Gotowe bibliografie tematyczne / Predictive modelling

Gotowa bibliografia na temat „Predictive modelling”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 20 lutego 2023

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Artykuły w czasopismach na temat "Predictive modelling"

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Riemann, Hans P., i K. R. Davey. "Predictive modelling". Letters in Applied Microbiology 14, nr 4 (kwiecień 1992): 127–28. http://dx.doi.org/10.1111/j.1472-765x.1992.tb00666.x.

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Farkas, J. "Predictive modelling". International Journal of Food Microbiology 23, nr 3-4 (listopad 1994): v. http://dx.doi.org/10.1016/0168-1605(94)90154-6.

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Savage, Neil. "Modelling: Predictive yield". Nature 501, nr 7468 (wrzesień 2013): S10—S11. http://dx.doi.org/10.1038/501s10a.

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Vora, Deepali, i Kamatchi Iyer. "Evaluating the Effectiveness of Machine Learning Algorithms in Predictive Modelling". International Journal of Engineering & Technology 7, nr 3.4 (25.06.2018): 197. http://dx.doi.org/10.14419/ijet.v7i3.4.16773.

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Predictive modelling is a statistical technique to predict future behaviour. Machine learning is one of the most popular methods for predicting the future behaviour. From the plethora of algorithms available it is always interesting to find out which algorithm or technique is most suitable for data under consideration. Educational Data Mining is the area of research where predictive modelling is most useful. Predicting the grades of the undergraduate students accurately can help students as well as educators in many ways. Early prediction can help motivating students in better ways to select their future endeavour. This paper presents the results of various machine learning algorithms applied to the data collected from undergraduate studies. It evaluates the effectiveness of various machine learning algorithms when applied to data collected from undergraduate studies. Two major challenges are addressed as: choosing the right features and choosing the right algorithm for prediction.
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Lehman, John T., Lars Hakanson i Robert H. Peters. "Predictive Limnology: Methods for Predictive Modelling." Ecology 78, nr 1 (styczeń 1997): 326. http://dx.doi.org/10.2307/2266003.

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Oden, J. Tinsley. "Adaptive multiscale predictive modelling". Acta Numerica 27 (1.05.2018): 353–450. http://dx.doi.org/10.1017/s096249291800003x.

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The use of computational models and simulations to predict events that take place in our physical universe, or to predict the behaviour of engineered systems, has significantly advanced the pace of scientific discovery and the creation of new technologies for the benefit of humankind over recent decades, at least up to a point. That ‘point’ in recent history occurred around the time that the scientific community began to realize that true predictive science must deal with many formidable obstacles, including the determination of the reliability of the models in the presence of many uncertainties. To develop meaningful predictions one needs relevant data, itself possessing uncertainty due to experimental noise; in addition, one must determine model parameters, and concomitantly, there is the overriding need to select and validate models given the data and the goals of the simulation.This article provides a broad overview of predictive computational science within the framework of what is often called the science of uncertainty quantification. The exposition is divided into three major parts. In Part 1, philosophical and statistical foundations of predictive science are developed within a Bayesian framework. There the case is made that the Bayesian framework provides, perhaps, a unique setting for handling all of the uncertainties encountered in scientific prediction. In Part 2, general frameworks and procedures for the calculation and validation of mathematical models of physical realities are given, all in a Bayesian setting. But beyond Bayes, an introduction to information theory, the maximum entropy principle, model sensitivity analysis and sampling methods such as MCMC are presented. In Part 3, the central problem of predictive computational science is addressed: the selection, adaptive control and validation of mathematical and computational models of complex systems. The Occam Plausibility Algorithm, OPAL, is introduced as a framework for model selection, calibration and validation. Applications to complex models of tumour growth are discussed.
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Peppler-Lisbach, Cord. "Predictive modelling of historical and recent land-use patterns". Phytocoenologia 33, nr 4 (19.11.2003): 565–90. http://dx.doi.org/10.1127/0340-269x/2003/0033-0565.

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Rossiter, J. A., i B. Kouvaritakis. "Modelling and implicit modelling for predictive control". International Journal of Control 74, nr 11 (styczeń 2001): 1085–95. http://dx.doi.org/10.1080/00207170110054129.

