Results 151 to 160 of about 60,295 (249)

From “Mirror Flower, Water Moon” to Multi‐Task Visual Prospective Representation Learning for Unmanned Aerial Vehicles Indoor Mapless Navigation

Journal of Field Robotics, Volume 43, Issue 2, Page 655-680, March 2026.
ABSTRACT Vision‐based deep learning models have been widely adopted in autonomous agents, such as unmanned aerial vehicles (UAVs), particularly in reactive control policies that serve as a key component of navigation systems. These policies enable agents to respond instantaneously to dynamic environments without relying on pre‐existing maps.
Yingxiu Chang, Yongqiang Cheng, John Murray, Muhammad Khalid, Umar Manzoor   +4 more
wiley   +1 more source

TSG‐Net: A Multiscale Decomposition and Spatio‐Temporal Graph Neural Network Framework for High‐Precision Wind Power Forecasting

Wind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT Wind energy's intermittency poses significant challenges for power grid stability. Existing forecasting methods exhibit notable limitations: traditional machine learning models struggle with long‐term temporal dependencies, while deep learning approaches often overlook spatial relationships among turbines.
YuChen Zhang
wiley   +1 more source

AI‐Driven Deep Learning Framework for Detecting Subtle Surface Defects on Wind Turbine Blades

Wind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT Wind turbine blade surface defect detection is of great significance in ensuring the safety and operational efficiency of wind power systems. However, accurately detecting subtle and small‐scale defects remains challenging under complex imaging conditions.
Shoutu Li, Jiawei Pan, Ling Liu, Jin Zhao, Nianxi Yue, Zhen Wu   +5 more
wiley   +1 more source

Federated Learning and 5G/6G‐Based Internet of Medical Things (IoMT): Applications, Key Enabling Technologies, Open Issues and Future Research Directions

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.
This review examines the integration of federated learning (FL) in the Internet of Medical Things (IoMT), enhanced by 5G/6G technologies, to improve healthcare systems with decentralized data processing, enhanced privacy, reduced latency, and efficient resource utilization, while addressing emerging challenges and future research directions.
Abdul Ahad, Kazi Istiaque Ahmed, Farhan Ullah, Muhammad Aman Sheikh, Mohammad Tahir, Mohammad Hayajneh, Ivan Miguel Pires   +6 more
wiley   +1 more source

Edge Computing in Healthcare Using Machine Learning: A Systematic Literature Review

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 1, March 2026.
Three key parts of our review. This review examines recent research on integrating machine learning with edge computing in healthcare. It is structured around three key parts: the demographic characteristics of the selected studies; the themes, tools, motivations, and data sources; and the key limitations, challenges, and future research directions ...
Amir Mashmool, Giorgio Delzanno, Hamid Saadatfar, Aakash Ahmad, Rainer Koschke, Roohallah Alizadehsani, U. Rajendra Acharya, Daniele D'Agostino   +7 more
wiley   +1 more source

Analysis of flow cytometry data by matrix relevance learning vector quantization. [PDF]

PLoS One, 2013
Biehl M, Bunte K, Schneider P.
europepmc   +1 more source

Plasma Proteomic Signatures of Pediatric Sepsis Reveal Persistent Inflammation and Phase‐Specific Biomarkers

FASEB BioAdvances, Volume 8, Issue 3, March 2026.
We enrolled a pediatric sepsis cohort at acute and recovery phases for plasma proteomics analysis. Abundantly expressed proteins in sepsis were identified and subjected to machine learning. In addition, the data was compared to adult sepsis and sterile inflammation.
Fahd Alhamdan, Yi‐Cheng Sin, Erik Malm, Hanna Van Pelt, Samuel Kim, LeeAnn Higgins, Yue Chen, Koichi Yuki   +7 more
wiley   +1 more source

Prototype-Based Classifiers and Vector Quantization on a Quantum Computer-Implementing Integer Arithmetic Oracles for Nearest Prototype Search. [PDF]

Entropy (Basel)
Engelsberger A, Pšeničkova M, Villmann T.   +2 more
europepmc   +1 more source

animal2vec and MeerKAT: A self‐supervised transformer for rare‐event raw audio input and a large‐scale reference dataset for bioacoustics

Methods in Ecology and Evolution, Volume 17, Issue 3, Page 875-888, March 2026.
Abstract Bioacoustic research, vital for promoting conservation and understanding animal behaviour and ecology, faces a monumental challenge: analysing vast datasets where animal vocalizations are rare. While deep learning techniques are becoming standard, adapting them to bioacoustics remains difficult.
Julian C. Schäfer‐Zimmermann, Vlad Demartsev, Baptiste Averly, Kiran L. Dhanjal‐Adams, Mathieu Duteil, Gabriella Gall, Marius Faiß, Lily Johnson‐Ulrich, Dan Stowell, Marta B. Manser, Marie A. Roch, Ariana Strandburg‐Peshkin   +11 more
wiley   +1 more source

Explaining the Comprehension–Production Vocabulary Gap Through Neural Networks and Cross‐Syndrome Evidence: Insights From Williams Syndrome

Developmental Science, Volume 29, Issue 2, March 2026.
ABSTRACT The comprehension–production vocabulary gap is a well‐documented hallmark of language development; however, anecdotal evidence suggests that this asymmetry may be reduced in children with Williams syndrome (WS). Here, we use empirical data to characterise the comprehension–production gap and computational modelling to investigate potential ...
Dean D'Souza, Hana D'Souza, Julien Mayor, Ángel Eugenio Tovar   +3 more
wiley   +1 more source