Jove
Visualize
Contact Us

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

53
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
53

You might also read

Related Articles

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

Sort by
Same author

An Intelligent Sensing Framework for Early Ransomware Detection Using MHSA-LSTM Machine Learning.

Sensors (Basel, Switzerland)·2026
Same author

Trustworthy High-Performance Multiplayer Games with Trust-but-Verify Protocol Sensor Validation.

Sensors (Basel, Switzerland)·2024
Same author

Temporal Data Correlation Providing Enhanced Dynamic Crypto-Ransomware Pre-Encryption Boundary Delineation.

Sensors (Basel, Switzerland)·2023
Same author

A Survey of Crypto Ransomware Attack Detection Methodologies: An Evolving Outlook.

Sensors (Basel, Switzerland)·2022
Same author

Examining the Performance of Fog-Aided, Cloud-Centered IoT in a Real-World Environment.

Sensors (Basel, Switzerland)·2021
Same author

IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: Jun 29, 2025

A Model for Epilepsy of Infectious Etiology using Theiler's Murine Encephalomyelitis Virus
05:33

A Model for Epilepsy of Infectious Etiology using Theiler's Murine Encephalomyelitis Virus

Published on: June 23, 2022

2.8K

eMIFS: A Normalized Hyperbolic Ransomware Deterrence Model Yielding Greater Accuracy and Overall Performance.

Abdullah Alqahtani1,2, Frederick T Sheldon2

  • 1College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia.

Sensors (Basel, Switzerland)
|March 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced Mutual Information Feature Selection (eMIFS) method for early ransomware detection. The technique improves accuracy by better identifying unique feature characteristics before encryption occurs.

Keywords:
MIFScrypto-ransomwarecyber securityearly detectionfeature selectionransomware

More Related Videos

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
08:05

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

9.8K
A Conflict Model of Reward-seeking Behavior in Male Rats
06:11

A Conflict Model of Reward-seeking Behavior in Male Rats

Published on: February 20, 2019

7.4K

Related Experiment Videos

Last Updated: Jun 29, 2025

A Model for Epilepsy of Infectious Etiology using Theiler's Murine Encephalomyelitis Virus
05:33

A Model for Epilepsy of Infectious Etiology using Theiler's Murine Encephalomyelitis Virus

Published on: June 23, 2022

2.8K
A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
08:05

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

9.8K
A Conflict Model of Reward-seeking Behavior in Male Rats
06:11

A Conflict Model of Reward-seeking Behavior in Male Rats

Published on: February 20, 2019

7.4K

Area of Science:

  • Cybersecurity
  • Machine Learning

Background:

  • Early detection of ransomware is crucial for mitigating damage.
  • Feature selection is key to developing effective ransomware detection models.

Purpose of the Study:

  • To propose an enhanced Mutual Information Feature Selection (eMIFS) technique using a normalized hyperbolic function for improved early ransomware detection.
  • To address challenges in feature characteristic perception with limited attack data.

Main Methods:

  • Utilized Term Frequency-Inverse Document Frequency (TF-IDF) for numerical feature representation.
  • Incorporated a normalized hyperbolic function (tanh) within the MIFS framework to evaluate feature relevance and redundancy individually.
  • Adapted MIFS for pre-encryption detection by improving redundancy coefficient estimation.

Main Results:

  • The eMIFS method demonstrated superior efficacy in early-stage ransomware detection compared to traditional MIFS techniques.
  • The normalized hyperbolic function significantly enhanced the feature selection process.
  • Achieved a more robust and accurate ransomware detection model.

Conclusions:

  • The proposed eMIFS technique offers a significant advancement in early ransomware detection.
  • Individual feature evaluation using hyperbolic functions improves model performance.
  • This approach provides a more effective solution for detecting ransomware before encryption.