Relevant bibliographies by topics / Value prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Value prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Value prediction"

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Sherstan, Craig, Shibhansh Dohare, James MacGlashan, Johannes Günther, and Patrick M. Pilarski. "Gamma-Nets: Generalizing Value Estimation over Timescale." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5717–25. http://dx.doi.org/10.1609/aaai.v34i04.6027.

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Temporal abstraction is a key requirement for agents making decisions over long time horizons—a fundamental challenge in reinforcement learning. There are many reasons why value estimates at multiple timescales might be useful; recent work has shown that value estimates at different time scales can be the basis for creating more advanced discounting functions and for driving representation learning. Further, predictions at many different timescales serve to broaden an agent's model of its environment. One predictive approach of interest within an online learning setting is general value function (GVFs), which represent models of an agent's world as a collection of predictive questions each defined by a policy, a signal to be predicted, and a prediction timescale. In this paper we present Γ-nets, a method for generalizing value function estimation over timescale, allowing a given GVF to be trained and queried for arbitrary timescales so as to greatly increase the predictive ability and scalability of a GVF-based model. The key to our approach is to use timescale as one of the value estimator's inputs. As a result, the prediction target for any timescale is available at every timestep and we are free to train on any number of timescales. We first provide two demonstrations by 1) predicting a square wave and 2) predicting sensorimotor signals on a robot arm using a linear function approximator. Next, we empirically evaluate Γ-nets in the deep reinforcement learning setting using policy evaluation on a set of Atari video games. Our results show that Γ-nets can be effective for predicting arbitrary timescales, with only a small cost in accuracy as compared to learning estimators for fixed timescales. Γ-nets provide a method for accurately and compactly making predictions at many timescales without requiring a priori knowledge of the task, making it a valuable contribution to ongoing work on model-based planning, representation learning, and lifelong learning algorithms.
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Abdel-Karim, Benjamin M., Alexander Benlian, and Oliver Hinz. "The Predictive Value of Data from Virtual Investment Communities." Machine Learning and Knowledge Extraction 3, no. 1 (2020): 1–13. http://dx.doi.org/10.3390/make3010001.

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Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.
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Tullsen, Dean M., and John S. Seng. "Storageless value prediction using prior register values." ACM SIGARCH Computer Architecture News 27, no. 2 (1999): 270–79. http://dx.doi.org/10.1145/307338.301002.

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Calder, Brad, Glenn Reinman, and Dean M. Tullsen. "Selective value prediction." ACM SIGARCH Computer Architecture News 27, no. 2 (1999): 64–74. http://dx.doi.org/10.1145/307338.300985.

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Koster, Maurice, Sascha Kurz, Ines Lindner, and Stefan Napel. "The prediction value." Social Choice and Welfare 48, no. 2 (2016): 433–60. http://dx.doi.org/10.1007/s00355-016-1012-x.

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Lipasti, Mikko H., Christopher B. Wilkerson, and John Paul Shen. "Value locality and load value prediction." ACM SIGOPS Operating Systems Review 30, no. 5 (1996): 138–47. http://dx.doi.org/10.1145/248208.237173.

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Lipasti, Mikko H., Christopher B. Wilkerson, and John Paul Shen. "Value locality and load value prediction." ACM SIGPLAN Notices 31, no. 9 (1996): 138–47. http://dx.doi.org/10.1145/248209.237173.

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Mieruch, S., H. Feldmann, G. Schädler, C. J. Lenz, S. Kothe, and C. Kottmeier. "The regional MiKlip decadal forecast ensemble for Europe: the added value of downscaling." Geoscientific Model Development 7, no. 6 (2014): 2983–99. http://dx.doi.org/10.5194/gmd-7-2983-2014.

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Abstract. The prediction of climate on time scales of years to decades is attracting the interest of both climate researchers and stakeholders. The German Ministry for Education and Research (BMBF) has launched a major research programme on decadal climate prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal Climate Prediction) in order to investigate the prediction potential of global and regional climate models (RCMs). In this paper we describe a regional predictive hindcast ensemble, its validation, and the added value of regional downscaling. Global predictions are obtained from an ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs), which were downscaled for Europe using the COSMO-CLM regional model. Decadal hindcasts were produced for the 5 decades starting in 1961 until 2001. Observations were taken from the E-OBS data set. To identify decadal variability and predictability, we removed the long-term mean, as well as the long-term linear trend from the data. We split the resulting anomaly time series into two parts, the first including lead times of 1–5 years, reflecting the skill which originates mainly from the initialisation, and the second including lead times from 6–10 years, which are more related to the representation of low frequency climate variability and the effects of external forcing. We investigated temperature averages and precipitation sums for the summer and winter half-year. Skill assessment was based on correlation coefficient and reliability. We found that regional downscaling preserves, but mostly does not improve the skill and the reliability of the global predictions for summer half-year temperature anomalies. In contrast, regionalisation improves global decadal predictions of half-year precipitation sums in most parts of Europe. The added value results from an increased predictive skill on grid-point basis together with an improvement of the ensemble spread, i.e. the reliability.
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Mundada, Dhiraj. "Stock Value Prediction System." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 4 (2015): 2217–19. http://dx.doi.org/10.17762/ijritcc2321-8169.150498.

