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Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this paper describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This study applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The novelty of this study lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. This study compares three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving; however, these strategies have been more successful in drones. The experiment has been carried out in a virtual environment made available by AirSim. Using Unreal Engine 4, the various training and testing scenarios were created for understanding and analyzing the behavior of RL algorithms for drones. According to the training results, SAC outperformed the other two algorithms. PPO was the least successful among the algorithms, indicating that on-policy algorithms are ineffective in extensive 3D environments with dynamic actors. DQN and SAC, two off-policy algorithms, produced encouraging outcomes. However, due to its constrained discrete action space, DQN may not be as advantageous as SAC in narrow pathways and twists. Concerning further findings, when it comes to autonomous drones, off-policy algorithms, such as DQN and SAC, perform more effectively than on-policy algorithms, such as PPO. The findings could have practical implications for the development of safer and more efficient drones in the future.

There has been a sharp increase in liver disease globally, and many people are dying without even knowing that they have it. As a result of its limited symptoms, it is extremely difficult to detect liver disease until the very last stage. In the event of early detection, patients can begin treatment earlier, thereby saving their lives. It has become increasingly popular to use ensemble learning algorithms since they perform better than traditional machine learning algorithms. In this context, this paper proposes a novel architecture based on ensemble learning and enhanced preprocessing to predict liver disease using the Indian Liver Patient Dataset (ILPD). Six ensemble learning algorithms are applied to the ILPD, and their results are compared to those obtained with existing studies. The proposed model uses several data preprocessing methods, such as data balancing, feature scaling, and feature selection, to improve the accuracy with appropriate imputations. Multivariate imputation is applied to fill in missing values. On skewed columns, log1p transformation was applied, along with standardization, min–max scaling, maximum absolute scaling, and robust scaling techniques. The selection of features is carried out based on several methods including univariate selection, feature importance, and correlation matrix. These enhanced preprocessed data are trained on Gradient boosting, XGBoost, Bagging, Random Forest, Extra Tree, and Stacking ensemble learning algorithms. The results of the six models were compared with each other, as well as with the models used in other research works. The proposed model using extra tree classifier and random forest, outperformed the other methods with the highest testing accuracy of 91.82% and 86.06%, respectively, portraying our method as a real-world solution for detecting liver disease.