Results for 'Neural Network'

663 found
Order:
  1. 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.
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  2. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  3.  50
    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)
    Download  
     
    Export citation  
     
    Bookmark  
  4. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  5.  13
    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, (...)
    Download  
     
    Export citation  
     
    Bookmark  
  6.  87
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  7.  63
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  8.  77
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  9. Knowledge Bases and Neural Network Synthesis.Todd R. Davies - 1991 - In Hozumi Tanaka (ed.), Artificial Intelligence in the Pacific Rim: Proceedings of the Pacific Rim International Conference on Artificial Intelligence. IOS Press. pp. 717-722.
    We describe and try to motivate our project to build systems using both a knowledge based and a neural network approach. These two approaches are used at different stages in the solution of a problem, instead of using knowledge bases exclusively on some problems, and neural nets exclusively on others. The knowledge base (KB) is defined first in a declarative, symbolic language that is easy to use. It is then compiled into an efficient neural network (...)
    Download  
     
    Export citation  
     
    Bookmark  
  10.  17
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  11.  62
    Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear Quarter Car Active Suspension System.Mustefa Jibril - 2020 - International Research Journal of Modernization in Engineering Technology and Science 2 (3):178-188.
    Recently, active suspension system will become important to the vehicle industries because of its advantages in improving road managing and ride comfort. This paper offers the development of mathematical modelling and design of a neural network control approach. The paper will begin with a mathematical model designing primarily based at the parameters of the active suspension system. A nonlinear three by four-way valve-piston hydraulic actuator became advanced which will make the suspension system under the active condition. Then, the (...)
    Download  
     
    Export citation  
     
    Bookmark  
  12.  98
    Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation Train.Mustefa Jibril - 2020 - International Journal of Advance Research and Innovative Ideas in Education 6 (2):801-807.
    Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as (...)
    Download  
     
    Export citation  
     
    Bookmark  
  13. Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear EMS Magnetic Levitation Train.Mustefa Jibril & Eliyas Alemayehu - 2020 - Report and Opinion Journal 12 (5):21-25.
    Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as (...)
    Download  
     
    Export citation  
     
    Bookmark  
  14. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark   8 citations  
  15.  31
    Classification of Animal Species Using Neural Network.Rand Suhail Abu Al-Araj, Shaima Khalil Abed, Ahmed Nabil Al-Ghoul & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 4 (10):23-31.
    Abstract: Over 1.5 million living animal species have been described—of which around 1 million are insects—but it has been estimated there are over 7 million animal species in total. Animals range in length from 8.5 micrometres to 33.6 metres. In this paper an Artificial Neural Network (ANN) model, was developed and tested to predict animal species. There are a number of features that influence the classification of animal species. Such as the existence of hair/ feather, if the animal (...)
    Download  
     
    Export citation  
     
    Bookmark  
  16.  59
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  17.  20
    Design and Control of Steam Flow in Cement Production Process Using Neural Network Based Controllers.Mustefa Jibril - 2020 - Researcher 12 (5):76-84.
    In this paper a NARMA L2, model reference and neural network predictive controller is utilized in order to control the output flow rate of the steam in furnace by controlling the steam flow valve. The steam flow control system is basically a feedback control system which is mostly used in cement production industries. The design of the system with the proposed controllers is done with Matlab/Simulink toolbox. The system is designed for the actual steam flow output to track (...)
    Download  
     
    Export citation  
     
    Bookmark  
  18.  42
    DC Motor Speed Control with the Presence of Input Disturbance Using Neural Network Based Model Reference and Predictive Controllers.Mustefa Jibril - 2020 - International Research Journal of Modernization in Engineering Technology and Science 2 (4):103-110.
    In this paper we describe a technical system for DC motor speed control. The speed of DC motor is controlled using Neural Network Based Model Reference and Predictive controllers with the use of Matlab/Simulink. The analysis of the DC motor is done with and without input side Torque disturbance input and the simulation results obtained by comparing the desired and actual speed of the DC motor using random reference and sinusoidal speed inputs for the DC motor with Model (...)
    Download  
     
