YOLO (You Only Look Once) is an extensively utilized object detection algorithm that has found applications in various medical object detection tasks. This has been accompanied by the emergence of numerous novel variants in recent years, such as YOLOv7 and YOLOv8. This study encompasses a systematic exploration of the PubMed database to identify peer-reviewed articles published between 2018 and 2023. The search procedure found 124 relevant studies that employed YOLO for diverse tasks including lesion detection, skin lesion classification, retinal abnormality identification, cardiac abnormality detection, brain tumor segmentation, and personal protective equipment detection. The findings demonstrated the effectiveness of YOLO in outperforming alternative existing methods for these tasks. However, the review also unveiled certain limitations, such as well-balanced and annotated datasets, and the high computational demands. To conclude, the review highlights the identified research gaps and proposes future directions for leveraging the potential of YOLO for medical object detection.

Deep Reinforcement Learning (DRL) has gained significant adoption in diverse fields and applications, mainly due to its proficiency in resolving complicated decision-making problems in spaces with high-dimensional states and actions. Deep Deterministic Policy Gradient (DDPG) is a well-known DRL algorithm that adopts an actor-critic approach, synthesizing the advantages of value-based and policy-based reinforcement learning methods. The aim of this study is to provide a thorough examination of the latest developments, patterns, obstacles, and potential opportunities related to DDPG. A systematic search was conducted using relevant academic databases (Scopus, Web of Science, and ScienceDirect) to identify 85 relevant studies published in the last five years (2018-2023). We provide a comprehensive overview of the key concepts and components of DDPG, including its formulation, implementation, and training. Then, we highlight the various applications and domains of DDPG, including Autonomous Driving, Unmanned Aerial Vehicles, Resource Allocation, Communications and the Internet of Things, Robotics, and Finance. Additionally, we provide an in-depth comparison of DDPG with other DRL algorithms and traditional RL methods, highlighting its strengths and weaknesses. We believe that this review will be an essential resource for researchers, offering them valuable insights into the methods and techniques utilized in the field of DRL and DDPG.

Feature selection or dimensionality reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multi-objective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multi-objective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.

The Arithmetic Optimization Algorithm (AOA) is a recently proposed metaheuristic algorithm that has been shown to perform well in several benchmark tests. The AOA is a metaheuristic that uses the main arithmetic operators’ distribution behavior, such as multiplication, division, subtraction, and addition. This paper proposes a binary version of the Arithmetic Optimization Algorithm (BAOA) to tackle the feature selection problem in classification. The algorithm’s search space is converted from a continuous to a binary one using the sigmoid transfer function to meet the nature of the feature selection task. The classifier uses a method known as the wrapper-based approach K-Nearest Neighbors (KNN), to find the best possible solutions. This study uses 18 benchmark datasets from the University of California, Irvine (UCI) repository to evaluate the suggested binary algorithm’s performance. The results demonstrate that BAOA outperformed the Binary Dragonfly Algorithm (BDF), Binary Particle Swarm Optimization (BPSO), Binary Genetic Algorithm (BGA), and Binary Cat Swarm Optimization (BCAT) when various performance metrics were used, including classification accuracy, selected features as well as the best and worst optimum fitness values.

Air pollution is one of the world’s most significant challenges. Predicting air pollution is critical for air quality research, as it affects public health. The Air Pollution Index (API) is a convenient tool to describe air quality. Air pollution predictions can provide accurate information on the future pollution situation, effectively controlling air pollution. Governments have expressed growing concern about air pollution due to its global effect on human health and sustainable growth. This paper proposes a novel forecasting model using One-Dimensional Deep Convolutional Neural Network (1D-CNN) and Exponential Adaptive Gradients (EAG) optimization to predict API for a selected location, Klang, a city in Malaysia. The proposed 1D-CNN–EAG exponentially accumulates past model gradients to adaptively tune the learning rate and converge in both convex and non-convex areas. We use hourly air pollution data over three years (January 2012 to December 2014) for training. Parameter optimization and model evaluation was accomplished by a grid-search with k-folds cross-validation. Results have confirmed that the proposed approach achieves better prediction accuracy than the benchmark models in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and the Correlation Coefficient (R-Squared) with values of 2.036, 2.354, 4.214 and 0.966, respectively, and time complexity.