatives. The Monte Carlo methods are valuable for development of schedule and cost contingencies only. Various Monte Carlo methodologies may have high systematic errors if they overlook main components of project risk exposure. Activity and line-item ranging methods miss out risk events; cost risk analyses miss out schedule-driven costs; all Monte Carlo methods overlook risk interactions as attributes of complex project systems.
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Kitchin, Rob. "Big Brother is Watching and Controlling You." In Data Lives. Policy Press, 2021. http://dx.doi.org/10.1332/policypress/9781529215144.003.0020.
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This chapter examines how data-driven technologies are deployed as mass surveillance and social credit scoring in China and their threat to democracy. Over the last decade, China has put in place a state-sponsored system of mass automated surveillance. It has successfully managed to limit the Internet to state-approved websites, apps, and social media, corralling users into a monitored, non-anonymous environment and preventing access to overseas media and information. From December of 2019, all mobile phone users registering new SIM cards must agree to a facial recognition scan to prove their identity. The state has also facilitated the transition from anonymous cash to traceable digital transactions. Most significantly, the state has created a social credit scoring system that pulls together various forms of data into a historical archive and uses it to assign each citizen and company a set of scores that affects their lifestyles and ability to trade. On the one hand, this is about making the credit information publicly accessible, so that those who are deemed untrustworthy are publicly shamed and lose their reputation. On the other hand, it is about guilt-by-association and administering collective punishment. This sociality works to minimize protest and unrest and reinforce the logic of the system.
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Clark, Walter Aaron. "Angel, the Proteus." In Los Romeros. University of Illinois Press, 2018. http://dx.doi.org/10.5622/illinois/9780252041907.003.0018.
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Angel’s career may remind one of the Greek water god Proteus, who was able to change shape. In addition to being one of the leading guitarists of the last few decades, Angel has also demonstrated talent for art, martial arts, acting, and film scoring. Most of all, he has cultivated a second career as a symphony conductor, leading major ensembles in the Americas, Europe, and Asia. The self-described “daredevil” in the family, he is driven by a need to escape from the long shadow cast by his father and brothers, to establish his own identity. This produces a competitive streak in his personality, combining insecurity and self-promotion, but not at the expense of artistic integrity.
Conference papers on the topic "Scoring drivers"
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Bergasa, Luis M., Daniel Almeria, Javier Almazan, J. Javier Yebes, and Roberto Arroyo. "DriveSafe: An app for alerting inattentive drivers and scoring driving behaviors." In 2014 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2014. http://dx.doi.org/10.1109/ivs.2014.6856461.
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ZEWDIE, Retta, and Pavel KIC. "ANALYSIS OF PRINCIPAL STRESS FACTORS AFFECTING DRIVERS IN DIFFERENT VEHICLES." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.090.
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The aim of this research paper is to compare the effect of the heart rate variabilities on different vehicle drivers. The most common urban transport vehicles (bus, tram and metro) are compared with typical mobile agricultural machines (tractors and combine harvesters) and passenger car. The measurements were focused on the complexity and traffic routes, velocities of each vehicle and ages of drivers related to changes in the heart rate of drivers as main parameters. The authors characterize cardinal factors affecting the contentment of drivers while undertaking their performance. The results of the measurements showed that the lowest mean heart rate value was registered on metro (subway car) drivers scoring 59 bpm, whereas the bus drivers 80 bpm and tram 77 bpm. The mean heart rate value of the combine harvester drivers scored 106 bpm and tractor drivers 108 bpm, which indicates that the driver’s heart rate of the agricultural machineries were significantly greater. The heart rate of tram drivers has increased significantly in a short period of time by 75%, metro (subway) drivers by 59%, combine harvester drivers by 23% and tractor drivers by 19%, whereas the passenger car drivers’ heart rate value increase was by 30%. The driving velocity, drivers’ age and weight showed its effect on the heart rate variabilities. The data collections were made by a heart rate measurement device. Relevant data on technical conditions stretch and shape design routes; as well as the heart rate variabilities on the respective drivers are collected carefully for detail statistical analysis.