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KAWAI, Kimio, Anthony BEAUCAMP, Noriyuki IMAIZUMI, Masatoshi SAKURAI i Yoshimi TAKEUCHI. "1902 Modelling of Drill Shapes by a Novel Predictive System". Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2015.8 (2015): _1902–1_—_1902–4_. http://dx.doi.org/10.1299/jsmelem.2015.8._1902-1_.

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Ma, Jungmok. "Data driven TRL Transition Predictions for Early Technology Development in Defence". Defence Science Journal 71, nr 6 (22.10.2021): 777–83. http://dx.doi.org/10.14429/dsj.71.16771.

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This paper proposes the framework of TRL (Technology Readiness Level) transition predictions for early technology development in defense. Though predicting future TRLs is an important planning tool, it has been studied less actively than the other critical issues on TRL, and previous studies mostly have resorted to domain experts. The proposed framework is data-driven and utilises both explanatory and predictive modelling techniques. As a case study, the proposed framework is applied to real technology development data from DTiMS (Defense Technology InforMation Service) which is identified as a key resource. The result of explanatory modelling shows that the two predictor variables, TRL before R&D and project cost, are statistically significant for future TRLs. Also, popular predictive models are fitted and compared with various performance indices using 10-fold cross validation. The two selected predictive models are linear regression and support vector machine models with the lowest prediction errors.
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Rozprawy doktorskie na temat "Predictive modelling"

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Welfoot, J. St J. "Predictive modelling of membrane nanofiltration". Thesis, Swansea University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639377.

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The main objective of this work was to develop predictive models for nanofiltration (NF) membrane processes. A one-parameter model (pore radius) for uncharged solute rejection has been developed. The good agreement between the proposed model and experimental data confirmed that uncharged solute rejection is well described by continuum models. A two-parameter model (pore radius and membrane charge) for electrolyte rejection has also been developed. Dielectric exclusion was included as an energy barrier to ion partitioning into the pores, the reassessment of which using NaCl rejection at the membrane isoelectric point introduced a third model parameter, the average pore solvent dielectric constant. The predicted membrane charge densities with the three-parameter model were more realistic in magnitude than those from previous models and their variation with concentration for divalent salts was in better agreement with physical models of ion adsorption. Analysis of experimental rejection data with truncated pore size distributions and a variation of viscosity with pore radius resulted in model parameters that represented the average value over all pore sizes. Further, analysis of salt mixtures showed that large experimentally observed negative rejections were very well described with fitted charge densities of similar magnitude to those from single salts. Finite Difference linearisation of pore concentration gradient greatly simplified the numerical solution of the three-parameter model. The validity of the linearised model was tested both experimentally and theoretically, showing the model to be a powerful tool for characterisation of NF membranes and subsequent prediction of separation performance. Overall, the work presented in this thesis has improved the understanding of the separation mechanisms of NF membranes, especially dielectric exclusion. The developed models are more rigorous than those proposed previously and represent a significant contribution to the field of predictive NF modelling.
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Kampakis, S. "Predictive modelling of football injuries". Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1508067/.