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Kalaitzidis, Kleovoulos, and André Seznec. "Leveraging Value Equality Prediction for Value Speculation." ACM Transactions on Architecture and Code Optimization 18, no. 1 (2021): 1–20. http://dx.doi.org/10.1145/3436821.

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Dissertations / Theses on the topic "Value prediction"

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SUSOYKINA, Alina. "Customer Lifetime Value Prediction Using Statistical Modeling: Predicting Online Payments in an Industry Setting." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246010.

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Customer lifetime value (CLV) provides a measure of customer revenue contribution. It allows to justify marketing campaigns, overall budgeting, and strategy planning. CLV is an estimated cash flow attributed to the entire relationship with customers in the future. Ability to utilize information gained from CLV analysis at the most efficient way, provides a strong competitive advantage. The concept of CLV was studied and modeled with application to the online payments industry which is relatively new and at its growing phase. Ability to predict CLV accurately conveys a great value for guiding the industry (i.e. emerging companies) to maturity. CLV analysis in this case becomes complex due to the fact that usually the databases of such companies are huge and include transactions from different industries: e-commerce, financial services, travel, gaming etc. This paper aims to define an appropriate model for CLV prediction in the online payments setting. The proposed model segments customers first in order to improve performance of the predictive model. Then Pareto/NBD model was applied to predict CLV at the customer-level for each customer segment separately. Although the results show that it is possible to predict CLV at some extent, the model needs to be further improved and possible pitfalls need to be scrutinized. Discussion on these issues is provided in the following sections.<br>Kundens livstidsvärde (Customer lifetime value) är ett mått på hur en kund bidrar till företagets omsättning. Det tillåter att åskådliggöra försäljningskampanjer, företagets budget och företagets strategi. Kundens livstidsvärde är en estimering av betalningsflöde som ett företag kan tjäna av kunder i framtiden. Möjligheten att nyttiggöra informationen från kundens livstidsvärde analys ger företag en starkt konkurrenskraftig fördel. Kundens livstidsvärde var studerat och modellerat med anknytning till online betalningstjänster industri, vilken har utvecklats kraftigt inom senaste åren. Möjligheten att predikera kundens livstidsvärde med hög noggranhet medför ett starkt värde för företag som erbjuder tjänster inom online betalningar och kan driva dessa till mognad. Att predikera kundens livstidsvärde inom denna bransch anses vara en komplex process, då databaser hos såna föratag är stora och inkluderar information om transaktioner från olika industrier såsom: elektronisk handel, finansiella tjänster, rese- och spelbolag. I denna studie definieras en modell för att kunna predikera kundens livstidsvärde baserat på data från ett företag som tillhandahåller online betalningstjänster. För att uppnå bättre prestanda, segmenterar den föreslagna modellen kunder först. Därefter en Pareto/NBD modell används, för att predikera kundens livstidsvärde för varje kundsegment. Trots att resultat visar att kundens livtidsvärde kan modelleras till en viss nivå, modellen behöver förbättras och möjliga blindskär måste granskas.
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Adesogan, Adegbola Tolulope. "Prediction of the nutritive value of whole crop wheat." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320195.

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Perais, Arthur. "Increasing the performance of superscalar processors through value prediction." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S070/document.

