Results 141 to 150 of about 64,275 (248)

Autoencoders for genomic variation analysis. [PDF]

Genome Res
Geleta M, Montserrat DM, Giro-I-Nieto X, Ioannidis AG.   +3 more
europepmc   +1 more source

Artificial intelligence and machine learning‐assisted digital applications for biopharmaceutical manufacturing

Biotechnology Progress, EarlyView.
Abstract Artificial intelligence and automation are no longer just buzzwords in the biopharmaceutical industry. The manufacturing of a class of biologics, comprising monoclonal antibodies, cell therapies, and gene therapies, is far more complex than that of traditional small molecule drugs.
Shyam Panjwani, Hao Wei, John Mason
wiley   +1 more source

Enhancing zero-shot scene recognition through semantic autoencoders and visual relation transfer. [PDF]

Sci Rep
Wang C, Wang M, Peng G, De Baets B, Pan X.   +4 more
europepmc   +1 more source

Predicting solar cell efficiencies using historical data from a manufacturing process

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract The solar cell manufacturing data of a passivated emitter and rear cell solar cell manufacturing plant was studied to assess the effects of tool usage and the processing time spent on each tool on the solar cell efficiency. Since manufacturing processes involve several steps with multiple tools, tracing their quality parameters back to the ...
Sushmita Mittra, Vinay Prasad
wiley   +1 more source

Correction: Deep learning for atrial electrogram estimation: toward non-invasive arrhythmia mapping using variational autoencoders. [PDF]

Front Physiol
Gutiérrez-Fernández M, López-Linares K, Fambuena-Santos C, Guillem MS, Climent AM, Barquero-Pérez Ó.   +5 more
europepmc   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

Compressing and expanding optical matrix-vector multipliers for enabling optical image encoder-decoders and generators. [PDF]

Light Sci Appl
Stern A.
europepmc   +1 more source

Neural Network‐Based Detection of Adulterants in Opioid Samples Using IR Absorption Spectroscopy

Drug Testing and Analysis, EarlyView.
We construct a neural network for the classification of bromazolam and fluorofentanyl in illicit opioid samples. The model outperforms a random forest classifier and shows elevated performance for low concentration samples. ABSTRACT Community‐based drug checking services are challenged in their ability to reliably detect low concentration adulterants ...
Joshua Jai, Lea Gozdzialski, Bruce Wallace, Chris G. Gill, Dennis Hore   +4 more
wiley   +1 more source

Enhancing crayfish sex identification with Kolmogorov-Arnold networks and stacked autoencoders. [PDF]

Sci Rep
Atilkan Y, Kirik B, Acikbas ET, Ekinci F, Acici K, Asuroglu T, Benzer R, Guzel MS, Benzer S.   +8 more
europepmc   +1 more source

Research progress and current status of dynamic wave propagation characteristics in rock mass: A review

Deep Underground Science and Engineering, EarlyView.
This review elucidates the velocity–dispersion–attenuation coupling mechanisms of wave propagation in rock masses, compares six representative models, and reveals how pressure, temperature, mineral composition, and anisotropy jointly control dynamic responses in complex geological media.
Jiajun Shu, Tao Li, Ruiqi Yuan, Yue Li, Bingni Wu, Bo Liu, Zhengding Deng, Galindo Rubén, Fausto Molina‐Gómez   +8 more
wiley   +1 more source