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 Experiment Videos

An advanced artificial intelligence tool for menu design.

Abdus Salam Khan1, Achim Hoffmann

  • 1Artificial Intelligence Laboratory, School of Computer Science and Engineering, The University of New South Wales, Sydney 2052, Australia. askhan@cse.unsw.edu.au

Nutrition and Health
|June 14, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Comparing the Lecture-Based Learning With the Four-Component Instructional Design (4C/ID) Model of Learning in Enhancing the Skills of Consent-Taking in the Emergency Department: A Quasi-experimental Study.

Cureus·2025
Same author

Cognitive biases regarding utilization of emergency severity index among emergency nurses.

The American journal of emergency medicine·2023
Same author

Survival of an 80-Year-Old Male With a Successful Split-Thickness Skin Graft for End-Stage Necrotizing Fasciitis: A Case Report.

Cureus·2022
Same author

Giant Cell Tumour of the Patella: A Missing Differential Diagnosis in the Young.

Cureus·2022
Same author

Documenting response to COVID-individual and systems successes and challenges: a longitudinal qualitative study.

BMC health services research·2022
Same author

Accuracy of lung ultrasound and chest X-rays in diagnosing acute pulmonary oedema in patients presenting with acute dyspnoea in emergency department.

JPMA. The Journal of the Pakistan Medical Association·2022

This study introduces MIKAS, an AI tool for computer-assisted menu design. MIKAS uses incremental knowledge acquisition, Case-Based Reasoning, and Ripple Down Rules to improve dietary planning.

Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Nutrition Science

Background:

  • Computer-assisted menu design is challenging due to hard-coded knowledge limitations.
  • Existing systems struggle with unanticipated client needs in menu planning.

Purpose of the Study:

  • To develop an artificial intelligence (AI) system, MIKAS, for incremental knowledge acquisition in menu design.
  • To enhance computer-assisted menu planning by addressing limitations in handling diverse client requirements.

Main Methods:

  • Developed MIKAS (menu construction using incremental knowledge acquisition system).
  • Integrated Case-Based Reasoning (CBR) and Ripple Down Rules (RDR) for knowledge acquisition.
  • Utilized a Case Base for storing menu design instances and expert hints.

Related Experiment Videos

Main Results:

  • MIKAS enables incremental development of a knowledge base for menu design.
  • The system allows continuous improvement through expert interaction during routine use.
  • Combines CBR and RDR for effective knowledge acquisition and classification.

Conclusions:

  • MIKAS facilitates better dietary practice by leveraging dietitian expertise.
  • The system has potential applications in institutions providing dietary advice.
  • Addresses limitations of traditional computer-assisted menu design for varied client needs.