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

Machines: Problem Solving II01:30

Machines: Problem Solving II

617
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
617
Machines: Problem Solving I01:22

Machines: Problem Solving I

653
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
653
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

462
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
462
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

464
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
464
Machines01:19

Machines

533
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
533

You might also read

Related Articles

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

Sort by
Same author

Antidiabetic and antioxidant effects of Armillaria gallica and Armillaria cepistipes extracts and their silver nanoparticles against streptozotocin induced diabetes in mice.

Scientific reports·2026
Same author

Smartphone-Based Postoperative Wound Assessment Following Laparoscopic Surgery in a Resource-Limited Setting: A Prospective Cohort Study.

Bioengineering (Basel, Switzerland)·2026
Same author

Gastrointestinal endoscopic image classification using a hybrid modified inception network and a customized vision transformer with tree growth feature selection.

Scientific reports·2026
Same author

Generative AI and student learning performance in medical higher education: a social cognitive theory perspective.

Frontiers in medicine·2026
Same author

GLP-1 Receptor Agonists and Primary Prevention of Cancer Therapy-Related Cardiac Dysfunction.

The American journal of cardiology·2026
Same author

Leveraging 3D Heart Visualisation and Data Balancing Techniques for ECG Classification.

Bioengineering (Basel, Switzerland)·2026
Same journal

A tri-axis optomechanical accelerometer with plasmonic MIM waveguide and structural direction-dependent optical signatures.

Scientific reports·2026
Same journal

Holographic leaky-wave antennas with independently controlled multiple counter-rotating vortex beams.

Scientific reports·2026
Same journal

Differential associations of longitudinal hearing and vision trajectories with dementia and mild cognitive impairment in older adults.

Scientific reports·2026
Same journal

Abdominal obesity and leisure-time sedentary behavior in relation to gastroesophageal reflux disease risk: a prospective cohort study from the UK Biobank.

Scientific reports·2026
Same journal

Effect of nitrogen-rich COF incorporation on the structure and separation performance of polyamide nanofiltration membranes.

Scientific reports·2026
Same journal

Withanolide A inhibits hIAPP aggregation: An In silico, biophysical, and drosophila-based In vivo validation.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.4K

Orchestrating machine learning models in a swarm architecture for IoT inline malware detection.

Muhammad Hanif1, Ehsan Ullah Munir1, Muhammad Maaz Rehan1,2

  • 1COMSATS University Islamabad, G.T Road, Wah Cantt, Islamabad, Pakistan.

Scientific Reports
|December 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Swarm-based Inline Machine Learning (SIML) to enhance Internet of Things (IoT) security against cyber threats. SIML effectively detects malware, achieving high accuracy and precision for a safer digital future.

Keywords:
Active learningCyber securityFOG computingInternet of thingsIntrusion detection systemML model-based swarmMachine learning

Related Experiment Videos

Last Updated: Jan 7, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.4K

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Internet of Things (IoT)

Background:

  • The Internet of Things (IoT) ecosystem is rapidly expanding, increasing susceptibility to cyber threats.
  • Vulnerabilities in IoT devices make them prime targets for malicious actors, posing significant security risks.
  • Existing standalone threat detection systems are inadequate for securing the complex IoT landscape.

Purpose of the Study:

  • To introduce an innovative Swarm-based Inline Machine Learning (SIML) approach for enhanced IoT security.
  • To develop a distributed and end-to-end security solution to counter emerging malware threats in IoT environments.
  • To reduce the risk of IoT devices being exploited for launching cyber-attacks.

Main Methods:

  • The proposed Swarm-based Inline Machine Learning (SIML) approach leverages coordinated swarm data processing.
  • The Gradient-Boosting Tree algorithm was applied within the SIML framework for threat detection.
  • Rigorous testing was conducted using the UNSW-NB15, BoT-Iot, and Edge-IIoTset datasets.

Main Results:

  • The SIML approach achieved an accuracy rate of 93.7% and a precision rate of 95% on the UNSW-NB15 dataset.
  • The Gradient Boosting algorithm demonstrated superior performance compared to traditional methods in an inline setting.
  • The method showed outperformance on BoT-Iot and Edge-IIoTset datasets, with minor degradation at higher throughput.

Conclusions:

  • SIML offers a robust, distributed, and effective solution for securing Internet of Things environments against cyber threats.
  • The proposed method significantly enhances the security of IoT devices, reducing their vulnerability to exploitation.
  • This research contributes to a safer and more resilient digital future through advanced IoT security measures.