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Bien que les processeurs actuels possèdent plus de 10 cœurs, de nombreux programmes restent purement séquentiels. Cela peut être dû à l'algorithme que le programme met en œuvre, au programme étant vieux et ayant été écrit durant l'ère des uni-processeurs, ou simplement à des contraintes temporelles, car écrire du code parallèle est notoirement long et difficile. De plus, même pour les programmes parallèles, la performance de la partie séquentielle de ces programmes devient rapidement le facteur limitant l'augmentation de la performance apportée par l'augmentation du nombre de cœurs disponibles, ce qui est exprimé par la loi d'Amdahl. Conséquemment, augmenter la performance séquentielle reste une approche valide même à l'ère des multi-cœurs.Malheureusement, la façon conventionnelle d'améliorer la performance (augmenter la taille de la fenêtre d'instructions) contribue à l'augmentation de la complexité et de la consommation du processeur. Dans ces travaux, nous revisitons une technique visant à améliorer la performance de façon orthogonale : La prédiction de valeurs. Au lieu d'augmenter les capacités du moteur d'exécution, la prédiction de valeurs améliore l'utilisation des ressources existantes en augmentant le parallélisme d'instructions disponible.En particulier, nous nous attaquons aux trois problèmes majeurs empêchant la prédiction de valeurs d'être mise en œuvre dans les processeurs modernes. Premièrement, nous proposons de déplacer la validation des prédictions depuis le moteur d'exécution vers l'étage de retirement des instructions. Deuxièmement, nous proposons un nouveau modèle d'exécution qui exécute certaines instructions dans l'ordre soit avant soit après le moteur d'exécution dans le désordre. Cela réduit la pression exercée sur ledit moteur et permet de réduire ses capacités. De cette manière, le nombre de ports requis sur le fichier de registre et la complexité générale diminuent. Troisièmement, nous présentons un mécanisme de prédiction imitant le mécanisme de récupération des instructions : La prédiction par blocs. Cela permet de prédire plusieurs instructions par cycle tout en effectuant une unique lecture dans le prédicteur. Ces trois propositions forment une mise en œuvre possible de la prédiction de valeurs qui est réaliste mais néanmoins performante<br>Although currently available general purpose microprocessors feature more than 10 cores, many programs remain mostly sequential. This can either be due to an inherent property of the algorithm used by the program, to the program being old and written during the uni-processor era, or simply to time to market constraints, as writing and validating parallel code is known to be hard. Moreover, even for parallel programs, the performance of the sequential part quickly becomes the limiting improvement factor as more cores are made available to the application, as expressed by Amdahl's Law. Consequently, increasing sequential performance remains a valid approach in the multi-core era. Unfortunately, conventional means to do so - increasing the out-of-order window size and issue width - are major contributors to the complexity and power consumption of the chip. In this thesis, we revisit a previously proposed technique that aimed to improve performance in an orthogonal fashion: Value Prediction (VP). Instead of increasing the execution engine aggressiveness, VP improves the utilization of existing resources by increasing the available Instruction Level Parallelism. In particular, we address the three main issues preventing VP from being implemented. First, we propose to remove validation and recovery from the execution engine, and do it in-order at Commit. Second, we propose a new execution model that executes some instructions in-order either before or after the out-of-order engine. This reduces pressure on said engine and allows to reduce its aggressiveness. As a result, port requirement on the Physical Register File and overall complexity decrease. Third, we propose a prediction scheme that mimics the instruction fetch scheme: Block Based Prediction. This allows predicting several instructions per cycle with a single read, hence a single port on the predictor array. This three propositions form a possible implementation of Value Prediction that is both realistic and efficient
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Gunnarsson, Linnea. "Design Space Exploration for Value Prediction in Security Applications." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-409971.

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With the introduction of Spectre and Meltdown, two new attacks thattarget the speculative instructions due to Out-of-Order execution intoday's processors, a new way to handle speculative loads has beenproposed. Instead of performing the speculative load, the approach isto predict them. This is a new way to use value predictors. In thiswork, the Last Value Predictor, which predicts based on the previouslyseen value, Value TAgged GEometric history length Predictor (VTAGE),which predicts based on the global branch history, and a stridepredictor, which predicts with help of strides, has been compared tosee which one has the best fit for this new use. They have been runwith the SPEC CPU 2017 benchmark suite in three different tests,different sizes, different threshold confidence and for VTAGE,different associativity. The VTAGE predictor performed best in terms ofvalues predicted and values correctly predicted. The thresholdconfidence level plays an important role in how many incorrectpredictions were made. The associativity in the VTAGE did not do muchdifference to the results.
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Liu, Ziyang. "Prediction of Soil Layer R-Value Dependence on Moisture Content." PDXScholar, 2011. https://pdxscholar.library.pdx.edu/open_access_etds/125.

