Results 201 to 210 of about 114,074 (272)

Semi‐automated seal detection on the Western Antarctic Peninsula: an unsupervised machine learning approach for detecting ice seals in aerial survey data

Remote Sensing in Ecology and Conservation, EarlyView.
This study presents a semi‐automated, rule‐based image analysis pipeline to detect ice seals in aerial surveys of the Western Antarctic Peninsula during an unusually low sea ice year. By using simple hierarchical clustering instead of deep learning, the method substantially reduced human annotation effort while achieving 82% recall, identifying 758 ...
Claire McGinnity, Connor C.G. Bamford, Nathan Fenney, Andrew Fleming, Jaume Forcada, Michael S. Tift, Luis A. Hückstädt, Daniel P. Costa, Peter T. Fretwell   +8 more
wiley   +1 more source

Evaluating the Role of Social Capital for the Design of Sustainable River Governance Strategies: The Case of Tagus River

Sustainable Development, EarlyView.
ABSTRACT This study examines the role of social capital in designing and implementing more effective governance strategies to improve the state of the Tagus River's significantly deteriorated middle reach (Spain). We employed social network analysis (SNA) to evaluate the bonding, bridging and linking social capital and the quality of stakeholder ...
Leticia Blázquez, Juan A. García, José M. Bodoque   +2 more
wiley   +1 more source

How AI and Digital Technologies Can Enhance Sustainable Livestock Manure Management: An Overview From Treatment to Distribution

Sustainable Development, EarlyView.
ABSTRACT Sustainable livestock manure management sits at the nexus of climate, nutrient circularity and water quality. This review explores how artificial intelligence (AI) and digital platforms are used across four management stages, that is, treatment, storage, valorisation and distribution, and figures out where integration fails to deliver ...
Zhan Shi, Feihong Liang, Francesco Marinello, Andrea Pezzuolo   +3 more
wiley   +1 more source

DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection

IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley   +1 more source

From prediction to intervention: Paradigm shifts in causal AI for precision medicine and large‐scale cohorts

VIEW, EarlyView.
Large‐scale cohorts and multimodal biomedical data have enabled powerful predictive models for clinical risk stratification, but prediction alone cannot guide effective interventions. This review introduces causal artificial intelligence as a design‐first framework that integrates target trial emulation, causal discovery, and robust effect estimation ...
Linlin Cao, Yudian Xu, Yifan Chen, Honghao Huang, Wei Xu, Jun Pu   +5 more
wiley   +1 more source

The performance of drones and artificial intelligence for monitoring sage‐grouse at leks

Wildlife Biology, EarlyView.
Accurately monitoring sage‐grouse populations is critical for conservation, yet traditional ground‐based visual surveys face challenges in scalability and consistency, prompting the exploration of innovative drone‐based methodologies enhanced by artificial intelligence.
Lance B. McNew, Jason Hanlon, Ilya Buzytsky   +2 more
wiley   +1 more source

Estimating red deer Cervus elaphus population density using drones in a steep and rugged terrain

Wildlife Biology, EarlyView.
Precise and accurate information about population density, crucial for wildlife management, is difficult to obtain for elusive species living in dense forests or steep and inaccessible terrain. Using unmanned aerial vehicles (UAVs), we developed a method for obtaining absolute population estimates of ungulates living in steep, rugged, and partly ...
Julie Bommerlund, Torbjørn Ergon, Simon Filhol, Atle Mysterud   +3 more
wiley   +1 more source

Micro‐Mechanism Informed Neural Networks for Process‐Property Prediction in Laser Powder Bed Fusion

Artificial Intelligence for Engineering, EarlyView.
Hard physics embedding, where neural networks learn residuals relative to analytical baselines, substantially outperforms soft loss‐function constraints for extrapolation in LPBF process–property prediction. Physics integration architecture determines generalization capability more than constraint quantity.
Yo‐Lun Yang
wiley   +1 more source

Intelligent Advisor System for Prescriptive Maintenance of Engineered Assets Using Failure Modes, Effects and Criticality Analysis, Knowledge Graph and Machine Learning

Artificial Intelligence for Engineering, EarlyView.
This paper presents an intelligent advisor for prescriptive maintenance that integrates FMECA, machine learning, and knowledge graphs to support fault diagnosis and root‐cause analysis. Validated on a linear actuator, the framework enhances diagnostic interpretability and traceability, linking sensor‐level anomalies to transparent knowledge‐driven ...
Hongyi Lin, Agusmian Partogi Ompusunggu
wiley   +1 more source

Multitype Game Optimisation: A Two‐Stage Fine‐Tuning Framework for Multi‑Game Optimisation With Large Language Models

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Large language models (LLMs) have made remarkable advances in natural language processing, demonstrating great potential in modelling structured sequences. However, adapting these capabilities to machine gaming tasks such as Go remains challenging due to limitations in strategy generalisation and optimisation efficiency.
Xiali Li, Jingshi Gu, Feifan He, Yang Xiao, Yuanli Jia, Ping Lan   +5 more
wiley   +1 more source