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The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the recovery time of football injuries using the UEFA injury recordings: The UEFA recordings is a common standard for recording injuries in professional football. For this investigation, three datasets of UEFA injury recordings were available: one from THFC, one from WW and one that was constructed by merging both. Poisson, negative binomial and ordinal regression were used to model the recovery time after an injury and assess the significance of various injury-related covariates. Then, different machine learning algorithms (support vector machines, Gaussian processes, neural networks, random forests, naïve Bayes and k-nearest neighbours) were used in order to build a predictive model. The performance of the machine learning models is then improved by using feature selection conducted through correlation-based subset feature selection and random forests. 2. Predicting injuries in professional football using exposure records: The relationship between exposure (in training hours and match hours) in professional football athletes and injury incidence was studied. A common problem in football is understanding how the training schedule of an athlete can affect the chance of him getting injured. The task was to predict the number of days a player can train before he gets injured. The dataset consisted of the exposure records of professional footballers in Tottenham Hotspur Football Club from the season 2012-2013. The problem was approached by a Gaussian process model equipped with a dynamic time warping kernel that allowed the calculation of the similarity of exposure records of different lengths. 3. Predicting intrinsic injury incidence using in-training GPS measurements: A significant percentage of football injuries can be attributed to overtraining and fatigue. GPS data collected during training sessions might provide indicators of fatigue, or might be used to detect very intense training sessions which can lead to overtraining. This research used GPS data gathered during training sessions of the first team of THFC, in order to predict whether an injury would take place during a week. The data consisted of 69 variables in total. Two different binary classification approaches were followed and a variety of algorithms were applied (supervised principal component analysis, random forests, naïve Bayes, support vector machines, Gaussian process, neural networks, ridge logistic regression and k-nearest neighbours). Supervised principal component analysis shows the best results, while it also allows the extraction of components that reduce the total number of variables to 3 or 4 components which correlate with injury incidence. The first investigation contributes the following to the field: • It provides models based on the UEFA injury recordings, a standard used by many clubs, which makes it easier to replicate and apply the results. • It investigates which variables seem to be more highly related to the prediction of recovery after an injury. • It provides a comparison of models for predicting the time to return to play after injury. The second investigation contributes the following to the field: • It provides a model that can be used to predict the time when the first injury of the season will take place. • It provides a kernel that can be utilized by a Gaussian process in order to measure the similarity of training and match schedules, even if the time series involved are of different lengths. The third investigation contributes the following to the field: • It provides a model to predict injury on a given week based on GPS data gathered from training sessions. • It provides components, extracted through supervised principal component analysis, that correlate with injury incidence and can be used to summarize the large number of GPS variables in a parsimonious way.
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Davis, Luke M. "Predictive modelling of bone ageing". Thesis, University of East Anglia, 2013. https://ueaeprints.uea.ac.uk/45085/.

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Bone age assessment (BAA) is a task performed daily by paediatricians in hospitalsworldwide. The main reasons for BAA to be performed are: fi�rstly, diagnosis of growth disorders through monitoring skeletal development; secondly, prediction of final adult height; and fi�nally, verifi�cation of age claims. Manually predicting bone age from radiographs is a di�fficult and time consuming task. This thesis investigates bone age assessment and why automating the process will help. A review of previous automated bone age assessment systems is undertaken and we investigate why none of these systems have gained widespread acceptance. We propose a new automated method for bone age assessment, ASMA (Automated Skeletal Maturity Assessment). The basic premise of the approach is to automatically extract descriptive shape features that capture the human expertise in forming bone age estimates. The algorithm consists of the following six modularised stages: hand segmentation; hand segmentation classifi�cation; bone segmentation; feature extraction; bone segmentation classifi�cation; bone age prediction. We demonstrate that ASMA performs at least as well as other automated systems and that models constructed on just three bones are as accurate at predicting age as expert human assessors using the standard technique. We also investigate the importance of ethnicity and gender in skeletal development. Our conclusion is that the feature based system of separating the image processing from the age modelling is the best approach, since it off�ers flexibility and transparency, and produces accurate estimates.
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Kalathenos, Panayiotis. "Predictive modelling of wine spoilage microorganisms". Thesis, University of Reading, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260584.

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Kougoulos, Eleftherios. "Predictive modelling of organic crystallization processes". Thesis, University College London (University of London), 2005. http://discovery.ucl.ac.uk/1445645/.

