ABSTRACT This article seeks to understand women’s underrepresentation in STEM professions by investigating the educational factors contributing to female students’ identity development. The current study undertakes a comparative approach between STEM, and the most popular field of study with women, Management and Commerce. Data were collected from a University in Australia. Structural equation modelling was employed for data analysis. Our findings showed that teacher support enhances students’ identity development with their fields of study by lowering feelings of alienation from school and improving the valence of heterogeneity. The comparative approach of this study revealed that teachers’ gender explains students’ identity development with the field of study of STEM. The comparative nature of the research provides deep insights into the role that teachers’ gender plays in strengthening the relationship between teacher support and students’ identity development with STEM discipline. This study is one of the very few shifting the research attention to the education sector, where women’s identity with STEM professions is developed initially. This research makes a significant contribution to the literature by illuminating the mechanisms through which teaching support and educational environment enhance female students’ identity development.

Respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD), are affecting a huge percentage of the world’s population with mortality rates exceeding those of lung cancer and breast cancer combined. The major challenge is the number of patients who are incorrectly diagnosed. To address this, we developed an expert diagnostic system that can differentiate among patients with asthma, COPD or a normal lung function based on measurements of lung function and information about patient’s symptoms. To develop accurate classification algorithms, data from 3657 patients were used and then independently verified using data from 1650 patients collected over a period of two years. Our results demonstrate that the expert diagnostic system can correctly identify patients with asthma and COPD with sensitivity of 96.45% and specificity of 98.71%. Additionally, 98.71% of the patients with a normal lung function were correctly classified, which contributed to a 49.23% decrease in demand for conducting additional tests, therefore decreasing financial cost.