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Journal articles on the topic 'Exploration models'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Phillips, Thelma M. "Exploration of Theoretical Models." Nursing Science Quarterly 27, no. 4 (September 22, 2014): 308–14. http://dx.doi.org/10.1177/0894318414546409.

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2

Wimsatt, William C. "Models and experiments? An exploration." Biology & Philosophy 30, no. 2 (July 10, 2014): 293–98. http://dx.doi.org/10.1007/s10539-014-9451-1.

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3

Doganova, Liliana. "Economic models as exploration devices." Journal of Economic Methodology 22, no. 2 (April 3, 2015): 249–53. http://dx.doi.org/10.1080/1350178x.2015.1037544.

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4

Rylskiу, Ilya. "Lidar virtual models for seismic exploration." InterCarto. InterGIS 27, no. 1 (2021): 304–16. http://dx.doi.org/10.35595/2414-9179-2021-1-27-304-316.

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Seismic exploration is important for development of new fields and the resumption of production at old areas of oil production. These works involve studying of underground geological structures using seismic profiling and seismotomography methods. In fact, detailed information about the territory is necessary for almost every participant in the process—to find the optimal routes for movement on terrain, to optimize the volume of forest felling on profiles, for georeferencing and checking coordinate measurements at points, etc. The availability of spatial data is also important for increasing the level of safety and trouble-free operation during work. One of possible solutions is to perform field surveys using aerial photography and airborne laser scanning, followed creation of virtual models based on them with instrumentation adapted to the specifics of these works. Modern GIS, with all their development, has number of disadvantages. It is difficult to use GIS by non-specialists; full-featured GIS are expensive, and free solutions has limited analysis functionality and can’t make good 3D visualizations, it is difficult to protect spatial information from unauthorized copying. These problems can be solved without using classic GIS packages. Instead, it is proposed to use virtual environments closed from editing and access to the original data. Test area of 69 km2 in the Khanty-Mansiysk Autonomous Okrug was selected for testing virtual modeling technologies. LIDAR and aerial photography datasets were obtained, and subsequent processing of the resulting was done, forming highly-detailed virtual environment. The article discusses the main features and functionality of this model.
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5

de Souza e Silva, Edmundo, and Pedro Mejiá Ochoa. "State space exploration in Markov models." ACM SIGMETRICS Performance Evaluation Review 20, no. 1 (June 1992): 152–66. http://dx.doi.org/10.1145/149439.133100.

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6

Otto, Gordon H. "Statistical Models for Optimizing Mineral Exploration." Technometrics 32, no. 4 (November 1990): 460–61. http://dx.doi.org/10.1080/00401706.1990.10484750.

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7

Roman, Gruia-Catalin, and Jamie Payton. "A principled exploration of coordination models." Theoretical Computer Science 336, no. 2-3 (May 2005): 367–401. http://dx.doi.org/10.1016/j.tcs.2004.11.012.

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8

Beyer, Uwe, and Frank Śmieja. "Data exploration with reflective adaptive models." Computational Statistics & Data Analysis 22, no. 2 (July 1996): 193–211. http://dx.doi.org/10.1016/0167-9473(95)00048-8.

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9

Thompson, David R., Alberto Candela, David S. Wettergreen, Eldar Noe Dobrea, Gregg A. Swayze, Roger N. Clark, and Rebecca Greenberger. "Spatial Spectroscopic Models for Remote Exploration." Astrobiology 18, no. 7 (July 2018): 934–54. http://dx.doi.org/10.1089/ast.2017.1782.

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10

Łucki, Zbigniew, and Zbigniew Szkutnik. "Petroleum exploration models—Estimation and applications." Mathematical Geology 21, no. 5 (July 1989): 495–512. http://dx.doi.org/10.1007/bf00894665.

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11

Gaudel, Marie-Claude, Alain Denise, Sandrine-Dominique Gouraud, Richard Lassaigne, Johan Oudinet, and Sylvain Peyronnet. "Coverage-biased Random Exploration of Models." Electronic Notes in Theoretical Computer Science 220, no. 1 (December 2008): 3–14. http://dx.doi.org/10.1016/j.entcs.2008.11.002.

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12

Wälder, Konrad. "Additive models in mining and exploration." Nonrenewable Resources 6, no. 1 (March 1997): 11–25. http://dx.doi.org/10.1007/bf02816921.