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This thesis is concerned with the development of a predictive model for batch cooling suspension pharmaceutical crystallizations, with a focus on product performance. A major challenge involved in the design of industrial pilot plant pharmaceutical crystallizers, is to predict the influence of crystallizer geometry, scale and operating conditions on the process behaviour and crystal size distribution (CSD). The design of industrial crystallizers is hindered by the lack of scale-up rules due to the absence of reliable predictive process models. Currently no reliable predictive or 'dial up a particle size' tool exists for scale-up predictions. The research involves the development of a novel predictive compartmental modelling framework for the scale-up of an organic fine chemical. A new approach of using compartments is developed in order to facilitate scale-up design and process modelling by separating crystallization kinetic and hydrodynamic phenomena. Application of this technique involves determining key process engineering information on a laboratory scale, which is critical for technology transfer, and combining this data with hydrodynamic information on transfer to large scale for predictive scale-up purposes. The key process engineering information required for predictive modelling includes the determination of solubility characteristics, thermodynamic properties and crystallization kinetics of the organic fine chemical. Attenuated Total Reflectance Ultra-Violet (ATR-UV) spectroscopy is used as an 'in-situ' measurement technique to measure solute concentration. A modified continuous Mixed Suspension Mixed Product Removal (MSMPR) crystallizer is designed specifically for innovative drug candidates available in limited quantities to derive steady state crystallization kinetics with minimal influence from hydrodynamic phenomena. Batch attrition experiments were carried out to determine the effects of specific power input on the CSD using Lasentec Focussed Beam Reflectance Monitoring (FBRM) to monitor the process on-line and to develop an attrition rate model. Computational Fluid Dynamics (CFD) is a simulation tool that is also introduced to provide valuable insight into mixing, heat transfer and hydrodynamic phenomena within agitated batch cooling suspension crystallization vessels including investigating the effects of scale-up. CFD is used to aid the development of the compartmental modelling framework. The design of the compartmental structure is based on high spatial resolution CFD simulations of internal flow, mixing and heat transfer within crystallizers upon scale-up. The great advantage of using a compartmental modelling framework is that the spatial resolution is reduced and the full population balance with kinetic models can be implemented. The detailed compartmental framework is based on the overall flow pattern, local energy dissipation rate, solids concentration and temperature distribution obtained from CFD. The number, location, cross-sectional area and volume of compartments are determined from CFD results based on the physical crystallizer dimensions. The compartments are selected such that they have approximately uniform temperature, local energy dissipation and solids concentration. Each dynamic compartment has a mass, concentration, enthalpy and population balance combined with MSMPR crystallization kinetic models. The compartments are therefore well mixed and physically connected via interconnecting flows determined from CFD. A general process modelling tool, gPROMS (Process Systems Enterprises) that supports both steady state and dynamics simulations is used to solve sets of ordinary differential and algebraic equations in each compartment. A single compartmental modelling approach is used initially as a first approach without taking into account local variations in process conditions. Predictions on a laboratory scale for an MSMPR and batch cooling crystallizer were satisfactory but upon scale-up the effects of mixing and hydrodynamics is not taken into account and therefore the predictions become less reliable. A compartmentalization approach can be introduced into gPROMS whereby the compartments are modelled as individual units with input and output streams using CFD hydrodynamic information.
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Valvatne, Per Henrik. "Predictive pore-scale modelling of multiphase flow". Thesis, Imperial College London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405885.

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Robertson, Mark Peter. "Predictive modelling of species' potential geographical distributions". Thesis, Rhodes University, 2003. http://hdl.handle.net/10962/d1007189.

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Models that are used for predicting species' potential distributions are important tools that have found applications in a number of areas of applied ecology. The majority of these models can be classified as correlative, as they rely on strong, often indirect, links between species distribution records and environmental predictor variables to make predictions. Correlative models are an alternative to more complex mechanistic models that attempt to simulate the mechanisms considered to underlie the observed correlations with environmental attributes. This study explores the influence of the type and quality of the data used to calibrate correlative models. In terms of data type, the most popular techniques in use are group discrimination techniques, those that use both presence and absence locality data to make predictions. However, for many organisms absence data are either not available or are considered to be unreliable. As the available range of profile techniques (those using presence only data) appeared to be limited, new profile techniques were investigated and evaluated. A new profile modelling technique based on fuzzy classification (the Fuzzy Envelope Model) was developed and implemented. A second profile technique based on Principal Components Analysis was implemented and evaluated. Based on quantitative model evaluation tests, both of these techniques performed well and show considerable promise. In terms of data quality, the effects on model performance of false absence records, the number of locality records (sample size) and the proportion of localities representing species presence (prevalence) in samples were investigated for logistic regression distribution models. Sample size and prevalence both had a significant effect on model performance. False absence records had a significant influence on model performance, which was affected by sample size. A quantitative comparison of the performance of selected profile models and group discrimination modelling techniques suggests that different techniques may be more successful for predicting distributions for particular species or types of organism than others. The results also suggest that several different model design! sample size combinations are capable of making predictions that will on average not differ significantly in performance for a particular species. A further quantitative comparison among modelling techniques suggests that correlative techniques can perform as well as simple mechanistic techniques for predicting potential distributions.
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Lindström, Martin. "Predictive Modelling of Heavy Metals in Urban Lakes". Doctoral thesis, Uppsala University, Department of Earth Sciences, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-530.

