Jove
Visualize
Contact Us

Related Concept Videos

Survival Tree01:19

Survival Tree

166
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...
166
Classification of Signals01:30

Classification of Signals

925
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
925
Force Classification01:22

Force Classification

1.7K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.7K
Classification of Systems-I01:26

Classification of Systems-I

319
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
319
Classification of Leukocytes01:30

Classification of Leukocytes

2.8K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
2.8K
Classification of Systems-II01:31

Classification of Systems-II

242
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
242

You might also read

Related Articles

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

Sort by
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·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: Sep 18, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Machine learning techniques for imbalanced multiclass malware classification through adaptive feature selection.

Binayak Panda1, Sudhanshu Shekhar Bisoyi2, Sidhanta Panigrahy3

  • 1Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be) University, Bhubaneswar, Odisha, India.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an Adaptive Multiclass Malware Classification (AMMC) framework with adaptive feature selection (AFS) to efficiently detect malware variants. The proposed method achieves state-of-the-art results on multiple datasets, enhancing malware detection performance.

Keywords:
API sequenceGreedy feature selectionMachine learningMulticlass malware classificationSkip-gramTF-IDF

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

919

Related Experiment Videos

Last Updated: Sep 18, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

919

Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • Detecting polymorphic and metamorphic malware variants is a significant challenge.
  • Artificial intelligence (AI) offers advantages over traditional signature-based methods for malware detection.
  • The proliferation of malware necessitates efficient and accurate classification techniques.

Purpose of the Study:

  • To propose an Adaptive Multiclass Malware Classification (AMMC) framework.
  • To introduce a novel adaptive feature selection (AFS) technique for improved malware classification.
  • To address challenges in imbalanced multiclass malware detection.

Main Methods:

  • Developed an Adaptive Multiclass Malware Classification (AMMC) framework.
  • Implemented an adaptive feature selection (AFS) technique using a greedy strategy on TF-IDF weights.
  • Evaluated the framework on three imbalanced multiclass malware datasets (VirusShare, VirusSample, MAL-API-2019) using Windows API sequence features.

Main Results:

  • The AMMC framework with AFS achieved state-of-the-art performance across all tested datasets.
  • Macro F1-scores of 0.92, 0.94, and 0.84 were obtained for VirusShare, VirusSample, and MAL-API-2019, respectively.
  • Macro AUC scores of 0.99, 0.99, and 0.98 were achieved for VirusShare, VirusSample, and MAL-API-2019, respectively.

Conclusions:

  • The proposed AMMC framework with AFS is highly effective for imbalanced multiclass malware classification.
  • The AFS technique successfully identifies influential features, boosting classification performance.
  • The framework demonstrates promising results for detecting diverse malware variants efficiently.