Results 131 to 140 of about 25,600 (268)

Cellular and subcellular specialization enables biology-constrained deep learning. [PDF]

Cell Rep
Galloni AR, Peddada A, Chennawar Y, Milstein AD.   +3 more
europepmc   +1 more source

Decentralized Federated Learning for Wind Turbine Bearing Prognostics Under Data Scarcity and Statistical Heterogeneity

Energy Science &Engineering, EarlyView.
This paper proposes a decentralized peer‐to‐peer federated learning framework for wind turbine bearing remaining useful life prediction, introducing a virtual client paradigm in which statistical health indicators serve as independent feature‐level clients—enabling privacy‐preserving collaborative prognostics from a single physical asset under ...
Jihene Sidhom, Lotfi Saidi, Eric Bechhoefer   +2 more
wiley   +1 more source

The Reward Positivity Tracks Positive Reward Prediction Errors From Feedback to Cues During Reinforcement Learning. [PDF]

Psychophysiology
Gao Y, Wilson R, Karpov G, Baker TE.
europepmc   +1 more source

Stability Evaluation and Parametric Optimization of Coal‐Concrete Composite Bearing Systems Under Mine‐Water‐Induced Deterioration: Experiments and FEINN Analysis

Energy Science &Engineering, EarlyView.
Mine‐water immersion tests reveal pronounced coal weakening (vs. minor concrete degradation), identifying coal pillars as the stability‐limiting component in composite dams. A coupled FEINN framework quantifies extreme‐pressure stability and ranks multi‐parameter designs via a normalized multi‐indicator scheme, enabling optimized dam configuration for ...
He Wen, Hongchuan Dai, Guixin Zhang, Pengzhe Li, Tong Yang, Cang Gong, Renbo Gao   +6 more
wiley   +1 more source

Closed-form feedback-free learning with forward projection. [PDF]

Nat Commun
O'Shea R, Rajendran B.
europepmc   +1 more source

Real‐Time Incremental Learning Artificial Neural Networks Maximum Power Point Tracking With Raspberry Pi‐Based Meteorological Data Acquisition

Energy Science &Engineering, EarlyView.
We present a smart solar tracking method using artificial intelligence to improve the efficiency of solar panels. Unlike traditional techniques, our system learns and adapts to changing sunlight conditions, ensuring faster and more reliable power generation for real‐world energy needs.
Rida Amine, Noureddine El Barbri, Mourad Yessef, Mohammed M. Alammar, Z.M.S. El‐Barbary, Hatim Ameziane   +5 more
wiley   +1 more source

Hybrid Simulation–Machine Learning Surrogates for Coordinate‐Based Solar and Wind Energy Yield Assessment in Iraq: A Streamlit Decision‐Support Tool

Energy Science &Engineering, EarlyView.
This study integrates climatic simulations with machine learning to predict solar and wind energy across Iraq. Results show Random Forest excels for solar (R2 = 0.98) and neural networks for wind (R2 = 0.97), enabling a practical web tool for renewable energy planning. ABSTRACT Driven by the global shift away from fossil fuels, solar and wind resources
Bassam Musheer Kareem, Abdolsalam Ebrahimpour, Jafar Ghafouri, Roghayyeh Motallebzadeh   +3 more
wiley   +1 more source

Adaptive and lightweight surrogate gradients: enhancing training efficiency of spiking neural networks. [PDF]

Front Neurosci
Hou K, Wu K, Zhou Y.
europepmc   +1 more source

RETRACTED: Polygenic risk score and prostate specific antigen predict death from prostate cancer in men with intermediate aggressive cancer

International Journal of Cancer, EarlyView.
What's New? Using 21 SNPs, two novel PRS were constructed and used to develop two new machine‐learning classifiers, one for the detection of prostate cancer and the other for the prediction of its aggressiveness and subsequent mortality. The classifier for disease detection is built using the PRS as the sole feature, whereas the one for disease ...
Leandro Rodrigues Santiago, Efthymios Ladoukakis, Dorota Scibior‐Bentkowska, Belinda Nedjai   +3 more
wiley   +1 more source

Constructing biologically constrained RNNs via Dale's backpropagation and topologically informed pruning. [PDF]

Sci Adv
Balwani A, Wang AQ, Najafi F, Choi H.
europepmc   +1 more source