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GSO ISO/IEC TR 24030:2024
GSO - Cover page
تقديم خليجي
Gulf Foreword
ISO - Cover page
Foreword
Introduction
1 Scope
2 Normative references
3 Terms and definitions
4 Abbreviated terms
5 Applications
 5.1 General
 5.2 Application domains
 5.3 Deployment models
 5.4 Examples of AI applications
6 Use cases
 6.1 General
 6.2 Acceptable sources of use cases
 6.3 Guidance for submitting use cases
 6.4 Properties
  6.4.1 General information on use case
  6.4.2 References of use case
 6.5 Basic statistics
  6.5.1 Use cases by application domain
  6.5.2 Use cases by status
  6.5.3 Use cases by task
 6.6 Societal concerns
  6.6.1 General
  6.6.2 Impact analysis
 6.7 Use case analysis for standardization opportunities and requirements
7 Use cases summaries
 7.1 General
 7.2 Agriculture
  7.2.1 AI to understand adulteration in commonly used food items (use case 19)
  7.2.2 bioBotGuard (use case 54)
  7.2.3 Ecosystems management from causal relation inference from observational data (use case 96)
  7.2.4 Real-time segmentation and prediction of plant growth dynamics using low-power embedded systems equipped with AI (use case 126)
 7.3 Digital marketing
  7.3.1 Improving conversion rates and return on investment (RoI) with AI technologies (use case 53)
  7.3.2 Logo and trademark detection (use case 56)
  7.3.3 Flavorlens (use case 76)
 7.4 Education
  7.4.1 VTrain recommendation engine (use case 23)
  7.4.2 RAVE (use case 55)
  7.4.3 IFLYTEK intelligent marking system (use case 83)
  7.4.4 Intelligent educational robot (use case 84)
  7.4.5 AI solution to intelligent campus (use case 85)
  7.4.6 AI adaptive learning platform for personalized learning (use case 102)
  7.4.7 AI adaptive learning mobile app (use case 124)
 7.5 Energy
  7.5.1 AI-dispatcher (operator) of large-scale distributed energy system infrastructure (use case 109)
 7.6 Fintech
  7.6.1 Detection of frauds based on collusions (use case 20)
  7.6.2 Credit scoring using KYC data (use case 27)
  7.6.3 Virtual bank assistant (use case 57)
  7.6.4 Forecasting prices of commodities (use case 91)
  7.6.5 Finance advising and asset management with AI (use case 114)
  7.6.6 Loan in 7 minutes (use case 119)
 7.7 Healthcare
  7.7.1 Explainable artificial intelligence for genomic medicine (use case 1)
  7.7.2 Improve clinical decision-making and risk assessment in mental healthcare (use case 2)
  7.7.3 Computer-aided diagnosis in medical imaging based on machine learning (use case 6)
  7.7.4 AI solution to predict post-operative visual acuity for LASIK surgeries (use case 24)
  7.7.5 Chromosome segmentation and deep classification (use case 44)
  7.7.6 AI solution for quality control of electronic medical records (EMR) in real time (use case 50)
  7.7.7 Dialogue-based social care services for people with mental illness, dementia and the elderly living alone (use case 63)
  7.7.8 Pre-screening of cavity and oral diseases based on 2D digital images (use case 67)
  7.7.9 Real-time patient support and medical information service applying spoken dialogue system (use case 68)
  7.7.10 Integrated recommendation solution for prosthodontic treatments (use case 69)
  7.7.11 Sudden infant death syndrome (SIDS) (use case 74)
  7.7.12 Discharge summary classifier (use case 79)
  7.7.13 Generation of clinical pathways (use case 80)
  7.7.14 Hospital management tools (use case 81)
  7.7.15 Predicting relapse of a dialysis patient during treatment (use case 87)
  7.7.16 Instant triaging of wounds (use case 89)
  7.7.17 Accelerated acquisition of magnetic resonance images (use case 101)
  7.7.18 AI based text to speech services with personal voices for people with speech impairments (use case 103)
  7.7.19 AI platform for chest CT-scan analysis (early stage lung cancer detection) (use case 105)
  7.7.20 AI-based design of pharmacologically relevant targets with target properties (use case 107)
  7.7.21 AI-based mapping of optical to multi-electrode catheter recordings for atrial fibrillation treatment (use case 108)
