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

Microbial Biosensors

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

You might also read

Related Articles

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

Sort by

Same author

A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform.

Sensors (Basel, Switzerland)·2023

Same author

Current Status and Prospects in the Treatment of Erectile Dysfunction by Adipose-Derived Stem Cells in the Diabetic Animal Model.

Sexual medicine reviews·2020

Same author

Low-Complexity Adaptive Signal Detection for Mobile Molecular Communication.

IEEE transactions on nanobioscience·2020

Same author

Corrigendum to "HMGB1 contributes to adriamycin-induced cardiotoxicity via up-regulating autophagy" [Toxicol. Lett. 292 (2018) 115-121].

Toxicology letters·2019

Same author

Programming nanoparticle valence bonds with single-stranded DNA encoders.

Nature materials·2019

Same author

Novel pH-sensitive films based on starch/polyvinyl alcohol and food anthocyanins as a visual indicator of shrimp deterioration.

International journal of biological macromolecules·2019

Search research articles

Related Experiment Video

Updated: Jul 9, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data.

Victor Vlădăreanu¹, Valentin-Gabriel Voiculescu², Vlad-Alexandru Grosu²

¹Institute of Solid Mechanics of the Romanian Academy, 010141 Bucharest, Romania.

Sensors (Basel, Switzerland)

|May 17, 2020

Summary

This study introduces a machine learning approach using software sensors to detect anomalous behavior in smartphones. Three classifiers were evaluated for their effectiveness in identifying unusual activity.

Keywords:

machine-learning classifier smartphone security software sensor data

More Related Videos

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Published on: November 9, 2018

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Related Experiment Videos

Last Updated: Jul 9, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

Published on: November 9, 2018

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

Computer Science
Machine Learning
Cybersecurity

Background:

Modern smartphones generate vast amounts of data.
Detecting anomalous behavior is crucial for security and performance.
Existing methods may not fully leverage software-based sensing capabilities.

Purpose of the Study:

To develop and evaluate a machine learning-based method for detecting anomalous behavior in smartphones.
To investigate the efficacy of different machine learning classifiers for this task.
To establish a framework for anomaly detection extensible to other computing devices.

Main Methods:

Utilized software-based sensors to collect data from smartphones.
Implemented and compared three machine learning classification models: logistic regressions, shallow neural nets, and support vector machines.
Designed, implemented, and comparatively evaluated the performance of each model.

Main Results:

Successfully obtained relevant data sources for anomaly detection.
Comparative evaluation of logistic regressions, shallow neural nets, and support vector machines was performed.
Demonstrated the feasibility of using machine learning classifiers for smartphone anomaly detection.

Conclusions:

The developed method effectively detects anomalous behavior in smartphones using machine learning.
The choice of classifier impacts detection performance.
The approach has potential for extension to other computing devices with necessary adaptations.