    Export citation  
     
    Bookmark  
  19.  15
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  20.  21
    Detection and Classification of Gender-Type Using Convolution Neural Network.Husam R. Almadhoun - 2021 - International Journal of Academic Engineering Research (IJAER) 4 (12):15-20.
    Deep learning has a vital role in computer vision to discover things. Deep learning techniques, especially convolutional neural networks, are being exploited in identifying and extracting relevant features of a specific set of images. In this research we suggested that it could help in detecting the gender-type of individuals and classifying them using convolutional neural networks, as it achieved superior predictive performance in classifying individuals according to gender, and the experimental results showed that the proposed system works accurately (...)
    Download  
     
    Export citation  
     
    Bookmark  
  21. Networks of Gene Regulation, Neural Development and the Evolution of General Capabilities, Such as Human Empathy.Alfred Gierer - 1998 - Zeitschrift Für Naturforschung C - A Journal of Bioscience 53:716-722.
    A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  22. Dynamic Change of Awareness During Meditation Techniques: Neural and Physiological Correlates.Jerath Ravinder, Vernon A. Barnes, David Dillard-Wright, Shivani Jerath & Brittany Hamilton - 2012 - Frontiers in Human Neuroscience 6:1-5.
    Recent fndings illustrate how changes in consciousness accommodated by neural correlates and plasticity of the brain advance a model of perceptual change as a function of meditative practice. During the mindbody response neural correlates of changing awareness illustrate how the autonomic nervous system shifts from a sympathetic dominant to a parasympathetic dominant state. Expansion of awareness during the practice of meditation techniques can be linked to the Default Mode Network (DMN), a network of brain regions that (...)
    Download  
     
    Export citation  
     
    Bookmark   4 citations  
  23. The Cognitive Gap, Neural Darwinism & Linguistic Dualism —Russell, Husserl, Heidegger & Quine.Hermann G. W. Burchard - 2014 - Open Journal of Philosophy 4 (3):244-264.
    Guided by key insights of the four great philosophers mentioned in the title, here, in review of and expanding on our earlier work (Burchard, 2005, 2011), we present an exposition of the role played by language, & in the broader sense, λογοζ, the Logos, in how the CNS, the brain, is running the human being. Evolution by neural Darwinism has been forcing the linguistic nature of mind, enabling it to overcome & exploit the cognitive gap between an animal and (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  24.  18
    Tumor Classification Using Artificial Neural Networks.Jamal Khamis El-Mahelawi, Jinan Usama Abu-Daqah, Rasha Ibrahim Abu-Latifa, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 4 (11):8-15.
    Abstract: Tumor is a group of diseases that involve abnormal increases in the number of cells, with the potential to invade or spread to other parts of the body. Not all tumors or lumps are cancerous; benign tumors are not classified as being cancer because they do not spread to other parts of the body. There are over 100 different known Tumors that affect humans. Tumors are often described by the body part that they originated in. However, some body parts (...)
    Download  
     
    Export citation  
     
    Bookmark  
  25.  29
    Getting Over Atomism: Functional Decomposition in Complex Neural Systems.Daniel C. Burnston - forthcoming - British Journal for the Philosophy of Science:000-000.
    Functional decomposition is an important goal in the life sciences, and is central to mechanistic explanation and explanatory reduction. A growing literature in philosophy of science, however, has challenged decomposition-based notions of explanation. ‘Holists’ posit that complex systems exhibit context-sensitivity, dynamic interaction, and network dependence, and that these properties undermine decomposition. They then infer from the failure of decomposition to the failure of mechanistic explanation and reduction. I argue that complexity, so construed, is only incompatible with one notion of (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  26. Predicting Whether a Couple is Going to Get Divorced or Not Using Artificial Neural Networks.Ibrahim M. Nasser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (10):49-55.
    In this paper, an artificial neural network (ANN) model was developed and validated to predict whether a couple is going to get divorced or not. Prediction is done based on some questions that the couple answered, answers of those questions were used as the input to the ANN. The model went through multiple learning-validation cycles until it got 100% accuracy.
    Download  
     
