Results for 'Predictive Model'

999 found
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  1.  69
    When Are Purely Predictive Models Best?Robert Northcott - 2017 - Disputatio 9 (47):631-656.
    Can purely predictive models be useful in investigating causal systems? I argue ‘yes’. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to use theory to (...)
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  2. ANN Model for Predicting Protein Localization Sites in Cells.Mohammed Nafez Abu Samra, Bilal Ezz El-Din Abed, Hossam Abdel Nasser Zaqout & Samy S. Abu-Naser - 2020 - International Journal of Academic and Applied Research (IJAAR) 4 (9):43-50.
    To automate examination of massive amounts of sequence data for biological function, it is important to computerize interpretation based on empirical knowledge of sequence-function relationships. For this purpose, we have been constructing an Artificial Neural Network (ANN) by organizing various experimental and computational observations as a collection ANN models. Here we propose an ANN model which utilizes the Dataset for UCI Machine Learning Repository, for predicting localization sites of proteins. We collected data for 336 proteins with known localization sites (...)
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  3. Predictive Processing and the Phenomenology of Time Consciousness: A Hierarchical Extension of Rick Grush’s Trajectory Estimation Model.Wanja Wiese - 2017 - Philosophy and Predictive Processing.
    This chapter explores to what extent some core ideas of predictive processing can be applied to the phenomenology of time consciousness. The focus is on the experienced continuity of consciously perceived, temporally extended phenomena (such as enduring processes and successions of events). The main claim is that the hierarchy of representations posited by hierarchical predictive processing models can contribute to a deepened understanding of the continuity of consciousness. Computationally, such models show that sequences of events can be represented (...)
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  4. The Risk GP Model: The Standard Model of Prediction in Medicine.Jonathan Fuller & Luis J. Flores - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 54:49-61.
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  5. Predictive Coding and Representationalism.Paweł Gładziejewski - 2016 - Synthese 193 (2).
    According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of (...)
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  6. Diabetes Prediction Using Artificial Neural Network.Nesreen Samer El_Jerjawi & Samy S. Abu-Naser - 2018 - International Journal of Advanced Science and Technology 121:54-64.
    Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to (...)
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  7. Predicting Birth Weight Using Artificial Neural Network.Mohammed Al-Shawwa & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (1):9-14.
    In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in (...)
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  8.  11
    Standardised Predictive Linear Models of Managerial Processes and the Sustainability of Graduate Programmes (SGPs) in Universities: A Case Study.Valentine Joseph Owan & Oni Enene Offu - 2021 - Contemporary Mathematics and Science Education 2 (1):Article ep21006.
    The exploration of the literature indicated that much studies abound in related areas. Much seems yet to be known about the nature of the relationship that exists between managerial variables and the sustainability of graduate programmes. To bridge this gap, we utilized a standardised multiple regression approach to build up linear models that examine three managerial processes (strategic planning, staff and information/communication management) and how they affect three proxies of the sustainability of graduate programmes (availability of funds and facilities, as (...)
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  9. Extending the Argument From Unconceived Alternatives: Observations, Models, Predictions, Explanations, Methods, Instruments, Experiments, and Values.Darrell Patrick Rowbottom - 2016 - Synthese (10).
    Stanford’s argument against scientific realism focuses on theories, just as many earlier arguments from inconceivability have. However, there are possible arguments against scientific realism involving unconceived (or inconceivable) entities of different types: observations, models, predictions, explanations, methods, instruments, experiments, and values. This paper charts such arguments. In combination, they present the strongest challenge yet to scientific realism.
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  10. Prediction and Topological Models in Neuroscience.Bryce Gessell, Matthew Stanley, Benjamin Geib & Felipe De Brigard - forthcoming - In Fabrizio Calzavarini & Marco Viola (eds.), Neural Mechanisms: New challenges in the philosophy of neuroscience. Springer.
    In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that (...)
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  11. Predicting Overall Car Performance Using Artificial Neural Network.Osama M. Al-Mubayyed, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic and Applied Research (IJAAR) 3 (1):1-5.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.62 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study (...)
