Results for 'diabetes, neural network'

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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 (...)
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  2. Artificial Neural Network for Forecasting Car Mileage Per Gallon in the City.Mohsen Afana, Jomana Ahmed, Bayan Harb, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2018 - International Journal of Advanced Science and Technology 124:51-59.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City (...)
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  3. Artificial Neural Network for Predicting Animals Category.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic and Applied Research (IJAAR) 3 (2):18-24.
    Abstract: In this paper an Artificial Neural Network (ANN) model, was developed and tested for predicting the category of an animal. There is a number of factors that influence the classification of animals. Such as the existence of hair/ feather, if the animal gives birth or spawns, it is airborne, aquatic, predator, toothed, backboned, venomous, has –fins, has-tail, cat-sized, and domestic. They were then used as input variables for the ANN model. A model based on the Multilayer Perceptron (...)
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  4. 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 (...)
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  5. Glass Classification Using Artificial Neural Network.Mohmmad Jamal El-Khatib, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Pedagogical Research (IJAPR) 3 (23):25-31.
    As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass classification (...)
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  6. 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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  7. 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 (...)
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  8. Predicting Overall Car Performance Using Artificial Neural Network.Osama M. Al-Mubayyed, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic and Applied Research (IJAAR) 3 (1):1-5.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.62 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study (...)
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  9. Tic-Tac-Toe Learning Using Artificial Neural Networks.Mohaned Abu Dalffa, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (2):9-19.
    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 better (...)
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  10.  57
    Discourseology of Linguistic Consciousness: Neural Network Modeling of Some Structural and Semantic Relationships.Vitalii Shymko - 2021 - Psycholinguistics 29 (1):193-207.
    Objective. Study of the validity and reliability of the discourse approach for the psycholinguistic understanding of the nature, structure, and features of the linguistic consciousness functioning. -/- Materials & Methods. This paper analyzes artificial neural network models built on the corpus of texts, which were obtained in the process of experimental research of the coronavirus quarantine concept as a new category of linguistic consciousness. The methodology of feedforward artificial neural networks (multilayer perceptron) was used in order to (...)
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  11. Predicting Birth Weight Using Artificial Neural Network.Mohammed Al-Shawwa & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (1):9-14.
    In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. (...)
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  12. Predicting Tumor Category Using Artificial Neural Networks.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (2):1-7.
    In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and (...)
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  13. 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, often appealing (...)
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  14. 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 (...)
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  15. 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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  16. 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 (...)
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  17.  14
    Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map.Birgitta Dresp-Langley - 2021 - Symmetry 13:299.
    Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human (...)
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  18. Lung Cancer Detection Using Artificial Neural Network.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (3):17-23.
    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 (...)
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  19.  55
    THE SPECTACLE OF REFLECTION: ON DREAMS, NEURAL NETWORKS AND THE VISUAL NATURE OF THOUGHT.Magdalena Szalewicz - manuscript
    The article considers the problem of images and the role they play in our reflection turning to evidence provided by two seemingly very distant theories of mind together with two sorts of corresponding visions: dreams as analyzed by Freud who claimed that they are pictures of our thoughts, and their mechanical counterparts produced by neural networks designed for object recognition and classification. Freud’s theory of dreams has largely been ignored by philosophers interested in cognition, most of whom focused solely (...)
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    Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Electromagnetic Space Vehicle Suspension System.Mustefa Jibril, Mesay Tadesse & Nurye Hassen - 2021 - Journal of Engineering and Applied Sciences 16 (10):313-317.
    Electromagnetic Suspension System (EMS) is mostly used in the field of high-speed vehicle. In this study, a space exploring vehicle quarter electromagnetic suspension system is modelled, designed and simulated using Neural network-based control problem. NARMA-L2, Model reference and predictive controllers are designed to improve the body travel of the vehicle using bump road profile. Comparison between the proposed controllers is done and a promising simulation result have been analyzed.
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  21. 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 (...)
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  22. 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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  23. 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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  24. 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 validating (...)
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  25. Type of Tomato Classification Using Deep Learning.Mahmoud A. Alajrami & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):21-25.
    Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...)
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  26. What Does It Mean to Understand? Neural Networks Case.Albert Ierusalem & Aleksandr Senin - manuscript
    We can say that we understand neural networks then and only then if you will come to me and say that the best model ever for some task has a 100 layers, and I will answer "No! 101 layers model is the best!".
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  27. 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 (...)
