To mitigate the curse of dimensionality in high-dimensional datasets, feature selection has become a crucial step in most data mining applications. However, no feature selection method consistently delivers the best performance across different domains. For this reason and in order to improve the stability of the feature selection process, ensemble feature selection frameworks have become increasingly popular. While many have examined the construction of ensemble techniques under various considerations, little work has been done to shed light on the influence of the aggregation process on the stability of the ensemble feature selection. In contribution to this field, this work aims to explore the impact of some selected aggregation strategies on the ensemble’s stability and accuracy. Using twelve classification real datasets from various domains, the stability and accuracy of five different aggregation techniques were examined under four standard filter feature selection methods. The experimental analysis revealed significant differences in both the stability and accuracy behavior of the ensemble under different aggregations, especially between score-based and rank-based aggregation strategies. Moreover, it was observed that the simpler score-based strategies based on the Arithmetic Mean or L2-norm aggregation appear to be efficient and compelling in most cases. Given the data structure or associated application domain, this work’s findings can guide the construction of feature selection ensembles using the most efficient and suitable aggregation rules.

Feature selection is essential in machine learning and data science. Recently, there has been a growing effort to apply various mathematical methods to construct novel feature selection algorithms. In this study, we present a comprehensive state-of-the-art review of such techniques. We propose a new mathematical framework-based taxonomy to group the existing literature and provide an analysis of the research in each category from a mathematical perspective. The key frameworks discussed include variance-based methods, regularization methods, and Bayesian methods. By analyzing the strengths and limitations of each technique, we provide insights into their applicability across various domains. The review concludes with emerging trends and future research directions for mathematical methods in feature selection.

This study investigates the factors influencing employees’ pro-environmental behavior (PEB) within organizations in the United Arab Emirates (UAE), a nation with a strong policy focus on sustainability. Utilizing a questionnaire-based survey of 146 employees in an automotive division of a UAE company and structural equation modeling (SEM), the research examines the impact of green entrepreneurial orientation, green leadership, environmental commitment, and Green Human Resource Management (GHRM) on employees’ willingness to engage in eco-friendly practices at work. The findings reveal that GHRM and green leadership significantly influence employees’ green entrepreneurial orientation, which in turn, alongside environmental commitment, positively impacts PEB. These results emphasize the importance of integrating sustainability into organizational culture, leadership, and human resource practices to foster a workforce that actively participates in environmental initiatives, thereby contributing to the development of sustainable communities and enhancing stakeholder engagement. The study provides valuable insights into the specific factors that drive PEB in the UAE context, where national policies prioritize sustainability, highlighting that the importance of implementing green practices and promoting a supportive environment encourages employees and stakeholders to embrace environmental sustainability. The research also sheds light on the role of green entrepreneurial orientation, suggesting that empowering employees to develop innovative environmental solutions can be a key driver of PEB. The SEM analysis also confirmed the positive impact of GHRM and green leadership on green entrepreneurial orientation. Additionally, green entrepreneurial orientation and environmental commitment were found to significantly influence PEB. These results have practical implications for organizations in the UAE and beyond, emphasizing that by integrating eco-friendly practices and fostering stakeholder engagement, organizations can enhance their environmental performance, strengthen their reputation, and attract environmentally conscious customers and employees, contributing to the development of sustainable communities.

Deep Learning has contributed significantly to the advances made in the fields of Medical Imaging and Computer Aided Diagnosis (CAD). Although a variety of Deep Learning (DL) models exist for the purposes of image classification in the medical domain, more analysis needs to be conducted on their decision-making processes. For this reason, several novel Explainable AI (XAI) techniques have been proposed in recent years to better understand DL models. Currently, medical professionals rely on visual inspections to diagnose potential diseases in endoscopic imaging in the preliminary stages. However, we believe that the use of automated systems can enhance both the efficiency for such diagnoses. The aim of this study is to increase the reliability of model predictions within the field of endoscopic imaging by implementing several transfer learning models on a balanced subset of Kvasir-capsule, a Wireless Capsule Endoscopy imaging dataset. This subset includes the top 9 classes of the dataset for training and testing. The results obtained were an F1-score of 97%±1% for the Vision Transformer model, although other models such as MobileNetv3Large and ResNet152v2 were also able to achieve F1-scores of over 90%. These are currently the highest-reported metrics on this data, improving upon prior studies done on the same dataset. The heatmaps of several XAI techniques, including GradCAM, GradCAM++, LayersCAM, LIME, and SHAP have been presented in image form and evaluated according to their highlighted regions of importance. This is in an effort to better understand the decisions of the top-performing DL models and look beyond their black-box nature.

In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection is that different approaches can give very different results when applied to similar datasets. Aggregating the results of different selection methods helps to resolve this concern and control the diversity of selected feature subsets. In this work, we implemented a general framework for the ensemble of multiple feature selection methods. Based on diversified datasets generated from the original set of observations, we aggregated the importance scores generated by multiple feature selection techniques using two methods: the Within Aggregation Method (WAM), which refers to aggregating importance scores within a single feature selection; and the Between Aggregation Method (BAM), which refers to aggregating importance scores between multiple feature selection methods. We applied the proposed framework on 13 real datasets with diverse performances and characteristics. The experimental evaluation showed that WAM provides an effective tool for determining the best feature selection method for a given dataset. WAM has also shown greater stability than BAM in terms of identifying important features. The computational demands of the two methods appeared to be comparable. The results of this work suggest that by applying both WAM and BAM, practitioners can gain a deeper understanding of the feature selection process.

With the aim of appraising the impact of Emergency Remote Teaching (ERT) amidst the COVID-19 pandemic on college students, an online survey was conducted in December 2020 on a total of 588 undergraduate students at the American University of Sharjah in the United Arab Emirates. The purpose of the study was to probe into the perceptions of college students regarding their learning process and its influence on their mental health with the abrupt transition from face-to-face classes to ERT in the Spring 2020 semester. A comprehensive analysis was performed using structural equation modeling and other statistical techniques to reveal crucial results associated with the factors that have an effect on the students’ psychological distress, such as quality of courses, academic performance, and readiness for future work or studies. Findings suggest that the students’ perceived quality of courses under ERT has a significant impact on their academic performance and readiness for future work or studies. Moreover, they indicate that these factors serve as a vital mediating role in provoking psychological distress among the students. The study also shows that gender, previous history of anxiety/distress, education being at risk due to financial issues caused by COVID-19, and year of study significantly affect the students’ distress levels. In order to ensure and prioritize the well-being of college students during these turbulent times, new strategies are urgently needed to develop and enhance resilient ERT environments in higher education. The study concludes with limitations and suggestions for further research.