AI is utilized in the marine industry for applications such as autonomous navigation, vessel monitoring, and marine resource management. It employs machine learning algorithms and sensor data analysis to enhance safety, optimize operations, and facilitate sustainable practices in maritime transportation, fisheries, and offshore activities.
Researchers at Cornell University have developed a machine learning-based method using underwater microphones to estimate North Atlantic Right whale numbers, offering a safer and cost-effective alternative to aerial surveys.
Researchers at Harvard SEAS and NTT Research used machine-learning-directed optimization (ML-DO) to design biohybrid mini-rays that swim twice as efficiently as conventional biomimetic models, marking a breakthrough in biohybrid robotics.
Australian researchers are developing a global real-time monitoring system using machine learning and AI to track coral reef health and combat the rapid decline caused by climate change, aiming for quicker conservation intervention.
Researchers at Rutgers University-New Brunswick have developed an AI tool that predicts endangered whale habitats, guiding ships to avoid deadly collisions and supporting marine conservation efforts.
AI’s role in education took center stage at Westminster’s Evidence Week, where policymakers were urged to shape regulations that ensure equitable and effective use of AI in classrooms while addressing potential digital divides.
Study reveals that 91% of glaciers in Svalbard, a global warming hotspot, have shrunk significantly over the past 40 years, losing over 800 km². Using AI to analyze satellite data, researchers found that glacier retreats have accelerated in recent years due to rising temperatures and extreme weather events.
Researchers combined machine learning and physics-based models to predict and visualize sea-surface debris movement around Malta, enhancing marine conservation efforts.
Researchers developed the "Deepdive" dataset and benchmarked deep learning models to automate the classification of deep-sea biota in the Great Barrier Reef, achieving significant accuracy with the Inception-ResNet model.
Researchers introduced innovative computer vision techniques to the maritime industry, incorporating ensemble learning and domain knowledge. These methods significantly improve detection accuracy and optimize video viewing on vessels, offering advancements for marine operations and communication.
Researchers have developed a markerless computer vision method to measure aircraft model attitudes during dynamic wind tunnel testing, providing accurate estimations without altering aerodynamic properties. This novel technique, validated through simulations and real-world data, significantly improves accuracy over traditional methods and offers versatility for various aircraft designs and applications.
Researchers introduced biSAMNet, a cutting-edge model integrating word embedding and deep neural networks, for classifying vessel trajectories. Tested in the Taiwan Strait, it significantly outperformed other models, enhancing maritime safety and traffic management.
Recent research in few-shot fine-grained image classification (FSFGIC) has seen the development of various methods, including class representation learning and global/local deep feature representation techniques. These advancements aim to improve generalization, overcome distribution biases, and enhance discriminative feature representation, yet challenges such as overfitting and efficiency persist, necessitating further investigation.
Researchers introduce METEOR, a deep meta-learning methodology addressing diverse Earth observation challenges. This innovative approach adapts to different resolutions and tasks using satellite data, showcasing impressive performance across various downstream problems.
Korean researchers introduce a groundbreaking framework marrying Explainable AI (XAI) and Zero-Trust Architecture (ZTA) for robust cyberdefense in marine communication networks. Their deep neural network, Zero-Trust Network Intrusion Detection System (NIDS), not only exhibits remarkable accuracy in classifying cyber threats but also integrates XAI methodologies, SHAP and LIME, to provide interpretable insights. This innovative approach fosters transparency and collaboration between AI systems and human experts, promising enhanced cybersecurity in marine, and potentially other, critical infrastructures.
Study by Global Fishing Watch and partners, using machine learning and satellite imagery, reveals 75% of the world's industrial fishing vessels are untracked, highlighting extensive "dark" ocean activity, including in Africa and South Asia.
This study introduces innovative unsupervised machine-learning techniques to analyze and interpret high-resolution global storm-resolving models (GSRMs). By leveraging variational autoencoders and vector quantization, the researchers systematically break down massive datasets, uncover spatiotemporal patterns, identify inconsistencies among GSRMs, and even project the impact of climate change on storm dynamics.
Researchers present an intelligent framework, integrating a Group Method of Data Handling (GMDH) neural network and Shapley Additive Explanations (SHAP) analysis, to predict free atmospheric corrosion in marine steel structures. Leveraging historical sensor data, the framework demonstrates high forecasting accuracy, with optimal parameter selection enhancing performance. The SHAP analysis reveals the impact of environmental factors on corrosion, providing valuable insights into the dynamics of atmospheric corrosion in marine settings.
Researchers propose a novel deep learning (DL) method utilizing convolutional neural networks (CNNs) for automatic sediment core analysis. The DL-based approach employs semantic segmentation on digital images of sediment cores, demonstrating high accuracy in interpreting sedimentary facies, offering a precise, efficient tool for subsurface stratigraphic modeling in geoscience applications.
This article in Nature features a groundbreaking approach for monitoring marine life behavior using Lite3D, a lightweight deep learning model. The real-time anomalous behavior recognition system, focusing on cobia and tilapia, outperforms traditional and AI-based methods, offering precision, speed, and efficiency. Lite3D's application in marine conservation holds promise for monitoring and protecting underwater ecosystems impacted by global warming and pollution.
Researchers introduced the MDCNN-VGG, a novel deep learning model designed for the rapid enhancement of multi-domain underwater images. This model combines multiple deep convolutional neural networks (DCNNs) with a Visual Geometry Group (VGG) model, utilizing various channels to extract local information from different underwater image domains.
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