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

Prediction Intervals01:03

Prediction Intervals

3.5K
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. 
3.5K
Classification of Systems-I01:26

Classification of Systems-I

649
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:
649
Classification of Systems-II01:31

Classification of Systems-II

544
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,
544
Cognitive Learning01:21

Cognitive Learning

1.5K
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
1.5K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

455
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 of...
455
Associative Learning01:27

Associative Learning

1.7K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Nanoparticles in wheat (<i>Triticum aestivum</i> L) cultivation: a promising solution to alleviate heavy metal toxicity.

Physiology and molecular biology of plants : an international journal of functional plant biology·2026
Same author

Nutritional, Physicochemical, and Phytochemical Characterization of Pumpkin Seed Flour-Enriched Waffles.

Journal of food science·2026
Same author

The Laennec technique: A potential candidate for the standardization of right robotic donor hepatectomies.

Surgery·2026
Same author

Contrast-Enhanced Ultrasound as a Next-Step Tool After Indeterminate CT in ESRD.

Ultrasound quarterly·2026
Same author

Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.

Biotechnology journal·2026
Same author

Prognostic significance of IDH1 promoter methylation and associated genome-wide alterations in breast cancer.

Scientific reports·2026

Related Experiment Video

Updated: Mar 14, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.6K

A robust E learning recommendation system based on novel interval valued bipolar fuzzy hypersoft set theory.

Muhammad Imran Harl1, Muhammad Saeed1, Muhammad Haris Saeed2

  • 1Department of Mathematics, University of Management and Technology, Lahore, 54700, Punjab, Pakistan.

Scientific Reports
|March 13, 2026
PubMed
Summary

This study introduces a novel interval-valued bipolar fuzzy hypersoft set (IVBFHS) for decision-making with bipolar information. The new framework enhances multi-attribute decision-making (MADM) by processing complex, interval-based data effectively.

Keywords:
Bipolar hypersoft setBipolar soft SetDecision makingDecision support systemsFuzzy set theoryOptimizationSoft set theory

Related Experiment Videos

Last Updated: Mar 14, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.6K

Area of Science:

  • Fuzzy Set Theory
  • Decision Support Systems
  • Multi-Attribute Decision-Making (MADM)

Background:

  • Bipolar information is crucial for balanced decision-making, requiring tools that handle uncertainty.
  • Interval-valued bipolar fuzzy sets (IVBFS) offer a way to capture interval-valued bipolar information.
  • Bipolar hypersoft sets (BHSS) provide a framework for multi-attribute analysis up to sub-attributive levels.

Purpose of the Study:

  • To introduce a hybrid data structure, the interval-valued bipolar fuzzy hypersoft set (IVBFHS).
  • To merge the capabilities of IVBFS and BHSS for processing bipolar, interval-based, multi-attribute data.
  • To develop a decision support algorithm for MADM problems using the proposed IVBFHS framework.

Main Methods:

  • Development of the interval-valued bipolar fuzzy hypersoft set (IVBFHS) data structure.
  • Analysis of fundamental operations and properties (commutative, associative, distributive, De Morgan laws) of IVBFHS.
  • Creation of a preferential decision support algorithm for selecting optimal alternatives in e-learning scenarios.

Main Results:

  • The proposed IVBFHS effectively manipulates and processes bipolar information presented in intervals across multiple attributes and sub-attributes.
  • Essential features and operations of IVBFHS are defined and analyzed, ensuring its mathematical soundness.
  • A decision support algorithm based on IVBFHS demonstrates adaptability and reliability in MADM problems, validated through computation and structural comparisons.

Conclusions:

  • The interval-valued bipolar fuzzy hypersoft set (IVBFHS) is a powerful extension for handling complex decision-making scenarios with bipolar, interval-valued, and multi-attribute data.
  • The developed decision support algorithm provides a systematic approach for rational decision-making, particularly applicable in fields like e-learning.
  • The study validates the proposed framework's effectiveness and reliability for advanced decision support applications.