New Learning Approach for Unsupervised Neural Networks Model with Application to Agriculture Field

Belattar, Sara; Abdoun, Otman; Haimoudi, El Khatir

doi:10.14569/ijacsa.2020.0110548

Cited by 6 publications

(8 citation statements)

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“…The unsupervised learning method is used to solve classification and clustering tasks. One of the neural network paradigms that uses this method is the SOM map, which has been used in several classification tasks and has undergone several improvements and evolutions to increase the relevance of the classification and the learning speed [16], [17]. (See Figure 3).…”

Section: Fig 2 Examples Of Rna Paradigmsmentioning

confidence: 99%

“…The input data is presented as a matrix, the rows are the vectors of the objects and the columns are the components of these objects. This paradigm uses competitive learning, in which it tries to distribute the training set into groups (clusters), which are specific to the input data [16], [17]. This type of neural network processes only the input vectors X and thus implements the "unsupervised" learning procedure.…”

Section: Fig 3 Korhonen's Self-organizing Mapmentioning

confidence: 99%

“…The learning procedure starts with the normalization of the input data and the synaptic weights to an even length to reduce the learning time [16], [17]. This operation is based on the following algebraic formula:…”

Section: Fig 3 Korhonen's Self-organizing Mapmentioning

confidence: 99%

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Smart Model for Classification and Orientation of Learners in a MOOC

Dhaiouir

Ezziyyani

Khaldi

2022

Int. J. Emerg. Technol. Learn.

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Distance education (E-Learning) is experiencing significant, rapid and con-tinuous evolution all over the world, especially with the arrival of the covid-19 pandemic. MOOCs are considered as a personal learning process, which are addressed to a massive and varied number of learners. The problem of the free opening MOOCs puts us in front of a massive number of registrants, which means a large number of heterogeneous profiles, which makes the teacher's task more complicated, either in terms of follow-up or framing. As a solution to this problem, in this present work, we propose an approach that allows the classification and categorization of learner profiles via an intelli-gent and autonomous system developed on the basis of neural networks and in particular the self-organizing map (SOM). This approach which is based on the traceability of learners, allowed us to get homogenous groups in order to direct them towards MOOCs that meet their characteristics and needs. The tests carried out have shown that our approach is efficient in terms of classification and grouping of profiles, which allows us to manage a large number of learners either at the level of the choice of relevant contents or during the evaluation process.

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Section: Fig 2 Examples Of Rna Paradigmsmentioning

confidence: 99%

Section: Fig 3 Korhonen's Self-organizing Mapmentioning

confidence: 99%

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Smart Model for Classification and Orientation of Learners in a MOOC

Dhaiouir

Ezziyyani

Khaldi

2022

Int. J. Emerg. Technol. Learn.

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show abstract

“…In this study, their proposed strategy removed the relation complexes between patterns, resulting in better classification performance when compared to the standard K-SOM. The Gram-Schmidt algorithm was suggested in our first article [23] to increase the classification accuracy of the K-SOM in unsupervised learning. For validation in this study, we used two datasets.…”

Section: Related Workmentioning

confidence: 99%

“…After determining the winning neuron by the Euclidean Distance metric, the weights of the winning neuron and all its neighbors must be corrected and updated using the following equation [23], [41][42][43].…”

Section: B Counter Propagation Artificial Neural Networkmentioning

confidence: 99%