Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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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.
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): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 108
Country: Bangladesh
Subject: Global Journal of Computer Science and Technology - D: Neural & AI
Authors: Dr. Snehasish Sarker, Md.Sarwar Kamal, Puja Das (PhD/Dr. count: 1)
View Count (all-time): 257
Total Views (Real + Logic): 10382
Total Downloads (simulated): 2508
Publish Date: 2012 08, Wed
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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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