Results for 'prediction'

285 found
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  1. 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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  2.  62
    Causation, Prediction, and Search.Peter Spirtes, Clark Glymour, Scheines N. & Richard - 2000 - Mit Press: Cambridge.
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  3.  92
    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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  4. Experience and Prediction.Hans Reichenbach - 1938 - University of Chicago Press.
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  5. 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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  6. 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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  7. 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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  8. 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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  9. 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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  10.  90
    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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  11. 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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  12. 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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  13.  39
    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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  14. 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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  15.  95
    Enhancing the Prediction of Emotionally Intelligent Behavior: The PAT Integrated Framework Involving Trait EI, Ability EI, and Emotion Information Processing.Ashley Vesely Maillefer, Shagini Udayar & Marina Fiori - 2018 - Frontiers in Psychology 9.
    Emotional Intelligence (EI) has been conceptualized in the literature either as a dispositional tendency, in line with a personality trait (trait EI; Petrides and Furnham, 2001), or as an ability, moderately correlated with general intelligence (ability EI; Mayer and Salovey, 1997). Surprisingly, there have been few empirical attempts conceptualizing how the different EI approaches should be related to each other. However, understanding how the different approaches of EI may be interwoven and/or complementary is of primary importance for clarifying the conceptualization (...)
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  16. 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 of DCOP (...)
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  17. 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 topological (...)
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  18. Prediction in General Relativity.C. McCoy - 2017 - Synthese 194 (2):491-509.
    Several authors have claimed that prediction is essentially impossible in the general theory of relativity, the case being particularly strong, it is said, when one fully considers the epistemic predicament of the observer. Each of these claims rests on the support of an underdetermination argument and a particular interpretation of the concept of prediction. I argue that these underdetermination arguments fail and depend on an implausible explication of prediction in the theory. The technical results adduced in these (...)
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  19. The Prediction of Future Behavior: The Empty Promises of Expert Clinical and Actuarial Testimony.Andrés Páez - 2016 - Teoria Jurídica Contemporânea 1 (1):75-101.
    Testimony about the future dangerousness of a person has become a central staple of many judicial processes. In settings such as bail, sentencing, and parole decisions, in rulings about the civil confinement of the mentally ill, and in custody decisions in a context of domestic violence, the assessment of a person’s propensity towards physical or sexual violence is regarded as a deciding factor. These assessments can be based on two forms of expert testimony: actuarial or clinical. The purpose of this (...)
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  20. State of the Field: Why Novel Prediction Matters.Heather Douglas & P. D. Magnus - 2013 - Studies in History and Philosophy of Science Part A 44 (4):580-589.
    There is considerable disagreement about the epistemic value of novel predictive success, i.e. when a scientist predicts an unexpected phenomenon, experiments are conducted, and the prediction proves to be accurate. We survey the field on this question, noting both fully articulated views such as weak and strong predictivism, and more nascent views, such as pluralist reasons for the instrumental value of prediction. By examining the various reasons offered for the value of prediction across a range of inferential (...)
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  21.  52
    Prediction, History and Political Science.Robert Northcott - forthcoming - In Harold Kincaid & Jeroen Van Bouwel (eds.), Oxford Handbook of Philosophy of Political Science. Oxford University Press.
    To succeed, political science usually requires either prediction or contextual historical work. Both of these methods favor explanations that are narrow-scope, applying to only one or a few cases. Because of the difficulty of prediction, the main focus of political science should often be contextual historical work. These epistemological conclusions follow from the ubiquity of causal fragility, under-determination, and noise. They tell against several practices that are widespread in the discipline: wide-scope retrospective testing, such as much large-n statistical (...)
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  22. 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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  23. Against the Epistemic Value of Prediction Over Accommodation.Robin Collins - 1994 - Noûs 28 (2):210-224.
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  24. Motor Experience Interacts with Effector Information During Action Prediction.Lincoln Colling, William Thompson & John Sutton - 2013 - Proceedings of the 35th Annual Conference of the Cognitive Science Society:2082-2087.
    Recent theory suggests that action prediction relies of a motor emulation mechanism that works by mapping observed actions onto the observer action system so that predictions can be generated using that same predictive mechanisms that underlie action control. This suggests that action prediction may be more accurate when there is a more direct mapping between the stimulus and the observer. We tested this hypothesis by comparing prediction accuracy for two stimulus types. A mannequin stimulus which contained information (...)
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  25.  33
    MPG Prediction Using Artificial Neural Network.Yara Ibrahim Al Barsh, Maram Khaled Duhair, Hassan Jassim Ismail, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2020 - International Journal of Academic Information Systems Research (IJAISR) 4 (11):7-16.
    Abstract: During the course of this research, imposing the training of an artificial neural network to predicate the MPG rate for present thru forthcoming automobiles in the foremost relatively accurate evaluation for the approximated number which foresight the actual number to help through later design and manufacturing of later automobile, by training the ANN to accustom to the relationship between the skewing of each later stated attributes, the set of mathematical combination of the sequences that could be excavate the Miles (...)
