Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Prediction Intervals

Prediction Intervals

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.

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

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...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

You might also read

Related Articles

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

Sort by

Same author

Reduction of Vision-Based Models for Fall Detection.

Sensors (Basel, Switzerland)·2024

Same author

SLRProp: A Back-Propagation Variant of Sparse Low Rank Method for DNNs Reduction.

Sensors (Basel, Switzerland)·2023

Same author

A Generalization Performance Study Using Deep Learning Networks in Embedded Systems.

Sensors (Basel, Switzerland)·2021

Same author

Characterization of e-Government adoption in Europe.

PloS one·2020

Same author

Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People.

Sensors (Basel, Switzerland)·2019

Same author

A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface.

PloS one·2019

Search research articles

Related Experiment Video

Updated: Jan 30, 2026

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Published on: December 1, 2011

Smart Meeting Room Usage Information and Prediction by Modelling Occupancy Profiles.

Unai Saralegui¹, Miguel Ángel Antón², Olatz Arbelaitz³

¹Tecnalia Research & Innovation, Mikeletegi 2, Donostia 20009, Spain. unai.saralegui@tecnalia.com.

Sensors (Basel, Switzerland)

|January 19, 2019

Summary

This summary is machine-generated.

Internet of Things (IoT) sensors enable monitoring of building energy usage. This study shows simple sensing systems can predict room usage, aiding energy efficiency strategies.

Keywords:

Internet of Things (IoT)ambient intelligence behaviour modelling buildings occupancy detection sensor networks smart meeting room

More Related Videos

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Published on: May 2, 2018

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Related Experiment Videos

Last Updated: Jan 30, 2026

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Prediction of HIV-1 Coreceptor Usage Tropism by Sequence Analysis using a Genotypic Approach

Published on: December 1, 2011

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Published on: May 2, 2018

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Area of Science:

Building energy management
Smart building technology
Occupancy sensing

Background:

Buildings are significant energy consumers, with human behavior causing discrepancies in energy simulations.
Advances in IoT sensors and low-power communication facilitate building monitoring.
Accurate monitoring and prediction of room usage are crucial for energy conservation.

Purpose of the Study:

To investigate the feasibility of using IoT sensors for non-intrusive monitoring of room occupancy and usage patterns.
To collect data on room usage for developing predictive models.
To explore the potential for integrating sensor data into advanced building energy control strategies.

Main Methods:

Deployment of a simple, non-intrusive sensing system in four meeting rooms.
Collection of data on room usage over a specified period.
Analysis of collected data to identify usage patterns and predict future occupancy.

Main Results:

Demonstrated the effectiveness of a simple sensing system for collecting detailed room usage data.
Showcased the potential to predict future room usage based on historical data.
Validated the feasibility of using sensor outputs for developing sophisticated control strategies.

Conclusions:

A simple, non-intrusive sensing system can effectively monitor room usage.
The collected data can be utilized to predict future room occupancy, improving energy management.
This approach supports the development of advanced control strategies for reducing building energy consumption.