Primary Education Status Analysis in Bangladesh Based On Neural Networks and Baysian Networks

1
Dr. Md.Sarwar kamal
Dr. Md.Sarwar kamal
2
Dr. Snehasish Sarker
Dr. Snehasish Sarker
3
Md.Sarwar Kamal
Md.Sarwar Kamal
4
Puja Das
Puja Das
1 BGC Trust Uiversity Bangladesh

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GJCST Volume 12 Issue D10

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In this research work we have concentrate to measure the primary education status in Bangladesh, a developing country of South Asia. It known that the literacy rate of South Asian country is very slow and it is not the different in Bangladesh. Here we measure the dropout rate of primary school kids at different classes at different sessions. We have collected the data from various primary schools from Chittagong region of Bangladesh. Here we use K -Nearest Neighbor (KNN) algorithm to classify the data from irrelevant data like secondary school and tertiary level data. After then we have applied Neural Network (NN) to train the data set for better result. Finally we have compared the result by calculating the result with Bayesian Network (BN). Here we found that if the dropout rate is small Neural Network is best to measure the result and NN generate more error when the dropout rate is large. On the contrary BN is better when the rate is large.

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No external funding was declared for this work.

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The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

Dr. Md.Sarwar kamal. 2012. \u201cPrimary Education Status Analysis in Bangladesh Based On Neural Networks and Baysian Networks\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 12 (GJCST Volume 12 Issue D10): .

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GJCST Volume 12 Issue D10
Pg. 1- 10
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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August 22, 2012

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In this research work we have concentrate to measure the primary education status in Bangladesh, a developing country of South Asia. It known that the literacy rate of South Asian country is very slow and it is not the different in Bangladesh. Here we measure the dropout rate of primary school kids at different classes at different sessions. We have collected the data from various primary schools from Chittagong region of Bangladesh. Here we use K -Nearest Neighbor (KNN) algorithm to classify the data from irrelevant data like secondary school and tertiary level data. After then we have applied Neural Network (NN) to train the data set for better result. Finally we have compared the result by calculating the result with Bayesian Network (BN). Here we found that if the dropout rate is small Neural Network is best to measure the result and NN generate more error when the dropout rate is large. On the contrary BN is better when the rate is large.

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Primary Education Status Analysis in Bangladesh Based On Neural Networks and Baysian Networks

Dr. Snehasish Sarker
Dr. Snehasish Sarker
Md.Sarwar Kamal
Md.Sarwar Kamal
Puja Das
Puja Das

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