A new presentation and exploration of human cerebral vasculature correlated with surface and sectional neuroanatomy     

Wieslaw L. Nowinski  Arumugam Thirunavuukarasuu  Ihar Volkau  Yevgen Marchenko  Bivi Aminah  Arnaud Gelas  Su Huang  Looi Chow Lee  Jimin Liu  Ting Ting Ng  Natalia G. Nowinska  Guoyu Yu Qian  Fiftarina Puspitasari  Val M. Runge 《Anatomical sciences education》2009,2(1):24-33

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Multi-label Arabic text categorization: A benchmark and baseline comparison of multi-label learning algorithms     

Bassam Al-Salemi  Masri Ayob  Graham Kendall  Shahrul Azman Mohd Noah 《Information processing & management》2019,56(1):212-227

Multi-label text categorization refers to the problem of assigning each document to a subset of categories by means of multi-label learning algorithms. Unlike English and most other languages, the unavailability of Arabic benchmark datasets prevents evaluating multi-label learning algorithms for Arabic text categorization. As a result, only a few recent studies have dealt with multi-label Arabic text categorization on non-benchmark and inaccessible datasets. Therefore, this work aims to promote multi-label Arabic text categorization through (a) introducing “RTAnews”, a new benchmark dataset of multi-label Arabic news articles for text categorization and other supervised learning tasks. The benchmark is publicly available in several formats compatible with the existing multi-label learning tools, such as MEKA and Mulan. (b) Conducting an extensive comparison of most of the well-known multi-label learning algorithms for Arabic text categorization in order to have baseline results and show the effectiveness of these algorithms for Arabic text categorization on RTAnews. The evaluation involves four multi-label transformation-based algorithms: Binary Relevance, Classifier Chains, Calibrated Ranking by Pairwise Comparison and Label Powerset, with three base learners (Support Vector Machine, k-Nearest-Neighbors and Random Forest); and four adaptation-based algorithms (Multi-label kNN, Instance-Based Learning by Logistic Regression Multi-label, Binary Relevance kNN and RFBoost). The reported baseline results show that both RFBoost and Label Powerset with Support Vector Machine as base learner outperformed other compared algorithms. Results also demonstrated that adaptation-based algorithms are faster than transformation-based algorithms.  相似文献   

Malaysian students' preferences for counsellors: Effects of the sex and ethnic group of counsellors and students     

John M. Littrell  Aminah Haji Hashim  Stanley K. Scheiding 《International journal for the advancement of counseling》1989,12(3):181-190

Iowa State University  相似文献   

Learning from group work in science     

R. F. Kempa  Aminah Ayob 《International Journal of Science Education》2013,35(6):743-754

Abstract

This paper addresses itself to the question of how effective group work is in promoting ‘learning from others’. It follows an earlier report in which verbal interactions between pupils engaged in group work were analysed. The tasks attended to during the group work were all concerned with the planning of scientific investigations.

The findings obtained indicate that a significant amount of ‘learning from others’ occurs as the result of pupils being involved in group work: in the present case, about 40% of information points included in pupils’ independent written accounts had previously been contributed to the group discussion by other pupils. However, the accounts also contained information points that had not been mentioned during the preceding group discussions.

The extent of pupils’ achievement and ‘learning from others’ in group work appeared unrelated to their actual group behaviours, which suggests that even seemingly ‘inactive’ group members benefit from their involvement in group learning experiences.  相似文献