Results for 'neural binding'

746 found
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  1. Neuroelectrical Approaches to Binding Problems.Mostyn W. Jones - 2016 - Journal of Mind and Behavior 2 (37).
    How do separate brain processes bind to form unified, conscious percepts? This is the perceptual binding problem, which straddles neuroscience and psychology. In fact, two problems exist here: (1) the easy problem of how neural processes are unified, and (2) the hard problem of how this yields unified perceptual consciousness. Binding theories face familiar troubles with (1) and they do not come to grips with (2). This paper argues that neuroelectrical (electromagnetic-field) approaches may help with both problems. (...)
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  2. Neural Correlate of Consciousness in a Single Electron: Radical Answer to “Quantum Theories of Consciousness”.Victor Argonov - 2012 - Neuroquantology 12 (2):276-285.
    We argue that human consciousness may be a property of single electron in the brain. We suppose that each electron in the universe has at least primitive consciousness. Each electron subjectively “observes” its quantum dynamics (energy, momentum, “shape” of wave function) in the form of sensations and other mental phenomena. However, some electrons in neural cells have complex “human” consciousnesses due to complex quantum dynamics in complex organic environment. We discuss neurophysiological and physical aspects of this hypothesis and show (...)
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  3. Mounting Evidence That Minds Are Neural EM Fields Interacting with Brains.Mostyn W. Jones - 2017 - Journal of Consciousness Studies 24 (1-2):159-183.
    Evidence that minds are neural electromagnetic fields comes from research into how separate brain activities bind to form unified percepts and unified minds. Explanations of binding using synchrony, attention, and convergence are all problematic. But the unity of EM fields explains binding without these problems. These unified fields neatly explain correlations and divergences between synchrony, attention, convergence, and unified minds. The simplest explanation for the unity of both minds and fields is that minds are fields. Treating minds (...)
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  4. Electromagnetic-Field Theories of Mind.Mostyn W. Jones - 2013 - Journal of Consciousness Studies 20 (11-12):124-149.
    Neuroscience investigates how neuronal processing circuits work, but it has problems explaining experiences this way. For example, it hasn’t explained how colour and shape circuits bind together in visual processing, nor why colours and other qualia are experienced so differently yet processed by circuits so similarly, nor how to get from processing circuits to pictorial images spread across inner space. Some theorists turn from these circuits to their electromagnetic fields to deal with such difficulties concerning the mind’s qualia, unity, privacy, (...)
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  5. Growing Evidence That Perceptual Qualia Are Neuroelectrical Not Computational.Mostyn W. Jones - 2019 - Journal of Consciousness Studies 26 (5-6):89-116.
    Computational neuroscience attributes coloured areas and other perceptual qualia to calculations that are realizable in multiple cellular forms. This faces serious issues in explaining how the various qualia arise and how they bind to form overall perceptions. Qualia may instead be neuroelectrical. Growing evidence indicates that perceptions correlate with neuroelectrical activity spotted by locally activated EEGs, the different qualia correlate with the different electrochemistries of unique detector cells, a unified neural-electromagnetic field binds this activity to form overall perceptions, and (...)
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  6.  17
    Mysteries of Visual Experience.Jerome Feldman - manuscript
    Science is a crowning glory of the human spirit and its applications remain our best hope for social progress. However, there are limitations to existing science and perhaps to any science. The general mind-body problem is known to be currently intractable and mysterious (8). This is one of many deep problems that are generally agreed to be beyond the present purview of Science, including many quantum phenomena, etc. However, all of these famous unsolved problems are either remote from everyday experience (...)
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  7. Necessary Ingredients of Consciousness: Integration of Psychophysical, Neurophysiological, and Consciousness Research for the Red-Green Channel.Ram Lakhan Pandey Vimal - 2009 - Vision Research Institute: Living Vision and Consciousness Research 1 (1).
    A general definition of consciousness is: ‘consciousness is a mental aspect of a system or a process, which is a conscious experience, a conscious function, or both depending on the context’, where the term context refers to metaphysical views, constraints, specific aims, and so on. One of the aspects of visual consciousness is the visual subjective experience (SE) or the first person experience that occurs/emerges in the visual neural-network of thalamocortical system (which includes dorsal and ventral visual pathways and (...)
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  8.  61
    Neural Organoids and the Precautionary Principle.Jonathan Birch & Heather Browning - 2021 - American Journal of Bioethics 21 (1):56-58.
    Human neural organoid research is advancing rapidly. As Greely notes in the target article, this progress presents an “onrushing ethical dilemma.” We can’t rule out the possibility that suff...
