scholarly journals Application of Brain Neural Network in Personalized English Education System

2018 ◽  
Vol 13 (10) ◽  
pp. 15
Author(s):  
Songlin Yang ◽  
Min Zhang
Keyword(s):  
Neural Network ◽  
Education System ◽  
English Education ◽  
High Rise ◽  
Word Memory ◽  
Learning Content ◽  
Personalized Education ◽  
Study Case ◽  
Feedback Data ◽  
The Brain

The Personalized Education System (PES) provides appropriate counseling pro-gram as per the different demands and the natures of learners. Its education quali-ty depends on the individuality to a great extent. The Brain Neural Network (BNN) can automatically analyze the learners’ profiles from their feedback data. In light of the above, this paper analyzes the forgetting curve of the learners in the system by building the brain neural network. Take the word memory in English learning as a study case. This curve will help customize the learning content for those learners precisely to hit their strides with a new high-rise personalized edu-cation. Experiment bears out that the forgetting curve generated by the BNN more adapts to the learner's memory law than the traditional universal Ebbinghaus memory curve. The new memory curve makes it possible to improve the effect of PES more effectively and the teaching principle more scientifically.

Neural network controller with on-line inventory feedback data in RFID-enabled supply chain

2010 ◽  
Vol 48 (9) ◽  
pp. 2613-2632 ◽  
Author(s):  
Seong Rok Hong ◽  
Shin Tae Kim ◽  
Chang Ouk Kim
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Neural Network Controller ◽  
Network Controller ◽  
On Line ◽  
Feedback Data

scholarly journals Generalization of Patterns by Identification with Polynomial Neural Network

2010 ◽  
Vol 61 (2) ◽  
pp. 120-124 ◽  
Author(s):  
Ladislav Zjavka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Dependence ◽  
Polynomial Neural Network ◽  
New Type ◽  
Input Variables ◽  
The Brain ◽  
Functional Output ◽  
Simplified Form

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

scholarly journals Tracheal occlusion-evoked respiratory load compensation and inhibitory neurotransmitter expression in rats

2014 ◽  
Vol 116 (8) ◽  
pp. 1006-1016 ◽  
Author(s):  
Hsiu-Wen Tsai ◽  
Paul W. Davenport
Keyword(s):  
Neural Network ◽  
Brain Stem ◽  
Breathing Pattern ◽  
Respiratory Rhythm ◽  
Inhibitory Neurotransmitter ◽  
Load Compensation ◽  
Efferent Projections ◽  
Motor Reflex ◽  
Sensory Pathway ◽  
The Brain

Respiratory load compensation is a sensory-motor reflex generated in the brain stem respiratory neural network. The nucleus of the solitary tract (NTS) is thought to be the primary structure to process the respiratory load-related afferent activity and contribute to the modification of the breathing pattern by sending efferent projections to other structures in the brain stem respiratory neural network. The sensory pathway and motor responses of respiratory load compensation have been studied extensively; however, the mechanism of neurogenesis of load compensation is still unknown. A variety of studies has shown that inhibitory interconnections among the brain stem respiratory groups play critical roles for the genesis of respiratory rhythm and pattern. The purpose of this study was to examine whether inhibitory glycinergic neurons in the NTS were activated by external and transient tracheal occlusions (ETTO) in anesthetized animals. The results showed that ETTO produced load compensation responses with increased inspiratory, expiratory, and total breath time, as well as elevated activation of inhibitory glycinergic neurons in the caudal NTS (cNTS) and intermediate NTS (iNTS). Vagotomized animals receiving transient respiratory loads did not exhibit these load compensation responses. In addition, vagotomy significantly reduced the activation of inhibitory glycinergic neurons in the cNTS and iNTS. The results suggest that these activated inhibitory glycinergic neurons in the NTS might be essential for the neurogenesis of load compensation responses in anesthetized animals.

scholarly journals The Moral Imperative: The Case of the English Education System

2014 ◽  
Vol 12 (5) ◽  
pp. 730-740
Author(s):  
Sabine Spangenberg ◽  
Bryan McIntosh
Keyword(s):  
Education System ◽  
English Education ◽  
Moral Imperative

scholarly journals Using student feedback and professor-developed multimedia to improve instructor presence and student learning

2014 ◽  
pp. 12-30 ◽  
Author(s):  
William Albert Young II ◽  
Brett H. Hicks ◽  
Danielle Villa-Lobos ◽  
Teresa J. Franklin
Keyword(s):  
Real Time ◽  
Social Presence ◽  
Learning Experience ◽  
Student Feedback ◽  
Learning Needs ◽  
Time Data ◽  
Online Distance Education ◽  
Learning Content ◽  
Online Distance ◽  
Feedback Data

