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Prediction Intervals01:03

Prediction Intervals

2.7K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

883
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
883
Improving Translational Accuracy02:07

Improving Translational Accuracy

12.5K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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3.3K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

2.0K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Related Experiment Videos

Optimising Deep Learning at the Edge for Accurate Hourly Air Quality Prediction.

I Nyoman Kusuma Wardana1,2, Julian W Gardner1, Suhaib A Fahmy3,1

  • 1School of Engineering, University of Warwick, Coventry CV4 7AL, UK.

Sensors (Basel, Switzerland)
|February 9, 2021
PubMed
Summary

This study introduces a hybrid deep learning model for accurate hourly PM2.5 air quality prediction on edge devices. The optimized model offers high accuracy and low latency, making it suitable for real-world environmental monitoring.

Keywords:
PM2.5 predictionair quality predictiondeep learningedge computingpost-training quantisation

Related Experiment Videos

Area of Science:

  • Environmental Science
  • Computer Science
  • Machine Learning

Background:

  • Centralized machine learning models struggle with resource-constrained edge devices for air quality monitoring.
  • Processing multi-dimensional, multi-location sensor data for accurate air quality prediction is challenging.

Purpose of the Study:

  • To design a novel hybrid deep learning model for hourly PM2.5 prediction.
  • To optimize this model for edge device deployment, focusing on accuracy and latency.
  • To evaluate the performance of the optimized model on Raspberry Pi devices.

Main Methods:

  • Developed a hybrid deep learning model combining 1D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM).
  • Optimized the model for edge devices (Raspberry Pi 3 Model B+ and 4 Model B) using post-training quantization.
  • Evaluated model performance using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), and measured execution latency.

Main Results:

  • The proposed hybrid CNN-LSTM model outperformed existing deep learning models in PM2.5 prediction accuracy.
  • Model optimization reduced file size significantly, with quantization further decreasing it.
  • The optimized model demonstrated efficient performance on edge devices, with Raspberry Pi 4 executing twice as fast as Raspberry Pi 3.

Conclusions:

  • The novel hybrid deep learning model is effective for accurate, low-latency PM2.5 prediction on resource-constrained edge devices.
  • Model optimization and quantization are crucial for deploying advanced AI on edge hardware.
  • This approach enables practical, real-time air quality monitoring in diverse environments.