Procedural knowledge processing based on area representation using a neural network

2002 ◽  
Author(s):  
S. Fujinaga ◽  
M. Hagiwara
Keyword(s):  
Neural Network ◽  
Procedural Knowledge ◽  
Knowledge Processing

Procedural Knowledge Processing Based on Area Representation Using a Neural Network

2000 ◽  
Vol 4 (1) ◽  
pp. 46-51
Author(s):  
Seiya Fujinaga ◽  
◽  
Masafumi Hagiwara
Keyword(s):  
Neural Network ◽  
Computer Simulations ◽  
Procedural Knowledge ◽  
High Efficiency ◽  
Hebbian Learning ◽  
Distributed Representation ◽  
Information Representation ◽  
Main Theme ◽  
Knowledge Processing ◽  
Knowledge Based

In this paper, a neural network that treats procedural knowledge based on area representation is proposed. The main theme of this paper is to propose a novel neural network that processes procedural knowledge. The network employs formerly proposed ideas such as "area representation" and "improved Hebbian learning." Area representation expresses information by a group of neurons. Since it is considered as a combination of localized and distributed representation, it has many advantages such as robustness, high efficiency for information representation, and potential ability to treat similarity of data. The proposed network based on area representation is constructed to store and recall procedural knowledge. We performed various kinds of computer simulations to examine the validity and effectiveness of the proposed network.

scholarly journals Convolutional Neural Network-Assisted Strategies for Improving Teaching Quality of College English Flipped Class

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tiankun Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Student Behavior ◽  
Flipped Classroom ◽  
Classroom Behavior ◽  
Procedural Knowledge ◽  
Teaching Style ◽  
Teaching Quality ◽  
Classroom Teaching ◽  
College English

The “flipped classroom” teaching paradigm not only follows the cognitive rules of the learners, but it also subverts and reverses the standard classroom teaching process. Problem-oriented, teacher-led, student-centered, and mixed teaching approaches are the key teaching methods in the flipped classroom teaching model, which focuses on students’ procedural knowledge acquisition and critical thinking training. There are a lot of studies on the specific practice path of the “flipped classroom” teaching style right now, but there are not many on the learning involvement of college English students in this approach. According to studies, the level of student participation in classroom learning is the most important factor limiting the efficiency of teaching. The lack of research in this subject greatly limits the “flipped classroom” teaching model’s ability to improve college English classroom teaching quality. The degree of engagement between teachers and students, the enthusiasm of students in class, and the competence of teachers to educate are all reflected in student conduct in the classroom. Understanding and evaluating the behaviors and activities of students in the classroom are helpful in determining the state of students in the classroom, as well as improving the flipped classroom teaching technique and quality. As a result, the convolutional neural network is used to recognize student behavior in the classroom. The loss function of VGG-16 has been enhanced, the distance inside the class has been lowered, the distance between classes has been increased, and the recognition accuracy has improved. Accurate recognition of classroom behavior is beneficial in developing methods to improve teaching quality.

scholarly journals Neuronless Knowledge Processing in Forests

2020 ◽  
Vol 10 (7) ◽  
pp. 2509
Author(s):  
Aviv Segev ◽  
Dorothy Curtis ◽  
Christine Balili ◽  
Sukhwan Jung
Keyword(s):  
Neural Network ◽  
Processing System ◽  
Basic Knowledge ◽  
Resource Usage ◽  
Knowledge Processing ◽  
Single Tree ◽  
Canopy Tree ◽  
The Us ◽  
Forest Use ◽  
Tree Coverage

Neurons are viewed as the basic cells that process and transmit information. Trees and neurons share a similar structure and neurotransmitter-like substances. No evidence for structures such as neurons, synapses, or a brain has been found inside plants. Consequently, the ability of a network of trees to process information in a method similar to that of a neural network and to make decisions regarding the usage of resources is unperceived. We show that the network between trees is used for knowledge processing to implement decisions that prioritize the forest over a single tree regarding forest use and optimization of resources, similar to the processes of a biological neural network. We found that when there is resection of a network of trees in a forest, namely a trail, each network part will try optimizing its overall access to light resources, represented by canopy tree coverage, independently. This was analyzed in 323 forests in different locations across the US where forest resection is performed by trails. Our results demonstrate that neuron-like relations can occur in a forest knowledge processing system. We anticipate that other systems exist in nature where the basic knowledge processing for resource usage is performed by components other than neurons.

scholarly journals Effective Methodology for Co-Referential Aspect Based Sentiment Analysis of Tourist Reviews

2020 ◽  
pp. 523-529
Author(s):  
Kamlesh A. Waghmare ◽  
Sheetal K. Bhala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sentiment Analysis ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Knowledge Processing ◽  
The Neural Network ◽  
The World ◽  
Sentiment Score

Tourist reviews are the source of data that is going to be used for the travelers around the world to find the hotels for their stay according to their comfort. In this the hotels are ranked over the parameters or aspects considered keeping travelers in mind. This computation of data sets is done with the help of the machine learning algorithms and the neural network. The knowledge processing done over the reviews generates the sentiment score for each hotel with respect to the aspects defined. Here, the explicit , implicit and co-referential aspects are identified by suppressing the noise. This paper proposes the method that can be best used for the detection of the sentiments with the high accuracy.

Knowledge Processing System Using Chaotic Associative Memory

2000 ◽  
Vol 4 (1) ◽  
pp. 39-45
Author(s):  
Yuko Osana ◽  
◽  
Masafumi Hagiwara
Keyword(s):  
Neural Network ◽  
Computer Simulations ◽  
Associative Memory ◽  
Semantic Network ◽  
Processing System ◽  
External Input ◽  
Input Pattern ◽  
Chaotic Neural Network ◽  
Knowledge Processing ◽  
Noisy Input

In this paper, we propose a knowledge processing system using chaotic associative memory (KPCAM). KPCAM is based on a chaotic neural network (CAM) composed of chaotic neurons. In conventional chaotic neural network, when a stored pattern is given continuously to the network as an external input, the input pattern vicinity is searched. The CAM makes use of this property to separate superimposed patterns and to deal with many-tomany associations. In this research, the CAM is applied to knowledge processing in which knowledge is represented in a form of semantic network. The proposed KPCAM has the following features: (1) it can deal with knowledge represented in a form of semantic network; (2) it can deal with characteristic inheritance; (3) it is robust for noisy input. A series of computer simulations shows the effectiveness of the proposed system.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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