Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

104
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
104
Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

600
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
600
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

198
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
198
Random Error01:04

Random Error

3.5K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
3.5K
Correlation of Experimental Data01:23

Correlation of Experimental Data

334
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
334

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A systematic review of deep learning-based segmentation techniques for brain tumor detection (2013-2023).

Digital health·2025
Same author

Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage.

PeerJ. Computer science·2025
Same author

Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks.

PeerJ. Computer science·2025
Same author

Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction.

PeerJ. Computer science·2025
Same author

A comprehensive review of sensor node deployment strategies for maximized coverage and energy efficiency in wireless sensor networks.

PeerJ. Computer science·2024
Same author

Gauss Markov and Flow Balanced Vector Radial Learning network traffic classification on IoT with SDN.

PloS one·2024

Related Experiment Video

Updated: Oct 12, 2025

An Application for Pairing with Wearable Devices to Monitor Personal Health Status
06:58

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

3.0K

An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics.

Chanyoung Choi1, Haewoong Jung2, Jaehyuk Cho2

  • 1School of Statistics and Actuarial Science, Soongsil University, Seoul 06978, Korea.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

A new ensemble imputation method improves the reliability of Internet of Things (IoT)-based environmental sensors by effectively handling missing data. This approach uniquely combines time dependency and variable correlation for more accurate air quality monitoring.

Keywords:
ensemble methodenvironmental sensormachine learningmissing dataunivariate and multivariate imputation

More Related Videos

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

Related Experiment Videos

Last Updated: Oct 12, 2025

An Application for Pairing with Wearable Devices to Monitor Personal Health Status
06:58

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

3.0K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.4K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

Area of Science:

  • Environmental Science
  • Data Science
  • Sensor Technology

Background:

  • Increasing urbanization drives demand for environmental sensors measuring air quality.
  • Reliability of Internet of Things (IoT)-based environmental sensors is crucial but challenged by missing data.
  • Existing imputation methods often fail to utilize both temporal and correlational data characteristics.

Purpose of the Study:

  • To develop and evaluate a novel ensemble imputation method for missing values in environmental sensor data.
  • To address the limitations of existing methods by incorporating both time dependency and multivariate correlations.
  • To enhance the reliability of environmental sensor data through improved missing value imputation.

Main Methods:

  • Generated experimental data simulating four common missing value scenarios: communication and sensor errors.
  • Employed single univariate and multivariate imputation models to predict missing values.
  • Developed an ensemble imputation technique using weighted averaging and stacking on single model predictions.

Main Results:

  • The proposed ensemble imputation method demonstrated superior performance over single imputation methods.
  • Evaluation using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) confirmed the effectiveness of the ensemble approach.
  • The method successfully integrated variable correlation and time dependence for more robust imputation.

Conclusions:

  • The novel ensemble imputation technique effectively addresses missing values in environmental sensor data.
  • This approach enhances the overall reliability and accuracy of air quality monitoring systems.
  • The method offers a significant contribution to handling data gaps in IoT-based environmental sensing.