Relevant bibliographies by topics / Predictive Reasoning

Contents

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

Academic literature on the topic 'Predictive Reasoning'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Predictive Reasoning"

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Stauffer, E. Shannon. "HIGH TECH VS PREDICTIVE REASONING." Orthopedics 18, no. 10 (1995): 967. http://dx.doi.org/10.3928/0147-7447-19951001-04.

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Oslington, Gabrielle, Joanne Mulligan, and Penny Van Bergen. "Third-graders’ predictive reasoning strategies." Educational Studies in Mathematics 104, no. 1 (2020): 5–24. http://dx.doi.org/10.1007/s10649-020-09949-0.

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Białek, Michał. "Język, logika a rozumowanie – teoria reguł czy modele umysłowe?" Acta Universitatis Lodziensis. Folia Psychologica, no. 12 (January 1, 2008): 45–54. http://dx.doi.org/10.18778/1427-969x.12.03.

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In present research there was shown the issue of connection between reasoning and logical competence. The point of view of rules theory and mental models theory concerning discussed range of problems was presented and subsequently the prediction of rules theory was verified. The results of the survey conducted on 70 people: 37 students of philosophy and 33 students of other courses at University of Lodz let us reject rules theory as a predictive to the proceedings of reasoning process and induce to accept an alternative theory explaining the proceedings of the discussed process. The adduced re
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Li, Yi, Hridya Dhulipala, Aashish Yadavally, Xiaokai Rong, Shaohua Wang, and Tien N. Nguyen. "Blended Analysis for Predictive Execution." Proceedings of the ACM on Software Engineering 2, FSE (2025): 2987–3008. https://doi.org/10.1145/3729402.

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Although Large Language Models (LLMs) are highly proficient in understanding source code and descriptive texts, they have limitations in reasoning on dynamic program behaviors, such as execution trace and code coverage prediction, and runtime error prediction, which usually require actual program execution. To advance the ability of LLMs in predicting dynamic behaviors, we leverage the strengths of both approaches, Program Analysis (PA) and LLM, in building PredEx, a predictive executor for Python. Our principle is a blended analysis between PA and LLM to use PA to guide the LLM in predicting
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Fernbach, Philip M., Adam Darlow, and Steven A. Sloman. "Asymmetries in predictive and diagnostic reasoning." Journal of Experimental Psychology: General 140, no. 2 (2011): 168–85. http://dx.doi.org/10.1037/a0022100.

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Rodrigo, María J., Manuel de Vega, and Javier Castaneda. "Updating mental models in predictive reasoning." European Journal of Cognitive Psychology 4, no. 2 (1992): 141–57. http://dx.doi.org/10.1080/09541449208406247.

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Lim, Tow Keang. "The predictive brain model in diagnostic reasoning." Asia Pacific Scholar 6, no. 2 (2021): 1–8. http://dx.doi.org/10.29060/taps.2021-6-2/ra2370.

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Introduction: Clinical diagnosis is a pivotal and highly valued skill in medical practice. Most current interventions for teaching and improving diagnostic reasoning are based on the dual process model of cognition. Recent studies which have applied the popular dual process model to improve diagnostic performance by “Cognitive De-biasing” in clinicians have yielded disappointing results. Thus, it may be appropriate to also consider alternative models of cognitive processing in the teaching and practice of clinical reasoning. Methods: This is critical-narrative review of the predictive brain mo
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Lathifaturrahmah, Lathifaturrahmah, Toto Nusantara, Subanji Subanji, and Makbul Muksar. "Predictive reasoning of senior high school students in handling COVID-19 data." Eurasia Journal of Mathematics, Science and Technology Education 19, no. 4 (2023): em2253. http://dx.doi.org/10.29333/ejmste/13110.

