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In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.

In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.

The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.

Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.

The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

Gender disparity is a global phenomenon where females outnumber male participants. It has been observed that males are the early leaver from higher education, thus reflecting a severe concern about social instability. Malaysia is a prominent example where females outnumber males in higher education. In this context, this paper aims to examine the effect of individual, social and financial factors on the higher education self-efficacy of male and female students. It develops a comprehensive understanding of gender-based decision factors in pursuing higher education.

The hypothesis was formed based on a comprehensive literature review following the hypothetico-deductive positivist approach. These hypotheses were tested based on a sample of 250 respondents. A multiple regression analysis was deployed to test the relationship between the dependent variable and its predictors.

The results suggest that male and female students’ self-efficacy depends on five determinants, i.e. family influence, peer influence, career expectancy outcome, gender roles and institutional factors. Male students tend to be influenced more by these five determinants than females. Additionally, male students with better financial backgrounds are more likely to have higher self-efficacy, whereas gender roles negatively affect male and female students’ self-efficacy for higher education.

The breakout of COVID-19 resulted in the selection of limited students in Malaysia. Due to restricted movement orders, it was impossible to reach out to the students for data collection. Future research could include a broader area to include multiple other regions of Malaysia. For a broader aspect, the study could be conducted in other areas/countries where the problem of less male participation exists.

The relationship between higher education self-efficacy is assessed with social, financial and institutional factors for male and female students. It will enable the stakeholders and policymakers to make better decisions in increasing the self-efficacy of students to attain equity in higher education institutions.

The finding of this paper will assist in increasing male participation in higher education institutions to avoid any social instability.

This paper contributes to the literature in understanding the causes of gender gap reversal, focusing on Malaysian higher education institutions. It also provides empirical evidence to look at potential factors that affect the higher education self-efficacy of male and female students.