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  12. Prediction of Whether Mushroom is Edible or Poisonous Using Back-Propagation Neural Network.Eyad Sameh Alkronz, Khaled A. Moghayer, Mohamad Meimeh, Mohannad Gazzaz, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic and Applied Research (IJAAR) 3 (2):1-8.
    Abstract: Predication is an application of Artificial Neural Network (ANN). It is a supervised learning due to predefined input and output attributes. Multi-Layer ANN model is used for training, validating, and testing of the data. In this paper, Multi-Layer ANN model was used to train and test the mushroom dataset to predict whether it is edible or poisonous. The Mushrooms dataset was prepared for training, 8124 instances were used for the training. JustNN software was used to training and (...)
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  13. Predicting Tumor Category Using Artificial Neural Networks.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (2):1-7.
    In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established (...)
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  14. Prediction Versus Accommodation in Economics.Robert Northcott - 2019 - Journal of Economic Methodology 26 (1):59-69.
    Should we insist on prediction, i.e. on correctly forecasting the future? Or can we rest content with accommodation, i.e. empirical success only with respect to the past? I apply general considerations about this issue to the case of economics. In particular, I examine various ways in which mere accommodation can be sufficient, in order to see whether those ways apply to economics. Two conclusions result. First, an entanglement thesis: the need for prediction is entangled with the methodological role of orthodox (...)
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  15. Direct Perception and the Predictive Mind.Zoe Drayson - 2018 - Philosophical Studies 175 (12):3145-3164.
    Predictive approaches to the mind claim that perception, cognition, and action can be understood in terms of a single framework: a hierarchy of Bayesian models employing the computational strategy of predictive coding. Proponents of this view disagree, however, over the extent to which perception is direct on the predictive approach. I argue that we can resolve these disagreements by identifying three distinct notions of perceptual directness: psychological, metaphysical, and epistemological. I propose that perception is plausibly construed as (...)
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  16. Causal Models and the Logic of Counterfactuals.Jonathan Vandenburgh - manuscript
    Causal models provide a framework for making counterfactual predictions, making them useful for evaluating the truth conditions of counterfactual sentences. However, current causal models for counterfactual semantics face limitations compared to the alternative similarity-based approach: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper argues that these limitations arise from the theory of interventions where intervening on variables requires changing structural equations rather than the values of variables. Using an (...)
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  17. Noise, Uncertainty, and Interest: Predictive Coding and Cognitive Penetration.Jona Vance & Dustin Stokes - 2017 - Consciousness and Cognition 47:86-98.
    This paper concerns how extant theorists of predictive coding conceptualize and explain possible instances of cognitive penetration. §I offers brief clarification of the predictive coding framework and relevant mechanisms, and a brief characterization of cognitive penetration and some challenges that come with defining it. §II develops more precise ways that the predictive coding framework can explain, and of course thereby allow for, genuine top-down causal effects on perceptual experience, of the kind discussed in the context of cognitive (...)
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  18. Energy Efficiency Prediction Using Artificial Neural Network.Ahmed J. Khalil, Alaa M. Barhoom, Bassem S. Abu-Nasser, Musleh M. Musleh & Samy S. Abu-Naser - 2019 - International Journal of Academic Pedagogical Research (IJAPR) 3 (9):1-7.
    Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on (...)
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  19. A Theory of Predictive Dissonance: Predictive Processing Presents a New Take on Cognitive Dissonance.Roope Oskari Kaaronen - 2018 - Frontiers in Psychology 9.
    This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance.” First, (...)
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  20. Model Robustness as a Confirmatory Virtue: The Case of Climate Science.Elisabeth A. Lloyd - 2015 - Studies in History and Philosophy of Science Part A 49:58-68.
    I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. (...)
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  21.  82
    Hydrates Production Prediction With Computer Modelling Group (CMG) Stars. A Comprehensive Review.Daudi Matungwa Katabaro & Wang Jinjie - 2018 - International Journal of Academic Multidisciplinary Research (IJAMR) 2 (11):24-30.