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13

Rasch, Philip John. "Exploration of planetary geoengineering with climate models." IOP Conference Series: Earth and Environmental Science 6, no. 28 (February 1, 2009): 282027. http://dx.doi.org/10.1088/1755-1307/6/28/282027.

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14

Hewett, J. L., B. Lillie, and T. G. Rizzo. "Monte Carlo Exploration of Warped Higgsless Models." Journal of High Energy Physics 2004, no. 10 (October 7, 2004): 014. http://dx.doi.org/10.1088/1126-6708/2004/10/014.

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15

Chen, Y., X. L. Wang, X. Xiang, B. Z. Tang, and J. Z. Bu. "Network structure exploration via Bayesian nonparametric models." Journal of Statistical Mechanics: Theory and Experiment 2015, no. 10 (October 9, 2015): P10004. http://dx.doi.org/10.1088/1742-5468/2015/10/p10004.

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16

Kaufman, Evan, Kuya Takami, Taeyoung Lee, and Zhuming Ai. "Autonomous Exploration with Exact Inverse Sensor Models." Journal of Intelligent & Robotic Systems 92, no. 3-4 (October 28, 2017): 435–52. http://dx.doi.org/10.1007/s10846-017-0710-7.

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17

Wrigley, Neil, and Richard Dunn. "Graphical diagnostics for logistic oil exploration models." Mathematical Geology 18, no. 4 (May 1986): 355–74. http://dx.doi.org/10.1007/bf00906061.

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18

Obiedkov, Sergei, Derrick G. Kourie, and J. H. P. Eloff. "Building access control models with attribute exploration." Computers & Security 28, no. 1-2 (February 2009): 2–7. http://dx.doi.org/10.1016/j.cose.2008.07.011.

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19

Baylot, E. Alex, Drew Kelley, James Richards, and Deanna Hardin. "Introducing Cost Models to Conceptual Tradespace Exploration." INCOSE International Symposium 28, no. 1 (July 2018): 16–29. http://dx.doi.org/10.1002/j.2334-5837.2018.00464.x.

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20

Spafford, Kyle L., and Jeffrey S. Vetter. "Automated Design Space Exploration with Aspen." Scientific Programming 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/157305.

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Architects and applications scientists often use performance models to explore a multidimensional design space of architectural characteristics, algorithm designs, and application parameters. With traditional performance modeling tools, these explorations forced users to first develop a performance model and then repeatedly evaluate and analyze the model manually. These manual investigations proved laborious and error prone. More importantly, the complexity of this traditional process often forced users to simplify their investigations. To address this challenge of design space exploration, we extend our Aspen (Abstract Scalable Performance Engineering Notation) language with three new language constructs: user-defined resources, parameter ranges, and a collection of costs in the abstract machine model. Then, we use these constructs to enable automated design space exploration via a nonlinear optimization solver. We show how four interesting classes of design space exploration scenarios can be derived from Aspen models and formulated as pure nonlinear programs. The analysis tools are demonstrated using examples based on Aspen models for a three-dimensional Fast Fourier Transform, the CoMD molecular dynamics proxy application, and the DARPA Streaming Sensor Challenge Problem. Our results show that this approach can compose and solve arbitrary performance modeling questions quickly and rigorously when compared to the traditional manual approach.
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21

Johnson, Gwen. "Mathematical Exploration: Similar Triangles." Mathematics Teaching in the Middle School 16, no. 4 (November 2010): 248–53. http://dx.doi.org/10.5951/mtms.16.4.0248.

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Within school mathematics, similarity is a topic that has many connections to real life. Similarity is related to scale drawings, such as those used in architecture, and models, including models of trains, cars, and boats. It is also used to solve problems that involve similar figures, which are common in middle-grades textbooks.
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22

Martinelli, Gabriele, and Jo Eidsvik. "Dynamic exploration designs for graphical models using clustering with applications to petroleum exploration." Knowledge-Based Systems 58 (March 2014): 113–26. http://dx.doi.org/10.1016/j.knosys.2013.08.020.

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23

Tarvydas, Vilia M. "Decision Making Models in Ethics: Models for Increased Clarity and Wisdom." Journal of Applied Rehabilitation Counseling 18, no. 4 (December 1, 1987): 50–52. http://dx.doi.org/10.1891/0047-2220.18.4.50.

