## 5 Results and Discussions  
The experimental data collected are the contents of the university curriculum that is used to train the core courses and test the elective courses for predicting the domain of the course using deep learning models. The dataset has three attributes. The first attribute has the name of the course, the second consists of the contents of the course and the third specify the domain set for the course contents by SME. The dataset is initially trained and tested with basic NLP. Once the text is processed the data set is divided into the training and test data. The top 10 features of class 1 and class 2 with accuracy are depicted in Table 3. The results concerning the evaluation metrics and the prediction accuracy of each domain using spaCy are depicted in Table 4. The precision, recall, and f1-score, are derived by comparing the contents of each domain with the word corpus of spaCy. The mapping accuracy of the contents of the core courses with the specified domain is very less with 62%. These results are derived without using the deep learning models for mapping the contents of the courses with respect to their domains. Henceforth, to improve the accuracy of the domain prediction, the deep learning models are used. The training dataset are embedded as word embeddings and are mapped to the vectors for dimensionality reduction. The maximum word length of the content's column is observed as 13. Python code is implemented and was run on 'jupyter notebook'. The deep learning models GRU and LSTM were used to test and train the dataset using 'TensorFlow' as backend. Once the dataset of core course contents specified with respective domains is loaded, the nltk and spaCy package, a portable GloVec set of embedding vectors from Stanford are loaded. Later, the word embeddings are generated for a given input with the maximum word length 13, a Unicode string returns the time series vector with each word (token) transformed into a 300-dimension representation calculated using Glove Vector. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Domain Name** & **Precision** & **Recall** & **f1-Score** & **Support** \\ \hline Programming & 0.68 & 0.59 & 0.63 & 51 \\ System Structure & 0.77 & 0.72 & 0.74 & 78 \\ Algorithms & 0.56 & 0.67 & 0.61 & 58 \\ Discrete Maths & 0.64 & 0.83 & 0.72 & 41 \\ Network and communication & 0.53 & 0.68 & 0.60 & 103 \\ Software engineering & 0.64 & 0.38 & 0.48 & 47 \\ Data visualization & 0.67 & 0.39 & 0.49 & 51 \\ Differential Equation & 0.71 & 0.67 & 0.69 & 18 \\ Avg/total & 0.64 & 0.62 & 0.62 & 447 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training and test with spacy. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Top 10 Features Used to Predict: Class 1 Best**: & **Top 10 Features Used to Predict: Class 2 Best**: \\ \hline \((-0.7763155002835709,\) ‘test’) & (1.4679062226659, ‘database’) \\ \((-0.749938237584759,\) ‘management’) & (1.4003297383225637, ‘transaction’) \\ \((-0.7395680989261526,\) ‘computer’) & (1.393016683949513, ‘agent’) \\ \((-0.7368785762469924,\) ‘runlength’) & (1.2454164144551625, ‘relational’) \\ \((-0.7363024267563822,\) ‘theoretic’) & (1.2162776944205755,\) ‘pcompleteness’) \\ \((-0.7327033132674196,\) ‘array’) & (1.1859191745528392, ‘dynamic’) \\ \((-0.7327033132674196,\) ‘dimensional’) & (1.1423705049001032,\) ‘belief’) \\ \((-0.7028657817630912,\) ‘bgp’) & (1.139173249178543, ‘general’) \\ \((-0.7028657817630912,\) ‘csppf’) & (1.1324795144192399, ‘amortize’) \\ \((-0.6710890198266639,\) ‘complier’ & (1.132143958415285,\) ‘np’) \\ accuracy: 0.6241610738255033 & accuracy: 0.6241610738255033 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Feature extraction. Once the words are embedded, the word embedding is trained using LSTM and GRU models for 100 epochs.