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  28. Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation Train.Mustefa Jibril, Elias Alemayehu & Mesay Tadesse - 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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  29. 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 (...)
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  30. Uncovering the Antecedents of Trust in Social Commerce: An Application of the Non-Linear Artificial Neural Network Approach.Hussam Al Halbusi - 2022 - Competitiveness Review 4.
    Purpose – The internet creates ample opportunities to start a mobile social commerce business. The literature confirms the issue of customer trust for social commerce businesses is a challenge that must be addressed. Hence, this study aims to examine the antecedents of trust in mobile social commerce by applying linear and non-linear relationships based on partial least squares structural equation modeling and an artificial neural network model. -/- Design/methodology/approach – This study applied a non-linear artificial neural (...) approach to provide a further understanding of the determinants of trust in mobile social commerce based on a non-linear and non-compensatory model. Besides, a questionnaire was distributed to 340 social commerce customers in Malaysia. -/- Findings – The conceptual framework for investigating trust in mobile social commerce has various advantages and contributions to predicting consumer behaviour. The results of the study showed there is a positive and significant relationship between social support, presence and unified theory of acceptance and use of technology2 (UTAUT2). In addition, UTAUT2 has fully mediated the relationship between social support, presence and trust in social commerce. Finally, the results concluded the relationship between UTAUT2 and trust in social commerce would be stronger when the diffusion of innovation and innovation resistance is high and low, respectively. -/- Research limitations/implications – The current study provides a novel perspective on how customers can trust social m-commerce to provide real solutions to managers of encouraging e-marketing among consumers. -/- Practical implications – This paper shows how businesses can develop trust in social m-commerce in Malaysian markets. The findings of this study probably could be extended to other businesses in Asia or other countries. Because trust in social e-commerce has a dynamic role in consumer behaviour and intention to purchase. -/- Originality/value – This study provided a new perspective on mobile social commerce and paid more attention to an investigation of such emerging commerce. The originality of this study is embodied by investigating an integrated model that included different theories that presented new directions of trust in mobile social commerce through social and behavioural determinants. (shrink)
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  31. THE THEORY OF EVOLUTION: from the space vacuum to neural networks and moving forward.Oleg Bazaluk - 2014 - ISPC.
    In the book, the author defines the evolution as a continuous and nonlinear complex of the structure of matter, interaction types and environments of existence; analyzes existing in modern science and philosophy approaches to the study of the process of evolution, degree of development factors and causes of evolution. Unifying interdisciplinary research in cosmology, evolution, biology, neuroscience and philosophy, the author presents his vision of the evolution model of «Evolving matter», which allows us to consider not only the laws of (...)
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  32. 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.
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  33. 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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  34.  88
    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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  35.  32
    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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  36. 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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  37. 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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  38.  70
    How a Neural Net Grows Symbols.James Franklin - 1996 - In Peter Bartlett (ed.), Proceedings of the Seventh Australian Conference on Neural Networks, Canberra. Canberra, Australia: ACNN '96. pp. 91-96.
    Brains, unlike artificial neural nets, use symbols to summarise and reason about perceptual input. But unlike symbolic AI, they “ground” the symbols in the data: the symbols have meaning in terms of data, not just meaning imposed by the outside user. If neural nets could be made to grow their own symbols in the way that brains do, there would be a good prospect of combining neural networks and symbolic AI, in such a way as to combine (...)
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  39.  32
    Neural Chitchat.Barry Smith - 2021 - The Sherry Turkle Miracle.
    A constant theme in Sherry Turkle’s work is the idea that computers shape our social and psychological lives. This idea is of course in a sense trivial, as can be observed when walking down any city street and noting how many of the passers-by have their heads buried in screens. In The Second Self, however, Turkle makes a stronger claim to the effect that where people confront machines that seem to think this suggests a new way for us to think (...)
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    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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  41.  64
    Adaptive Control Using Nonlinear Autoregressive-Moving Average-L2 Model for Realizing Neural Controller for Unknown Finite Dimensional Nonlinear Discrete Time Dynamical Systems.Mustefa Jibril, Mesay Tadesse & Nurye Hassen - 2021 - Journal of Engineering and Applied Sciences 16 (3):130-137.