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The importance of appropriate decision-making models in ethical judgment by rehabilitation counseling practitioners is discussed. Several prominent models for decision making in ethics are reviewed, including professional self-exploration, moral reasoning discourse, developmental process, and multidimensional, integrative process models.
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24

Hou, B., A. J. Fabris, J. L. Keeling, and M. C. Fairclough. "Palaeochannel uranium signatures and exploration models, South Australia." ASEG Extended Abstracts 2006, no. 1 (December 2006): 1–5. http://dx.doi.org/10.1071/aseg2006ab067.

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25

FITZPATRICK, BEN G. "NUMERICAL ANALYSIS AND SIMULATION OF RESOURCE-EXPLORATION MODELS." Natural Resource Modeling 18, no. 3 (June 28, 2008): 261–79. http://dx.doi.org/10.1111/j.1939-7445.2005.tb00158.x.

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26

Luo, Wen-Lin, and Thomas E. Nichols. "Diagnosis and exploration of massively univariate neuroimaging models." NeuroImage 19, no. 3 (July 2003): 1014–32. http://dx.doi.org/10.1016/s1053-8119(03)00149-6.

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27

INOUE, ATSUSHI. "MEAN-PLUS-NOISE FACTOR MODELS: AN EMPIRICAL EXPLORATION." Japanese Economic Review 63, no. 3 (July 23, 2012): 289–309. http://dx.doi.org/10.1111/j.1468-5876.2012.00582.x.

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28

Lopez-Arreguin, A. J. R., and S. Montenegro. "Improving engineering models of terramechanics for planetary exploration." Results in Engineering 3 (September 2019): 100027. http://dx.doi.org/10.1016/j.rineng.2019.100027.

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29

Neelamraju, Sridhar, David J. Wales, and Shachi Gosavi. "Protein energy landscape exploration with structure-based models." Current Opinion in Structural Biology 64 (October 2020): 145–51. http://dx.doi.org/10.1016/j.sbi.2020.07.003.

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30

Silvano, Cristina, Giovanni Agosta, and Gianluca Palermo. "Efficient architecture/compiler co-exploration using analytical models." Design Automation for Embedded Systems 11, no. 1 (March 2007): 1–23. http://dx.doi.org/10.1007/s10617-006-9588-7.

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31

LAVER, MICHAEL, and KENNETH A. SHEPSLE. "Understanding Government Survival: Empirical Exploration or Analytical Models?" British Journal of Political Science 29, no. 2 (February 1999): 395–401. http://dx.doi.org/10.1017/s0007123499210174.

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We should begin our reply to Paul Warwick by stating how very flattered we are by the diligent attention that he has given to our work on government formation. In particular, we are delighted that he has taken up and applied the simulation technology that we suggested as a way to explore cabinet stability, a matter on which we were beginning to feel like voices wailing in the wilderness.This is not the place to bore readers of the Journal with detailed points of issue between us, of which there are obviously many. We want here to concentrate upon some larger issues provoked by Warwick's critique. These involve a debate on cabinet stability and duration between a school of thought, in Warwick's corner, that is essentially empiricist in its outlook and a school of thought, in ours, that takes analytical modelling as being more fundamental. This is a theme touched upon by Kaare Strom in his recent review essay on parliamentary democracy.
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32

Guo, Junfei, Qi Han, Guangzhi Ma, Hong Liu, and Seth van Hooland. "Tunable discounting and visual exploration for language models." Neurocomputing 269 (December 2017): 73–81. http://dx.doi.org/10.1016/j.neucom.2016.08.145.

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33

Mei, Honghui, Fangzhou Guo, Haidong Chen, Yi Chen, and Fangzhou Guo. "Visual Exploration of Diffierences Among DTI Fiber Models." Chinese Journal of Electronics 27, no. 5 (September 1, 2018): 959–67. http://dx.doi.org/10.1049/cje.2018.06.015.

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34

Glazebrook, K. D., and R. J. Boys. "A Class of Bayesian Models for Optimal Exploration." Journal of the Royal Statistical Society: Series B (Methodological) 57, no. 4 (November 1995): 705–20. http://dx.doi.org/10.1111/j.2517-6161.1995.tb02057.x.

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35

Tuna, Gurkan, Kayhan Gulez, and V. Cagri Gungor. "The effects of exploration strategies and communication models on the performance of cooperative exploration." Ad Hoc Networks 11, no. 7 (September 2013): 1931–41. http://dx.doi.org/10.1016/j.adhoc.2012.07.011.