    Export citation  
     
    Bookmark  
  27. Geometry for a Brain. Optimal Control in a Network of Adaptive Memristors.Ignazio Licata & Germano Resconi - 2013 - Adv. Studies Theor. Phys., (no.10):479-513.
    In the brain the relations between free neurons and the conditioned ones establish the constraints for the informational neural processes. These constraints reflect the systemenvironment state, i.e. the dynamics of homeocognitive activities. The constraints allow us to define the cost function in the phase space of free neurons so as to trace the trajectories of the possible configurations at minimal cost while respecting the constraints imposed. Since the space of the free states is a manifold or a non orthogonal (...)
    Download  
     
    Export citation  
     
    Bookmark  
  28. Meditation-Induced Bliss Viewed as Release From Conditioned Neural (Thought) Patterns That Block Reward Signals in the Brain Pleasure Center.P. E. Sharp - 2013 - Religion, Brain and Behavior 3 (4):202-229.
    The nucleus accumbens orchestrates processes related to reward and pleasure, including the addictive consequences of repeated reward (e.g., drug addiction and compulsive gambling) and the accompanying feelings of craving and anhedonia. The neurotransmitters dopamine and endogenous opiates play interactive roles in these processes. They are released by natural rewards (i.e., food, water, sex, money, play, etc.) and are released or mimicked by drugs of abuse. Repeated drug use induces conditioned down-regulation of these neurotransmitters, thus causing painful suppression of everyday pleasure. (...)
    Download  
     
    Export citation  
     
    Bookmark  
  29. Nonconscious Perception, Conscious Awareness and Attention.Rajendra D. Badgaiyan - 2012 - Consciousness and Cognition 21 (1):584-586.
    Because it is unclear how a nonconscious stimulus is cognitively processed, there is uncertainty concerning variables that modulate the processing. In this context recent findings of a set of neuroimaging experiments are important. These findings suggest that conscious and nonconscious stimuli activate same areas of the brain during performance of a similar task. Further, different areas are activated when a task is performed with or without awareness of processing. It appears that the neural network involved in cognitive processing (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  30.  83
    Do We Live In An Intelligent Universe?William H. Green - manuscript
    This essay hypothesizes that the Universe contains a self-reproducing neural network of Black Holes with computational abilities—i.e., the Universe can “think”! It then rephrases the Final Anthropic Principle to state: “Intelligent information-processing must come into existence in each new Universe to assure the birth of intelligent successor universes”. Continued research into the theory of Early Universe and Black Hole information storage, processing and retrieval is recommended, as are observational searches for time-correlated electromagnetic and gravitational wave emission patterns from (...)
    Download  
     
    Export citation  
     
    Bookmark  
  31. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  32. Plant Seedlings Classification Using Deep Learning.Belal A. M. Ashqar, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Information Systems Research (IJAISR) 3 (1):7-14.
    Agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Preceding instrument vision methods established for selective weeding have confronted with major challenges for trustworthy and precise weed recognition. In this paper, (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  33.  34
    Evolving Efficient Classification Patterns in Lymphography Using EasyNN.Ahmed Suhail Jaber, Ahmed Khalil Humid, Mohammed Ahmed Hussein & Samy S. Abu-Naser - 2020 - International Journal of Academic Information Systems Research (IJAISR) 4 (9):66-73.
    A neural network exploits the non-linearity of a problem to define a set of desired inputs. Neural networks are important in realizing a better way for classification in machine learning and finds application in various fields such as data mining, pattern recognition, forensics etc. In this paper, our focus is to classify of patient records obtained from clinical data. Feature selection is a supervised method that attempts to select a subset of the predictor features based on the (...)
    Download  
     