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  9. The Neural Correlates of Consciousness.Jorge Morales & Hakwan Lau - 2020 - In Uriah Kriegel (ed.), The Oxford Handbook of the Philosophy of Consciousness. Oxford University Press. pp. 233-260.
    In this chapter, we discuss a selection of current views of the neural correlates of consciousness (NCC). We focus on the different predictions they make, in particular with respect to the role of prefrontal cortex (PFC) during visual experiences, which is an area of critical interest and some source of contention. Our discussion of these views focuses on the level of functional anatomy, rather than at the neuronal circuitry level. We take this approach because we currently understand more about (...)
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  10. Binding and its Consequences.Christopher J. G. Meacham - 2010 - Philosophical Studies 149 (1):49-71.
    In “Bayesianism, Infinite Decisions, and Binding”, Arntzenius et al. (Mind 113:251–283, 2004 ) present cases in which agents who cannot bind themselves are driven by standard decision theory to choose sequences of actions with disastrous consequences. They defend standard decision theory by arguing that if a decision rule leads agents to disaster only when they cannot bind themselves, this should not be taken to be a mark against the decision rule. I show that this claim has surprising implications for (...)
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  11. Neural Correlates of Moral Sensitivity and Moral Judgment Associated with Brain Circuitries of Selfhood: A Meta-Analysis.Hyemin Han - 2017 - Journal of Moral Education 46 (2):97-113.
    The present study meta-analyzed 45 experiments with 959 subjects and 463 activation foci reported in 43 published articles that investigated the neural mechanism of moral functions by comparing neural activity between the moral-task and non-moral-task conditions with the Activation Likelihood Estimate method. The present study examined the common activation foci of morality-related task conditions. In addition, this study compared the neural correlates of moral sensibility with the neural correlates of moral judgment, which are the two functional (...)
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  12. Artificial Neural Network for Forecasting Car Mileage Per Gallon in the City.Mohsen Afana, Jomana Ahmed, Bayan Harb, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2018 - International Journal of Advanced Science and Technology 124:51-59.
    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: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with (...)
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  13. 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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  14. 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 (...)
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  15. Binding Bound Variables in Epistemic Contexts.Brian Rabern - 2021 - Inquiry: An Interdisciplinary Journal of Philosophy 64 (5-6):533-563.
    ABSTRACT Quine insisted that the satisfaction of an open modalised formula by an object depends on how that object is described. Kripke's ‘objectual’ interpretation of quantified modal logic, whereby variables are rigid, is commonly thought to avoid these Quinean worries. Yet there remain residual Quinean worries for epistemic modality. Theorists have recently been toying with assignment-shifting treatments of epistemic contexts. On such views an epistemic operator ends up binding all the variables in its scope. One might worry that this (...)
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  16. 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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  17. 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 (...)
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  18. Oppressive Double Binds.Sukaina Hirji - 2021 - Ethics 131 (4):643-669.
    I give an account of the structure of “oppressive double binds,” the double binds that exist in virtue of oppression. I explain how these double binds both are a product of and serve to reinforce o...
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  19. 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 (...)
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  20. The Neural Correlates of Consciousness: New Experimental Approaches Needed?Jakob Hohwy - 2009 - Consciousness and Cognition 18 (2):428-438.
    It appears that consciousness science is progressing soundly, in particular in its search for the neural correlates of consciousness. There are two main approaches to this search, one is content-based (focusing on the contrast between conscious perception of, e.g., faces vs. houses), the other is state-based (focusing on overall conscious states, e.g., the contrast between dreamless sleep vs. the awake state). Methodological and conceptual considerations of a number of concrete studies show that both approaches are problematic: the content-based approach (...)
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  21. Email Classification Using Artificial Neural Network.Ahmed Alghoul, Sara Al Ajrami, Ghada Al Jarousha, Ghayda Harb & Samy S. Abu-Naser - 2018 - International Journal of Academic Engineering Research (IJAER) 2 (11):8-14.
    Abstract: In recent years email has become one of the fastest and most economical means of communication. However increase of email users has resulted in the dramatic increase of spam emails during the past few years. Data mining -classification algorithms are used to categorize the email as spam or non-spam. Numerous email spam messages are marketable in nature but might similarly encompass camouflaged links that seem to be for acquainted websites but actually lead to phishing web sites or sites that (...)
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  22.  38
    Binding Specificity and Causal Selection in Drug Design.Oliver M. Lean - 2020 - Philosophy of Science 87 (1):70-90.
    Binding specificity is a centrally important concept in molecular biology, yet it has received little philosophical attention. Here I aim to remedy this by analyzing binding specificity as a causal property. I focus on the concept’s role in drug design, where it is highly prized and hence directly studied. From a causal perspective, understanding why binding specificity is a valuable property of drugs contributes to an understanding of causal selection—of how and why scientists distinguish between causes, not (...)