    This study considers the problem of using approximate way for realizing the neural supervisor for nonlinear multivariable systems. The Nonlinear Autoregressive-Moving Average (NARMA) model is an exact transformation of the input-output behavior of finite-dimensional nonlinear discrete time dynamical organization in a hoodlum of the equilibrium state. However, it is not convenient for intention of adaptive control using neural networks due to its nonlinear dependence on the control input. Hence, quite often, approximate technique are used for realizing the (...) supervisor to overcome computational complexity. In this study, we introduce two classes of ideal which are approximations to the NARMA model and which are linear in the control input, namely NARMA-L1 and NARMA-L2. The latter fact substantially simplifies both the theoretical breakdown as well as the practical request of the controller. Extensive imitation studies have shown that the neural controller designed using the proposed approximate models perform very well and in dozens situation even better than an approximate controller designed using the exact NARMA Model. In view of their mathematical tractability as well as their fate in simulation studies, a matter is made in this study that such approximate input-output paragon warrants a detailed study in their own right. (shrink)
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  42. 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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  43. 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 (...)
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  44. Neural Implants as Gateways to Digital-Physical Ecosystems and Posthuman Socioeconomic Interaction.Matthew E. Gladden - 2016 - In Łukasz Jonak, Natalia Juchniewicz & Renata Włoch (eds.), Digital Ecosystems: Society in the Digital Age. Digital Economy Lab, University of Warsaw. pp. 85-98.
    For many employees, ‘work’ is no longer something performed while sitting at a computer in an office. Employees in a growing number of industries are expected to carry mobile devices and be available for work-related interactions even when beyond the workplace and outside of normal business hours. In this article it is argued that a future step will increasingly be to move work-related information and communication technology (ICT) inside the human body through the use of neuroprosthetics, to create employees who (...)
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  45.  36
    Environmental Variability and the Emergence of Meaning: Simulational Studies Across Imitation, Genetic Algorithms, and Neural Nets.Patrick Grim - 2006 - In Angelo Loula & Ricardo Gudwin (eds.), Artificial Cognition Systems. Idea Group. pp. 284-326.
    A crucial question for artificial cognition systems is what meaning is and how it arises. In pursuit of that question, this paper extends earlier work in which we show that emergence of simple signaling in biologically inspired models using arrays of locally interactive agents. Communities of "communicators" develop in an environment of wandering food sources and predators using any of a variety of mechanisms: imitation of successful neighbors, localized genetic algorithms and partial neural net training on successful neighbors. Here (...)
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  46. Mounting Evidence That Minds Are Neural EM Fields Interacting with Brains.Mostyn W. Jones - 2017 - Journal of Consciousness Studies 24 (1-2):159-183.
    Evidence that minds are neural electromagnetic fields comes from research into how separate brain activities bind to form unified percepts and unified minds. Explanations of binding using synchrony, attention, and convergence are all problematic. But the unity of EM fields explains binding without these problems. These unified fields neatly explain correlations and divergences between synchrony, attention, convergence, and unified minds. The simplest explanation for the unity of both minds and fields is that minds are fields. Treating minds as the (...)
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  47.  47
    Getting Over Atomism: Functional Decomposition in Complex Neural Systems.Daniel C. Burnston - 2021 - British Journal for the Philosophy of Science 72 (3):743-772.
    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 (...)
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  48.  58
    The Neural, Evolutionary, Developmental, and Bodily Basis of Metaphor.Jay Seitz - 2005 - New Ideas in Psychology 23 (2):74-95.
    We propose that there are four fundamental kinds of metaphor that are uniquely mapped onto specific brain ‘‘networks’’ and present preliterate (i.e., evolutionary, including before the appearance of written language in the historical record), prelinguistic (i.e., developmental, before the appearance of speech in human development), and extralinguistic (i.e., neuropsychological, cognitive) evidence supportive of this view. We contend that these basic metaphors are largely nonconceptual and entail (a) perceptual–perceptual, (b) cross-modal, (c) movement–movement, and (d) perceptual-affective mappings that, at least, in the (...)
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  49.  51
    CONSCIOUS EXPERIENCE, THE NEURAL MECHANISM.Richard A. Sieb - manuscript
    The physical basis of conscious experience is revealed by direct observation and analysis of any conscious experience. Human conscious experience has an invariant structural mode of organization based on the three types of space-time intervals (light-like, time-like, space-like). Sensory input activates the autonomic nervous system, endocrine system, and ascending reticular activating system to produce the awake conscious state. The dorsal and ventral frontoparietal attention networks are activated. Dorsal and ventral cortical functional streams carry “what”, “where”, and “when” information to the (...)
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  50.  46
    Commentary: Integrative Modeling and the Role of Neural Constraints.Brice Bantegnie - 2017 - Frontiers in Psychology 8:1531.
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