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

Cluster Sampling Method01:20

Cluster Sampling Method

13.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

261
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...
261
Manipulation and Analysis01:21

Manipulation and Analysis

274
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
274
The Midpoint Formula01:24

The Midpoint Formula

590
In coordinate geometry, determining the central point between two locations is common. This central point, or midpoint, lies exactly halfway along the line segment connecting two points in a two-dimensional space. It has applications in mathematics, physics, engineering, and various planning disciplines.Given two points labeled as A (x1, y1) and B (x2, y2) on a coordinate plane, a straight line segment can be plotted between them. The midpoint, labeled point M, divides this segment into two...
590
Column Efficiency: Rate Theory01:12

Column Efficiency: Rate Theory

853
The rate theory of chromatography provides quantitative insight into the shapes and widths of elution bands. These bands are based on the random-walk mechanism governing molecular migration within a column. The Gaussian profile of chromatographic bands arises from the cumulative effect of random molecular motions as they progress through the column.
During elution, a solute molecule experiences numerous transitions between stationary and mobile phases, exhibiting irregular residence times in...
853

You might also read

Related Articles

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

Sort by
Same author

Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.

Tomography (Ann Arbor, Mich.)·2025
Same author

The Influence of Gold Nanoparticles Addition on Sugarcane Leaves-Derived Silica Xerogel Catalyst for the Production of Biodiesel.

Gels (Basel, Switzerland)·2025
Same author

Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos.

Sensors (Basel, Switzerland)·2022
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

Related Experiment Video

Updated: Jan 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K

Advanced Clustering for Mobile Network Optimization: A Systematic Literature Review.

Claude Mukatshung Nawej1, Pius Adewale Owolawi2, Tom Mmbasu Walingo1

  • 1Department of Electrical, Electronics, and Computer Engineering, University of Kwa-Zulu Natal, Durban 4041, South Africa.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

Advanced clustering methods significantly enhance 5G/6G mobile networks by optimizing performance and resource management. These techniques improve anomaly detection, data delivery rates, and handover predictions for intelligent network infrastructures.

Keywords:
advanced clustering techniquesmobile network optimizationquality-of-service (QoS) prediction

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K

Related Experiment Videos

Last Updated: Jan 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.1K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K

Area of Science:

  • Telecommunications Engineering
  • Computer Science
  • Network Optimization

Background:

  • 5G technology offers enhanced latency, throughput, and connectivity beyond LTE.
  • Implementing 5G necessitates intelligent resource management and optimal network performance.
  • Heterogeneous and dynamic network conditions pose significant optimization challenges.

Purpose of the Study:

  • To explore the role of advanced clustering methods in optimizing cellular networks.
  • To analyze the effectiveness of various clustering approaches for 5G/6G networks.
  • To identify methodological trends and performance outcomes of clustering techniques.

Main Methods:

  • Systematic literature review of 40 studies from the Semantic Scholar Open Research Corpus.
  • Analysis of diverse clustering approaches: spectral, density-based (DBSCAN), and deep representation (DEMC, DANCE).
  • Examination of clustering parameters, mechanisms, experimental setups, and quality metrics.

Main Results:

  • Clustering techniques, especially with machine learning, show significant performance improvements.
  • Reported outcomes include anomaly detection accuracy up to 98.8% and delivery rate improvements up to 89.4%.
  • Handover prediction accuracy improved by approximately 43%.

Conclusions:

  • Advanced clustering models are vital for intelligent spectrum sensing and mobility management in 5G/6G.
  • These methods contribute to efficient resource allocation and the development of intelligent mobile network infrastructures.
  • Clustering techniques offer a robust framework for addressing the complexities of modern cellular networks.