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The purpose of this study is to describe the characteristics of predictive reasoning made by students in solving graph-related problems, particularly related to COVID-19. This is a descriptive qualitative study with data collected from a sample size of 25 senior high school students and analyzed using the <i>generalization-prediction task</i>. The result revealed that there are three types of students’ predictive reasoning made based on (1) data observation, (2) data observation coupled with prior experience, and (3) data observation coupled with prior experience or knowledge. The
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Yuan, Ye, Zhong Kai Yang, and Qing Fu Li. "End Effect Processing for Empirical Mode Decomposition Using Fuzzy Inductive Reasoning." Applied Mechanics and Materials 55-57 (May 2011): 407–12. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.407.

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This paper focuses on the end effect problem of the empirical mode decomposition (EMD) algorithm, which results in a serious distortion in the EMD sifting process. A new method based on fuzzy inductive reasoning (FIR) is proposed to overcome the end effect. Fuzzy inductive reasoning method has simple inferring rules and strong predictive capability. The fuzzy inductive reasoning based method uses the sequence near the end as the input signal of fuzzy inductive reasoning model. This predictive value can be obtained after fuzzification, qualitative modeling ,qualitative simulation and debluring.
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Wang, W. C. "Personalized Prediction Model for Hepatocellular Carcinoma With a Bayesian Clinical Reasoning Approach." Journal of Global Oncology 4, Supplement 2 (2018): 210s. http://dx.doi.org/10.1200/jgo.18.84600.

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Background: Predictive models for the risk of hepatocellular carcinoma (HCC) are often appropriate for average-risk population but not tailored for a personalized prediction model for individual risk of hepatocellular carcinoma (HCC), namely personalized prediction model. Aim: The objective of this study is to build up an individually tailored predictive model for HCC by using a Bayesian clinical reasoning algorithm to stratify risk groups of the underlying population. Methods: Data were derived from a community-based screening cohort consisting of 98,552 subjects between 1999 and 2007. Inform
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Dissertations / Theses on the topic "Predictive Reasoning"

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Bell, J. "Predictive conditionals, nonmonotonicity and reasoning about the future." Thesis, University of Essex, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235132.

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Ng, Sin Wa Serena. "Towards an understanding of the staged model of predictive reasoning." Thesis, University of Leicester, 2009. http://hdl.handle.net/2381/7868.

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This study set out to examine the clinical practice of experienced occupational therapists in mental health vocational rehabilitation service in Hong Kong. A combined qualitative and quantitative methodological approach was used to enhance the methodological rigour of the research. Three sub-studies were carried out including a pre-study survey; a semi-structured interview for 6 experienced therapists and a multiple case studies to verify the model of predictive reasoning generated in this research. The findings of this study confirmed the consecutive staged model of decision making, the cycli
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Vallée-Tourangeau, Frédéric. "Adjustment to disconfirming evidence in a covariation judgment task : the role of alternative predictive relationships." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=41208.

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This project investigated the impact of sustained disconfirmation on an acquired belief in a covariation judgment task. Both epistemology and the philosophy of science suggest that data which oppose a hypothesis might not dictate the revision of the hypothesis unless an alternative hypothesis can explain the negative evidence and replace the previous hypothesis. As well, the literature on human categorization and reasoning documents a preference for examples and test instances which confirm rather than disconfirm a prior hypothesis. It was therefore predicted that upon the presentation of nega
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Alaya, Mili Nourhene. "Managing the empirical hardness of the ontology reasoning using the predictive modelling." Thesis, Paris 8, 2016. http://www.theses.fr/2016PA080062/document.

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Multiples techniques d'optimisation ont été implémentées afin de surmonter le compromis entre la complexité des algorithmes du raisonnement et l'expressivité du langage de formulation des ontologies. Cependant les compagnes d'évaluation des raisonneurs continuent de confirmer l'aspect imprévisible et aléatoire des performances de ces logiciels à l'égard des ontologies issues du monde réel. Partant de ces observations, l'objectif principal de cette thèse est d'assurer une meilleure compréhension du comportement empirique des raisonneurs en fouillant davantage le contenu des ontologies. Nous avo
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Abbas, Kaja Moinudeen. "Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5302/.