    Export citation  
     
    Bookmark  
  34. The Trans-Species Core SELF: The Emergence of Active Cultural and Neuro-Ecological Agents Through Self-Related Processing Within Subcortical-Cortical Midline Networks.Jaak Panksepp & Georg Northoff - 2009 - Consciousness and Cognition 18 (1):193–215.
    The nature of “the self” has been one of the central problems in philosophy and more recently in neuroscience. This raises various questions: Can we attribute a self to animals? Do animals and humans share certain aspects of their core selves, yielding a trans-species concept of self? What are the neural processes that underlie a possible trans-species concept of self? What are the developmental aspects and do they result in various levels of self-representation? Drawing on recent literature from both (...)
    Download  
     
    Export citation  
     
    Bookmark   8 citations  
  35. On the Solvability of the Mind-Body Problem.Jan Scheffel - manuscript
    The mind-body problem is analyzed in a physicalist perspective. By combining the concepts of emergence and algorithmic information theory in a thought experiment employing a basic nonlinear process, it is shown that epistemically strongly emergent properties may develop in a physical system. Turning to the significantly more complex neural network of the brain it is subsequently argued that consciousness is epistemically emergent. Thus reductionist understanding of consciousness appears not possible; the mind-body problem does not have a reductionist solution. (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  36. Measuring the World: Olfaction as a Process Model of Perception.Ann-Sophie Barwich - 2018 - In John A. Dupre & Daniel Nicholson (eds.), Everything Flows: Towards a Processual Philosophy of Biology. pp. 337-356.
    How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation  
  37. Robot Pain.Simon van Rysewyk - 2014 - International Journal of Synthetic Emotions 4 (2):22-33.
    Functionalism of robot pain claims that what is definitive of robot pain is functional role, defined as the causal relations pain has to noxious stimuli, behavior and other subjective states. Here, I propose that the only way to theorize role-functionalism of robot pain is in terms of type-identity theory. I argue that what makes a state pain for a neuro-robot at a time is the functional role it has in the robot at the time, and this state is type identical (...)
    Download  
    Translate
     
     
    Export citation  
     
    Bookmark   2 citations  
  38. Evolving Artificial Minds and Brains.Alex Vereschagin, Mike Collins & Pete Mandik - 2007 - In Drew Khlentzos & Andrea Schalley (eds.), Mental States Volume 1: Evolution, function, nature. John Benjamins.
    We explicate representational content by addressing how representations that ex- plain intelligent behavior might be acquired through processes of Darwinian evo- lution. We present the results of computer simulations of evolved neural network controllers and discuss the similarity of the simulations to real-world examples of neural network control of animal behavior. We argue that focusing on the simplest cases of evolved intelligent behavior, in both simulated and real organisms, reveals that evolved representations must carry information about (...)
    Download  
     
    Export citation  
     
    Bookmark   5 citations  
  39.  53
    Universal Metadata Standard.Andrej Poleev - 2011 - Scientific and Technical Information Processing 38 (2):119-122.
    Consciousness is based on the association of notions or a neural network. Similarly, the creation of the next generation Internet (semantic web) is impossible without attributes that allow the semantic association of documents and their integration into an information context. To achieve these goals, the Universal Metadata Standard (UMS) may serve as a basis for documentography and is functionally required for interpretation of documents by automatic operating systems.
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  40. Brain, Mind and Limitations of a Scientific Theory of Human Consciousness.Alfred Gierer - 2008 - Bioessays 30 (5):499-505.
    In biological terms, human consciousness appears as a feature associated with the func- tioning of the human brain. The corresponding activities of the neural network occur strictly in accord with physical laws; however, this fact does not necessarily imply that there can be a comprehensive scientific theory of conscious- ness, despite all the progress in neurobiology, neuropsychology and neurocomputation. Pre- dictions of the extent to which such a theory may become possible vary widely in the scien- tific community. (...)
    Download  
     