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  23. Neural Synchrony and the Causal Efficacy of Consciousness.David Yates - 2020 - Topoi 39 (5):1057-1072.
    The purpose of this paper is to address a well-known dilemma for physicalism. If mental properties are type identical to physical properties, then their causal efficacy is secure, but at the cost of ruling out mentality in creatures very different to ourselves. On the other hand, if mental properties are multiply realizable, then all kinds of creatures can instantiate them, but then they seem to be causally redundant. The causal exclusion problem depends on the widely held principle that realized properties (...)
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  24. Glass Classification Using Artificial Neural Network.Mohmmad Jamal El-Khatib, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Pedagogical Research (IJAPR) 3 (23):25-31.
    As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass classification (...)
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  25.  78
    The Neural and Cognitive Mechanisms of Knowledge Attribution: An EEG Study.Adam Michael Bricker - 2020 - Cognition 203:104412.
    Despite the ubiquity of knowledge attribution in human social cognition, its associated neural and cognitive mechanisms are poorly documented. A wealth of converging evidence in cognitive neuroscience has identified independent perspective-taking and inhibitory processes for belief attribution, but the extent to which these processes are shared by knowledge attribution isn't presently understood. Here, we present the findings of an EEG study designed to directly address this shortcoming. These findings suggest that belief attribution is not a component process in knowledge (...)
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  26. Variable Binding Term Operators.John Corcoran, William Hatcher & John Herring - 1972 - Zeitschrift fur mathematische Logik und Grundlagen der Mathematik 18 (12):177-182.
    Chapin reviewed this 1972 ZEITSCHRIFT paper that proves the completeness theorem for the logic of variable-binding-term operators created by Corcoran and his student John Herring in the 1971 LOGIQUE ET ANALYSE paper in which the theorem was conjectured. This leveraging proof extends completeness of ordinary first-order logic to the extension with vbtos. Newton da Costa independently proved the same theorem about the same time using a Henkin-type proof. This 1972 paper builds on the 1971 “Notes on a Semantic Analysis (...)
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  27. Artificial Neural Network for Predicting Diabetes Using JNN.Hussam Hatem Harz, Ahmed Osama Rafi, Musbah Osama Hijazi & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 4 (10):14-22.
    Abstract 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). Therefore, in this paper, we used artificial (...) networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 987.3% . (shrink)
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  28. Beyond the Neural Correlates of Consciousness.Uriah Kriegel - 2020 - In U. Kriegel (ed.), Oxford Handbook of the Philosophy of Consciousness. Oxford University Press. pp. 261-276.
    The centerpiece of the scientific study of consciousness is the search for the neural correlates of consciousness. Yet science is typically interested not only in discovering correlations, but also – and more deeply – in explaining them. When faced with a correlation between two phenomena in nature, we typically want to know why they correlate. The purpose of this chapter is twofold. The first half attempts to lay out the various possible explanations of the correlation between consciousness and its (...)
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  29. Neural Correlates of Visuospatial Consciousness in 3D Default Space: Insights From Contralateral Neglect Syndrome.Ravinder Jerath & Molly W. Crawford - 2014 - Consciousness and Cognition 28:81-93.
    One of the most compelling questions still unanswered in neuroscience is how consciousness arises. In this article, we examine visual processing, the parietal lobe, and contralateral neglect syndrome as a window into consciousness and how the brain functions as the mind and we introduce a mechanism for the processing of visual information and its role in consciousness. We propose that consciousness arises from integration of information from throughout the body and brain by the thalamus and that the thalamus reimages visual (...)
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  30. Artificial Neural Network for Lung Cancer Detection.Ola Mohammed Abu Kweik, Mohammed Atta Abu Hamid, Samer Osama Sheqlieh, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 4 (11):1-7.
    Abstract: The effectiveness of cancer prediction system helps the people to know their cancer risk with low cost and it also helps the people to take the appropriate decision based on their cancer risk status. The dataset is collected from the data world website. In this paper, we proposed an Artificial Neural Network for detecting whether lung cancer is found or not in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, (...)
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  31. Developing Artificial Neural Network for Predicting Mobile Phone Price Range.Ibrahim M. Nasser, Mohammed Al-Shawwa & Samy S. Abu-Naser - 2019 - International Journal of Academic Information Systems Research (IJAISR) 3 (2):1-6.