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Heavy metals are well-known environmental pollutants. In this thesis predictive models for heavy metals in urban lakes are discussed and new models presented. The base of predictive modelling is empirical data from field investigations of many ecosystems covering a wide range of ecosystem characteristics. Predictive models focus on the variabilities among lakes and processes controlling the major metal fluxes.

Sediment and water data for this study were collected from ten small lakes in the Stockholm area, the Eastern parts of Lake Mälaren, the innermost areas of the Stockholm archipelago and from literature studies. By correlating calculated metal loads to the land use of the catchment areas (describing urban and natural land use), the influences of the local urban status on the metal load could be evaluated. Copper was most influenced by the urban status and less by the regional background. The opposite pattern was shown for cadmium, nickel and zinc (and mercury). Lead and chromium were in-between these groups.

It was shown that the metal load from the City of Stockholm is considerable. There is a 5-fold increase in sediment deposition of cadmium, copper, mercury and lead in the central areas of Stockholm compared to surrounding areas.

The results also include a model for the lake characteristic concentration of suspended particulate matter (SPM), and new methods for empirical model testing. The results indicate that the traditional distribution (or partition) coefficient Kd (L kg-1) is unsuitable to use in modelling of the particle association of metals. Instead the particulate fraction, PF (-), defined as the ratio of the particulate associated concentration to the total concentration, is recommended. Kd is affected by spurious correlations due to the definition of Kd as a ratio including SPM and also secondary spurious correlations with many variables correlated to SPM. It was also shown that Kd has a larger inherent within-system variability than PF. This is important in modelling.

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Lindström, Martin. "Predictive modelling of heavy metals in urban lakes /". Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2000. http://publications.uu.se/theses/91-554-4854-2/.

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Yeh, Der-Ming. "Manipulation and predictive modelling of flowering in cineraria". Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309594.

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Książki na temat "Predictive modelling"

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Håkanson, Lars. Predictive limnology: Methods for predictive modelling. Amsterdam: SPB Academic, 1995.

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Verhagen, Philip. Case studies in archaeological predictive modelling [sic. [Leiden]: Leiden University Press, 2007.

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Verhagen, Philip. Case studies in archaeological predictive modelling [sic. [Leiden]: Leiden University Press, 2007.

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Roy, Sudipta, Lalit Mohan Goyal i Mamta Mittal, red. Advanced Prognostic Predictive Modelling in Healthcare Data Analytics. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0538-3.

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Camacho, E. F. Model predictive control. Wyd. 2. New York: Springer, 2004.

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Wescott, K. Practical Applications of GIS for Archaeologists: A Predictive Modelling Toolkit. London: CRC Press, 1999.

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1962-, Bordons C., red. Model predictive control. Berlin: Springer, 1999.

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Camacho, E. F. Model predictive control. London: Springer, 2003.

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Henryk, Maciejewski. Predictive modelling in high-dimensional data: Prior domain knowledge-based approaches. Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 2013.

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Mora, Peter, Mitsuhiro Matsu’ura, Raul Madariaga i Jean-Bernard Minster, red. Microscopic and Macroscopic Simulation: Towards Predictive Modelling of the Earthquake Process. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-7695-7.

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Części książek na temat "Predictive modelling"

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Dawson, Catherine. "Predictive modelling". W A–Z of Digital Research Methods, 301–6. Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9781351044677-46.