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Abdelrahman, Abdalla, Hossam S. Hassanein, and Najah Abu-Ali. "Data-driven Robust Scoring Approach for Driver Profiling Applications." In GLOBECOM 2018 - 2018 IEEE Global Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/glocom.2018.8647971.
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Arsovski, Sasa, Branko Markoski, Predrag Pecev, Ladislav Ratgeber, and Nikola Petrov. "An ontology driven credit risk scoring model." In 2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2014. http://dx.doi.org/10.1109/cinti.2014.7028694.
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Kar, Gorkem, Batuhan Asiroglu, and Fatih Sinan Bir. "Scotto: Real-Time Driver Behavior Scoring Using In-Vehicle Data." In 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring). IEEE, 2019. http://dx.doi.org/10.1109/vtcspring.2019.8746461.
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Perez, Jerico, David Weir, Caroline Seguin, and Refaul Ferdous. "Development and Implementation of a Liquid Pipeline Quantitative Risk Assessment Model." In 2014 10th International Pipeline Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/ipc2014-33705.
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To the end of 2012, Enbridge Pipelines employed an in-house developed indexed or relative risk assessment algorithm to model its liquid pipeline system. Using this model, Enbridge was able to identify risk control or treatment projects (e.g. valve placement) that could mitigate identified high risk areas. A changing understanding of the threats faced by a liquid pipeline system and their consequences meant that the model changed year over year making it difficult to demonstrate risk reduction accomplished on an annual basis using a relative scoring system. As the development of risk management evolved within the company, the expectations on the model also evolved and significantly increased. For example, questions were being asked such as “what risk is acceptable and what risk is not acceptable?”, “what is the true risk of failure for a given pipe section that considers the likelihood of all threats applicable to the pipeline”, and “is enough being done to reduce these risks to acceptable levels?” To this end, starting in 2012 and continuing through to the end of 2013, Enbridge Pipelines developed a quantitative mainline risk assessment model. This tool quantifies both threat likelihood and consequence and offers advantages over the indexed risk assessment model in the following areas: • Models likely worst case (P90) rupture scenarios • Enables independent evaluation of threats and consequences in order to understand the drivers • Produces risk assessment results in uniform units for all consequence criteria and in terms of frequencies of failure for likelihood • Aggregates likelihood and consequence at varying levels of granularity • Uses the risk appetite of the organization and its quantification allows for the setting of defined high, medium, and low risk targets • Quantifies the amount of risk in dollars/year facilitating cost-benefit analyses of mitigation efforts and risk reduction activities • Grounds risk assessment results on changes in product volume-out and receptor sensitivity • Balances between complexity and utility by using enough information and data granularity to capture all factors that have a meaningful impact on risk Development and implementation of the quantitative mainline risk assessment tool has had a number of challenges and hurdles. This paper provides an overview of the approach used by Enbridge to develop its quantitative mainline risk assessment model and examines the challenges, learnings and successes that have been achieved in its implementation.
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Diaconescu, Paul, and Victor-Emil Neagoe. "Credit Scoring Using Deep Learning Driven by Optimization Algorithms." In 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2020. http://dx.doi.org/10.1109/ecai50035.2020.9223139.
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Jones, Douglas A., and Gregory M. Rusk. "Toward a scoring function for quality-driven machine translation." In the 18th conference. Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/990820.990875.
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Loukina, Anastassia, Nitin Madnani, and Aoife Cahill. "Speech- and Text-driven Features for Automated Scoring of English Speaking Tasks." In Proceedings of the Workshop on Speech-Centric Natural Language Processing. Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-4609.
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Zhai, Jiachen, and Ma Conggan. "Sound Quality Evaluation of Centralized Drive PMSM Based on Grade Scoring Method." In WCX™ 17: SAE World Congress Experience. SAE International, 2017. http://dx.doi.org/10.4271/2017-01-1061.
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