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This study focuses on how green roof thermal performance is affected by the soil moisture in summer condition. It aims to determine whether moist soil is a better insulator during the summer months than dry soil. A soil model is developed to predict simultaneous conduction, convection, and surface evaporation for a layer of moist soil representing a green roof. It used to analyze evaporation process and its affect on the soil resistance. The model considers only bare soil without vegetation on the roof. The model predicts the soil surface temperature as it is affected by soil moisture content, which can then be used to calculate heat transfer through the soil layer. An experimental dry out test was conducted to measure the soil moisture and soil temperature histories. Comparison of the predicted and measured sol surface temperature shows that the model reasonably captures the actual behavior. The evaporative cooling effectively reduces the soil surface temperature and heat flux in moist soil and can be used as an effective way to insulate the roof.
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Li, Gang. "A study of value-at-risk models and their prediction power." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B32029718.

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ANDO, Hideki, and Yusuke TANAKA. "Register File Size Reduction through Instruction Pre-Execution Incorporating Value Prediction." Institute of Electronics, Information and Communication Engineers, 2010. http://hdl.handle.net/2237/14941.

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Li, Gang, and 李剛. "A study of value-at-risk models and their prediction power." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B32029718.

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Lindblom, Rebecca. "News Value Modeling and Prediction using Textual Features and Machine Learning." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167062.

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News value assessment has been done forever in the news media industry and is today often done in real-time without any documentation. Editors take a lot of different qualitative aspects into consideration when deciding what news stories will make it to the first page. This thesis explores how the complex news value assessment process can be translated into a quantitative model, and also how those news values can be predicted in an effective way using machine learning and NLP. Two models for news value were constructed, for which the correlation between modeled and manual news values was measured, and the results show that the more complex model gives a higher correlation. For prediction, different types of features are extracted, Random Forest and SVM are used, and the predictions are evaluated with accuracy, F1-score, RMSE, and MAE. Random Forest shows the best results for all metrics on all datasets, the best result being on the largest dataset, probably due to the smaller datasets having a less even distribution between classes.
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Tanaka, Yusuke, and Hideki Ando. "Reducing register file size through instruction pre-execution enhanced by value prediction." IEEE, 2009. http://hdl.handle.net/2237/13892.

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Books on the topic "Value prediction"

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Triplett, Theodore L. Illustrations of the value of subsidence prediction in the Illinois coal baasin. s.n, 1992.

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Franke, Richard H. The use of observed data for the initial value problem in numerical weather prediction. Naval Postgraduate School, 1987.

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Johnny, Henderson, and Ouahab Abdelghani, eds. Impulsive differential inclusions: A fixed point approach. Walter de Gruyter GmbH & Co., KG, 2013.

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Mason, Gerald C. The value of transabdominal ultrasound assessment of the cervix in the prediction of premature delivery. University of Manchester, 1992.

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Meehl, Paul E. Clinical versus statistical prediction: A theoretical analysis and a review of the evidence. J. Aronson, 1996.

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Sakaiya, Taichi. The knowledge-value revolution, or, A history of the future. Kodansha International, 1991.

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Manitoba. Manitoba Education and Training. Student Support Branch. Possibilities for practice: An education of value for at risk students : a program support document. Manitoba Education and Training, 1993.

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Schaefer, Tanja B. Sex-role orientation and the type "A" personality: Their value in prediction the responses and the recovery rate for a cognitive stressor. Laurentian University, Department of Psychology, 1998.

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Binsbergen, Jules H. van. Predictive regressions: A present-value approach. National Bureau of Economic Research, 2010.

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1954-, Padron Victor, ed. Classification of radial solutions arising in the study of thermal structures with thermal equilibrium or no flux at the boundary. American Mathematical Society, 2010.

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Book chapters on the topic "Value prediction"

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Szepesvári, Csaba. "Value Prediction Problems." In Algorithms for Reinforcement Learning. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01551-9_2.

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Sangeetha, R. G., C. Hemanth, Naman Arora, Karan Singh, and Vithika Mulye. "Bitcoin Value Prediction." In Hybrid Intelligent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96305-7_14.

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Ziegler, G., Z. Palotai, T. Cinkler, P. Arató, and A. Lörincz. "Value Prediction in Engineering Applications." In Engineering of Intelligent Systems. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45517-5_4.

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Habyarimana, Ephrem, and Sofia Michailidou. "Genomic Prediction and Selection in Support of Sorghum Value Chains." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_16.