    Abstract: Hydrates are an enormous energy resource with global circulation in the permafrost and in the oceans. Even if conventional estimates are deliberated and only a small fraction is recoverable, the pure size of the resource is so huge that it demands assessment as a potential energy source. In this research work, we discuss the hydrate production prediction with Computer Modeling Group STARS (CMG STARS). In this paper different literatures reviews have been visited concerning hydrate production prediction with CMG STARS (...)
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  22. Experiential Fantasies, Prediction, and Enactive Minds.Michael David Kirchhoff - 2015 - Journal of Consciousness Studies 22 (3-4):68-92.
    A recent surge of work on prediction-driven processing models--based on Bayesian inference and representation-heavy models--suggests that the material basis of conscious experience is inferentially secluded and neurocentrically brain bound. This paper develops an alternative account based on the free energy principle. It is argued that the free energy principle provides the right basic tools for understanding the anticipatory dynamics of the brain within a larger brain-body-environment dynamic, viewing the material basis of some conscious experiences as extensive--relational and thoroughly world-involving.
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  23. Models and Explanation.Alisa Bokulich - 2017 - In Lorenzo Magnani & Tommaso Wayne Bertolotti (eds.), Springer Handbook of Model-Based Science. Springer. pp. 103-118.
    Detailed examinations of scientific practice have revealed that the use of idealized models in the sciences is pervasive. These models play a central role in not only the investigation and prediction of phenomena, but in their received scientific explanations as well. This has led philosophers of science to begin revising the traditional philosophical accounts of scientific explanation in order to make sense of this practice. These new model-based accounts of scientific explanation, however, raise a number of key questions: Can (...)
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  24. Artificial Neural Network for Predicting Car Performance Using JNN.Awni Ahmed Al-Mobayed, Youssef Mahmoud Al-Madhoun, Mohammed Nasser Al-Shuwaikh & Samy S. Abu-Naser - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (9):139-145.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study (...)
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  25. Artificial Neural Network for Predicting Animals Category.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic and Applied Research (IJAAR) 3 (2):18-24.
    Abstract: In this paper an Artificial Neural Network (ANN) model, was developed and tested for predicting the category of an animal. There is a number of factors that influence the classification of animals. Such as the existence of hair/ feather, if the animal gives birth or spawns, it is airborne, aquatic, predator, toothed, backboned, venomous, has –fins, has-tail, cat-sized, and domestic. They were then used as input variables for the ANN model. A model based on the Multilayer (...)
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  26. Prediction in Selectionist Evolutionary Theory.Rasmus Grønfeldt Winther - 2009 - Philosophy of Science 76 (5):889-901.
    Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli, and the origin of eukaryotic cells through endosymbiosis.
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  27. Predicting the Age of Abalone From Physical Measurements Using Artificial Neural Network.Ghaida Riyad Mohammed, Jaffa Riad Abu Shbikah, Mohammed Majid Al-Zamili, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2020 - International Journal of Academic and Applied Research (IJAAR) 4 (11):7-12.
    Abalones have long been a valuable food source for humans in every area of the world where a species is abundant. Predicting the age of abalone is done using physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age of abalone is using Artificial Neural Network (...)
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  28. Vanilla PP for Philosophers: A Primer on Predictive Processing.Wanja Wiese & Thomas Metzinger - 2017 - Philosophy and Predictive Processing.
    The goal of this short chapter, aimed at philosophers, is to provide an overview and brief explanation of some central concepts involved in predictive processing (PP). Even those who consider themselves experts on the topic may find it helpful to see how the central terms are used in this collection. To keep things simple, we will first informally define a set of features important to predictive processing, supplemented by some short explanations and an alphabetic glossary. -/- The features (...)
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  29.  23
    Modelling Competing Legal Arguments Using Bayesian Model Comparison and Averaging.Martin Neil, Norman Fenton, David Lagnado & Richard David Gill - 2019 - Artificial Intelligence and Law 27 (4):403-430.
    Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them (...)
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  30. Understanding From Machine Learning Models.Emily Sullivan - forthcoming - British Journal for the Philosophy of Science:axz035.
    Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? (...)
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  31. Moral Identity Predicts the Development of Presence of Meaning During Emerging Adulthood.Hyemin Han, Indrawati Liauw & Ashley Floyd Kuntz - forthcoming - Emerging Adulthood.
    We examined change over time in the relationship between moral identity and presence of meaning during early adulthood. Moral identity refers to a sense of morality and moral values that are central to one’s identity. Presence of meaning refers to the belief that one’s existence has meaning, purpose, and value. Participants responded to questions on moral identity and presence of meaning in their senior year of high school and two years after. Mixed effects model analyses were used to examine (...)
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  32.  58
    Nonlinear Active Suspension System Control Using Fuzzy Model Predictive Controller.Mustefa Jibril, Mesay Tadesse & Nurye Hassen - 2021 - Journal of Engineering and Applied Sciences 16 (9):289-295.
    Recent years, active suspension system has been widely used in automobiles to improve the road holding ability and the riding comfort. This study presents a new fuzzy model predictive control for a nonlinear quarter car active suspension system. A nonlinear dynamical model of active suspension is established, where the nonlinear dynamical characteristic of the spring and damper are considered. Based on the proposed fuzzy model predictive control method is presented to stabilize the displacement of the (...)
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  33. Models, Brains, and Scientific Realism.Fabio Sterpetti - 2006 - In Lorenzo Magnani & Claudia Casadio (eds.), Model Based Reasoning in Science and Technology. Logical, Epistemological, and Cognitive Issues. Springer. pp. 639-661.
    Prediction Error Minimization theory (PEM) is one of the most promising attempts to model perception in current science of mind, and it has recently been advocated by some prominent philosophers as Andy Clark and Jakob Hohwy. Briefly, PEM maintains that “the brain is an organ that on aver-age and over time continually minimizes the error between the sensory input it predicts on the basis of its model of the world and the actual sensory input” (Hohwy 2014, p. 2). (...)
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  34. Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear EMS Magnetic Levitation Train.Mustefa Jibril & Eliyas Alemayehu - 2020 - Report and Opinion Journal 12 (5):21-25.
    Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have (...)
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  35.  13
    Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Electromagnetic Space Vehicle Suspension System.Mustefa Jibril, Mesay Tadesse & Nurye Hassen - 2021 - Journal of Engineering and Applied Sciences 16 (10):313-317.
    Electromagnetic Suspension System (EMS) is mostly used in the field of high-speed vehicle. In this study, a space exploring vehicle quarter electromagnetic suspension system is modelled, designed and simulated using Neural network-based control problem. NARMA-L2, Model reference and predictive controllers are designed to improve the body travel of the vehicle using bump road profile. Comparison between the proposed controllers is done and a promising simulation result have been analyzed.
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  36. Minds, Models and Mechanisms: A New Perspective on Intentional Psychology.Eric Hochstein - 2012 - Journal of Experimental & Theoretical Artificial Intelligence 24 (4):547-557.
    In this article, I argue that intentional psychology (i.e. the interpretation of human behaviour in terms of intentional states and propositional attitudes) plays an essential role in the sciences of the mind. However, this role is not one of identifying scientifically respectable states of the world. Rather, I argue that intentional psychology acts as a type of phenomenological model, as opposed to a mechanistic one. I demonstrate that, like other phenomenological models in science, intentional psychology is a methodological tool (...)
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  37.  52
    Unification by Fiat: Arrested Development of Predictive Processing.Piotr Litwin & Marcin Miłkowski - 2020 - Cognitive Science 44 (7).
    Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains only loosely connected both to its computational framework and to its hypothetical biological underpinnings, which makes its fundamentals unclear. Instead of offering explanations that refer to the same set (...)
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  38. Intensional Models for the Theory of Types.Reinhard Muskens - 2007 - Journal of Symbolic Logic 72 (1):98-118.