    In this paper an Artificial Neural Network (ANN) model, was developed and tested for predicting the price range of a mobile phone. We used a dataset that contains mobile phones information, and there was a number of factors that influence the classification of mobile phone price. Factors as battery power, CPU clock speed, has dual sim support or not, Front Camera mega pixels, has 4G or not, has Wi-Fi or not, etc…. 20 attributes were used as input variables for (...)
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  32. Artificial Neural Network for Mushroom Prediction.Kamel Jamal Dawood, Mohamed Hussam Zaqout, Riad Mohammed Salem & Samy S. Abu-Naser - 2020 - International Journal of Academic Information Systems Research (IJAISR) 4 (10):9-17.
    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 dataset. In this paper, Multi-Layer ANN model was used to train and test the mushroom dataset to predict whether mushroom is edible or poisonous. The Mushrooms dataset was prepared for training, 8124 instances were used for the training. JNN tool was used for training and validating (...)
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  33.  62
    The Neural Correlates of Visual Imagery: A Co-Ordinate-Based Meta-Analysis.C. Winlove, F. Milton, J. Ranson, J. Fulford, M. MacKisack, Fiona Macpherson & A. Zeman - 2018 - Cortex 105 (August 2018):4-25.
    Visual imagery is a form of sensory imagination, involving subjective experiences typically described as similar to perception, but which occur in the absence of corresponding external stimuli. We used the Activation Likelihood Estimation algorithm (ALE) to identify regions consistently activated by visual imagery across 40 neuroimaging studies, the first such meta-analysis. We also employed a recently developed multi-modal parcellation of the human brain to attribute stereotactic co-ordinates to one of 180 anatomical regions, the first time this approach has been combined (...)
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  34.  58
    Neural phase: a new problem for the modal account of epistemic luck.Adam Michael Bricker - 2019 - Synthese (8):1-18.
    One of the most widely recognised intuitions about knowledge is that knowing precludes believing truly as a matter of luck. On Pritchard’s highly influential modal account of epistemic luck, luckily true beliefs are, roughly, those for which there are many close possible worlds in which the same belief formed in the same way is false. My aim is to introduce a new challenge to this account. Starting from the observation—as documented by a number of recent EEG studies—that our capacity to (...)
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  35. Artificial Neural Network for Diagnose Autism Spectrum Disorder.Ibrahim M. Nasser, Mohammed Al-Shawwa & Samy S. Abu-Naser - 2019 - International Journal of Academic Information Systems Research (IJAISR) 3 (2):27-32.
    In this paper an Artificial Neural Network (ANN) model, was developed and tested for diagnosing Autism Spectrum Disorder (ASD). A dataset collected from ASD screening app was used in this paper, it contains ASD tests results based upon questions answers from users. Test data evaluation shows that the ANN model is able to correctly diagnose ASD with 100% accuracy.
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  36. A Proposed Artificial Neural Network for Predicting Movies Rates Category.Ibrahim M. Nasser, Mohammed Al-Shawwa & Samy S. Abu-Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 3 (2):21-25.
    We proposed an Artificial Neural Network (ANN) in this paper for predicting the rate category of movies. A dataset used obtained from UCI repository created for research purposes. Our ANN prediction model was developed and validated; validation results showed that the ANN model is able to 92.19% accurately predict the category of movies’ rate.
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  37. Binding On the Fly: Cross-Sentential Anaphora in Variable— Free Semantics.Anna Szabolcsi - 2003 - In R. Oehrle & J. Kruijff (eds.), Resource Sensitivity, Binding, and Anaphora. Kluwer Academic Publishers. pp. 215--227.
    Combinatory logic (Curry and Feys 1958) is a “variable-free” alternative to the lambda calculus. The two have the same expressive power but build their expressions differently. “Variable-free” semantics is, more precisely, “free of variable binding”: it has no operation like abstraction that turns a free variable into a bound one; it uses combinators—operations on functions—instead. For the general linguistic motivation of this approach, see the works of Steedman, Szabolcsi, and Jacobson, among others. The standard view in linguistics is that (...)
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  38.  35
    Neural Computation of Surface Border Ownership and Relative Surface Depth From Ambiguous Contrast Inputs.Birgitta Dresp-Langley & Stephen Grossberg - 2016 - Frontiers in Psychology 7.
    The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the (...)
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  39. Cultural Influences on the Neural Correlate of Moral Decision Making Processes.Hyemin Han, Gary H. Glover & Changwoo Jeong - 2014 - Behavioural Brain Research 259:215-228.
