Results 61 to 70 of about 270,692 (266)

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Deep learning approaches for energy consumption forecasting: analyzing stress factors and optimizing models for future demand

Discover Applied Sciences
Energy demand forecasting is crucial to the creation of reliable and sustainable energy systems, given the rising global consumption and the increasing integration of renewable energy sources.
Soham Navale, Nivedita Mishra, Sheetal Borhade   +2 more
doaj   +1 more source

The Emergence of Artificial Intelligence: Learning to Learn.

AI Mag., 1985
The classical approach to the acquisition of knowledge and reason in artificial intelligence is to program the facts and rules into the machine. Unfortunately, the amount of time required to program the equivalent of human intelligence is prohibitively large. An alternative approach allows an automaton to learn to solve problems through iterative trial-
openaire   +2 more sources

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

Machine Learning and Artificial Intelligence in Nanomedicine

WIREs Nanomedicine and Nanobiotechnology
ABSTRACTNanomedicine harnesses nanoscale materials, such as lipid, polymeric, and inorganic nanoparticles, to deliver diagnostic or therapeutic agents for cancer, infectious disease, and neurological disorders, among others. However, translating promising nanoparticle designs into clinically approved products remains a challenge.
Wei‐Chun Chou, Alexa Canchola, Fan Zhang, Zhoumeng Lin   +3 more
openaire   +4 more sources

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Probabilistic machine learning and artificial intelligence [PDF]

Nature, 2015
How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience.
openaire   +2 more sources

Intermediate Resistive State in Wafer‐Scale Vertical MoS2 Memristors Through Lateral Silver Filament Growth for Artificial Synapse Applications

Advanced Functional Materials, EarlyView.
In MOCVD MoS2 memristors, a current compliance‐regulated Ag filament mechanism is revealed. The filament ruptures spontaneously during volatile switching, while subsequent growth proceeds vertically through the MoS2 layers and then laterally along the van der Waals gaps during nonvolatile switching.
Yuan Fa, Milan Buttberg, Ke Ran, Rana Walied Ahmad, Dennis Braun, Lukas Völkel, Jimin Lee, Sofía Cruces, Bart Macco, Bárbara Canto, Holger Lerch, Thorsten Wahlbrink, Holger Kalisch, Michael Heuken, Andrei Vescan, Joachim Mayer, Zhenxing Wang, Ilia Valov, Stephan Menzel, Max C. Lemme   +19 more
wiley   +1 more source

Optimizing poultry audio signal classification with deep learning and burn layer fusion

Journal of Big Data
This study introduces a novel deep learning-based approach for classifying poultry audio signals, incorporating a custom Burn Layer to enhance model robustness.
Esraa Hassan, Samar Elbedwehy, Mahmoud Y. Shams, Tarek Abd El-Hafeez, Nora El-Rashidy   +4 more
doaj   +1 more source

Real-time aerial fire detection on resource-constrained devices using knowledge distillation

International Journal of Applied Earth Observations and Geoinformation
Wildfire catastrophes cause significant environmental degradation, human losses, and financial damage. To mitigate these severe impacts, early fire detection and warning systems are crucial.
Sabina Jangirova, Branislava Jankovic, Waseem Ullah, Latif U. Khan, Mohsen Guizani   +4 more
doaj   +1 more source