Machine learning (ML) has been instrumental in solving complex problems and significantly advancing different areas of our lives. Decision tree-based methods have gained significant popularity among the diverse range of ML algorithms due to their simplicity and interpretability. This paper presents a comprehensive overview of decision trees, including the core concepts, algorithms, applications, their early development to the recent high-performing ensemble algorithms and their mathematical and algorithmic representations, which are lacking in the literature and will be beneficial to ML researchers and industry experts. Some of the algorithms include classification and regression tree (CART), Iterative Dichotomiser 3 (ID3), C4.5, C5.0, Chi-squared Automatic Interaction Detection (CHAID), conditional inference trees, and other tree-based ensemble algorithms, such as random forest, gradient-boosted decision trees, and rotation forest. Their utilisation in recent literature is also discussed, focusing on applications in medical diagnosis and fraud detection.

Deep learning (DL), a branch of machine learning (ML), is the core technology in today’s technological advancements and innovations. Deep learning-based approaches are the state-of-the-art methods used to analyse and detect complex patterns in large datasets, such as credit card transactions. However, most credit card fraud models in the literature are based on traditional ML algorithms, and recently, there has been a rise in applications based on deep learning techniques. This study reviews the recent DL-based literature and presents a concise description and performance comparison of the widely used DL techniques, including convolutional neural network (CNN), simple recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Additionally, an attempt is made to discuss suitable performance metrics, common challenges encountered when training credit card fraud models using DL architectures and potential solutions, which are lacking in previous studies and would benefit deep learning researchers and practitioners. Meanwhile, the experimental results and analysis using a real-world dataset indicate the robustness of the deep learning architectures in credit card fraud detection.

Recent advances in machine learning (ML) have shown great promise in detecting heart disease. However, to ensure the clinical adoption of ML models, they must not only be generalizable and robust but also transparent and explainable. Therefore, this research introduces an approach that integrates the robustness of ensemble learning algorithms with the precision of Bayesian optimization for hyperparameter tuning and the interpretability offered by Shapley additive explanations (SHAP). The ensemble classifiers considered include adaptive boosting (AdaBoost), random forest, and extreme gradient boosting (XGBoost). The experimental results on the Cleveland and Framingham datasets demonstrate that the optimized XGBoost model achieved the highest performance, with specificity and sensitivity values of 0.971 and 0.989 on the Cleveland dataset and 0.921 and 0.975 on the Framingham dataset, respectively.

Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At the core of this transformation is deep learning (DL), a subset of ML that is robust in processing and analyzing complex and large datasets. This paper provides a comprehensive overview of key deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief Networks (DBNs), Transformers, Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (Deep RL). Beyond summarizing their mathematical foundations and learning processes, this study offers new insights into how these models are applied in real-world financial contexts, highlighting their specific advantages and limitations in tasks such as algorithmic trading, risk management, and portfolio optimization. It also examines recent advances and emerging trends in the financial industry alongside critical challenges such as data quality, model interpretability, and computational complexity. These insights can guide future research directions toward developing more efficient, robust, and explainable financial models that address the evolving needs of the financial sector.

Since the introduction of foundational models such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), there has been rapid evolution in both the scale and application of large language models (LLMs). This review provides a concise overview of LLMs, their architecture, training methodologies, and recent innovative applications, focusing on notable models such as the GPT series, BERT, Pathways Language Model (PaLM), and Large Language Model Meta AI (LLaMA), and recently the DeepSeek-R1 model. Additionally, this paper presents a taxonomy for categorizing LLMs based on three critical dimensions: scalability, application domains, and ethical considerations. This taxonomy aims to enable researchers and practitioners to better understand these models in terms of their potential and limitations. Lastly, by reviewing contributions from numerous publications, this study identifies emerging trends, gaps, and opportunities in LLM research, providing a structured guide for future investigations.

Gender difference in how technology is used has been a long-time concern in education. This study examined the impact of the rapid transition from face-to-face learning to remote learning (RL) for students of selected higher education institutions in South Africa. The study employed a quantitative census sampling method as an electronic questionnaire administered to 243 respondents, mainly students enrolled in accounting and finance department in their second, third, and fourth year of study. In designing the questionnaire, ten items from the Technology Readiness Index (TRI 2.0) were adapted to determine the technology readiness of respondents. Furthermore, descriptive statistics were used to establish the common Information and Communications Technology (ICT) tools, platforms, and teaching approaches adopted by lecturers and estimate the technological readiness of students. This article revealed some challenges encountered by students while schools were closed due to the lockdown measures to contain the COVID-19 virus. From the findings in this study, it can be seen that male students are more likely to adjust faster than female students to RL. This result is consistent with some of the findings in the literature. Learning from home or other environments outside the university has been proven ineffective as students can get easily distracted by external factors that can affect their learning and understanding. This becomes more difficult for female students, especially in Africa, where it is believed that specific responsibilities are for girls. Statistically, there is a high level of anxiety among female students. This can be attributed to the exceptional living conditions and situations during the ongoing pandemic, such as uncertainty of the future, social isolation, challenges of RL, childcare, babysitting, and gender-based violence. The study recommended that policymakers introduce policies that address students’ challenges, especially female students while working from home.