"Trendingworthiness" and "prosumers" on Weibo

Liu, Zhengjia

doi:10.17077/etd.obippjet

Cited by 1 publication

(1 citation statement)

References 68 publications

(64 reference statements)

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“…Correlation C1 relates to hypotheses H0, H1 and H2 and in this case the model was built with one input layer, two hidden layers and one output layer which took an input dimension of 7, 7 representing 7 features (or questions) based on the three hypotheses. The input layer contains 14 nodes with initial weight being uniform and activation function being Sigmoid [11]. The two hidden layers contain 8 nodes and 4 nodes respectively, both having initial weights being uniform and activation function being Sigmoid, while the output layer has one node.…”

Section: Backpropagation Neural Network (Bpn)mentioning

confidence: 99%

Deep Learning Based Analysis of Student Aptitude for Programming at College Freshman Level

Narasimhan

Basupi

2023

Preprint

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Predicting Freshman student’s aptitude for computing is critical for researchers to understand the underlying aptitude for programming. Dataset out of a questionnaire taken from various Senior students in a high school in the city of Kanchipuram, Tamil Nadu, India was used, where the questions related to their social and cultural backgrounds and their experience with computers. Several hypotheses were also generated. The datasets were analyzed using three machine learning algorithms namely, Back-propagation Neural Network (BPN) and Recurrent Neural Network (RNN) (and its variant, Gated Recurrent Network (GNN)) with K-Nearest Neighbor (KNN) used as the clas-sifier. Various models were obtained to validate the underpinning set of hypotheses clusters. The results show that the BPN model achieved a high degree of accuracies on various metrics in predicting Freshman student’s aptitude for computer programming.

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Section: Backpropagation Neural Network (Bpn)mentioning

confidence: 99%