    This study compares the neural substrate of moral decision making processes between Korean and American participants. By comparison with Americans, Korean participants showed increased activity in the right putamen associated with socio-intuitive processes and right superior frontal gyrus associated with cognitive control processes under a moral-personal condition, and in the right postcentral sulcus associated with mental calculation in familiar contexts under a moral-impersonal condition. On the other hand, American participants showed a significantly higher degree of activity in the bilateral (...)
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  40. Agent-Causal Libertarianism, Statistical Neural Laws and Wild Coincidences.Jason Runyan - 2018 - Synthese 195 (10):4563-4580.
    Agent-causal libertarians maintain we are irreducible agents who, by acting, settle matters that aren’t already settled. This implies that the neural matters underlying the exercise of our agency don’t conform to deterministic laws, but it does not appear to exclude the possibility that they conform to statistical laws. However, Pereboom (Noûs 29:21–45, 1995; Living without free will, Cambridge University Press, Cambridge, 2001; in: Nadelhoffer (ed) The future of punishment, Oxford University Press, New York, 2013) has argued that, if these (...)
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  41. 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 (...)
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  42. The Search for Neural Correlates of Consciousness.Jakob Hohwy - 2007 - Philosophy Compass 2 (3):461–474.
    Most consciousness researchers, almost no matter what their views of the metaphysics of consciousness, can agree that the first step in a science of consciousness is the search for the neural correlate of consciousness (the NCC). The reason for this agreement is that the notion of ‘correlation’ doesn’t by itself commit one to any particular metaphysical view about the relation between (neural) matter and consciousness. For example, some might treat the correlates as causally related, while others might view (...)
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  43. 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 (...)
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  44. Neural and Environmental Modulation of Motivation: What's the Moral Difference?Thomas Douglas - forthcoming - In David Birks & Thomas Douglas (eds.), Treatment for Crime: Philosophical Essays on Neurointerventions in Criminal Justice. Oxford: Oxford University Press.
    Interventions that modify a person’s motivations through chemically or physically influencing the brain seem morally objectionable, at least when they are performed nonconsensually. This chapter raises a puzzle for attempts to explain their objectionability. It first seeks to show that the objectionability of such interventions must be explained at least in part by reference to the sort of mental interference that they involve. It then argues that it is difficult to furnish an explanation of this sort. The difficulty is that (...)
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  45. Evolving Self-Taught Neural Networks: The Baldwin Effect and the Emergence of Intelligence.Nam Le - 2019 - In AISB Annual Convention 2019 -- 10th Symposium on AI & Games.
    The so-called Baldwin Effect generally says how learning, as a form of ontogenetic adaptation, can influence the process of phylogenetic adaptation, or evolution. This idea has also been taken into computation in which evolution and learning are used as computational metaphors, including evolving neural networks. This paper presents a technique called evolving self-taught neural networks – neural networks that can teach themselves without external supervision or reward. The self-taught neural network is intrinsically motivated. Moreover, the self-taught (...)
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  46.  67
    Beyond Cognitive Myopia: A Patchwork Approach to the Concept of Neural Function.Philipp Haueis - 2018 - Synthese 195 (12):5373-5402.
    In this paper, I argue that looking at the concept of neural function through the lens of cognition alone risks cognitive myopia: it leads neuroscientists to focus only on mechanisms with cognitive functions that process behaviorally relevant information when conceptualizing “neural function”. Cognitive myopia tempts researchers to neglect neural mechanisms with noncognitive functions which do not process behaviorally relevant information but maintain and repair neural and other systems of the body. Cognitive myopia similarly affects philosophy of (...)
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  47. 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 (...)
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  48. Scope and Binding.Anna Szabolcsi - 2011 - In von Heusinger, Maienborn & Portner (eds.), Semantics: An International Handbook of Natural Language Meaning, Vol. 2. de Gruyter Mouton.
    The first part of this article (Sections 1–5) focuses on the classical notions of scope and binding and their formal foundations. It argues that once their semantic core is properly understood, it can be implemented in various different ways: with or without movement, with or without variables. The second part (Sections 6–12) takes up the empirical issues that have redrawn the map in the past two decades. It turns out that scope is not a primitive. Existential scope and distributive (...)
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  49.  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 (...)
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  50. Crosscutting Psycho-Neural Taxonomies: The Case of Episodic Memory.Muhammad Ali Khalidi - 2017 - Philosophical Explorations 20 (2):191-208.
    I will begin by proposing a taxonomy of taxonomic positions regarding the mind–brain: localism, globalism, revisionism, and contextualism, and will go on to focus on the last position. Although some versions of contextualism have been defended by various researchers, they largely limit themselves to a version of neural contextualism: different brain regions perform different functions in different neural contexts. I will defend what I call “environmental-etiological contextualism,” according to which the psychological functions carried out by various neural (...)
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