Results 171 to 180 of about 3,569 (224)

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

High-throughput neutralization measurements correlate strongly with evolutionary success of human influenza strains. [PDF]

Elife
Kikawa C, Loes AN, Huddleston J, Figgins MD, Steinberg P, Griffiths T, Drapeau EM, Peck H, Barr I, Englund JA, Hensley SE, Bedford T, Bloom JD.   +12 more
europepmc   +1 more source

Camera trap distance sampling for density estimation of tiger prey in a Sumatran ecosystem restoration concession

Wildlife Biology, EarlyView.
Estimating prey species densities is critical for implementing effective tiger Panthera tigris recovery strategies. Several statistical models exist for density estimation of unmarked species from camera trap data, all of which rely on the random placement of cameras.
Beno Fariza Syahri, Febri Anggriawan Widodo, Muhammad Rahmad Al Kahfi, Muhammad Rizki Qosyim, Heri Irawan, Dede Hendra Setiawan, Muhammad Ali Imron, Matthew J. Struebig, Nicolas J. Deere, Satyawan Pudyatmoko, Thomas Neill Edward Gray   +10 more
wiley   +1 more source

Leveraging data augmentation for machine learning models in predicting depression and anxiety using the Revised Child Anxiety and Depression Scale clinical reports. [PDF]

Front Psychiatry
Noor S, Hussain Z, Hamdan QU, Zaman M, Paracha RZ, Zahra Shamsi SA.   +5 more
europepmc   +1 more source

Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia   +4 more
wiley   +1 more source

Adaptive memory: The effects of survival-constrained retrieval on recognition depend on initial encoding conditions. [PDF]

Mem Cognit
Bell R, Mieth L, Kroneisen M.
europepmc   +1 more source

HSL‐CFS: Hybrid stacked learning with cooperative feature selection for cyberattack detection in smart grids

Energy Conversion and Economics, EarlyView.
Abstract An effective method for detecting cyberattacks is essential to the security of smart grids (SGs). In SGs, data from both cyber and physical domains can support attack detection. However, existing works insufficiently consider the heterogeneity, high dimensionality, and cross‐domain correlations of multi‐source data, affecting model ...
Qize Gao, Qiuyu Lu, June Li, Zhao Peng, Guo Shen, Ming Ni   +5 more
wiley   +1 more source

Learning and Motivation State Fluctuations from Motoric and Neurophysiologic Metrics during a Somatosensory Task in Mice. [PDF]

eNeuro
Bueno-Junior LS, Ghimire A, Ding M, Watson BO.   +3 more
europepmc   +1 more source

Residual Life Assessment of Oil‐Immersed Insulating Paper by FTIR Feature Interpretability Evolution

High Voltage, EarlyView.
ABSTRACT The ageing of insulating paper escalates the peril of insulation failure in oil‐impregnated‐paper power equipment. Consequently, the real‐time monitoring and non‐destructive assessment of insulating paper condition assume paramount significance.
Guangyi Liu, Yanan Liu, Chen Zhang, Jian Li, Zhengyong Huang, Feipeng Wang, Huaqiang Li, Jinghui Gao, Lisheng Zhong   +8 more
wiley   +1 more source

Systematic Evaluation of Machine Learning and Deep Learning Models for IoT Malware Detection Across Ransomware, Rootkit, Spyware, Trojan, Botnet, Worm, Virus, and Keylogger. [PDF]

Sensors (Basel)
Maghanaki M, Keramati S, Chen FF, Shahin M.   +3 more
europepmc   +1 more source