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Abstract Probabilistic reasoning under uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest. A Bayesian network representing the outbreak of influenza and pneumonia in a geographic
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SORMANI, RAUL. "Criticality assessment of terrorism related events at different time scales TENSOR clusTEriNg terroriSm actiOn pRediction." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/125509.

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Law Enforcement Agencies (LEAs) are nowadays taking advantage of a wide range of information and intelligence sources (e.g., human intelligence (HUMINT), open source intelligence (OSINT), image analysis (IMINT)) to anticipate potential terroristic actions. Urban environments are nowadays associated with a wide range of vulnerabilities, which create fertile ground for terrorists planning actions against assets and/or citizens. These vulnerabilities stem from the characteristics of the urban environment (e.g., presence of civilians, availability of many and diverse physical infrastructures, comp
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Castillo, Guevara Ramon Daniel. "The emergence of cognitive patterns in learning: Implementation of an ecodynamic approach." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396531855.

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Cao, Qiushi. "Semantic technologies for the modeling of predictive maintenance for a SME network in the framework of industry 4.0 Smart condition monitoring for industry 4.0 manufacturing processes: an ontology-based approach Using rule quality measures for rule base refinement in knowledge-based predictive maintenance systems Combining chronicle mining and semantics for predictive maintenance in manufacturing processes." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR04.

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Dans le domaine de la fabrication, la détection d’anomalies telles que les défauts et les défaillances mécaniques permet de lancer des tâches de maintenance prédictive, qui visent à prévoir les défauts, les erreurs et les défaillances futurs et à permettre des actions de maintenance. Avec la tendance de l’industrie 4.0, les tâches de maintenance prédictive bénéficient de technologies avancées telles que les systèmes cyberphysiques (CPS), l’Internet des objets (IoT) et l’informatique dématérialisée (cloud computing). Ces technologies avancées permettent la collecte et le traitement de données d
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Bjurén, Johan. "USING CASE-BASED REASONING FOR PREDICTING ENERGY USAGE." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-9436.

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In this study, the inability to in a future meet the electricity demand and the urge to change the consumption behavior considered. In a smart grid context there are several possible ways to do this. Means include ways to increase the consumer’s awareness, add energy storages or build smarter homes which can control the appliances. To be able to implement these, indications on how the future consumption will be could be useful. Therefore we look further into how a framework for short-term consumption predictions can be created using electricity consumption data in relation to external factors.
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Khajotia, Burzin K. "CASE BASED REASONING – TAYLOR SERIES MODEL TO PREDICT CORROSION RATE IN OIL AND GAS WELLS AND PIPELINES." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1173828758.

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Books on the topic "Predictive Reasoning"

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Klein, Patrick. Combining Expert Knowledge and Deep Learning with Case-Based Reasoning for Predictive Maintenance. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-46986-3.

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Wolsey, Thomas DeVere. Learning to predict and predicting to learn: Cognitive strategies and instructional routines. Pearson/Allyn & Bacon, 2009.

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Bridgeman, Brent. Predictions of freshman grade-point average from the revised and recentered SAT I, Reasoning Test. College Entrance Examination Board, 2000.

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Trevor, Hastie, Tibshirani Robert, and SpringerLink (Online service), eds. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag New York, 2009.

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Matwijkiw, Bronik. Predictive Reasoning in Legal Theory (Applied Legal Philosophy). Ashgate Pub Ltd, 2003.

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Essex, University of, ed. Predictive conditionals, nonmonotonicity and reasoning about the future. 1988.

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Reason and Prediction. Cambridge University Press, 2009.

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Rosenberg, Alex. Blunt Instrument. The MIT Press, 2025. https://doi.org/10.7551/mitpress/15672.001.0001.

