## 4 Experiments and Results
### Results of the Multiway ADI Problem  
For the multiway ADI problem, a four-way ADI problem was run including all of the four classes in the collected data, and a three-way ADI problem, which included the Hijazi, Najdi, and Hasawi dialects. Table 4 shows the results of the four-way ADI problem for all of our considered algorithms, including the CNN approach (Models 1, 2, 3, and 4) and the classical machine learning algorithms. For the CNN approach, the best result was achieved for Model 1, which reached an accuracy of 35.4%. In the model, the tashkeel was considered as a character (resulting in a 67-character list), and there were no drop layers. In the case of removing tashkeel and adding drop layers (Model 4), the performance of the CNN model slightly increased in performance by 0.8 percent compared with the performance without the drop layers (Model 3). On the other hand, when considering tashkeel in the character list and without adding the drop layers (Model 1), the performance increased by 1.7% compared with the same case but with the drop layers (Model 3). Therefore, the results indicated that this slight improvement might come from the dual effects of two factors, the addition of tashkeel in the character list and the lack of drop layers being added in the CNN architecture, not from the tashkeel alone. Thus, the decision was made to normalise the dataset by removing the tashkeel from the Arabic sentences and running the considered classical machine learning algorithms for the three- and two-way ADI problems. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Precision** & **Recall** & **F-Measure** & **Accuracy** \\ \hline Model 5 & 43.0\% & 43.7\% & 41.1\% & 43.1\% \\ \hline Model 6 & 65.4\% & 46.1\% & 42.4\% & 47.0\% \\ \hline Model 7 & 44.78\% & 44.0\% & 43.7\% & 43.8\% \\ \hline Model 8 & 61.7\% & 42.1\% & 38.1\% & 43.2\% \\ \hline MultinomialNB & & & & \\ max\_df = 0.75, min\_df = 1, & 44.5\% & 44.4\% & 43.9\% & 44.3\% \\ ngram\_range = (1, 2) & & & & \\ \hline BernoulliN & & & & \\ max\_df = 0.75, min\_df = 1, & 45.8\% & 44.1\% & 43.9\% & 43.6\% \\ ngram\_range = (1, 1) & & & & \\ \hline SGDC & & & & \\ max\_df = 0.75, min\_df = 1, & 44.1\% & 44.3\% & 43.8\% & 43.9\% \\ ngram\_range = (1, 4) & & & & \\ \hline SVC & & & & \\ max\_df = 0.75, min\_df = 1, & 45.6\% & 45.6\% & 45.5\% & 45.8\% \\ ngram\_range = (1, 3) & & & & \\ \hline Logistic Regression & & & & \\ max\_df = 0.75, min\_df = 1, & 47.1\% & 47.1\% & 46.7\% & 46.8\% \\ ngram\_range = (1, 4) & & & & \\ \hline LinearSVC & & & & \\ max\_df = 0.75, min\_df = 1, & 46.0\% & 46.1\% & 45.7\% & 45.8\% \\ ngram\_range = (1, 4) & & & & \\ \hline NuSVC & & & & \\ max\_df = 0.75, min\_df = 1, & 45.9\% & 46.0\% & 45.7\% & 45.8\% \\ ngram\_range = (1, 4) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of three-way ADI problem (classes 1 and 3). For the classical machine learning algorithms, our grid search clearly indicated that most of them achieved the highest performance using TF-IDF with max_df being equal to 0.75 and min_df being equal to 1, with the ngram_range being equal to (1, 4). In the case of the four-way ADI problem, all the considered classical machine learning algorithms outperformed the CNN models, where the best performance was achieved using LR and NuSVC, reaching an accuracy of 40.9%. Table 5 shows the results of the three-way ADI problem. In this problem, the worst class was deleted, which was Janobi, while keeping the other classes to test the performance of the considered algorithms in that case. However, the best performance was achieved by using the CNN approach (Model 6), in which the tashkeel was removed and no drop layer was added, reaching an accuracy of 47.0%, followed by a close performance of the LR algorithm achieving 46.8%. The classical machine learning algorithms also outperformed the other CNN models in the three-way ADI problem.