Results for 'artificial neural network'

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  1. 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 (...)
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  2. Predicting Books’ Overall Rating Using Artificial Neural Network.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 3 (8):11-17.
    We developed an Artificial Neural Network (ANN) model for predicting the overall rating of books. The prediction is based on some Factors (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which used as input variables and (average_rating) as output for our ANN predictive model. Our model established, trained, and validated using data set, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.90% of the validation instances.
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  3.  72
    Classification of Mushroom Using Artificial Neural Network.Alkronz Sameh, Meimeh Moghayer, Gazaz Mohanad & AlKahlout Mohammad - 2020 - International Journal of Academic and Applied Research (IJAAR) 3 (2):1-5.
    Predication is an application of Artificial Neural Network (ANN). It is a supervised learning due to predefined input and output attributes. Multi-Layer ANN model is used for training, validating, and testing of the data. In this paper, Multi-Layer ANN model was used to train and test the mushroom dataset to predict whether it is edible or poisonous. The Mushrooms dataset was prepared for training, 8124 instances were used for the training. JustNN software was used to training and (...)
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  4. 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 (...)
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  5. Predicting Titanic Survivors Using Artificial Neural Network.Alaa M. Barhoom, Ahmed J. Khalil, Bassem S. Abu-Nasser, Musleh M. Musleh & Samy S. Abu Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 3 (9):8-12.
    Although the Titanic disaster happened just over one hundred years ago, it still appeals researchers to understand why some passengers survived while others did not. With the use of a machine learning tool (JustNN) and the provided dataset we study which factors or classifications of passengers have a strong relationship with survival for passengers that took that trip on 15th of April, 1912. The analysis seeks to identify characteristics of passengers - cabin class, age, and point of departure – and (...)
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  6. 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 (...)
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  7.  68
    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 (...)
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  8. Classifying Nuts Types Using Convolutional Neural Network.Ibtesam M. Dheir, Alaa Soliman Abu Mettleq, Abeer A. Elsharif & Samy S. Abu-Naser - 2020 - International Journal of Academic Information Systems Research (IJAISR) 3 (12):12-18.
    Abstract: Nuts are nutrient-dense foods with complex matrices rich in unsaturated fatty and other bioactive compounds. By virtue of their unique composition, all types of nuts are likely to beneficially impact health outcomes. In this paper, we classified five types of Nuts with a dataset that contains 2868 images. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used for this task. The trained model achieved an accuracy of 98% on a held-out (...)
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  9. Revelation and Artificial Neural Networks.Lascelles G. B. James - manuscript
    The grammatical forms and material of the book of Revelation suggest a complex interplay of Old Testament and 1st century literature and language. As well, the book does not lack its own peculiarity and character that is unparalleled in the literate world. Various analytical tools including historical-comparative methodologies have been employed to reconstruct the linguistic paradigm of the book. Artificial intelligence and its derivatives provide alternate methods of probing this paradigm.
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  10. ANN for Predicting Mobile Phone Price Range.Fatthy Khillha & Nassar Shawwa - 2020 - 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 (...)
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  11. Our Tactile Brain Computed World and Platonic Brain Web Wikipedia.Jahan N. Schad (ed.) - 2016 - Charleston, USA: CreateSpace.
    It is not likely that we will ever convincingly know how and why we came to be on this planet; of course, this has never prevented inquisitive minds from pushing the frontiers of understanding and discovery further. Our origin is the subject of scientific theories and continuous inquiries with no end in sight, as the shells of related complexities are getting much harder to crack. Paraphrasing philosopher and historian Will Durant, a very few people are getting to know more and (...)
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  12.  28
    ANN for Predicting Birth Weight.Shawwah Mohammad & Murshidy Suheil - 2020 - International Journal of Academic Health and Medical Research (IJAHMR) 1 (3):9-12.
    In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in (...)
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  13.  27
    ANN for Tic-Tac-Toe Learning.Dalffa Abu-Mohaned - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 3 (2):9-17.
    Throughout this research, imposing the training of an Artificial Neural Network (ANN) to play tic-tac-toe bored game, by training the ANN to play the tic-tac-toe logic using the set of mathematical combination of the sequences that could be played by the system and using both the Gradient Descent Algorithm explicitly and the Elimination theory rules implicitly. And so on the system should be able to produce imunate amalgamations to solve every state within the game course to make (...)
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  14.  23
    ANN for Predicting Animals Category.Nassar Ibraheem & AlKahlout Mohammad - 2020 - International Journal of Academic and Applied Research (IJAAR) 3 (2):18-23.
    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 (...)
