Results for 'Prediction'

999 found
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  1. 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 sorts, (...)
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  2. 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 (...)
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  3.  59
    Predictive Policing and the Ethics of Preemption.Daniel Susser - 2020 - In Ben Jones & Eduardo Mendieta (eds.), The Ethics of Policing: New Perspectives on Law Enforcement. New York: Nyu Press.
    The American justice system, from police departments to the courts, is increasingly turning to information technology for help identifying potential offenders, determining where, geographically, to allocate enforcement resources, assessing flight risk and the potential for recidivism amongst arrestees, and making other judgments about when, where, and how to manage crime. In particular, there is a focus on machine learning and other data analytics tools, which promise to accurately predict where crime will occur and who will perpetrate it. Activists and academics (...)
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  4. On Predicting.Fabrizio Cariani - forthcoming - Ergo: An Open Access Journal of Philosophy.
    I propose an account of the speech act of prediction that denies that the contents of prediction must be about the future and illuminates the relation between prediction and assertion. My account is a synthesis of two ideas: (i) that what is in the future in prediction is the time of discovery and (ii) that, as Benton and Turri recently argued, prediction is best characterized in terms of its constitutive norms.
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  5.  97
    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 hospitals. The evaluation (...)
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  6. Predicting Books’ Overall Rating Using Artificial Neural Network.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 3 (8):11-17.
    We developed an Artificial Neural Network (ANN) model for predicting the overall rating of books. The prediction is based on some Factors (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which used as input variables and (average_rating) as output for our ANN predictive model. Our model established, trained, and validated using data set, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.90% of the validation instances.
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  7. 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 method is (...)
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  8. Classification Prediction of SBRCTs Cancers Using Artificial Neural Network.Remah Al-Massri, Yomna Al-Astel, Hanan Ziadia, Deyaa K. Mousa & Samy S. Abu-Naser - 2018 - International Journal of Academic Engineering Research (IJAER) 2 (11):1-7.
    Abstract: Small Blue Round Cell Tumors (SBRCTs) are a heterogeneous group of tumors that are difficult to diagnose because of overlapping morphologic, immunehistochemical, and clinical features. About two-thirds of EWSR1-negative SBRCTs are associated with CIC-DUX4-related fusions, whereas another small subset shows BCOR-CCNB3 X-chromosomal par acentric inversion. In this paper, we propose an ANN model to Classify and Predict SBRCTs Cancers. The accuracy of the classification reached 100%.
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  9. 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 validating the (...)
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  10. Predictive Coding Explains Binocular Rivalry: An Epistemological Review.Jakob Hohwy, Andreas Roepstorff & Karl Friston - 2008 - Cognition 108 (3):687-701.
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  11. 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 as states (...)
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  12. 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 and trained using (...)
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  13. 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 (...)
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  14. Causation, Prediction, and Search.Peter Spirtes, Clark Glymour, Scheines N. & Richard - 2000 - Mit Press: Cambridge.
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  15.  97
    Predicting Effect of Oxygen Consumption of Thylakoid Membranes (Chloroplasts) From Spinach After Inhibition Using Artificial Neural Network.Mohammed Al-Shawwa & Samy S. Abu-Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 3 (2):15-20.
    Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict effect of oxygen consumption of thylakoid membranes (chloroplasts) from spinach after inhibition. A number of factors were identified that may affect of oxygen consumption of thylakoid membranes from spinach. Factors such as curve, herbicide, dose, 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 inhibition of photosynthesis in farms. (...)
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  16. Predictability Crisis in Early Universe Cosmology.Chris Smeenk - 2014 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 46 (1):122-133.
    Inflationary cosmology has been widely accepted due to its successful predictions: for a “generic” initial state, inflation produces a homogeneous, flat, bubble with an appropriate spectrum of density perturbations. However, the discovery that inflation is “generically eternal,” leading to a vast multiverse of inflationary bubbles with different low-energy physics, threatens to undermine this account. There is a “predictability crisis” in eternal inflation, because extracting predictions apparently requires a well-defined measure over the multiverse. This has led to discussions of anthropic predictions (...)
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  17. Predicting Titanic Survivors Using Artificial Neural Network.Alaa M. Barhoom, Ahmed J. Khalil, Bassem S. Abu-Nasser, Musleh M. Musleh & Samy S. Abu Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 3 (9):8-12.
    Although the Titanic disaster happened just over one hundred years ago, it still appeals researchers to understand why some passengers survived while others did not. With the use of a machine learning tool (JustNN) and the provided dataset we study which factors or classifications of passengers have a strong relationship with survival for passengers that took that trip on 15th of April, 1912. The analysis seeks to identify characteristics of passengers - cabin class, age, and point of departure – and (...)
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  18. 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 psychologically indirect on (...)
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  19. Artificial Neural Network for Predicting Workplace Absenteeism.Raghad Adnan Abu Hassanein, Saja Ahmed Al-Qassas, Fatima Naji Abu Tir & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 4 (9):62-67.
    Associations can grow, succeed, and sustain if their employees are committed. The main assets of an association are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, Associations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or (...)