    In this paper we define intensional models for the classical theory of types, thus arriving at an intensional type logic ITL. Intensional models generalize Henkin's general models and have a natural definition. As a class they do not validate the axiom of Extensionality. We give a cut-free sequent calculus for type theory and show completeness of this calculus with respect to the class of intensional models via a model existence theorem. After this we turn our attention to applications. Firstly, (...)
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  39. Embodied Decisions and the Predictive Brain.Christopher Burr - 2016 - Dissertation, University of Bristol
    Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her (...)
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  40. Predicting Islamic Ethical Work Behavior Using the Theory of Planned Behavior and Religiosity in Brunei.Nur Amali Aminnuddin - 2019 - Journal of Behavioral Science 14 (1):1-13.
    The objective of this study was to employ the theory of planned behavior in examining the inclusion of Islamic religiosity in predicting Islamic ethical work behavior. Islamic religiosity was included as Islam plays a dominant role in Brunei’s society. Participants consisted of 370 Malay Muslim teachers. Structural equation modeling was used to test three proposed models. While Model 1 was based on the theory of planned behavior, it does not take into consideration the distinctive Islamic context of the Bruneian (...)
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  41.  67
    Three Ways in Which Pandemic Models May Perform a Pandemic.Philippe van Basshuysen, Lucie White, Donal Khosrowi & Mathias Frisch - 2021 - Erasmus Journal for Philosophy and Economics 14 (1):110-127.
    Models not only represent but may also influence their targets in important ways. While models’ abilities to influence outcomes has been studied in the context of economic models, often under the label ‘performativity’, we argue that this phenomenon also pertains to epidemiological models, such as those used for forecasting the trajectory of the Covid-19 pandemic. After identifying three ways in which a model by the Covid-19 Response Team at Imperial College London may have influenced scientific advice, policy, and individual (...)
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  42. The Interplay Between Models and Observations.Claudio Masolo, Alessander Botti Benevides & Daniele Porello - 2018 - Applied ontology 13 (1):41-71.
    We propose a formal framework to examine the relationship between models and observations. To make our analysis precise,models are reduced to first-order theories that represent both terminological knowledge – e.g., the laws that are supposed to regulate the domain under analysis and that allow for explanations, predictions, and simulations – and assertional knowledge – e.g., information about specific entities in the domain of interest. Observations are introduced into the domain of quantification of a distinct first-order theory that describes their nature (...)
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  43. Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear Quarter Car Active Suspension System.Mustefa Jibril - 2020 - International Research Journal of Modernization in Engineering Technology and Science 2 (3):178-188.
    Recently, active suspension system will become important to the vehicle industries because of its advantages in improving road managing and ride comfort. This paper offers the development of mathematical modelling and design of a neural network control approach. The paper will begin with a mathematical model designing primarily based at the parameters of the active suspension system. A nonlinear three by four-way valve-piston hydraulic actuator became advanced which will make the suspension system under the active condition. Then, the (...) can be analyzed thru MATLAB/Simulink software program. Finally, the NARMA-L2, model reference and predictive controllers are designed for the active suspension system. The results are acquired after designing the simulation of the quarter-car nonlinear active suspension system. From the simulation end result using MATLAB/Simulink, the response of the system might be as compared between the nonlinear active suspension system with NARMA-L2, model reference and predictive controllers. Besides that, the evaluation has been made between the proposed controllers thru the characteristics of the manage objectives suspension deflection, body acceleration and body travel of the active suspension system. . As a conclusion, designing a nonlinear active suspension system with a nonlinear hydraulic actuator for quarter car model has improved the car performance by using a NARMA-L2 controller. The improvements in performance will improve road handling and ride comfort performance of the active suspension system. (shrink)
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  44.  60
    DC Motor Speed Control with the Presence of Input Disturbance Using Neural Network Based Model Reference and Predictive Controllers.Mustefa Jibril - 2020 - International Research Journal of Modernization in Engineering Technology and Science 2 (4):103-110.
