Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography review paper comprehensively reviews computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The review is meticulously carried out, adhering closely to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are comprehensively reviewed. Evaluating a deep learning architecture based on self-extracted features for classification tasks demonstrated outstanding performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations’ sustainability development goals.

University dropout rates in Europe pose a significant challenge to achieving the European Union’s target of 45% higher education attainment among 25–34-year-olds by 2030. This pilot study introduces an innovative framework, the Integrated Theory-Driven ML Dropout (ITMD) Model, which integrates advanced Machine Learning (ML) techniques with established educational theories, including Tinto’s Dropout Theory, Expectancy-Value Theory, Self-Determination Theory, and the Hardré and Reeve Model. The UnitelmaSapienza (2021) dataset was employed for this initial study to develop and implement a stacked classifier architecture. The model integrates Random Forest, Support Vector Machine (SVM), and Gradient Boosting at the base level, with eXtreme Gradient Boosting (XGBoost) serving as the meta-learner, creating a robust and efficient predictive framework. The model achieves 93% accuracy, precision and recall rates of 91–95%, and an AUC-ROC of 0.94, demonstrating robust and balanced dropout prediction. This ITMD Model proposed an ensemble-based approach that significantly improves over traditional methods, particularly in the early identification of at-risk students, offering educators and policymakers a powerful tool for targeted interventions. By bridging ML advancements with theoretical foundations, the research presents a novel and impactful solution to address dropout challenges globally and especially for European higher education, supporting both institutional and societal goal.