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  15.  20
    ANN for Predicting Overall Car Performance.Mubayyed Osamma & Gazaz Ahmed - 2020 - International Journal of Academic and Applied Research (IJAAR) 1 (3):1-4.
    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 (...)
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  16.  19
    ANN for Predicting the Effect of Oxygen Consumption of Thylakoid Membranes (Chloroplasts) From Spinach After Inhibition.Shawah Mohammad - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (2):15-19.
    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 (...)
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  17.  16
    ANN for Tic-Tac-Toe Learning.Dalffa Muhannad - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 3 (2):9-17.
    Throughout this research, imposing the training of an Artificial Neural Network (ANN) to play tic-tac-toe bored game, by training the ANN to play the tic-tac-toe logic using the set of mathematical combination of the sequences that could be played by the system and using both the Gradient Descent Algorithm explicitly and the Elimination theory rules implicitly. And so on the system should be able to produce imunate amalgamations to solve every state within the game course to make (...)
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  18.  28
    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 (...)
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  19.  16
    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 (...)
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  20.  20
    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 (...)
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  21.  98
    NEW PRINCIPLE FOR ENCODING INFORMATION TO CREATE SUBJECTIVE REALITY IN ARTIFICIAL NEURAL NETWORKS.Alexey Bakhirev - manuscript
    The paper outlines an analysis of two types of information - ordinary and subjective, consideration is given to the difference between the concepts of intelligence and perceiving mind. It also provides description of some logical functional features of consciousness. A technical approach is proposed to technical obtaining of subjective information by changing the signal’s time degree of freedom to the spatial one in order to obtain the "observer" function in the system and information signals appearing in relation to it, that (...)
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  22.  10
    Face Recognition Using Dct And Neural Micro-Classifier Network.Abdellatief Hussien AbouAli - 2018 - International Journal of Engineering and Information Systems (IJEAIS) 2 (3):27-35.
    Abstract— In this study, a proposed faces recognition methodology based on the neural micro-classifier network. The proposed methodology uses simple well known feature extraction methodology. The feature extraction used is the discrete cosine transformation low frequencies coefficients. The micro-classifier network is a deterministic four layers neural network, the four layers are: input, micro-classifier, counter, and output. The network provide confidence factor, and proper generalization is guaranteed. Also, the network allows incremental learning, and more (...)
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  23.  29
    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 (...)
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  24. 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 (...)
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  25.  19
    Enhanced Artificial Intelligence System for Diagnosing and Predicting Breast Cancer Using Deep Learning.Mona Alfifi, Mohamad Shady Alrahhal, Samir Bataineh & Mohammad Mezher - 2020 - International Journal of Advanced Computer Science and Applications 11 (7):1-17.
    Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, (...)
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  26.  91
    AISC 17 Talk: The Explanatory Problems of Deep Learning in Artificial Intelligence and Computational Cognitive Science: Two Possible Research Agendas.Antonio Lieto - 2018 - In Proceedings of AISC 2017.
    Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the (...)
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  27.  19
    Connectionism and the Intentionality of the Programmer.Mark Ressler - 2003 - Dissertation, San Diego State University
    Connectionism seems to avoid many of the problems of classical artificial intelligence, but has it avoided all of them? In this thesis I examine the problem that Intentionality, the directedness of thought to an object, raises for connectionism. As a preliminary approach, I consider the role of Intentionality in classical artificial intelligence from the programmer’s point of view. In this investigation, one problem I identify with classical artificial intelligence is that the Intentionality of the programmer seems to (...)
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  28. Potato Classification Using Deep Learning.Abeer A. Elsharif, Ibtesam M. Dheir, Alaa Soliman Abu Mettleq & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):1-8.
    Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of (...)
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  29. Assessing the Future Plausibility of Catastrophically Dangerous AI.Alexey Turchin - 2018 - Futures.
    In AI safety research, the median timing of AGI creation is often taken as a reference point, which various polls predict will happen in second half of the 21 century, but for maximum safety, we should determine the earliest possible time of dangerous AI arrival and define a minimum acceptable level of AI risk. Such dangerous AI could be either narrow AI facilitating research into potentially dangerous technology like biotech, or AGI, capable of acting completely independently in the real world (...)
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  30. 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 (...)
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  31. 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 (...)
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  32.  67
    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 (...)
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  33.  41
    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 (...)
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  34.  30
    ANN for Diagnosing Autism Spectrum Disorder.Mohammad Nassar - 2020 - International Journal of Academic Information Systems Research (IJAISR) 3 (12):12-17.
    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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  35.  19
    ANN for Predicting Movies Rates Category.Nassar Shawwa & Fatthy Khillha - 2020 - 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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  36.  19
    ANN for Predicting Books’ Overall Rating.Nassar AlIbrahim & Gazaz Ahmed - 2020 - International Journal of Academic Engineering Research (IJAER) 8 (3):1-5.
