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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

486
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...
486
Fault Types01:18

Fault Types

422
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
422
Network Function of a Circuit01:25

Network Function of a Circuit

685
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
685
Nuclear Fusion02:45

Nuclear Fusion

33.8K
The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
33.8K
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

2.1K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
2.1K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K

You might also read

Related Articles

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

Sort by
Same author

A lightweight CNN for enhanced non-small cell lung cancer classification using CT scan image.

Scientific reports·2026
Same author

A 3-tier information fusioned framework featuring explainable deep active optimized CRNet for accurate heart disease prediction.

Journal of translational medicine·2026
Same author

Developing a novel framework using optimized active stacking and explainable AI for heart disease prediction.

Computer methods and programs in biomedicine·2025
Same author

HyFusion-X: hybrid deep and traditional feature fusion with ensemble classifiers for breast cancer detection using mammogram and ultrasound images.

Scientific reports·2025
Same author

Smart defense based on explainable stacked machine learning architecture for securing internet of health things with K-means clustering.

Scientific reports·2025
Same author

Acute lymphoblastic leukemia cancer diagnosis in children and adults using transforming blood fluorescence microscopy imaging.

PeerJ. Computer science·2025

Related Experiment Video

Updated: Jan 27, 2026

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

7.4K

Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor

Atia Javaid1, Nadeem Javaid2, Zahid Wadud3

  • 1Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan. atiajavaid477@gmail.com.

Sensors (Basel, Switzerland)
|March 20, 2019
PubMed
Summary

This study introduces four enhanced classification techniques for Wireless Sensor Networks (WSNs) to improve decision fusion and detect sensor faults. Enhanced Recurrent Extreme Learning Machine (ERELM) demonstrated the best performance in belief function fusion and fault detection.

Keywords:
ELMKNNRELMSVMWireless Sensor Networksbelief functionmachine learning classifiers

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

952

Related Experiment Videos

Last Updated: Jan 27, 2026

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

7.4K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

952

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Decision fusion enhances classification accuracy and reduces data transmission costs in Wireless Sensor Networks (WSNs).
  • Decentralized classification fusion in WSNs necessitates belief function-based approaches.
  • WSNs are susceptible to faults due to hardware/software issues and environmental factors, requiring efficient fault detection.

Purpose of the Study:

  • To improve belief function-based decision fusion in WSNs.
  • To propose and evaluate four enhanced classification techniques: EKNN, EELM, ESVM, and ERELM.
  • To address sensor failure issues in WSNs through enhanced classification methods for fault detection.

Main Methods:

  • Proposed four enhanced classification techniques: Enhanced K-Nearest Neighbor (EKNN), Enhanced Extreme Learning Machine (EELM), Enhanced Support Vector Machine (ESVM), and Enhanced Recurrent Extreme Learning Machine (ERELM).
  • Induced four types of sensor faults: offset, gain, stuck-at, and out of bounds.
  • Evaluated fault detection performance using Detection Accuracy (DA), True Positive Rate (TPR), and Error Rate (ER).

Main Results:

  • ERELM achieved the best performance in improving belief function fusion.
  • ESVM, EELM, and EKNN provided the second, third, and fourth best results, respectively.
  • The proposed enhanced classifiers outperformed existing techniques in belief function fusion and fault detection.

Conclusions:

  • The proposed enhanced classification methods effectively improve belief function fusion and fault detection in WSNs.
  • ERELM is the most effective among the proposed methods for these tasks.
  • The study highlights the potential of enhanced classifiers for robust WSN operation despite sensor failures.