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

Time-Series Graph00:54

Time-Series Graph

4.5K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.5K
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

436
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...
436
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

107
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
107
Survival Tree01:19

Survival Tree

129
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
129
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.8K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.8K

You might also read

Related Articles

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

Sort by
Same author

Retraction notice to "An improved energy-efficient cloud-optimized load-balancing for IoT frameworks" [Heliyon 9 (2023) e21947].

Heliyon·2025
Same author

Road traffic noise prediction model based on artificial neural networks.

Heliyon·2024
Same author

Retraction Note: Audio-Visual Automatic Speech Recognition Towards Education for Disabilities.

Journal of autism and developmental disorders·2024
Same author

An improved energy-efficient cloud-optimized load-balancing for IoT frameworks.

Heliyon·2023
Same author

Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models.

African health sciences·2023
Same author

Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model.

Neural computing & applications·2022
Same journal

Big Data-Driven Video Anomaly Detection Using VideoMAE for Visual Analytics in CCTV Surveillance.

Big data·2026
Same journal

Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of Delirium Through Genomic Big Data Integration.

Big data·2026
Same journal

Personalized Driven Instruction Through Explainable Agentic AI in Multicultural Higher Education Environments.

Big data·2026
Same journal

Big Data-Driven Explainable Agentic AI Decision Frameworks for Enterprise Innovation in FinTech Ecosystems.

Big data·2026
Same journal

An Edge-Enabled Low-Latency Cross-Lingual Speech-to-Text Framework for Efficient Human-Robot Interaction.

Big data·2026
Same journal

DS<sup>2</sup>PT: A Deep Two-Stage Patent Text Segmentation Framework Informed by Low-Latency Neural Network Characteristics.

Big data·2026
See all related articles

Related Experiment Video

Updated: Aug 9, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

Enhanced Neural Network-Based Univariate Time-Series Forecasting Model for Big Data.

Suyel Namasudra1, S Dhamodharavadhani2, R Rathipriya2

  • 1Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India.

Big Data
|February 24, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a Data Trust Method (DTM) to handle anomalies in big data for improved univariate time series (UTS) forecasting. The enhanced neural network (NN) model effectively identifies and replaces untrustworthy data, boosting prediction accuracy.

Keywords:
health care datalayer recurrent neural networknonlinear autoregressive neural networkstatistical measure-based data trust method

More Related Videos

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.9K

Related Experiment Videos

Last Updated: Aug 9, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.9K

Area of Science:

  • Data Science
  • Machine Learning

Background:

  • Big data presents challenges due to anomalies and inconsistencies.
  • Untrustworthy data significantly impacts analytical application accuracy.
  • Existing methods require robust preprocessing for reliable big data analysis.

Purpose of the Study:

  • To propose an enhanced neural network (NN) model for big data.
  • To incorporate a Data Trust Method (DTM) for preprocessing univariate time series (UTS) data.
  • To improve the quality and accuracy of UTS forecasting algorithms.

Main Methods:

  • A novel approach combining statistical untrustworthy data detection and replacement methods.
  • Integration of the DTM as a preprocessing step within an NN-based UTS forecasting model.
  • Utilizing coefficient variance root mean squared error for optimal UTS data selection.

Main Results:

  • The proposed enhanced NN model effectively identifies and replaces untrustworthy big data.
  • The DTM significantly improves the forecast quality of UTS.
  • The method demonstrates effectiveness in enhancing prediction processes for big data.

Conclusions:

  • The integration of DTM with NN models offers a robust solution for big data preprocessing.
  • Accurate forecasting of UTS is achievable with reliable, preprocessed big data.
  • The proposed method enhances the trustworthiness and predictive power of big data analytics.