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  20. Prediction, Authority, and Entitlement in Shared Activity.Abraham Sesshu Roth - 2014 - Noûs 48 (4):626-652.
    Shared activity is often simply willed into existence by individuals. This poses a problem. Philosophical reflection suggests that shared activity involves a distinctive, interlocking structure of intentions. But it is not obvious how one can form the intention necessary for shared activity without settling what fellow participants will do and thereby compromising their agency and autonomy. One response to this problem suggests that an individual can have the requisite intention if she makes the appropriate predictions about fellow participants. I argue (...)
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  21. What? Now. Predictive Coding and Enculturation.Richard Menary - 2015 - In Thomas Metzinger & Jennifer M. Windt (eds.), Open Mind. M.I.T. Press.
    Regina Fabry has proposed an intriguing marriage of enculturated cognition and predictive processing. I raise some questions for whether this marriage will work and warn against expecting too much from the predictive processing framework. Furthermore I argue that the predictive processes at a sub-personal level cannot be driving the innovations at a social level that lead to enculturated cognitive systems, like those explored in my target paper.
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  22. Assertion, Knowledge and Predictions.Matthew A. Benton - 2012 - Analysis 72 (1):102-105.
    John N. Williams (1994) and Matthew Weiner (2005) invoke predictions in order to undermine the normative relevance of knowledge for assertions; in particular, Weiner argues, predictions are important counterexamples to the Knowledge Account of Assertion (KAA). I argue here that they are not true counterexamples at all, a point that can be agreed upon even by those who reject KAA.
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  23. Experience and Prediction: An Analysis of the Foundations and the Structure of Knowledge.Hans Reichenbach - 1938 - University of Chicago Press.
    First published in 1949 expressly to introduce logical positivism to English speakers. Reichenbach, with Rudolph Carnap, founded logical positivism, a form of epistemofogy that privileged scientific over metaphysical truths.
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  24. 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, the hierarchical PP (...)
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  25.  92
    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 and (...)
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  26. 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 penetration. §III develops (...)
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  27. Predictive Brains: Forethought and the Levels of Explanation.Giuseppe Boccignone & Roberto Cordeschi - 2012 - Frontiers in Psychology 3.
    Is any unified theory of brain function possible? Following a line of thought dat- ing back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gain- ing the “Grand Unified Theory of the Mind”—or “painting the big picture,” as Edelman (2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted with experimental (...)
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  28. 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 a (...)
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  29. Modularity and the Predictive Mind.Zoe Drayson - 2017 - T. Metzinger and W. Weise, (Eds), Philosophy and Predictive Processing.
    Modular approaches to the architecture of the mind claim that some mental mechanisms, such as sensory input processes, operate in special-purpose subsystems that are functionally independent from the rest of the mind. This assumption of modularity seems to be in tension with recent claims that the mind has a predictive architecture. Predictive approaches propose that both sensory processing and higher-level processing are part of the same Bayesian information-processing hierarchy, with no clear boundary between perception and cognition. Furthermore, it is not (...)
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  30. Explanatory Pluralism: An Unrewarding Prediction Error for Free Energy Theorists.Matteo Colombo & Cory Wright - 2017 - Brain and Cognition 112:3–12.
    Courtesy of its free energy formulation, the hierarchical predictive processing theory of the brain (PTB) is often claimed to be a grand unifying theory. To test this claim, we examine a central case: activity of mesocorticolimbic dopaminergic (DA) systems. After reviewing the three most prominent hypotheses of DA activity—the anhedonia, incentive salience, and reward prediction error hypotheses—we conclude that the evidence currently vindicates explanatory pluralism. This vindication implies that the grand unifying claims of advocates of PTB are unwarranted. More (...)
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  31. 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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  32. The Body as Laboratory: Prediction-Error Minimization, Embodiment, and Representation.Christopher Burr & Max Jones - 2016 - Philosophical Psychology 29 (4):586-600.
    In his paper, Jakob Hohwy outlines a theory of the brain as an organ for prediction-error minimization, which he claims has the potential to profoundly alter our understanding of mind and cognition. One manner in which our understanding of the mind is altered, according to PEM, stems from the neurocentric conception of the mind that falls out of the framework, which portrays the mind as “inferentially-secluded” from its environment. This in turn leads Hohwy to reject certain theses of embodied (...)
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  33. Parkinson’s Disease Prediction Using Artificial Neural Network.Ramzi M. Sadek, Salah A. Mohammed, Abdul Rahman K. Abunbehan, Abdul Karim H. Abdul Ghattas, Majed R. Badawi, Mohamed N. Mortaja, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (1):1-8.
    Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying (...)
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  34. 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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  35. Neural Network Approach to Predict Forest Fires Using Meteorological Data.Mutasim Mahmoud Al-Kahlout, Ahmed Mahmoud Abu Ghaly, Donia Zaher Mudawah & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 4 (9):68-72.