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Gonzales-Barron, Ursula Andrea. "Predictive Microbial Modelling". W Handbook of Food Safety Engineering, 108–52. Oxford, UK: Wiley-Blackwell, 2012. http://dx.doi.org/10.1002/9781444355321.ch6.

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van Kuijk, Sander M. J., Frank J. W. M. Dankers, Alberto Traverso i Leonard Wee. "Preparing Data for Predictive Modelling". W Fundamentals of Clinical Data Science, 75–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99713-1_6.

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AbstractThis is the first chapter of five that cover an introduction to developing and validating models for predicting outcomes for the individual patient. Such prediction models can be used for predicting the occurrence or recurrence of an event, or of the most likely value on a continuous outcome. We will mainly focus on the prediction of binary outcomes, such as the occurrence of a complication, recurrence of disease, the presence of metastases, remission, survival, etc. This chapter deals with the selection of an appropriate study design for a study on prediction, and on methods to manipulate the data before the statistical modelling can begin.
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Tolovski, Ilin, Sašo Džeroski i Panče Panov. "Semantic Annotation of Predictive Modelling Experiments". W Discovery Science, 124–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61527-7_9.

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Abstract In this paper, we address the task of representation, semantic annotation, storage, and querying of predictive modelling experiments. We introduce OntoExp, an OntoDM module which gives a more granular representation of a predictive modeling experiment and enables annotation of the experiment’s provenance, algorithm implementations, parameter settings and output metrics. This module is incorporated in SemanticHub, an online system that allows execution, annotation, storage and querying of predictive modeling experiments. The system offers two different user scenarios. The users can either define their own experiment and execute it, or they can browse the repository of completed experimental workflows across different predictive modelling tasks. Here, we showcase the capabilities of the system with executing multi-target regression experiment on a water quality prediction dataset using the Clus software. The system and created repositories are evaluated based on the FAIR data stewardship guidelines. The evaluation shows that OntoExp and SemanticHub provide the infrastructure needed for semantic annotation, execution, storage, and querying of the experiments.
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Wu, Wen, i Athula Herath. "Chemometrics and Predictive Modelling". W Nonclinical Statistics for Pharmaceutical and Biotechnology Industries, 653–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23558-5_25.

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Collins, Frank, i Frédéric Blin. "Predictive modelling of ageing". W Ageing of Infrastructure, 61–74. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9780429455704-5.

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West, Catharine ML. "3 Radiotherapy predictive assays". W Radiobiological Modelling in Radiation Oncology, 35–50. 48–50 St John Street, London EC1M 4DG, UK: The British Institute of Radiology, 2007. http://dx.doi.org/10.1259/9780905749839.chapter03.

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Ghatak, Rahul. "Operational Analytics and Predictive Modelling". W Management for Professionals, 13–36. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3873-3_2.

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Moss, Gary P., Darren R. Gullick i Simon C. Wilkinson. "Other Approaches to Modelling Percutaneous Absorption". W Predictive Methods in Percutaneous Absorption, 103–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-47371-9_6.

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Cui, Sunan, Randall K. Ten Haken i Issam El Naqa. "Building a Predictive Model of Toxicity". W Modelling Radiotherapy Side Effects, 23–51. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2019] |: CRC Press, 2019. http://dx.doi.org/10.1201/b21956-2.

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Streszczenia konferencji na temat "Predictive modelling"

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Levin, Stanislav, i Amiram Yehudai. "Boosting Automatic Commit Classification Into Maintenance Activities By Utilizing Source Code Changes". W PROMISE: Predictive Modelling in Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127005.3127016.

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Noor, Tanzeem Bin, i Hadi Hemmati. "Studying Test Case Failure Prediction for Test Case Prioritization". W PROMISE: Predictive Modelling in Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127005.3127006.

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Minku, Leandro L., i Siqing Hou. "Clustering Dycom". W PROMISE: Predictive Modelling in Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127005.3127007.

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Coppola, Riccardo, Maurizio Morisio i Marco Torchiano. "Scripted GUI Testing of Android Apps". W PROMISE: Predictive Modelling in Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127005.3127008.

