Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering the three main ensemble methods: bagging, boosting, and stacking, their early development to the recent state-of-the-art algorithms. The study focuses on the widely used ensemble algorithms, including random forest, adaptive boosting (AdaBoost), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). An attempt is made to concisely cover their mathematical and algorithmic representations, which is lacking in the existing literature and would be beneficial to machine learning researchers and practitioners.

Recurrent neural networks (RNNs) have significantly advanced the field of machine learning (ML) by enabling the effective processing of sequential data. This paper provides a comprehensive review of RNNs and their applications, highlighting advancements in architectures, such as long short-term memory (LSTM) networks, gated recurrent units (GRUs), bidirectional LSTM (BiLSTM), echo state networks (ESNs), peephole LSTM, and stacked LSTM. The study examines the application of RNNs to different domains, including natural language processing (NLP), speech recognition, time series forecasting, autonomous vehicles, and anomaly detection. Additionally, the study discusses recent innovations, such as the integration of attention mechanisms and the development of hybrid models that combine RNNs with convolutional neural networks (CNNs) and transformer architectures. This review aims to provide ML researchers and practitioners with a comprehensive overview of the current state and future directions of RNN research.

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) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. However, the field of deep learning is constantly evolving, with recent innovations in both architectures and applications. Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolution and applications of foundational models like convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs), as well as recent architectures such as transformers, generative adversarial networks (GANs), capsule networks, and graph neural networks (GNNs). Additionally, the paper discusses novel training techniques, including self-supervised learning, federated learning, and deep reinforcement learning, which further enhance the capabilities of deep learning models. By synthesizing recent developments and identifying current challenges, this paper provides insights into the state of the art and future directions of DL research, offering valuable guidance for both researchers and industry experts.

Recent advancements in electronic commerce and communication systems have significantly increased the use of credit cards for both online and regular transactions. However, there has been a steady rise in fraudulent credit card transactions, costing financial companies huge losses every year. The development of effective fraud detection algorithms is vital in minimizing these losses, but it is challenging because most credit card datasets are highly imbalanced. Also, using conventional machine learning algorithms for credit card fraud detection is inefficient due to their design, which involves a static mapping of the input vector to output vectors. Therefore, they cannot adapt to the dynamic shopping behavior of credit card clients. This paper proposes an efficient approach to detect credit card fraud using a neural network ensemble classifier and a hybrid data resampling method. The ensemble classifier is obtained using a long short-term memory (LSTM) neural network as the base learner in the adaptive boosting (AdaBoost) technique. Meanwhile, the hybrid resampling is achieved using the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method. The effectiveness of the proposed method is demonstrated using publicly available real-world credit card transaction datasets. The performance of the proposed approach is benchmarked against the following algorithms: support vector machine (SVM), multilayer perceptron (MLP), decision tree, traditional AdaBoost, and LSTM. The experimental results show that the classifiers performed better when trained with the resampled data, and the proposed LSTM ensemble outperformed the other algorithms.

Deep convolutional neural networks (CNNs) have revolutionized medical image analysis by enabling the automated learning of hierarchical features from complex medical imaging datasets. This review provides a focused analysis of CNN evolution and architectures as applied to medical image analysis, highlighting their application and performance in different medical fields, including oncology, neurology, cardiology, pulmonology, ophthalmology, dermatology, and orthopedics. The paper also explores challenges specific to medical imaging and outlines trends and future research directions. This review aims to serve as a valuable resource for researchers and practitioners in healthcare and artificial intelligence.