    In this paper we describe a technical system for DC motor speed control. The speed of DC motor is controlled using Neural Network Based Model Reference and Predictive controllers with the use of Matlab/Simulink. The analysis of the DC motor is done with and without input side Torque disturbance input and the simulation results obtained by comparing the desired and actual speed of the DC motor using random reference and sinusoidal speed inputs for the DC motor with (...) Reference and Predictive controllers. The DC motor with Model Reference controller shows almost the actual speed is the same as the desired speed with a good performance than the DC motor with Predictive controller for the system with and without input side disturbance. Finally the comparative simulation result prove the effectiveness of the DC motor with Model Reference controller. (shrink)
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  45. Model-Selection Theory: The Need for a More Nuanced Picture of Use-Novelty and Double-Counting.Katie Steele & Charlotte Werndl - 2016 - British Journal for the Philosophy of Science:axw024.
    This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive (...)
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  46. Presence of Amphibian Species Prediction Using Features Obtained From GIS and Satellite Images.Nadia Shaker Habib, Omar Kamal Abu Maghasib, Ahmed Rashad Al-Ghazali, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2020 - International Journal of Academic and Applied Research (IJAAR) 4 (11):13-22.
    The establishment of the transport infrastructure is usually preceded by an EIA procedure, which should determine amphibian breeding sites and migration routes. However, evaluation is very difficult due to the large number of habitats spread over a vast area and the limited time available for field work. An artificial Neural Network (ANN) is proposed for predicting the presence of amphibians species near the water reservoirs based on features obtained from GIS systems and satellite images. The dataset collected from UCI Machine (...)
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  47. Bayesian Learning Models of Pain: A Call to Action.Abby Tabor & Christopher Burr - 2019 - Current Opinion in Behavioral Sciences 26:54-61.
    Learning is fundamentally about action, enabling the successful navigation of a changing and uncertain environment. The experience of pain is central to this process, indicating the need for a change in action so as to mitigate potential threat to bodily integrity. This review considers the application of Bayesian models of learning in pain that inherently accommodate uncertainty and action, which, we shall propose are essential in understanding learning in both acute and persistent cases of pain.
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  48. Just How Conservative is Conservative Predictive Processing?Paweł Gładziejewski - 2017 - Hybris. Internetowy Magazyn Filozoficzny 38:98-122.
    Predictive Processing (PP) framework construes perception and action (and perhaps other cognitive phenomena) as a matter of minimizing prediction error, i.e. the mismatch between the sensory input and sensory predictions generated by a hierarchically organized statistical model. There is a question of how PP fits into the debate between traditional, neurocentric and representation-heavy approaches in cognitive science and those approaches that see cognition as embodied, environmentally embedded, extended and (largely) representation-free. In the present paper, I aim to investigate (...)
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  49. Ramsey and Joyce on Deliberation and Prediction.Yang Liu & Huw Price - 2020 - Synthese 197:4365-4386.
    Can an agent deliberating about an action A hold a meaningful credence that she will do A? 'No', say some authors, for 'Deliberation Crowds Out Prediction' (DCOP). Others disagree, but we argue here that such disagreements are often terminological. We explain why DCOP holds in a Ramseyian operationalist model of credence, but show that it is trivial to extend this model so that DCOP fails. We then discuss a model due to Joyce, and show that Joyce's rejection (...)
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  50. Breast Cancer Diagnosis and Survival Prediction Using JNN.Mohammed Ziyad Abu Shawarib, Ahmed Essam Abdel Latif, Bashir Essam El-Din Al-Zatmah & Samy S. Abu-Naser - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (10):23-30.
    Abstract: Breast cancer is reported to be the most common cancer type among women worldwide and it is the second highest women fatality rate amongst all cancer types. Notwithstanding all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still not sufficient. In this paper, we propose an ANN model which outperforms all the previous supervised learning methods by reaching 99.57 in terms of accuracy in Wisconsin Breast Cancer dataset. Experimental results on Haberman’s (...)
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