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  26. Forgery: Prediction's Vile Twin.Joachim L. Dagg - 2003 - Science 302:783-784.
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  27. Prediction in Epidemiology and Medicine.Jonathan Fuller, Alex Broadbent & Luis J. Flores - 2015 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences.
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  28. "Deliberation and Prediction: It's Complicated".Vavova Katia - 2016 - Episteme 13 (4):529-538.
    Alan Hájek launches a formidable attack on the idea that deliberation crowds out prediction – that when we are deliberating about what to do, we cannot rationally accommodate evidence about what we are likely to do. Although Hájek rightly diagnoses the problems with some of the arguments for the view, his treatment falls short in crucial ways. In particular, he fails to consider the most plausible version of the view, the best argument for it, and why anyone would ever (...)
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  29.  24
    The Unvirtuous Prediction of the Pessimistic Induction.Seungbae Park - 2021 - Filosofija. Sociologija 32 (3):TBD.
    The pessimist predicts that future scientific theories will replace present scientific theories. However, she does not specify when the predicted events will take place, so we do not have the opportunity to blame her for having made a false prediction, although we might have the chance to praise her for having made a true prediction. Her prediction contrasts with the astronomer’s prediction. The astronomer specifies when the next solar eclipse will happen, so we have both the (...)
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  30.  53
    Breast Cancer Prediction Using JNN.Mohammed Abdul Hay Abu Bakr, Haitham Maher Al-Attar, Nader Kamal Mahra & Samy S. Abu-Naser - 2020 - International Journal of Academic Information Systems Research (IJAISR) 4 (10):1-8.
    Abstract- Breast Cancer is mostly identified amongst women and is a main reason for increasing the rate of mortality amongst women. Diagnosis of breast cancer takes time and due to the importance of the topic, it is necessary to develop a system that can automatically diagnose breast cancer in its early stages. Many Machine Learning Algorithms have been used for the detection breast cancer. The Wisconsin Breast Cancer Dataset has been used which contains 699 samples and 10 features. The paper (...)
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  31. 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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  32. Delusions: Between Phenomenology and Prediction.Przemysław Nowakowski - 2014 - Avant: Trends in Interdisciplinary Studies (3/2014):11-16.
    One of the leading and central figures in research on delusions, Max Coltheart, presents and summarises his heretofore work in a short text. Miyazono and Bortolotti present an interesting argument aimed at the charges against the doxastic concept of delusions. Adams, Brown and Friston showcase a predictive-Bayesian concept of delusions. Young criticizes the current changes in the two-factor account of delusions and argues that the role of experience should not be dismissed within it. Kapusta presents an interesting, phenomenological approach to (...)
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  33.  29
    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 (...)
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  34.  81
    Books’ Rating Prediction Using Just Neural Network.Alaa Mazen Maghari, Iman Ali Al-Najjar, Said Jamil Al-Laqtah & Samy S. Abu-Naser - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (10):17-22.
    Abstract: The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating of (...)
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  35. Blood Donation Prediction Using Artificial Neural Network.Eman Alajrami, Bassem S. Abu-Nasser, Ahmed J. Khalil, Musleh M. Musleh, Alaa M. Barhoom & S. S. Abu Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 3 (10):1-7.
    The aim of this research is to study the performance of JustNN environment that have not been previously examined to care of this blood donation problem forecasting. An Artificial Neural Network model was built to understand if performance is considerably enhanced via JustNN tool or not. The inspiration for this study is that blood request is steadily growing day by day due to the need of transfusions of blood because of surgeries, accidents, diseases etc. Accurate forecast of the number of (...)
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  36.  87
    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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  37.  20
    Low Birth Weight Prediction Using JNN.Osama Salah El-Din Al-Madhoun, Afnan Omar Abu Hasira, Soha Ahmed Hegazy & Samy S. Abu-Naser - 2020 - International Journal of Academic Health and Medical Research (IJAHMR) 4 (11):8-14.
    Abstract: 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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  38.  29
    More Connection and Less Prediction Please: Applying a Relationship Focus in Protected Area Planning and Management.Robert G. Dvorak & Jeffrey Brooks - 2013 - Journal of Park and Recreation Administration 31 (3):5-22.
    Integrating the concept of place meanings into protected area management has been difficult. Across a diverse body of social science literature, challenges in the conceptualization and application of place meanings continue to exist. However, focusing on relationships in the context of participatory planning and management allows protected area managers to bring place meanings into professional judgment and practice. This paper builds on work that has outlined objectives and recommendations for bringing place meanings, relationships, and lived experiences to the forefront of (...)
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  39.  86
    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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  40.  22
    ANN for English Alphabet Prediction.Hamza H. Heriz, Sharief M. Salah, Mohammad Abu Abdu & Qassas Randa - 2016 - International Journal of Academic Pedagogical Research (IJAPR) 11 (2):8-13.
    Abstract: In this paper an Artificial Neural Network (ANN) model, for predicting the Letters from twenty dissimilar fonts for each letter. The character images were, initially, based on twenty dissimilar fonts and each letter inside these twenty fonts was arbitrarily distorted to yield a file of 20,000 distinctive stimuli. Every stimulus was transformed into 16 simple numerical attributes (arithmetical moments and edge amounts) which were then ascended to be suitable into a range of numeral values from 0 to 15. We (...)