This paper explores the use of Professor-Developed Multimedia Content (PDMC) in online, distance education to build a community of inquiry (CoI) through enhanced social presence and real-time, student-driven, adaption of the learning content. The foundation of higher education has long been, developing curriculum to meet educational objectives. Most often faculty relies on assessment information gained at the end of each course. Then assessments, formative and summative, are re-designed based on student feedback/data from end of course surveys and educational materials such as textbooks, articles, and test banks are updated with newer editions. In the distance-learning environment, PDMC provides a creative, innovative, and interactive ways to engage the student for real-time learning. Still, the ability to target PDMC materials to the correct sub-sections of our classroom cohort can produce a richer, more immerse learning experience and perhaps become the closet recreation of in-seat, traditional classroom learning in a distance/online environment. By using PDMC with corresponding surveys, educators can obtain real-time data and metrics to alter content in the classroom immediately, and develop media content welcoming sub-sets of learners with desired content based on learning needs, desires, and feedback.

scholarly journals Spiritual Education Mission in the Mufassirin Perspective

Al-Albab ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 237-262
Author(s):  
Syarif Syarif
Keyword(s):  
Education System ◽  
Social Life ◽  
Research Method ◽  
Spiritual Values ◽  
Spiritual Education ◽  
Modern Humans ◽  
Educational Approach ◽  
Low Level ◽  
Library Research ◽  
The Brain

Spiritual crisis is a factor leading to disorientation in today’s modern humans and the decline of morality of the nation. The low level of spirituality is caused by an educational approach that only focuses on the brain and ignores spiritual values. The Qur'an has actually affirmed the mission of spiritual education which should be used as a reference for the current education system. This article employs library research method through a comparison of the way the mufassirin interpret verses about the mission of spiritual education. The results show that the mission of spiritual education carried out by the Prophet Muhammad can be seen for example in Surat Al-Anbiya' verse 107, Surat Saba' verse 28 and Surat Al-Ahzab verse 21, namely rahmatan lil 'alamin (mercy to all creations). The prophet has brought evidence the truth to perfect the akhlậq, as well as to become the followers of uswah hasanah (perfect example) which must be imitated by all humans. Meanwhile, the stages of increasing spirituality in the Surat Luqman Verses 12-19, include: (1) instilling the tauhid values, (2) being filial to parents, (3) understanding the reciprocity of each deed, (4) command to worship, (5) introducing politeness in social life. Elements of spiritual education contained in Surat al-Muzzammil Verses 1-10 include qiyamul lail or night prayer, reciting the Qur’an in a tartil way, getting used to zikr, patience, jihad fi sabilillah or fighting on the path of Allah, and always praying and begging forgiveness from Allah.

scholarly journals Reconstructing feedback representations in ventral visual pathway with a generative adversarial autoencoder

2020 ◽  
Author(s):  
Haider Al-Tahan ◽  
Yalda Mohsenzadeh
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Visual Pathway ◽  
Functional Magnetic Resonance ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
High Level ◽  
Feedback Connections ◽  
The Brain ◽  
Insight Into

AbstractWhile vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.Author summaryIt has been shown that the ventral visual cortex consists of a dense network of regions with feedforward and feedback connections. The feedforward path processes visual inputs along a hierarchy of cortical areas that starts in early visual cortex (an area tuned to low level features e.g. edges/corners) and ends in inferior temporal cortex (an area that responds to higher level categorical contents e.g. faces/objects). Alternatively, the feedback connections modulate neuronal responses in this hierarchy by broadcasting information from higher to lower areas. In recent years, deep neural network models which are trained on object recognition tasks achieved human-level performance and showed similar activation patterns to the visual brain. In this work, we developed a generative neural network model that consists of encoding and decoding sub-networks. By comparing this computational model with the human brain temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) response patterns, we found that the encoder processes resemble the brain feedforward processing dynamics and the decoder shares similarity with the brain feedback processing dynamics. These results provide an algorithmic insight into the spatiotemporal dynamics of feedforward and feedback processes in biological vision.

scholarly journals Artificial Neural Network in Drug Delivery and Pharmaceutical Research

2013 ◽  
Vol 7 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Vijaykumar Sutariya ◽  
Anastasia Groshev ◽  
Prabodh Sadana ◽  
Deepak Bhatia ◽  
Yashwant Pathak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Delivery ◽  
Pattern Recognition ◽  
Analytical Data ◽  
Pharmaceutical Research ◽  
Artificial Neural ◽  
The Brain

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

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