This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

Several studies have explored green economy and the needs for improvement on the standard of living among low-income families or households in many developing countries including Bangladesh. Similarly, there is an emphasis on economic growth and vision 2030 is regarded stressed. Nonetheless, there is less attention in exploring green economy in propelling sustainable financial inclusion among low-income families and households in Bangladesh in order to attain vision 2030 and overall economic growth.  The primary objective is to explore green economy in fostering sustainable financial inclusion among low-income families and households in Bangladesh in enhancing economic growth and vision 2030 in Bangladesh. Content Analysis (CA) and systematic literature review (SLR) as an integral part of qualitative research. Secondary data were gathered through different sources such as: Web of Science (WOS), related journals, published references, research papers, library sources and reports. The results indicated that poverty is a prime challenge impeding sustainable financial inclusion among low-income families and households in Bangladesh. The study has further established the potential of green economy in improving well-beings and social fairness in fostering sustainable and inclusive finance among families or households with low-income in the country. The paper also highlighted the necessity of implementing policy relating to vision 2030 by enhancing sustainable and inclusive finance among low-income households in particular and overall economic growth in the country in general. In conclusion, it has been reiterated that green economy has been a mechanism for achieving sustainable development in general and poverty eradication among low-income households in Bangladesh. It is therefore suggested that the government and economic policymakers should provide enabling environment for improving green economy among low-income households in achieving Vision 2030 and overall economic growth in the country.

Low-cost housing homeownership funding for junior staffers is challenging in private sector organisations, especially in developing countries. Motivating private sector investment in junior staffers’ homeownership via a developed expanded corporate social responsibility (ECSR) may promote achieving Sustainable Development Goal 11 (SDG 11). Therefore, the study investigates the role of the ECSR framework in improving Nigeria’s private sector junior staffers’ homeownership and achieving SDG 11. Data were collected via face-to-face interviews with selected participants in six of Nigeria’s geo-political zones. The study adopted thematic analysis to analyse the collected data. Six variables emerged from the 18 re-clustered sub-variables. This includes institutionalising ECSR in low-income homeownership, housing finance for junior staffers’ homeownership, and housing incentives and stakeholders’ participation for low-income earners. The research employed six variables and 18 sub-variables to develop the improved private sector’s junior staffers’ homeownership via ECSR and achieving SDG 11 (sustainable cities and communities) and their targets. The research presents a novel approach by attempting to integrate SDG 11 with Corporate Social Housing, an extension of corporate social responsibility, especially to align the SDGs with evolving perspectives on Expanded Corporate Social Responsibility in Nigeria.

The artificial intelligence (AI)-based architect's profile's selection (simply iSelection) uses a polymathic mathematical model and AI-subdomains' integration for enabling automated and optimized human resources (HR) processes and activities. HR-related processes and activities in the selection, support, problem-solving, and just-in-time evaluation of a transformation manager's or key team members' polymathic profile (TPProfile). Where a TPProfile can be a classical business manager, transformation manager, project manager, or an enterprise architect. iSelection-related selection processes use many types of artifacts, like critical success factors (CSF), AI-subdomain' integration environments, and an enterprise-wide decision-making system (DMS). iSelection focuses on TPProfiles for various kinds of transformation projects, like the case of the transformation of enterprises' HRs (EHR) processes, activities, and related fields, like enterprise resources planning (ERP) environments, financial systems, human factors (HF) evolution, and AI-subdomains. The iSelection tries to offer a well-defined (or specific) TPProfile, which includes HF's original-authentic capabilities, education, affinities, and possible polymathical characteristics. Such a profile can also be influenced by educational or training curriculum (ETC), which also takes into account transformation projects’ acquired experiences. Knowing that selected TPProfiles are supported by an internal (or external) transformation framework (TF), which can support standard transformation activities, and solving various types of iSelection’s problems. Enterprise transformation projects (simply projects) face extremely high failure rates (XHFR) of about 95%, which makes EHR selection processes very complex.