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AbstractGenomic prediction and selection models (GS) were deployed as part of DataBio project infrastructure and solutions. The work addressed end-user requirements, i.e., the need for cost-effectiveness of the implemented technologies, simplified breeding schemes, and shortening the time to cultivar development by selecting for genetic merit. Our solutions applied genomic modelling in order to sustainably improve productivity and profits. GS models were implemented in sorghum crop for several breeding scenarios. We fitted the best linear unbiased predictions data using Bayesian ridge regression, genomic best linear unbiased predictions, Bayesian least absolute shrinkage and selection operator, and BayesB algorithms. The performance of the models was evaluated using Monte Carlo cross-validation with 70% and 30%, respectively, as training and validation sets. Our results show that genomic models perform comparably with traditional methods under single environments. Under multiple environments, predicting non-field evaluated lines benefits from borrowing information from lines that were evaluated in other environments. Accounting for environmental noise and other factors, also this model gave comparable accuracy with traditional methods, but higher compared to the single environment model. The GS accuracy was comparable in genomic selection index, aboveground dry biomass yield and plant height, while it was lower for the dry mass fraction of the fresh weight. The genomic selection model performances obtained in our pilots are high enough to sustain sorghum breeding for several traits including antioxidants production and allow important genetic gains per unit of time and cost.
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Hardas, Manas S., and Lisa Purvis. "Computing the Value of a Crowd." In Social Computing, Behavioral - Cultural Modeling and Prediction. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29047-3_30.

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Xin, Fan, and Zubin Abraham. "Extreme Value Prediction for Zero-Inflated Data." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30217-6_27.

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Gianola, D., S. Im, and F. W. Macedo. "A Framework for Prediction of Breeding Value." In Advances in Statistical Methods for Genetic Improvement of Livestock. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-74487-7_11.

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Sato, Toshinori, Akihiko Hamano, Kiichi Sugitani, and Itsujiro Arita. "Influence of Compiler Optimizations on Value Prediction." In High-Performance Computing and Networking. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48228-8_32.

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Saito, Kazumi, Masahiro Kimura, Kouzou Ohara, and Hiroshi Motoda. "Detecting Changes in Opinion Value Distribution for Voter Model." In Social Computing, Behavioral-Cultural Modeling and Prediction. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19656-0_15.

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Fabijan, Aleksander, Helena Holmström Olsson, and Jan Bosch. "Early Value Argumentation and Prediction: An Iterative Approach to Quantifying Feature Value." In Product-Focused Software Process Improvement. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26844-6_2.

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Conference papers on the topic "Value prediction"

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Lipasti, Mikko H., Christopher B. Wilkerson, and John Paul Shen. "Value locality and load value prediction." In the seventh international conference. ACM Press, 1996. http://dx.doi.org/10.1145/237090.237173.

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Salehi, Mohammad, and Amirali Baniasadi. "Storage-Aware Value Prediction." In 2010 13th Euromicro Conference on Digital System Design: Architectures, Methods and Tools (DSD). IEEE, 2010. http://dx.doi.org/10.1109/dsd.2010.70.

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Rahman, Saifur. "Experimental K-value Prediction." In SPE/EAGE Reservoir Characterization and Simulation Conference. Society of Petroleum Engineers, 2009. http://dx.doi.org/10.2118/125413-ms.

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Rahman, Saifur. "Experimental K-value Prediction." In SPE/EAGE Reservoir Characterization & Simulation Conference. European Association of Geoscientists & Engineers, 2009. http://dx.doi.org/10.3997/2214-4609-pdb.170.spe125413.

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Kalaitzidis, Kleovoulos, and Andre Seznec. "Value Speculation through Equality Prediction." In 2019 IEEE 37th International Conference on Computer Design (ICCD). IEEE, 2019. http://dx.doi.org/10.1109/iccd46524.2019.00101.

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Cennamo, Greg. "Providing Value Through Reliability Prediction." In 2018 Annual Reliability and Maintainability Symposium (RAMS). IEEE, 2018. http://dx.doi.org/10.1109/ram.2018.8463005.

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Nakra, T., R. Gupta, and M. L. Soffa. "Global context-based value prediction." In Proceedings Fifth International Symposium on High-Performance Computer Architecture. IEEE, 1999. http://dx.doi.org/10.1109/hpca.1999.744311.

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Sheikh, Rami, Harold W. Cain, and Raguram Damodaran. "Load value prediction via path-based address prediction." In MICRO-50: The 50th Annual IEEE/ACM International Symposium on Microarchitecture. ACM, 2017. http://dx.doi.org/10.1145/3123939.3123951.

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Wang, Yan-Jhu, You-Sheng Guo, Changsheng Deng, and Ting-Lan Lin. "Analysis of chroma pixel value prediction using luma pixel values." In 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2019. http://dx.doi.org/10.1109/ispacs48206.2019.8986254.