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36

Graybeal, Frederick T. "Aspects of Mineral Exploration Thinking." SEG Discovery, no. 128 (January 1, 2022): 24–35. http://dx.doi.org/10.5382/geo-and-mining-14.

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Editor’s note: The aim of the Geology and Mining series is to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. Abstract Successful exploration requires an understanding of ore deposit models, the experience to recognize ore guides in an outcrop, nonlinear thinking, and some intuition. Models, using porphyry Cu deposits as examples, combine magmatic and hydrothermal processes; however, process and the results of process are different. Models provide important understanding of process but are not ore guides and do not drive discoveries; models function as rules that inhibit prediscovery exploration thinking. Results of the genetic process are recorded in descriptive models that do not reflect the considerable geologic variations existing between the hundreds of known porphyry Cu deposits. Discoveries and discovery cycles are driven by nonlinear thinking about ore guides visible in outcrop, not by genetic or descriptive models. Reality in an outcrop typically departs from generalized models. Reinterpretations that lead to drilling prospects rejected by previous exploration groups is what makes many discoveries. Increasingly, field-portable instruments for mineral and chemical analyses will add efficiencies. The most important product of early exploration work is the geologic map, defined here as a decision-making document. Mapping of ore guides in any ore-forming system invariably leads to sampling of outcrops where high grading can help geologists rig the odds in their favor. However, the objective is a highly profitable mine, not just a high-grade sample. That means the mineralization must be sufficiently continuous to build the inventory of recoverable metal required for a profitable mine, regardless of grade. High grade gets you interested, but continuity gets the mine. The principal intangible in any discovery is intuition, often described as nothing more or less than recognition, and it invariably involves experience. Perhaps the only tangible expression of intuition is displayed by individuals or teams that are unwilling to abandon a complex prospect, a behavior often described in case histories as tenacity.
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37

Manss, Christoph, and Dmitriy Shutin. "Global-Entropy Driven Exploration with Distributed Models under Sparsity Constraints." Applied Sciences 8, no. 10 (September 22, 2018): 1722. http://dx.doi.org/10.3390/app8101722.

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This paper focuses on exploration when using different data distribution schemes and ADMM as a solver for swarms. By exploration, we mean the estimation of new measurement locations that are beneficial for the model estimation. In particular, the different distribution schemes are splitting-over-features or heterogeneous learning and splitting-over-examples or homogeneous learning. Each agent contributes a solution to solve the joint optimization problem by using ADMM and the consensus algorithm. This paper shows that some information is unknown to the individual agent, and thus, the estimation of new measurement positions is not possible without further communication. Therefore, this paper shows results for how to distribute only necessary information for a global exploration. We show the benefits between the proposed global exploration scheme and benchmark exploration schemes such as random walk and systematic traversing, i.e., meandering. The proposed waypoint estimation methods are then tested against each other and with other movement methods. This paper shows that a movement method, which considers the current information within the model, is superior to the benchmark movement methods.
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38

Bather, J. A. "Search models." Journal of Applied Probability 29, no. 3 (September 1992): 605–15. http://dx.doi.org/10.2307/3214897.

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Mathematical models have been proposed for oil exploration and other kinds of search. They can be used to estimate the amount of undiscovered resources or to investigate optimal stopping times for the search. Here we consider a continuous search for hidden objects using a model which represents the number and values of the objects by mixtures of Poisson processes. The flexibility of the model and its complexity depend on the number of components in the mixture. In simple cases, optimal stopping rules can be found explicitly and more general qualitative results can sometimes be obtained.
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Bather, J. A. "Search models." Journal of Applied Probability 29, no. 03 (September 1992): 605–15. http://dx.doi.org/10.1017/s0021900200043424.

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Mathematical models have been proposed for oil exploration and other kinds of search. They can be used to estimate the amount of undiscovered resources or to investigate optimal stopping times for the search. Here we consider a continuous search for hidden objects using a model which represents the number and values of the objects by mixtures of Poisson processes. The flexibility of the model and its complexity depend on the number of components in the mixture. In simple cases, optimal stopping rules can be found explicitly and more general qualitative results can sometimes be obtained.
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40

Lu, Xushan, Colin Farquharson, Jean-Marc Miehé, Grant Harrison, and Patrick Ledru. "Computer modeling of electromagnetic data for mineral exploration: Application to uranium exploration in the Athabasca Basin." Leading Edge 40, no. 2 (February 2021): 139a1–139a10. http://dx.doi.org/10.1190/tle40020139a1.1.