  7.7.22 AI solution for end-to-end processing of cell microscopy images (use case 115)
  7.7.23 Generation of computer tomography scans from magnetic resonance images (use case 116)
  7.7.24 Improving the knowledge base of prescriptions for drug and non-drug therapy and its use as a tool in support of medical professionals (use case 117)
  7.7.25 Neural network formation of 3D-model orthopedic insoles (use case 121)
  7.7.26 Search for undiagnosed patients (use case 127)
  7.7.27 Support system for optimization and personalization of drug therapy (use case 129)
  7.7.28 Syntelly - computer aided organic synthesis (use case 130)
  7.7.29 WebioMed clinical decision support system (use case 131)
 7.8 Home/service robotics
  7.8.1 Robot consciousness (use case 61)
  7.8.2 Social humanoid technology capable of multi-modal context recognition and expression (use case 65)
  7.8.3 Application of strong artificial intelligence (use case 111)
 7.9 ICT
  7.9.1 Autonomous network and automation level definition (use case 30)
  7.9.2 Autonomous network scenarios (use case 31)
  7.9.3 A judging support system for gymnastics using 3D sensing (use case 70)
  7.9.4 Active antenna array satellite (use case 71)
  7.9.5 Carrier interference detection and removal for satellite communication (use case 72)
  7.9.6 Ontologies for smart buildings (use case 78)
  7.9.7 Product failure prediction for critical IT infrastructure (use case 86)
  7.9.8 Data compression with AI techniques (use case 98)
  7.9.9 Optimization of software configurations with AI techniques (use case 99)
  7.9.10 Better human-computer interaction with advanced language models (use case 100)
 7.10 Legal
  7.10.1 Tax rules updates and classification (use case 95)
  7.10.2 AI contract management (use case 120)
  7.10.3 Semantic analysis of legal documents (use case 128)
 7.11 Logistics
  7.11.1 Improving productivity for warehouse operation (use case 41)
  7.11.2 AI based dynamic routing SaaS (use case 92)
 7.12 Maintenance and support
  7.12.1 Anomaly detection in sensor data using deep learning techniques (use case 45)
  7.12.2 Jet engine predictive maintenance service (use case 73)
  7.12.3 Detection of fraudulent medical claims (use case 90)
  7.12.4 AI virtual assistant for customer support and service (use case 106)
 7.13 Manufacturing
  7.13.1 AI solution to calculate amount of contained material from mass spectrometry measurement data (use case 3)
  7.13.2 AI solution to quickly identify defects during quality assurance process on wind turbine blades (use case 4)
  7.13.3 Solution to detect signs of failures in wind power generation system (use case 5)
  7.13.4 Generative design of mechanical parts (use case 15)
  7.13.5 Information extraction from hand-marked industrial inspection sheets (use case 21)
  7.13.6 Automated defect classification on product surfaces (use case 33)
  7.13.7 Robotic task automation: insertion (use case 34)
  7.13.8 Powering remote drilling command centre (use case 36)
  7.13.9 Leveraging AI to enhance adhesive quality (use case 37)
  7.13.10 Machine learning-driven approach to identify weak spots in the manufacturing of circuit breakers (use case 38)
  7.13.11 Machine learning-driven analysis of batch process operation data to identify causes for poor batch performance (use case 39)
  7.13.12 Empowering autonomous flow meter control – reducing time taken for “proving of meters” (use case 40)
  7.13.13 Adaptable factory (use case 46)
  7.13.14 Order-controlled production (use case 47)
  7.13.15 Value-based service (use case 48)
  7.13.16 Improvement of productivity of semiconductor manufacturing (use case 82)
  7.13.17 AI decryption of magnetograms (use case 104)
  7.13.18 Analysing and predicting acid treatment effectiveness on bottom hole zone (use case 110)
  7.13.19 Automatic classification tool for full size core (use case 112)
  7.13.20 Intelligent technology to control manual operations via video — “Norma” (use case 118)
  7.13.21 Optimization of ferroalloy consumption for a steel production company (use case 123)
