This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia (ALL) subtypes. The two ALL subtypes considered are T-lymphoblastic leukaemia (pre-T) and B-lymphoblastic leukaemia (pre-B). They exhibit various characteristics which make it difficult to distinguish between subtypes from their mature cells, lymphocytes. In a common approach, handcrafted features must be well designed for this complex domain-specific problem. With deep learning approach, handcrafted feature engineering can be eliminated because a deep learning method can automate this task through the multilayer architecture of a convolutional neural network (CNN). In this work, we implement a CNN classifier to explore the feasibility of deep learning approach to identify lymphocytes and ALL subtypes, and this approach is benchmarked against a dominant approach of support vector machines (SVMs) applying handcrafted feature engineering. Additionally, two traditional machine learning classifiers, multilayer perceptron (MLP), and random forest are also applied for the comparison. The experiments show that our CNN classifier delivers better performance to identify normal lymphocytes and pre-B cells. This shows a great potential for image classification with no requirement of multiple preprocessing steps from feature engineering.

Discrimination in the workplace is illegal, yet discriminatory practices remain a persistent global problem. To identify discriminatory practices in the workplace, job advertisement analysis was used by previous studies. However, most of those studies adopted content analysis by manually coding the text from a limited number of samples since working with a large scale of job advertisements consisting of unstructured text data is very challenging. Encountering those limitations, the present study involves text mining techniques to identify multiple types of direct discrimination on a large scale of online job advertisements by designing a method called Direct Discrimination Detection (DDD). The DDD is constructed using a combination of N-grams and regular expressions (regex) with the exact match principle of a Boolean retrieval model. A total of 8,969 online job advertisements in English and Bahasa Indonesia, published from May 2005 to December 2017 were collected from bursakerja-jateng.com as the data. The results reveal that the practices of direct discrimination still exist during the job-hunting process including gender, marital status, physical appearances, and religion. The most recurrent type of discrimination which occurs in job advertisements is based on age (66.27%), followed by gender (38.76%), and physical appearances (18.42%). Additionally, female job seekers are found as the most vulnerable party to experience direct discrimination during recruitment. The results exhibit female job seekers face complex jeopardy in particular job positions comparing to their male counterparts. Not only excluded because of their gender, but female job seekers also had to fulfil more requirements for getting an opportunity to apply for the jobs such as being single, still at a young age, complying specific physical appearances and particular religious preferences. This study illustrates the power and potential of optimizing computational methods on a large scale of unstructured text data to analyze phenomena in the social field.

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Blended learning is a kind of classroom learning which is suitable to help students for promoting their learning with the 21st century skills. When the collaborative learning is integrated into classroom techniques, blended learning is a way for students to learn their basic knowledge with online technology before they come to classroom, and spend most of their time in the classroom performing team activities to solve new or complex problems. This study proposes a model for teaching and learning in order to develop the blended learning through learning by teaching. Based on the gradual release of responsibility model, rainbow talk is used to encourage students to take responsibility for collaborative learning combined with flipped learning which students are assigned to study lessons and take tests before they come to classroom. This aims at studying effectiveness of the proposed model in order to improve mathematical achievement in mathematical courses of general education program for higher education. The study is a practical research that provides statistical analysis of mathematical learning results of students from the proposed learning model. The results of this study indicate that mathematical achievement of students is attained in accordance with the learning outcomes of the course at a satisfactory level and is satisfied with the learning and teaching at a good level.