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Why economic theory—with no track-record of predictive success—is still an indispensable tool for protecting civilized life. Economic theory has never gotten any better at prediction. Its explanations are always after the fact. The mathematical models economists have devoted themselves to for more than a century can't be improved to enhance their empirical relevance. But from this research program that never paid off, a very useful tool has emerged—game theory. It's just what civilized society needs to protect itself from the rapaciousness that condemns all markets to fail. In Blunt Instrument
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White, Stephen J. Responsibility and the Demands of Morality. Edited by Kyla Ebels-Duggan and Berislav Marušić. Oxford University PressOxford, 2025. https://doi.org/10.1093/9780191997273.001.0001.

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Abstract Stephen J. White was developing a comprehensive view of responsibility and its limits when his life was tragically cut short. This volume contains his collected papers. White’s view of responsibility spans across ethics, action theory, and interpersonal epistemology. Its core idea is that to be responsible for doing or believing something is to be answerable for why one has done it or why one believes it. And to be responsible for a state of affairs is to be answerable for why things are that way, rather than some other way. White deploys this conception of responsibility to illuminat
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Gallagher, Shaun. Enactivist Interventions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198794325.001.0001.

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Enactivist Interventions explores central issues in the contemporary debates about embodied cognition, addressing interdisciplinary questions about intentionality, representation, affordances, the role of affect, and the problems of perception and cognitive penetration, action and free will, higher-order cognition, and intersubjectivity. It argues for a rethinking of the concept of mind, drawing on pragmatism, phenomenology, and cognitive science. It interprets enactivism as a philosophy of nature that has significant methodological and theoretical implications for the scientific investigation
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Book chapters on the topic "Predictive Reasoning"

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Ang, Zhendong, and Umang Mathur. "Predictive Monitoring with Strong Trace Prefixes." In Computer Aided Verification. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65630-9_9.

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AbstractRuntime predictive analyses enhance coverage of traditional dynamic analyses based bug detection techniques by identifying a space of feasible reorderings of the observed execution and determining if any reordering in this space witnesses the violation of some desired safety property. The most popular approach for modelling the space of feasible reorderings is through Mazurkiewicz’s trace equivalence. The simplicity of the framework also gives rise to efficient predictive analyses, and has been the de facto means for obtaining space and time efficient algorithms for monitoring concurre
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Wotawa, Franz. "Reasoning from First Principles for Self-adaptive and Autonomous Systems." In Predictive Maintenance in Dynamic Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05645-2_15.

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Bazin, Alexandre, Miguel Couceiro, Marie-Dominique Devignes, and Amedeo Napoli. "An Approach to Identifying the Most Predictive and Discriminant Features in Supervised Classification Problems." In Graph-Based Representation and Reasoning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86982-3_4.

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Riesterer, Nicolas, Daniel Brand, and Marco Ragni. "The Predictive Power of Heuristic Portfolios in Human Syllogistic Reasoning." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00111-7_35.

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Klein, Patrick. "Data Generation for AI-based Predictive Maintenance Research." In Combining Expert Knowledge and Deep Learning with Case-Based Reasoning for Predictive Maintenance. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-46986-3_3.

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Oladapo, Kayode Abiodun, Folasade Adedeji, Uchenna Jeremiah Nzenwata, Bao Pham Quoc, and Akinbiyi Dada. "Fuzzified Case-Based Reasoning Blockchain Framework for Predictive Maintenance in Industry 4.0." In Studies in Big Data. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38325-0_12.

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Martin, Luke J. W. "Predictive Reasoning and Machine Learning for the Enhancement of Reliability in Railway Systems." In Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33951-1_13.

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Klein, Patrick. "Infusing Expert Knowledge into a Siamese Neural Network for Encoding Time Series." In Combining Expert Knowledge and Deep Learning with Case-Based Reasoning for Predictive Maintenance. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-46986-3_7.

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Klein, Patrick. "Problem Definition and Introduction of Developed Constructs Used Across Application Scenarios." In Combining Expert Knowledge and Deep Learning with Case-Based Reasoning for Predictive Maintenance. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-46986-3_5.

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Klein, Patrick. "Introduction." In Combining Expert Knowledge and Deep Learning with Case-Based Reasoning for Predictive Maintenance. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-46986-3_1.

