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

Reducing Line Loss01:18

Reducing Line Loss

430
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
430
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

3.1K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
3.1K
Shrinkage in Concrete01:27

Shrinkage in Concrete

463
Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
When concrete is still in its plastic state, it can undergo a decrease in volume by about 1% of its absolute volume. This decrease is known as plastic shrinkage. It arises either...
463
Drying Shrinkage01:21

Drying Shrinkage

439
When hardened concrete is exposed to air with a relative humidity of less than 100 percent, it begins to lose the free water within its capillaries. As this water evaporates, the water initially adsorbed onto the calcium silicate hydrates migrates towards these now empty spaces and eventually evaporates as well. Over time, as more water leaves, the volume of the concrete decreases, a phenomenon known as drying shrinkage.
A portion of this drying shrinkage can be reversed; if the concrete is...
439
Optimization Problems01:26

Optimization Problems

124
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
124
Methods of Medium Optimization01:28

Methods of Medium Optimization

1
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
1

You might also read

Related Articles

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

Sort by
Same author

A Sensor-Aware Multi-Agent Reinforcement Learning Framework for Joint Data Offloading and Power Control in Edge-Assisted Wireless Sensor Networks.

Sensors (Basel, Switzerland)·2026
Same author

Validation of the Bangla PHQ-4 in rural and urban community samples from Bangladesh.

PloS one·2026
Same author

Efficacy and safety of jinlida granule in the treatment of diabetic kidney disease: a systematic review and meta-analysis of randomized controlled trials.

Frontiers in pharmacology·2026
Same author

Association between reduced cervical extensor muscle mass and postoperative outcomes after single-door laminoplasty in elderly patients with cervical spondylotic myelopathy: a retrospective study.

Annals of medicine·2026
Same author

Efficacy and safety of Buyang Huanwu Decoction combined with α-lipoic acid for diabetic peripheral neuropathy: a systematic review with in-depth heterogeneity deconstruction and methodological appraisal.

Frontiers in endocrinology·2026
Same author

Resolving the vertical versus horizontal conundrum: the potential of a "next generation" community health worker program to accelerate progress in TB control and at the same time strengthen primary health care.

Frontiers in public health·2026

Related Experiment Videos

A new good point set stepwise shrinkage optimization in machine learning model for fog node performance prediction.

Zhang Bo1,2, Mohammad Kamrul Hasan3, Elankovan A Sundararajan1

  • 1Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia.

Scientific Reports
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

A new method, Good Point Set Stepwise Shrinkage, optimizes machine learning hyperparameters for Internet of Things (IoT) fog computing. This approach reduces uncertainty and computational costs for better performance prediction.

Keywords:
Consumer electronicsGood point setHyperparameter optimizationMachine learningNode performance predictionSequential experimental design

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Network Engineering

Background:

  • The Internet of Things (IoT) is evolving with AI, enhancing connectivity but facing adaptation challenges due to increased service requests from decentralized devices.
  • Machine learning is vital for IoT, with hyperparameter optimization critical for model performance, yet current methods struggle with uncertainty, generalization, and cost.
  • Fog computing nodes in IoT require efficient hyperparameter optimization for accurate performance prediction.

Purpose of the Study:

  • To introduce and evaluate the Good Point Set Stepwise Shrinkage (GPSS) optimization approach for fog computing node performance prediction in IoT.
  • To address limitations of existing hyperparameter optimization methods, specifically uncertainty, poor model generalization, and high computational expenses.
  • To improve cross-validation techniques and search space methods for machine learning models used in IoT fog computing.

Main Methods:

  • The study proposes the Good Point Set Stepwise Shrinkage (GPSS) method for hyperparameter optimization.
  • GPSS was applied to optimize hyperparameters for Support Vector Machine (SVM), Back Propagation Network (BPN), and Convolutional Neural Network (CNN) models.
  • Performance data from IoT fog computing nodes was utilized to test the proposed optimization scheme.

Main Results:

  • The GPSS method demonstrated reduced uncertainty and improved model generalization.
  • GPSS resulted in lower computational costs compared to traditional methods.
  • Optimized models achieved Mean Squared Errors of 4.061 (SVM), 4.114 (BPN), and 3.963 ± 0.0323 (CNN).

Conclusions:

  • Good Point Set Stepwise Shrinkage (GPSS) is a highly suitable approach for hyperparameter optimization in IoT fog computing performance prediction.
  • GPSS effectively resolves cross-validation uncertainties and enhances search space randomness.
  • The proposed method offers a simpler and more efficient alternative to existing techniques like Sequential Uniform Designs.