Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Machine Learning-Driven Innovations in Microfluidics
by
Kim, Yang Woo
, Park, Jinseok
, Jeon, Hee-Jae
in
3-D printers
/ Accuracy
/ Algorithms
/ Analytical chemistry
/ Artificial intelligence
/ Automation
/ Bioengineering
/ Biomarkers
/ Biomedical research
/ Biosensing Techniques
/ biosensing technology
/ Biosensors
/ Costs
/ Data analysis
/ Decision making
/ Deep learning
/ Detectors
/ droplet generation
/ Environmental monitoring
/ Fabrication
/ Fluid dynamics
/ Humans
/ Information management
/ Injection molding
/ Innovations
/ Integrated circuit fabrication
/ Laboratories
/ Learning algorithms
/ Machine Learning
/ Medical research
/ Microfluidic devices
/ Microfluidics
/ Nanoparticles
/ Precision medicine
/ Rapid prototyping
/ Real time
/ Researchers
/ Review
/ Reynolds number
/ Technological change
/ Technology application
2024
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Machine Learning-Driven Innovations in Microfluidics
by
Kim, Yang Woo
, Park, Jinseok
, Jeon, Hee-Jae
in
3-D printers
/ Accuracy
/ Algorithms
/ Analytical chemistry
/ Artificial intelligence
/ Automation
/ Bioengineering
/ Biomarkers
/ Biomedical research
/ Biosensing Techniques
/ biosensing technology
/ Biosensors
/ Costs
/ Data analysis
/ Decision making
/ Deep learning
/ Detectors
/ droplet generation
/ Environmental monitoring
/ Fabrication
/ Fluid dynamics
/ Humans
/ Information management
/ Injection molding
/ Innovations
/ Integrated circuit fabrication
/ Laboratories
/ Learning algorithms
/ Machine Learning
/ Medical research
/ Microfluidic devices
/ Microfluidics
/ Nanoparticles
/ Precision medicine
/ Rapid prototyping
/ Real time
/ Researchers
/ Review
/ Reynolds number
/ Technological change
/ Technology application
2024
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Machine Learning-Driven Innovations in Microfluidics
by
Kim, Yang Woo
, Park, Jinseok
, Jeon, Hee-Jae
in
3-D printers
/ Accuracy
/ Algorithms
/ Analytical chemistry
/ Artificial intelligence
/ Automation
/ Bioengineering
/ Biomarkers
/ Biomedical research
/ Biosensing Techniques
/ biosensing technology
/ Biosensors
/ Costs
/ Data analysis
/ Decision making
/ Deep learning
/ Detectors
/ droplet generation
/ Environmental monitoring
/ Fabrication
/ Fluid dynamics
/ Humans
/ Information management
/ Injection molding
/ Innovations
/ Integrated circuit fabrication
/ Laboratories
/ Learning algorithms
/ Machine Learning
/ Medical research
/ Microfluidic devices
/ Microfluidics
/ Nanoparticles
/ Precision medicine
/ Rapid prototyping
/ Real time
/ Researchers
/ Review
/ Reynolds number
/ Technological change
/ Technology application
2024
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Machine Learning-Driven Innovations in Microfluidics
2024
Request Book From Autostore
and Choose the Collection Method
Overview
Microfluidic devices have revolutionized biosensing by enabling precise manipulation of minute fluid volumes across diverse applications. This review investigates the incorporation of machine learning (ML) into the design, fabrication, and application of microfluidic biosensors, emphasizing how ML algorithms enhance performance by improving design accuracy, operational efficiency, and the management of complex diagnostic datasets. Integrating microfluidics with ML has fostered intelligent systems capable of automating experimental workflows, enabling real-time data analysis, and supporting informed decision-making. Recent advances in health diagnostics, environmental monitoring, and synthetic biology driven by ML are critically examined. This review highlights the transformative potential of ML-enhanced microfluidic systems, offering insights into the future trajectory of this rapidly evolving field.
Publisher
MDPI AG,MDPI
Subject
This website uses cookies to ensure you get the best experience on our website.