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Conference papers on the topic "Predictive Reasoning"

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Khajotia, Burzin, Dusan Sormaz, and Srdjan Nesic. "Case-based Reasoning Model of CO2 Corrosion Based on Field Data." In CORROSION 2007. NACE International, 2007. https://doi.org/10.5006/c2007-07553.

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Abstract An important aspect in corrosion prediction for oil and gas wells and pipelines is to obtain a realistic estimate of the corrosion rate. Corrosion rate prediction involves developing a predictive model that utilizes commonly available operational parameters, existing lab/field data and theoretical models to obtain realistic assessments of corrosion rates. The Case-based Reasoning (CBR) model for CO2 corrosion prediction is designed to mimic the approach of experienced field corrosion personnel. The model takes knowledge of corrosion rates for existing cases and uses CBR techniques and
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Ratan, Anushree, Aman Agarwal, Hrudaya Kumar Tripathy, et al. "A Novel Big Data Reasoning enabled Predictive Model in Healthcare Domain for Disease Diagnosis." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721880.

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Yan, Jing Nathan, Tianqi Liu, Justin Chiu, et al. "Predicting Text Preference Via Structured Comparative Reasoning." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.541.

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Wang, Bingbing, Geng Tu, Bin Liang, et al. "Enhancing Emotion Reasoning for Image Multi-Emotion Prediction." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889829.

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Deng, Chenlong, Kelong Mao, Yuyao Zhang, and Zhicheng Dou. "Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.43.

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Walkinshaw, Neil. "Using evidential reasoning to make qualified predictions of software quality." In PROMISE '13: 9th International Conference on Predictive Models in Software Engineering. ACM, 2013. http://dx.doi.org/10.1145/2499393.2499402.

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Yue, Jia, Anita Raja, and William Ribarsky. "Predictive Analytics Using a Blackboard-Based Reasoning Agent." In 2010 IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT). IEEE, 2010. http://dx.doi.org/10.1109/wi-iat.2010.155.

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Vats, Shivam, Maxim Likhachev, and Oliver Kroemer. "Efficient Recovery Learning using Model Predictive Meta-Reasoning." In 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023. http://dx.doi.org/10.1109/icra48891.2023.10160382.

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Cao, Qiushi, Ahmed Samet, Cecilia Zanni-Merk, François de Beuvron, and Christoph Reich. "Combining Evidential Clustering and Ontology Reasoning for Failure Prediction in Predictive Maintenance." In 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008969506180625.

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Tiger, Mattias, and Fredrik Heintz. "Stream Reasoning Using Temporal Logic and Predictive Probabilistic State Models." In 2016 23rd International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2016. http://dx.doi.org/10.1109/time.2016.28.

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Reports on the topic "Predictive Reasoning"

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Perry, Marcus B., Patrick J. Vincent, and Jeremy D. Jordan. Human Predictive Reasoning for Group Interactions. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada535335.

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Pasupuleti, Murali Krishna. Quantum Cognition: Modeling Decision-Making with Quantum Theory. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi225.

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Abstract Quantum cognition applies quantum probability theory and mathematical principles from quantum mechanics to model human decision-making, reasoning, and cognitive processes beyond the constraints of classical probability models. Traditional decision theories, such as expected utility theory and Bayesian inference, struggle to explain context-dependent reasoning, preference reversals, order effects, and cognitive biases observed in human behavior. By incorporating superposition, interference, and entanglement, quantum cognitive models offer a probabilistic framework that better accounts
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Ruvinsky, Alicia, Maria Seale, R. Salter, and Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/46632.

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Techniques in prognostics and health management have advanced considerably in the last few decades, enabled by breakthroughs in computational methods and supporting technologies. These predictive models, whether data-driven or physics-based, target the modeling of a system’s aggregate performance. As such, they generalize assumptions about the modelled system’s components, and are thus limited in their ability to represent individual components and the dynamic environmental factors that affect composite system health. To address this deficiency, we have developed an epigenetics-inspired knowle
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