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

Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

759
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
759
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

570
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
570
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

302
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...
302
Machines01:19

Machines

563
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
563
Self-Help Support Groups01:28

Self-Help Support Groups

339
Self-help support groups are voluntary, community-based organizations that provide a platform for individuals with shared concerns to exchange support, insights, and practical strategies for coping with life challenges. Typically led by group members or paraprofessionals, these groups form a cornerstone of mental health care, especially in reaching populations that are underserved by traditional healthcare systems.
Accessibility and Cost-Effectiveness
One of the primary strengths of self-help...
339
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

403
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
403

You might also read

Related Articles

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

Sort by
Same author

Revisiting the Radical Quenching Activity of Ebselen Analogues in Homogeneous Phase and In Silico Anti-ferroptotic Activity with Histone Deacetylase.

Chembiochem : a European journal of chemical biology·2026
Same author

Optimized convolutional neural network - kernel ridge regression-error correction method: an advanced model for predicting soil saturated hydraulic conductivity.

Scientific reports·2026
Same author

Modeling soil water distribution under drip fertigation in chrysanthemum across soil types using HYDRUS-2D.

Scientific reports·2026
Same author

Development of soil surface wetness models using machine learning techniques in the selected sites in Punjab, North-Western India.

Scientific reports·2026
Same author

Metaheuristic-enhanced deep learning for monthly pan evaporation prediction under limited climatic data.

Scientific reports·2026
Same author

Foliar potassium-calcium nutrition enhances fruit yield, quality and mitigates cracking in guava (<i>Psidium guajava</i> L.) under humid subtropical conditions.

Frontiers in plant science·2026

Related Experiment Video

Updated: Jan 24, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

An improved model based on the support vector machine and cuckoo algorithm for simulating reference

Mohammad Ehteram1, Vijay P Singh2, Ahmad Ferdowsi1

  • 1Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran.

Plos One
|June 1, 2019
PubMed
Summary

A new method, the support vector machine (SVM) with cuckoo algorithm (CA), accurately simulates monthly reference evapotranspiration (ET0) in India. This SVM-CA model outperforms other methods, showing significant reductions in error for crucial agricultural water management.

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

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

Related Experiment Videos

Last Updated: Jan 24, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K
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

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

Area of Science:

  • Agricultural Science
  • Environmental Science
  • Data Science

Background:

  • Reference evapotranspiration (ET0) is critical for effective irrigated agriculture management.
  • Accurate ET0 estimation is essential for optimizing water resource allocation and crop yield.

Purpose of the Study:

  • To develop and evaluate an improved support vector machine (SVM) model, enhanced by the cuckoo algorithm (CA) (SVM-CA), for simulating monthly ET0.
  • To compare the performance of the proposed SVM-CA model against established methods like genetic programming (GP), model tree (M5T), and adaptive neuro-fuzzy inference system (ANFIS).

Main Methods:

  • Utilized meteorological data including maximum and minimum temperature, relative humidity, wind speed, and sunshine hours as inputs.
  • Implemented the SVM-CA model for monthly ET0 simulation.
  • Benchmarked SVM-CA against GP, M5T, and ANFIS using root mean square error (RMSE) and mean absolute error (MAE).

Main Results:

  • The SVM-CA model demonstrated superior accuracy in simulating monthly ET0 compared to GP, M5T, and ANFIS.
  • SVM-CA achieved reductions in RMSE of 5-15% (vs. GP), 12-21% (vs. M5T), and 7-15% (vs. ANFIS).
  • SVM-CA showed reductions in MAE of 5-17% (vs. GP), 10-22% (vs. M5T), and 5-18% (vs. ANFIS).

Conclusions:

  • The proposed SVM-CA model offers a highly accurate and reliable approach for simulating monthly ET0.
  • SVM-CA presents a promising tool for enhancing agricultural water management strategies through precise ET0 prediction.