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Larson, Eric, and Todd Austin. "Compiler controlled value prediction using branch predictor based confidence." In the 33rd annual ACM/IEEE international symposium. ACM Press, 2000. http://dx.doi.org/10.1145/360128.360164.

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Reports on the topic "Value prediction"

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Singh, Ravindra, Paul Hines, Sarah Howerter, and James Reilly. Beyond DERMS: Platform Development, Flexibility, Prediction, and Value Measurements. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1825329.

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Singh, Ravindra, Paul Hines, Sarah Howerter, and James Reilly. Beyond DERMS: Platform Development, Flexibility, Prediction, and Value Measurements. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1825329.

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Liu, Ziyang. Prediction of Soil Layer R-Value Dependence on Moisture Content. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.125.

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Wolc, Anna, Christine Stricker, Jesus Arango, et al. Breeding Value Prediction for Production Traits in Layers Using High-density SNP Markers. Iowa State University, 2011. http://dx.doi.org/10.31274/ans_air-180814-680.

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Branstator, Grant. Initial value predictability of intrinsic oceanic modes and implications for decadal prediction over North America. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1165163.

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Finn, Michael V. Value of Area Defense Impact Point Prediction in a Two Layer Defense with Perfect Attackers and Defenders. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada175219.

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Wang, Yingxuan, Cheng Yan, and Liqin Zhao. The value of radiomics-based machine learning for hepatocellular carcinoma after TACE: a systematic evaluation and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.6.0100.

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Review question / Objective: Meta-analysis was performed to predict the efficacy and survival status of patients with hepatocellular carcinoma after the application of TACE, applying clinical models, radiomic models and combined models for non-invasive assessment.We performed a Meta-analysis on the prediction of efficacy and survival status after TACE for hepatocellular carcinoma. Condition being studied: Patients were scanned using CT or MR machines, and some patients had multiple follow-up records, and imaging feature extraction software was applied to extract regions of interest and build multiple prediction models.Literature screening was conducted by two reviewers independently, who had more than 3 years’ experience in imaging diagnosis and was cross-checked. Disagreements were settled by a third reviewer.
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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, 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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Hendricks, Stefanie, Iryna Dykun, Bastian Balcer, Matthias Totzeck, Tienush Rassaf, and Amir A. Mahabadi. Higher BNP/NT-pro BNP levels stratify prognosis equally well in patients with and without heart failure – a meta-analysis with more than 89,000 patients. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.4.0175.

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Review question / Objective: We performed a meta-analysis to investigate, whether the value of BNP/NT-proBNP as predictors of long-term prognosis differentiates in cohorts with and without heart failure. Condition being studied: The standardised cut-off levels for BNP and NT-proBNP that are currently used in clinical practice are based on the stratification of patients with heart failure. In patients without heart failure, however, relatively lower values are observed. This leads to the assumption that the prognosis for patients with BNP/NT-proBNP levels at the upper limit of the normal range might be worse than the prognosis for patients with BNP/NT-proBNP levels lower in the range, even if both are determined to be within the normal boundaries. However, a specific cut-off level of BNP/NT-proBNP for the prediction of prognosis in patients without heart failure has not yet been determined. Therefore, we performed a meta-analysis of existing studies investigating the value of BNP/NT-proBNP as a predictor of long-term prognosis in patients with heart failure and the general population.
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Ouyang, Zhiqiang, Qian Li, Guangrong Zheng, Tengfei Ke, Jun Yang, and Chengde Liao. Radiomics for predicting tumor microenvironment phenotypes in non-small cell lung cance: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.9.0060.

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Review question / Objective: Tumor microenvironment (TIME) phenotype is an important factor to affect the response and prognosis of immunotherapy in non-small cell lung cancer (NSCLC). Recently, accumulating studies have noninvasivly perdited the TIME phenotypes of NSCLC by using CT or PET/CT based radiomics. We will conduct this study by means of meta-analysis to eveluate the power and value of CT or PET/CT based radiomics for predicting TIME phenotypes in NSCLC patients. Condition being studied: At present, several recent prospective or retrospective cohort studies and randomized controlled studies have confirmed that CT or PET/CT-based radiomics were the potential tools to predict TIME phenotypes in NSCLC. However, this conclusion is controversial because of the difference of prediction profermance of different studies. The published and unpublished investigations will be included in this study. We will comprehensively evaluate the heterogeneity of these investigations, and the power and value of radiomics for predicting TIME phenotypes.
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