Neural network–based models have shown tremendous achievements in various fields. However, standard AI‐based systems suffer from catastrophic forgetting when undertaking sequential learning of multiple tasks in dynamic environments. Continual learning has emerged as a promising approach to address catastrophic forgetting. It enables AI systems to learn, transfer, augment, fine‐tune, and reuse knowledge for future tasks. The techniques used to achieve continual learning are inspired by the learning processes of the human brain. In this study, we present a comprehensive review of research and recent developments in continual learning, highlighting key contributions and challenges. We discuss essential functions of the biological brain that are pivotal for achieving continual learning and map these functions to the recent machine‐learning methods to aid understanding. Additionally, we offer a critical review of five recent types of continual learning methods inspired by the biological brain. We also provide empirical results, analysis, challenges, and future directions. We hope that this study will benefit both general readers and the research community by offering a complete picture of the latest developments in this field.

Underwater sound classification has been an area of interest in the research community because of its applications in military, commercial, and environmental domains. Underwater sound classification is a challenging task because of the high background noise and complex sound propagation patterns in the sea environment. For underwater sound classification, deterministic as well as stochastic techniques are being used. However, in recent years, stochastic techniques which are learning-based are getting a lot of attention. There exist few survey studies with a limited scope that cover the limited number of studies. In this study, we present the most comprehensive review of research and the latest developments in the field of underwater sound classification by highlighting the contributions and challenges from over 250 recent research papers. We discuss machine learning as well as deep learning-based methods for marine vessel sound classification and fish sound classification. The study also includes details of sources of underwater sound, features, classifiers, datasets, related techniques, challenges, and future trends. We hope that the study will benefit the general reader as well as the research community to have a complete picture of the latest research in the field.

Abstract Virtual globes render planetary‐scale terrain and have limited support for 3D applications development. Game engines provide development environment for interactive 3D applications development and have limited support for world‐scale terrain rendering. The game engine based terrain rendering methods lacks hardware based tessellation for high‐performance. This work presents a novel method for a high‐performance large‐scale terrain rendering for high‐fidelity display systems using game engine. The proposed method performs patch‐based hierarchical culling of a multi‐resolution terrain model to reduce rendering load. A view‐based algorithm simplifies the patches with error control on GPU. Simplified patches are efficiently submitted for drawing using indirect mesh instancing feature of game engine. The proposed method utilizes hardware tessellation feature for high‐performance model tessellation and accurate earth's surface construction using displacement mapping. The proposed method is evaluated by rendering scenes for high‐quality output on consumer‐level hardware. Flights are performed with various settings and results are compared with clipmap‐based and state‐of‐the‐art hardware tessellation based adaptive methods. The proposed method achieved 750, 575, and 540 frames‐per‐second for HD, full‐HD, and ultra‐HD display resolutions.