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Businge, John, Simon Kawuma, Engineer Bainomugisha, Foutse Khomh i Evarist Nabaasa. "Code Authorship and Fault-proneness of Open-Source Android Applications". W PROMISE: Predictive Modelling in Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127005.3127009.

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Mitra, Joydeep, i Venkatesh-Prasad Ranganath. "Ghera". W PROMISE: Predictive Modelling in Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127005.3127010.

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Al-Zubaidi, Wisam Haitham Abbood, Hoa Khanh Dam, Aditya Ghose i Xiaodong Li. "Multi-objective search-based approach to estimate issue resolution time". W PROMISE: Predictive Modelling in Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127005.3127011.

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Alelyani, Turki, Ke Mao i Ye Yang. "Context-Centric Pricing". W PROMISE: Predictive Modelling in Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127005.3127012.

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Valdivia-Garcia, Harold, i Meiyappan Nagappan. "The Characteristics of False-Negatives in File-level Fault Prediction". W PROMISE: Predictive Modelling in Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127005.3127013.

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Thompson, Christopher, i David Wagner. "A Large-Scale Study of Modern Code Review and Security in Open Source Projects". W PROMISE: Predictive Modelling in Software Engineering. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127005.3127014.

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Raporty organizacyjne na temat "Predictive modelling"

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Chatwani, A. Predictive modelling of boiler fouling. Final report. Office of Scientific and Technical Information (OSTI), grudzień 1990. http://dx.doi.org/10.2172/233295.

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Regis, D. Predictive thermodynamic modelling of IOCG alteration facies at high-grade metamorphic conditions. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329172.

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Sinclair, J. L. Predictive modelling of particle-laden, turbulent flows. Quarterly progress report No. 2, January 1, 1993--March 31, 1993. Office of Scientific and Technical Information (OSTI), lipiec 1993. http://dx.doi.org/10.2172/10167383.

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, wrzesień 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
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Savosko, V., I. Komarova, Yu Lykholat, E. Yevtushenko i T. Lykholat. Predictive model of heavy metals inputs to soil at Kryvyi Rih District and its use in the training for specialists in the field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4511.

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The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining & metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Central Ukraine). In this work, classical scientific methods (such as analysis and synthesis, induction and deduction, analogy and formalization, abstraction and concretization, classification and modelling) were used. By summary the own research results and available scientific publications, the heavy metals total inputs to soils at Kryvyi Rih District was predicted. It is suggested that the current heavy metals content in soils of this region due to 1) natural and 2) anthropogenic flows, which are segmented into global and local levels. Predictive calculations show that heavy metals inputs to the soil of this region have the following values (mg ⋅ m2/year): Fe – 800-80 000, Mn – 125-520, Zn – 75-360, Ni – 20-30, Cu – 15-50, Pb – 7.5-120, Cd – 0.30-0.70. It is established that anthropogenic flows predominate in Fe and Pb inputs (60-99 %), natural flows predominate in Ni and Cd inputs (55-95 %). While, for Mn, Zn, and Cu inputs the alternate dominance of natural and anthropogenic flows are characterized. It is shown that the predictive model development for heavy metals inputs to soils of the industrial region can be used for efficient biological education (for example in bachelors of biologists training, discipline "Computer modelling in biology").
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Савосько, Василь Миколайович, Ірина Олександрівна Комарова, Юрій Васильович Лихолат, Едуард Олексійович Євтушенко, i Тетяна Юріївна Лихолат. Predictive Model of Heavy Metals Inputs to Soil at Kryvyi Rih District and its Use in the Training for Specialists in the Field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4266.

