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Examination is one of the common ways to evaluate the students’ cognitive levels in higher education institutions. Exam questions are labeled manually by educators in accordance with Bloom’s taxonomy cognitive domain. To ease the burden of the educators, several past research works have proposed the automated question classification based on Bloom’s taxonomy using the machine learning technique. Feature selection, feature extraction and term weighting are common ways to improve the accuracy of question classification. Commonly used term weighting method in the past work is unsupervised namely TF and TF-IDF. There are several variants of TF and TFIDF and the most optimal variant has yet to be identified in the context of question classification based on BT. Therefore, this paper aims to study the TF, TF-IDF and normalized TF-IDF variants and identify the optimal variant that can enhance the exam question classification accuracy. To investigate the variants two different classifiers were used, which are Support Vector Machine (SVM) and Naïve Bayes. The average accuracies achieved by TF-IDF and normalized TF-IDF variants using SVM classifier were 64.3% and 72.4% respectively, while using Naïve Bayes classifier the average accuracies for TF-IDF and normalized TF-IDF were 61.9% and 63.0% respectively. Generally, the normalized TF-IDF variants outperformed TF and TF-IDF variants in accuracy and F1-measure respectively. Further statistical analysis using t-test and Wilcoxon Signed also shows that the differences in accuracy between normalized TF-IDF and TF, TF-IDF are significant. The findings from this study show that the Normalized TF-IDF3 variant recorded the highest accuracy of 74.0% among normalized TF-IDF variants. Also, the differences in accuracy between Normalized TF-IDF3 and other normalized variants are generally significant, thus the optimal variant is Normalized TF-IDF3. Therefore, the normalized TF-IDF3 variant is important for benchmarking purposes, which can be used to compare with other term weighting techniques in future work.

Faces in an image consists of complex structures in object detection. The components of a face, which includes the eyes, nose and mouth of a person differs from that of ordinary objects, thus making face detecting a complex process. Some of the challenges encounter posed in face detection of unconstrained images includes background variation, pose variation, facial expression, occlusion and noise. Current research of Viola-Jones (V-J) face detection is limited to only 45 degrees in-plane rotation. This paper proposes only one technique for the V-J detection face in unconstrained images, which V-J face detection with invariant rotation. The technique begins by rotating the given image file with each step 30 degrees until 360 degrees. Each step of adding 30 degrees from origin, V-J face detection is applied, which covers more angles of a rotated face in unconstrained images. Robust detection in rotation invariant used in the above techniques will aid in the detecting of rotated faces in images. The images that have been utilized for testing and evaluation in this paper are from CMU dataset with 12 rotations on each image. Therefore, there are 12 test patterns generated. These images have been measured through the correct detection rate, true positive and false positive. This paper shows that the proposed V-J face detection technique in unconstrained images have the ability to detect rotated faces with high accuracy in correct detection rate. To summarize, V-J face detection in unconstrained images with proposed variation of rotation is the method utilized in this paper. This proposed enhancement improves the current V-J face detection method and further increase the accuracy of face detection in unconstrained images.

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