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

Classification of Systems-I01:26

Classification of Systems-I

272
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:
272
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

313
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
313
Classification of Systems-II01:31

Classification of Systems-II

215
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,
215
Chi-square Analysis02:46

Chi-square Analysis

38.7K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
38.7K
Survival Tree01:19

Survival Tree

133
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...
133
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.1K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.1K

You might also read

Related Articles

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

Sort by
Same author

Correction: Rashid et al. A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture. <i>Life</i> 2023, <i>13</i>, 133.

Life (Basel, Switzerland)·2025
Same author

Correction: Zafar et al. Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey. <i>Life</i> 2023, <i>13</i>, 146.

Life (Basel, Switzerland)·2025
Same author

A Dynamic Traffic Light Control Algorithm to Mitigate Traffic Congestion in Metropolitan Areas.

Sensors (Basel, Switzerland)·2024
Same author

An extension of the best-worst method based on the spherical fuzzy sets for multi-criteria decision-making.

Granular computing·2024
Same author

An Insight into the Characteristics of 3D Printed Polymer Materials for Orthoses Applications: Experimental Study.

Polymers·2024
Same author

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis.

Diagnostics (Basel, Switzerland)·2023

Related Experiment Video

Updated: Aug 25, 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

A Performance Comparison of Classification Algorithms for Rose Plants.

Muzamil Malik1, Waqar Aslam1, Emad Abouel Nasr2

  • 1Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.

Computational Intelligence and Neuroscience
|October 14, 2022
PubMed
Summary

Botanists can now identify rose types using machine learning analysis of leaf features. This approach leverages unique leaf characteristics for accurate flower identification, outperforming traditional methods.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

180
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K

Related Experiment Videos

Last Updated: Aug 25, 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
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

180
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.6K

Area of Science:

  • Botany
  • Computer Science
  • Machine Learning

Background:

  • Botanists face challenges in identifying the vast number of flower species.
  • Flower identification is crucial but difficult due to the sheer volume of species.
  • Leaf features offer a stable alternative for plant identification as they persist longer than flowers.

Purpose of the Study:

  • To propose a machine learning-based method for identifying rose types using leaf features.
  • To evaluate the performance of various machine learning algorithms for this task.
  • To optimize a Random Forest model for enhanced accuracy in rose identification.

Main Methods:

  • Leaf features from rose plants were used as input for machine learning models.
  • Algorithms analyzed included Naive Bayes, Generalized Linear Model, Multilayer Perceptron, Decision Tree, Random Forest, Gradient Boosted Trees, and Support Vector Machine.
  • The Random Forest model was optimized by tuning parameters like tree number, depth, and splitting criteria (gain ratio).

Main Results:

  • The Random Forest model, optimized with the gain ratio splitting criterion, demonstrated superior performance.
  • Optimizing the number and depth of trees in the Random Forest model improved accuracy.
  • Ensemble classifiers, using a voting method, showed better performance than individual models.

Conclusions:

  • Machine learning, particularly optimized Random Forest models using leaf features, offers a viable solution for accurate rose identification.
  • Ensemble methods provide superior accuracy for plant identification tasks compared to single models.
  • Leveraging stable leaf characteristics enhances the reliability of automated botanical identification systems.