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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

51
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...
51
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

137
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
137
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

69
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
69
Survival Tree01:19

Survival Tree

80
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...
80
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K

You might also read

Related Articles

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

Sort by
Same author

Pneumococcal meningitis among hospitalised children after introduction of pneumococcal conjugate vaccine in India: a sentinel hospital surveillance (2019-2022).

The Lancet regional health. Southeast Asia·2026
Same author

Serum 25-hydroxyvitamin D and tear-film stability in dry eye disease: A case-control correlation study.

Bioinformation·2026
Same author

A Survey of knowledge and perception of patients towards the serum uric acid levels in musculoskeletal symptoms and systemic diseases.

Journal of clinical orthopaedics and trauma·2026
Same author

Viral threats to pregnancy: Global health risks in the era of pandemics.

Advances in virus research·2026
Same author

Comparison of Surgical Outcomes Between Vertebral Body Stenting (VBS) and Balloon Kyphoplasty (BKP)-Multicenter Cohort Study.

Journal of clinical medicine·2026
Same author

Introducing the Investigator Global Assessment of Hidradenitis Suppurativa (I-GLASS) instrument.

The British journal of dermatology·2026

Related Experiment Video

Updated: Jun 26, 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

7.5K

MCN portfolio: An efficient portfolio prediction and selection model using multiserial cascaded network with hybrid

Meeta Sharma1, Pankaj Kumar Sharma1, Hemant Kumar Vijayvergia1

  • 1Government Mahila Engineering College Ajmer, Ajmer, Rajasthan, India.

Network (Bristol, England)
|May 8, 2024
PubMed
Summary

This study introduces a new framework for portfolio prediction and optimization. The developed Multi-serial Cascaded Network (MCNet) enhances prediction accuracy, leading to better investment choices.

Keywords:
1D convolutional neural networkPortfolio prediction and selectionautoencoderintegration of artificial rabbit and hummingbird algorithmminimization of RMSEmultiserial cascaded networkrecurrent neural networks

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

696
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

6.8K

Related Experiment Videos

Last Updated: Jun 26, 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

7.5K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

696
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

6.8K

Area of Science:

  • Financial modeling and computational intelligence.
  • Data science and machine learning applications in finance.

Background:

  • Portfolio management requires accurate financial investment predictions, which are often complicated by existing techniques.
  • Effective error analysis and performance measures are crucial for validating portfolio prediction models.

Purpose of the Study:

  • To develop a novel framework for portfolio prediction and optimization.
  • To enhance the accuracy of financial forecasting and identify optimal investment portfolios.
  • To address the complexities and issues in current portfolio prediction methods.

Main Methods:

  • A Multi-serial Cascaded Network (MCNet) was employed, integrating Autoencoder, 1D Convolutional Neural Network (1DCNN), and Recurrent Neural Network (RNN) for benefit forecasting.
  • A dataset of company portfolios was collected for training and validation.
  • The Integration of Artificial Rabbit and Hummingbird Algorithm (IARHA) was used to select the optimal portfolio based on predicted profits.

Main Results:

  • The MCNet model demonstrated strong performance in forecasting company benefits.
  • The framework achieved low error rates, with Root Mean Square Error (RMSE) at 0.89% and Mean Absolute Error (MAE) at 0.56%.
  • The developed model showed enriched performance in experimental analysis.

Conclusions:

  • The proposed portfolio prediction framework significantly increases prediction accuracy.
  • The integration of MCNet and IARHA effectively aids in selecting optimal investment portfolios.
  • The study highlights the potential of advanced machine learning techniques for improved financial decision-making.