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Electromagnetic (EM) methods are important geophysical tools for mineral exploration. Forward and inverse computer modeling are commonly used to interpret EM data. Real-life geology can be complex, and our computer modeling tools need to faithfully represent subsurface features to achieve accurate data interpretation. Traditional rectilinear meshes are less flexible and have difficulty conforming to the complex geometries of realistic geologic models, resulting in large numbers of mesh cells. In contrast, unstructured grids can represent complex geologic structures efficiently and accurately. However, building realistic geologic models and discretizing these models with unstructured grids suitable for EM modeling can be difficult and requires significant effort and specialized computer software tools. Therefore, it is important to develop workflows that can be used to facilitate model building and mesh generation. We have developed a procedure that can be used to build arbitrarily complex geologic models with topography using unstructured grids and a finite-volume time-domain code to calculate EM responses. We present an example of a trial-and-error modeling approach applied to a real data set collected at a uranium exploration project in the Athabasca Basin in Canada. The uranium mineralization is closely related to graphitic fault conductors in the basement. The deep burial depth and small thickness of the graphitic fault conductors demand accurate data interpretation results to guide subsequent drill testing. Our trial-and-error modeling approach builds initial realistic geologic models based on known geology and downhole data and creates initial geoelectrical models based on physical property measurements. Then, the initial model is iteratively refined based on the match between modeled and real data. We show that the modeling method can obtain 3D geoelectrical models that conform to known geology while achieving a good match between modeled and real data. The method can also provide guidance of where future drill holes should be directed.
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41

Awan, Mazhar Javed, Awais Yasin, Haitham Nobanee, Ahmed Abid Ali, Zain Shahzad, Muhammad Nabeel, Azlan Mohd Zain, and Hafiz Muhammad Faisal Shahzad. "Fake News Data Exploration and Analytics." Electronics 10, no. 19 (September 23, 2021): 2326. http://dx.doi.org/10.3390/electronics10192326.

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Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.
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42

Pereira, Ângela, Rúben Nunes, Leonardo Azevedo, Luís Guerreiro, and Amílcar Soares. "Geostatistical seismic inversion for frontier exploration." Interpretation 5, no. 4 (November 30, 2017): T477—T485. http://dx.doi.org/10.1190/int-2016-0171.1.

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Numerical 3D high-resolution models of subsurface petroelastic properties are key tools for exploration and production stages. Stochastic seismic inversion techniques are often used to infer the spatial distribution of the properties of interest by integrating simultaneously seismic reflection and well-log data also allowing accessing the spatial uncertainty of the retrieved models. In frontier exploration areas, the available data set is often composed exclusively of seismic reflection data due to the lack of drilled wells and are therefore of high uncertainty. In these cases, subsurface models are usually retrieved by deterministic seismic inversion methodologies based exclusively on the existing seismic reflection data and an a priori elastic model. The resulting models are smooth representations of the real complex geology and do not allow assessing the uncertainty. To overcome these limitations, we have developed a geostatistical framework that allows inverting seismic reflection data without the need of experimental data (i.e., well-log data) within the inversion area. This iterative geostatistical seismic inversion methodology simultaneously integrates the available seismic reflection data and information from geologic analogs (nearby wells and/or analog fields) allowing retrieving acoustic impedance models. The model parameter space is perturbed by a stochastic sequential simulation methodology that handles the nonstationary probability distribution function. Convergence from iteration to iteration is ensured by a genetic algorithm driven by the trace-by-trace mismatch between real and synthetic seismic reflection data. The method was successfully applied to a frontier basin offshore southwest Europe, where no well has been drilled yet. Geologic information about the expected impedance distribution was retrieved from nearby wells and integrated within the inversion procedure. The resulting acoustic impedance models are geologically consistent with the available information and data, and the match between the inverted and the real seismic data ranges from 85% to 90% in some regions.
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43

Pearson, Mitchell, Glen Livingston Jr, and Robert King. "An exploration of predictive football modelling." Journal of Quantitative Analysis in Sports 16, no. 1 (March 26, 2020): 27–39. http://dx.doi.org/10.1515/jqas-2019-0075.