    We developed an Artificial Neural Network (ANN) model for predicting the overall rating of books. The prediction is based on some Factors (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which used as input variables and (average_rating) as output for our ANN predictive model. Our model established, trained, and validated using data set, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.90% of the validation instances.
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  37. Why Build a Virtual Brain? Large-Scale Neural Simulations as Jump Start for Cognitive Computing.Matteo Colombo - 2016 - Journal of Experimental and Theoretical Artificial Intelligence.
    Despite the impressive amount of financial resources recently invested in carrying out large-scale brain simulations, it is controversial what the pay-offs are of pursuing this project. One idea is that from designing, building, and running a large-scale neural simulation, scientists acquire knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. It has been claimed that this knowledge may usher in a new era of neuromorphic, cognitive computing systems. This study (...)
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  38. 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 (...)
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  39. 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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  40. A Deeper Look at the "Neural Correlate of Consciousness".Sascha Benjamin Fink - 2016 - Frontiers in Psychology 7.
    A main goal of the neuroscience of consciousness is: find the neural correlate to conscious experiences (NCC). When have we achieved this goal? The answer depends on our operationalization of “NCC.” Chalmers (2000) shaped the widely accepted operationalization according to which an NCC is a neural system with a state which is minimally sufficient (but not necessary) for an experience. A deeper look at this operationalization reveals why it might be unsatisfactory: (i) it is not an operationalization of (...)
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  41. Challenges for Artificial Cognitive Systems.Antoni Gomila & Vincent C. Müller - 2012 - Journal of Cognitive Science 13 (4):452-469.
    The declared goal of this paper is to fill this gap: “... cognitive systems research needs questions or challenges that define progress. The challenges are not (yet more) predictions of the future, but a guideline to what are the aims and what would constitute progress.” – the quotation being from the project description of EUCogII, the project for the European Network for Cognitive Systems within which this formulation of the ‘challenges’ was originally developed (http://www.eucognition.org). So, we stick out our (...)
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  42. Philosophy and Theory of Artificial Intelligence 2017.Vincent C. Müller (ed.) - 2017 - Berlin: Springer.
    This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI (...)
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  43.  56
    Can the G Factor Play a Role in Artificial General Intelligence Research?Davide Serpico & Marcello Frixione - 2018 - In Proceedings of the Society for the Study of Artificial Intelligence and Simulation of Behaviour 2018. pp. 301-305.
    In recent years, a trend in AI research has started to pursue human-level, general artificial intelli-gence (AGI). Although the AGI framework is characterised by different viewpoints on what intelligence is and how to implement it in artificial systems, it conceptualises intelligence as flexible, general-purposed, and capable of self-adapting to different contexts and tasks. Two important ques-tions remain open: a) should AGI projects simu-late the biological, neural, and cognitive mecha-nisms realising the human intelligent behaviour? and b) what is (...)
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  44. Empiricism Without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.Cameron Buckner - 2018 - Synthese (12):1-34.
    In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, (...)
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  45. The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics.Roberto Cordeschi - 2002 - Kluwer Academic Publishers.
    Since the second half of the XXth century, researchers in cybernetics and AI, neural nets and connectionism, Artificial Life and new robotics have endeavoured to build different machines that could simulate functions of living organisms, such as adaptation and development, problem solving and learning. In this book these research programs are discussed, particularly as regard the epistemological issues of the behaviour modelling. One of the main novelty of this book consists of the fact that certain projects involving the (...)
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  46. 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 (...)
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  47. 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 (...)
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  48. 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 (...)
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  49.  60
    From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness, Part 1.Jeffrey White - 2016 - APA Newsletter on Philosophy and Computers 1 (16):13-23.
    Direct neurological and especially imaging-driven investigations into the structures essential to naturally occurring cognitive systems in their development and operation have motivated broadening interest in the potential for artificial consciousness modeled on these systems. This first paper in a series of three begins with a brief review of Boltuc’s (2009) “brain-based” thesis on the prospect of artificial consciousness, focusing on his formulation of h-consciousness. We then explore some of the implications of brain research on the structure of consciousness, (...)
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    Meinongian Semantics and Artificial Intelligence.William J. Rapaport - 2013 - Humana Mente 6 (25):25-52.
    This essay describes computational semantic networks for a philosophical audience and surveys several approaches to semantic-network semantics. In particular, propositional semantic networks are discussed; it is argued that only a fully intensional, Meinongian semantics is appropriate for them; and several Meinongian systems are presented.
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