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The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining & metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Central Ukraine). In this work, classical scientific methods (such as analysis and synthesis, induction and deduction, analogy and formalization, abstraction and concretization, classification and modelling) were used. By summary the own research results and available scientific publications, the heavy metals total inputs to soils at Kryvyi Rih District was predicted. It is suggested that the current heavy metals content in soils of this region due to 1) natural and 2) anthropogenic flows, which are segmented into global and local levels. Predictive calculations show that heavy metals inputs to the soil of this region have the following values ( mg ∙ m ଶ year ⁄ ): Fe – 800-80 000, Mn – 125-520, Zn – 75-360, Ni – 20-30, Cu – 15-50, Pb – 7.5-120, Cd – 0.30-0.70. It is established that anthropogenic flows predominate in Fe and Pb inputs (60-99 %), natural flows predominate in Ni and Cd inputs (55-95 %). While, for Mn, Zn, and Cu inputs the alternate dominance of natural and anthropogenic flows are characterized. It is shown that the predictive model development for heavy metals inputs to soils of the industrial region can be used for efficient biological education (for example in bachelors of biologists training, discipline “Computer modelling in biology”).
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison i in. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model development. Canada in 3D (C3D) is a vision and road map for transforming the Geological Survey of Canada's (GSC) work practice by leveraging emerging 3D technologies. Primarily the transformation from 2D geological mapping, to a well-structured 3D modelling practice that is both data-driven and knowledge-driven. It is tempting to imagine that advanced 3D computational methods, coupled with Artificial Intelligence and Big Data tools will automate the bulk of this process. To effectively apply these methods there is a need, however, for data to be in a well-organized, classified, georeferenced (3D) format embedded with key information, such as spatial-temporal relations, and earth process knowledge. Another key challenge for C3D is the relative infancy of 3D geoscience technologies for geological inference and 3D modelling using sparse and heterogeneous regional geoscience information, while preserving the insights and expertise of geoscientists maintaining scientific integrity of digital products. In most geological surveys, there remains considerable educational and operational challenges to achieve this balance of digital automation and expert knowledge. Emerging from the last two decades of research are more efficient workflows, transitioning from cumbersome, explicit (manual) to reproducible implicit semi-automated methods. They are characterized by integrated and iterative, forward and reverse geophysical modelling, coupled with stratigraphic and structural approaches. The full impact of research and development with these 3D tools, geophysical-geological integration and simulation approaches is perhaps unpredictable, but the expectation is that they will produce predictive, instructive models of Canada's geology that will be used to educate, prioritize and influence sustainable policy for stewarding our natural resources. On the horizon are 3D geological modelling methods spanning the gulf between local and frontier or green-fields, as well as deep crustal characterization. These are key components of mineral systems understanding, integrated and coupled hydrological modelling and energy transition applications, e.g. carbon sequestration, in-situ hydrogen mining, and geothermal exploration. Presented are some case study examples at a range of scales from our efforts in C3D.
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Rahman, Kazi, Grace Lee, Kristina Vine, Amba-Rose Atkinson, Michael Tong i Veronica Matthews. Impacts of climate change on health and health services in northern New South Wales: an Evidence Check rapid review. The Sax Institute, grudzień 2022. http://dx.doi.org/10.57022/xlsj7564.

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This rapid review investigated the effects of climate change on health and health services in northern NSW—a known ‘hotspot’ for natural disasters—over the next 10-20 years. It included 92 peer-reviewed articles and 9 grey literature documents, with 17% focused on Northern NSW. Climate change will cause both an increase in average temperatures and in extreme weather events and natural disasters. Impacts particularly affecting Northern NSW are expected to include increases and exacerbations of: mental illness; infectious diseases, including those transmitted by mosquitoes, water and food; heat-related illnesses; chronic diseases including respiratory and cardiac conditions; injuries; and mortality—with vulnerable groups being most affected. Demand for health services will increase, but there will also be disruptions to medication supply and service availability. A whole-of-system approach will be needed to address these issues. There are numerous gaps in the research evidence and a lack of predictive modelling and robust locally relevant data.
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Huntley, David, Rob Holman, Huib de Vriend, Tony Bowen, Rolf Deigaard, Ed Thornton i Richard Soulsby. Intermediate Scale Coastal Behaviour: Measurement, Modelling and Prediction. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 1999. http://dx.doi.org/10.21236/ada630166.

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Huntley, David, Rob Holman, Huib de Vriend, Tony Bowen, Rolf Deigaard, Ed Thornton i Richard Soulsby. Intermediate Scale Coastal Behaviour: Measurement, Modelling and Prediction. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 1997. http://dx.doi.org/10.21236/ada634918.

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