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AbstractPredictive football modelling has become progressively popular over the last two decades. Due to this, numerous studies have proposed different types of statistical models to predict the outcome of a football match. This study provides a review of three different models published in the academic literature and then implements these on recent match data from the top football leagues in Europe. These models are then compared utilising the rank probability score to assess their predictive capability. Additionally, a modification is proposed which includes the travel distance of the away team. When tested on football leagues from both Australia and Russia, it is shown to improve predictive capability according to the rank probability score.
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44

Kim, Kue Bum, and Dawei Han. "Exploration of sub-annual calibration schemes of hydrological models." Hydrology Research 48, no. 4 (August 30, 2016): 1014–31. http://dx.doi.org/10.2166/nh.2016.296.

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This study compared hydrological model performances under different sub-annual calibration schemes using two conceptual models, IHACRES and HYMOD. In several publications regarding sub-annual calibration, the authors showed that such an approach generally performed better than the conventional whole period method. Hence, there are advantages in dividing the data into sub-annual periods for calibration. However, little attention has been paid to the issue of how to calibrate the non-continuous sub-annual period. Unlike the conventional calibration which assumes time-invariant parameters for the calibration period, the model parameters vary in sub-annual calibration. We have explored two sub-annual calibration schemes, serial calibration scheme (SCS) and parallel calibration scheme (PCS). We assume that the relationships between the rainfall and runoff could be different for each sub-annual period and consider intra-annual variations of the system. The models are then evaluated for a different validation period to avoid over-fitting and the optimal sub-annual calibration period is explored. Overall, we have found that PCS performed slightly better than SCS and the optimal calibration periods are seasonal and bimonthly for IHACRES and biannual for HYMOD. Since there are pros and cons in both SCS and PCS, we recommend choosing the method depending on the purpose of the model usage.
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45

Perez Portilla, Karla. "Challenging Media (Mis)Representation: An Exploration of Available Models." International Journal for Crime, Justice and Social Democracy 7, no. 2 (June 1, 2018): 4–20. http://dx.doi.org/10.5204/ijcjsd.v7i2.510.

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This article is a theoretical analysis aimed at articulating the harm caused by media (mis)representation, and at showing existing ways in which this harm can be contested. The approaches analysed are largely from the United Kingdom. However, the issues they raise are not unique and the models explored are potentially transferable. The examples cover a range of media, including British right-wing press, television and Facebook; and characteristics protected by equality legislation in the UK such as sex, sexual orientation, race, religion and mental health stigma. Crucially, all the initiatives presented demonstrate the group-based nature of media (mis)representations, which cannot be understood and, therefore, cannot be addressed through individualistic approaches. Therefore, the article concludes that the role of groups as the targets of media (mis)representation and as potential claimants should be fully acknowledged and enabled.
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46

Swierzbinski, Joseph E., and Robert Mendelsohn. "Exploration and Exhaustible Resources: The Microfoundations of Aggregate Models." International Economic Review 30, no. 1 (February 1989): 175. http://dx.doi.org/10.2307/2526556.

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47

Zheng, Youyi, Han Liu, Julie Dorsey, and Niloy J. Mitra. "Ergonomics-Inspired Reshaping and Exploration of Collections of Models." IEEE Transactions on Visualization and Computer Graphics 22, no. 6 (June 1, 2016): 1732–44. http://dx.doi.org/10.1109/tvcg.2015.2448084.

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48

Carrasco, Juan A., Javier Escribá, and Angel Calderón. "Efficient exploration of availability models guided by failure distances." ACM SIGMETRICS Performance Evaluation Review 24, no. 1 (May 15, 1996): 242–51. http://dx.doi.org/10.1145/233008.233049.

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49

Murphy, Colm A., and Jade L. Dickinson. "FTG Gravity data for prospecting and exploration play models." ASEG Extended Abstracts 2010, no. 1 (December 2010): 1–3. http://dx.doi.org/10.1081/22020586.2010.12041983.

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50

Schulz, Adriana, Jie Xu, Bo Zhu, Changxi Zheng, Eitan Grinspun, and Wojciech Matusik. "Interactive design space exploration and optimization for CAD models." ACM Transactions on Graphics 36, no. 4 (July 20, 2017): 1–14. http://dx.doi.org/10.1145/3072959.3073688.

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