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  41.  25
    Tensile Properties Prediction of Natural Fibers Metal Laminates FMLs.Hicham Laribou & Abdelali El-Bakari - 2018 - International Journal of Academic Engineering Research (IJAER) 2 (7):1-5.
    Abstract: Fiber–metal laminates (FMLs) are high-performance hybrid structures based on alternating stacked arrangements of fiber-reinforced plastic (FRP) plies and metal alloy layers FMLs have benefits over both aluminum and fiber reinforced composites. In this work, a jute fibers and effect of fiberFE volume fraction on tensile behavior of this novel material is investigated. A numerical simulation method based on finite element modeling (FEM) is used to predict the tensile proprerties of FMLs and compared with a result obtained by a modified (...)
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  42.  31
    SBRCTs Cancers Prediction Using ANN.Remah Massri, Yomna Astl, Hanan Zada, Deyaa Mosa & Qassas Randa - 2016 - International Journal of Academic Engineering Research (IJAER) 2 (11):1-6.
    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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  43.  67
    Antibiotic Susceptibility Prediction Using JNN.Ahmed Jabara Abu Oriban, Shaima Naji Abdel-Al, Nourhan Abdel Moneim Fouda & Samy S. Abu-Naser - 2020 - International Journal of Academic Information Systems Research (IJAISR) 4 (11):1-6.
    Abstract: In this research, an Artificial Neural Network (ANN) model was developed and validated to predict efficiency of antibiotics in treating various bacteria types. Attributes that were taken in account are: organism name, specimen type, and antibiotic name as input and susceptibility as an output. A model based on one input layer, one hidden layer, and one output layer concept topology was developed and trained using a data from Queensland government's website. The evaluation shows that the proposed ANN model using (...)
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  44.  53
    Children’s Reasoning About the Efficiency of Others’ Actions: The Development of Rational Action Prediction.Gökhan Gönül & Markus Paulus - 2021 - Journal of Experimental Child Psychology 204 (105035).
    The relative efficiency of an action is a central criterion in action control and can be used to predict others’ behavior. Yet, it is unclear when the ability to predict on and reason about the efficiency of others’ actions develops. In three main and two followup studies, 3- to 6-year-old children (n = 242) were confronted with vignettes in which protagonists could take a short (efficient) path or a long path. Children predicted which path the protagonist would take and why (...)
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  45.  28
    Children’s Reasoning About the Efficiency of Others’ Actions: The Development of Rational Action Prediction.Gökhan Gönül & Markus Paulus - 2021 - Journal of Experimental Child Psychology 105035 (204).
    The relative efficiency of an action is a central criterion in action control and can be used to predict others’ behavior. Yet, it is unclear when the ability to predict on and reason about the efficiency of others’ actions develops. In three main and two follow-up studies, 3- to 6-year-old children (n = 242) were confronted with vignettes in which protagonists could take a short (efficient) path or a long path. Children predicted which path the protagonist would take and why (...)
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  46. Avoiding Reification: Heuristic Effectiveness of Mathematics and the Prediction of the Omega Minus Particle.Michele Ginammi - 2016 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 53:20-27.
    According to Steiner (1998), in contemporary physics new important discoveries are often obtained by means of strategies which rely on purely formal mathematical considerations. In such discoveries, mathematics seems to have a peculiar and controversial role, which apparently cannot be accounted for by means of standard methodological criteria. M. Gell-Mann and Y. Ne׳eman׳s prediction of the Ω− particle is usually considered a typical example of application of this kind of strategy. According to Bangu (2008), this prediction is apparently (...)
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  47. ANN for Parkinson’s Disease Prediction.Salah Sadek, Abdul Mohammed, Abdul Karim Abunbehan, Majed Abdul Ghattas & Mohamed Badawi - 2020 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (1):1-7.
    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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  48. A Meta-Doomsday Argument: Uncertainty About the Validity of the Probabilistic Prediction of the End of the World.Alexey Turchin - manuscript
    Abstract: Four main forms of Doomsday Argument (DA) exist—Gott’s DA, Carter’s DA, Grace’s DA and Universal DA. All four forms use different probabilistic logic to predict that the end of the human civilization will happen unexpectedly soon based on our early location in human history. There are hundreds of publications about the validity of the Doomsday argument. Most of the attempts to disprove the Doomsday Argument have some weak points. As a result, we are uncertain about the validity of DA (...)
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  49.  22
    Ethical Implications of Alzheimer’s Disease Prediction in Asymptomatic Individuals Through Artificial Intelligence.Frank Ursin, Cristian Timmermann & Florian Steger - 2021 - Diagnostics 11 (3):440.
    Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put forward (...)
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  50.  33
    ANN Car Mileage Per Gallon Prediction.Jomana Ahmed, Bayan Harb, Bassem S. Abu, Mohsen Afana & Rafiq Madhoun - 2017 - International Journal of Advanced Science and Technology 124:51-58.
    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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