    Export citation  
     
    Bookmark   2 citations  
  41. Scientism, Philosophy and Brain-Based Learning.Gregory M. Nixon - 2013 - Northwest Journal of Teacher Education 11 (1):113-144.
    [This is an edited and improved version of "You Are Not Your Brain: Against 'Teaching to the Brain'" previously published in *Review of Higher Education and Self-Learning* 5(15), Summer 2012.] Since educators are always looking for ways to improve their practice, and since empirical science is now accepted in our worldview as the final arbiter of truth, it is no surprise they have been lured toward cognitive neuroscience in hopes that discovering how the brain learns will provide a nutshell explanation (...)
    Download  
    Translate
     
     
    Export citation  
     
    Bookmark   1 citation  
  42. Wyjaśnianie w kognitywistyce.Marcin Miłkowski - 2013 - Przeglad Filozoficzny - Nowa Seria 86 (2):151-166.
    The paper defends the claim that the mechanistic explanation of information processing is the fundamental kind of explanation in cognitive science. These mechanisms are complex organized systems whose functioning depends on the orchestrated interaction of their component parts and processes. A constitutive explanation of every mechanism must include both appeal to its environment and to the role it plays in it. This role has been traditionally dubbed competence. To fully explain how this role is played it is necessary to explain (...)
    Download  
    Translate
     
     
    Export citation  
     
    Bookmark   1 citation  
  43. 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 (...)
    Download  
     
    Export citation  
     
    Bookmark   5 citations  
  44.  54
    Modeling Cognitive Development of the Balance Scale Task Using ANN.Yara Essam Al-Atrash, Ahmed Tariq Wishah, Tariq Hosni Abul-Omreen & Samy S. Abu-Naser - 2020 - International Journal of Academic Information Systems Research (IJAISR) 4 (9):74-81.
    In this paper we describe a Artificial Neural Network model of children's development on the balance scale task. In balance scale experiments, the child is asked to predict the outcome of placing certain numbers of equal weights at various distances to the left or right of a fulcrum. Both stage progressions and information salience effects have been found with children on this task. Artificial Neural Network provided better fits to these human data than did previous models, (...)
    Download  
     
    Export citation  
     
    Bookmark  
  45.  65
    ANN for Lung Cancer Detection.Nassar AlIbrahim & Murshidy Suheil - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 3 (3):17-21.
    In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is (...)
    Download  
     
    Export citation  
     
    Bookmark  
  46.  15
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  47. Image-Based Tomato Leaves Diseases Detection Using Deep Learning.Belal A. M. Ashqar & Samy S. Abu-Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 2 (12):10-16.
    : Crop diseases are a key danger for food security, but their speedy identification still difficult in many portions of the world because of the lack of the essential infrastructure. The mixture of increasing worldwide smartphone dispersion and current advances in computer vision made conceivable by deep learning has cemented the way for smartphone-assisted disease identification. Using a public dataset of 9000 images of infected and healthy Tomato leaves collected under controlled conditions, we trained a deep convolutional neural (...) to identify 5 diseases. The trained model achieved an accuracy of 99.84% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image dataset presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale. (shrink)
    Download  
     
    Export citation  
     
    Bookmark  
  48.  31
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  49.  14
    Effect of Oxygen Consumption of Thylakoid Membranes (Chloroplasts) From Spinach After Inhibition Using JNN.Hisham Ziad Belbeisi, Youssef Samir Al-Awadi, Muhammad Munir Abbas & Samy S. Abu-Naser - 2020 - International Journal of Academic Health and Medical Research (IJAHMR) 4 (11):1-7.
    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 (...)
    Download  
     
    Export citation  
     
    Bookmark  
  50. Human Brain Evolution, Theories of Innovation, and Lessons From the History of Technology.Alfred Gierer - 2004 - J. Biosci 29 (3):235-244.
    Biological evolution and technological innovation, while differing in many respects, also share common features. In particular, implementation of a new technology in the market is analogous to the spreading of a new genetic trait in a population. Technological innovation may occur either through the accumulation of quantitative changes, as in the development of the ocean clipper, or it may be initiated by a new combination of features or subsystems, as in the case of steamships. Other examples of the latter type (...)
    Download  
     
    Export citation  
     
    Bookmark  
1 — 50 / 663