    Forest fires are a major environmental issue, creating economical and ecological damage while endangering human lives. Fast detection is a key element for controlling such phenomenon. To achieve this, one alternative is to use automatic tools based on local sensors, such as provided by meteorological stations. In effect, meteorological conditions (e.g. temperature, wind) are known to influence forest fires and several fire indexes, such as the forest Fire Weather Index (FWI), use such data. In this work, we explore a Just (...)
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  36. 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 method is (...)
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  37.  68
    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 achieve explanation (...)
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  38. 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 described here (...)
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  39. Disgust Sensitivity Predicts Intuitive Disapproval of Gays.Yoel Inbar, David A. Pizarro, Joshua Knobe & Paul Bloom - 2009 - Emotion 9 (3): 435– 43.
    Two studies demonstrate that a dispositional proneness to disgust (“disgust sensitivity”) is associated with intuitive disapproval of gay people. Study 1 was based on previous research showing that people are more likely to describe a behavior as intentional when they see it as morally wrong (see Knobe, 2006, for a review). As predicted, the more disgust sensitive participants were, the more likely they were to describe an agent whose behavior had the side effect of causing gay men to kiss in (...)
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  40. Do Predictive Brain Implants Threaten Patient's Autonomy or Authenticity?Eldar Sarajlic - 2015 - American Journal of Bioethics Neuroscience 6 (4):30-32.
    The development of predictive brain implant (PBI) technology that is able to forecast specific neuronal events and advise and/or automatically administer appropriate therapy for diseases of the brain raises a number of ethical issues. Provided that this technology satisfies basic safety and functionality conditions, one of the most pressing questions to address is its relation to the autonomy of patients. As Frederic Gilbert in his article asks, if autonomy implies a certain idea of freedom, or self-government, how can an individual (...)
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  41. Predicting Liver Patients Using Artificial Neural Network.Musleh M. Musleh, Eman Alajrami, Ahmed J. Khalil, Bassem S. Abu-Nasser, Alaa M. Barhoom & S. S. Abu Naser - 2019 - International Journal of Academic Information Systems Research (IJAISR) 3 (10):1-11.
    Liver diagnosis at an early stage is essential for enhanced handling. Precise classification is required for automatic recognition of disease from data samples (utilizing data mining for classification of liver patients from healthy ones). In this study, an artificial neural network model was designed and developed using JustNN Tool for predicting weather a person is a liver patient or not based on a dataset for liver patients. The main factors for input variables are: Age, Gender, Total Bilirubin, Direct Bilirubin, Alkphos (...)
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  42. 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 Perceptron Topology was (...)
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  43.  76
    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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  44. Cognitive Penetration and Predictive Coding: A Commentary on Lupyan.Fiona Macpherson - 2015 - Review of Philosophy and Psychology 6 (4):571-584.
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  45.  67
    Knowledge Attributions and Behavioral Predictions.John Turri - 2017 - Cognitive Science:2253-2261.
    Recent work has shown that knowledge attributions affect how people think others should behave, more so than belief attributions do. This paper reports two experiments providing evidence that knowledge attributions also affect behavioral predictions more strongly than belief attributions do, and knowledge attributions facilitate faster behavioral predictions than belief attributions do. Thus, knowledge attributions play multiple critical roles in social cognition, guiding judgments about how people should and will behave.
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  46. Prediction in Social Science - The Case of Research on the Human Resource Management-Organisational Performance Link.SteveAnthony FleetwoodHesketh - 2006 - Journal of Critical Realism 5 (2):228-250.
    _ Source: _Volume 5, Issue 2, pp 228 - 250 Despite inroads made by critical realism against the ‘scientific method’ in social science, the latter remains strong in subject-areas like human resource management. One argument for the alleged superiority of the scientific method lies in the taken-for-granted belief that it alone can formulate empirically testable predictions. Many of those who employ the scientific method are, however, confused about the way they understand and practice prediction. This paper takes as a (...)
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  47. 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 how (...)
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  48. Stereotypes, Theory of Mind, and the Action–Prediction Hierarchy.Evan Westra - 2019 - Synthese 196 (7):2821-2846.
    Both mindreading and stereotyping are forms of social cognition that play a pervasive role in our everyday lives, yet too little attention has been paid to the question of how these two processes are related. This paper offers a theory of the influence of stereotyping on mental-state attribution that draws on hierarchical predictive coding accounts of action prediction. It is argued that the key to understanding the relation between stereotyping and mindreading lies in the fact that stereotypes centrally involve (...)
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  49. Risk, Predictability and Biomedical Neo-Pragmatism.Olaf Dammann - 2009 - Acta Paediatrica 98:1093–5.
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  50.  74
    Embodied Decisions and the Predictive Brain.Christopher Burr - 2017 - Philosophy and Predictive Processing.
    A cognitivist account of decision-making views choice behaviour as a serial process of deliberation and commitment, which is separate from perception and action. By contrast, recent work in embodied decision-making has argued that this account is incompatible with emerging neurophysiological data. We argue that this account has significant overlap with an embodied account of predictive processing, and that both can offer mutual development for the other. However, more importantly, by demonstrating this close connection we uncover an alternative perspective on the (...)
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