  7.13.22 Device control using AI consisting of cloud computing and embedded system (use case 132)
 7.14 Media and entertainment
  7.14.1 Predictive analytics for the behaviour and psycho-emotional conditions of eSports players using heterogeneous data and artificial intelligence (use case 125)
 7.15 Mobility
  7.15.1 Autonomous apron truck (use case 12)
  7.15.2 AI solution to help mobile phones to have better picture effect (use case 32)
 7.16 Public sector
  7.16.1 AI ideally matches children to day-care centres (use case 7)
  7.16.2 AI sign language interpretation system for people with hearing impairment (use case 62)
  7.16.3 AI situation explanation service for people with visual impairments (use case 64)
  7.16.4 Predictive maintenance of public housing lifts (use case 94)
 7.17 Retail
  7.17.1 Emotion-sensitive AI customer service (use case 42)
  7.17.2 Deep learning-based user intent recognition (use case 43)
 7.18 Security
  7.18.1 Behavioural and sentiment analytics (use case 14)
  7.18.2 AI (swarm intelligence) solution for attack detection in IoT environment (use case 22)
  7.18.3 Use of robotic solution for traffic policing and control (use case 25)
  7.18.4 Robotic solution for replacing human labour in hazardous conditions (use case 26)
  7.18.5 Non-intrusive detection of malware (use case 93)
 7.19 Social infrastructure
  7.19.1 Deep learning technology combined with topological data analysis successfully estimates degree of internal damage to bridge infrastructure (use case 8)
  7.19.2 Water crystal mapping (use case 77)
  7.19.3 System for real-time earthquake simulation with data assimilation (use case 97)
 7.20 Transportation
  7.20.1 AI components for vehicle platooning on public roads (use case 9)
  7.20.2 Self-driving aircraft towing vehicle (use case 10)
  7.20.3 Unstaffed protective vehicle for road works on motorways (use case 11)
  7.20.4 Enhancing traffic management efficiency and infraction detection accuracy with AI technologies (use case 29)
  7.20.5 AI solution for traffic signal optimization based on multi-source data fusion (use case 49)
  7.20.6 Automated travel pattern recognition using mobile network data for applications to mobility as a service (use case 52)
  7.20.7 Autonomous trains [unattended train operation (UTO)] (use case 113)
 7.21 Work and life
  7.21.1 Robotic prehension of objects (use case 16)
  7.21.2 Robotic vision – scene awareness (use case 17)
  7.21.3 Recommendation algorithm for improving member experience and discoverability of resorts in the booking portal of a hotel chain (use case 28)
  7.21.4 Cooking recipes without border (CRWB) recommendation benchmark (use case 75)
  7.21.5 Improving the quality of online interaction (use case 88)
 7.22 Others
  7.22.1 AI solution to automatically identify false positives from a specific check for “untranslated target segments” by an automated quality assurance tool (use case 13)
  7.22.2 AI solution for car damage classification (use case 18)
  7.22.3 Causality-based thermal prediction for data centre (use case 35)
  7.22.4 Machine learning tools in support of transformer diagnostics (use case 51)
  7.22.5 Video on demand publishing intelligence platform (use case 58)
  7.22.6 Predictive testing (use case 59)
  7.22.7 Predictive data quality (use case 60)
  7.22.8 Expansion of AI training dataset and contents using artificial intelligence techniques (use case 66)
  7.22.9 Open spatial dataset for developing AI algorithms based on remote sensing (satellite, drone, aerial imagery) data (use case 122)
Annex A (informative) Impact analysis items
Annex B (informative) Use case template
Annex C (informative) In-depth analysis of machine learning tools in support of transformer diagnostics use case
Bibliography
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
GSO ISO/IEC TR 24030:2024 Gulf Standard
GSO ISO/IEC TR 24030:2024
ISO/IEC TR 24030:2021
مواصفة قياسية خليجية
تقنية المعلومات - الذكاء الاصطناعي (AI) - حالات استخدام
Gulf Standard
Information technology — Artificial intelligence (AI) — Use cases
ICS:
35.020


Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
GSO ISO/IEC TR 24030:2024 Gulf Standard

تقديم خليجي

هيئة التقييس لدول مجلس التعاون لدول الخليج العربية هيئة إقليمية تضم في عضويتها أجهزة التقييس الوطنية في الدول الأعضاء ، ومن مهام الهيئة إعداد المواصفات القياسية واللوائح الفنية الخليجية بواسطة لجان فنية متخصصة.
قرر المجلس الفني لهيئة التقييس لدول مجلس التعاون لدول الخليج العربية في الاجتماع الـ (57) بتاريخ 1445/07/20 هـ ،الموافق 2024/01/31م اعتماد المواصفة القياسية الخليجية رقم GSO ISO/IEC TR 24030:2024 وعنوانها "تقنية المعلومات - الذكاء الاصطناعي (AI) - حالات استخدام" باللغة الانجليزية التي تم دراستها وتبنيها عن المنظمة الدولية للتقييس رقم ISO/IEC TR 24030:2021 بدون إدخال تعديلات فنية عليها ضمن برنامج عمل اللجنة الفنية الخليجية لمواصفات المعلومات وتقنية المعلومات (TC08) المدرجة في خطة الإمارات العربية المتحدة .

Gulf Foreword

GCC Standardization Organization (GSO) is a regional organization which consists of the national standardization bodies of GCC member states. One of GSO main functions is to issue gulf standards and technical regulations through specialized technical committees.
GSO Technical Council in its meeting number (57) held on 20/07/1445 H, which corresponds to 31/01/2024, has approved Gulf Standard GSO ISO/IEC TR 24030:2024 titled "Information technology — Artificial intelligence (AI) — Use cases" in English language which was studied and adopted from International Organization for Standardization Standard ISO/IEC TR 24030:2021 without any technical modifications through the technical program of GSO Technical Committee for Information and Information Technology (TC08) in UNITED ARAB EMIRATES schedule .
ISO/IEC TR 24030:2021(en)
ISO/IEC TR 24030:2021(en)
© GSO 2024 © ISO/IEC 2021 – All rights reserved.
© GSO 2024 © ISO/IEC 2021 – All rights reserved.
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
GSO ISO/IEC TR 24030:2024 Gulf Standard

Technical

Report

ISO/IEC 24030
Information technology — Artificial intelligence (AI) — Use cases
Technologies de l'information — Intelligence artificielle (IA) — Cas pratiques
Reference number
ISO/IEC TR 24030:2021(en)
First edition
2021-05
ISO/IEC TR 24030:2021(en)
ISO/IEC TR 24030:2021(en)
© GSO 2024 © ISO/IEC 2021 – All rights reserved.
© GSO 2024 © ISO/IEC 2021 – All rights reserved.
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
GSO ISO/IEC TR 24030:2024 Gulf Standard

ISO/IEC TR 24030:2021(en)
ISO/IEC TR 24030:2021(en)
© GSO 2024 © ISO/IEC 2021 – All rights reserved.
© GSO 2024 © ISO/IEC 2021 – All rights reserved.
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
GSO ISO/IEC TR 24030:2024 Gulf Standard
Contents Page

Foreword

ISO (the International Organization for Standardization) and IEC (the International Electrotechnical Commission) form the specialized system for worldwide standardization. National bodies that are members of ISO or IEC participate in the development of International Standards through technical committees established by the respective organization to deal with particular fields of technical activity. ISO and IEC technical committees collaborate in fields of mutual interest. Other international organizations, governmental and non-governmental, in liaison with ISO and IEC, also take part in the work.
The procedures used to develop this document and those intended for its further maintenance are described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types of document should be noted. This document was drafted in accordance with the editorial rules of the ISO/IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_experts/refdocs).
Attention is drawn to the possibility that some of the elements of this document may be the subject of patent rights. ISO and IEC shall not be held responsible for identifying any or all such patent rights. Details of any patent rights identified during the development of the document will be in the Introduction and/or on the ISO list of patent declarations received (see www.iso.org/patents) or the IEC list of patent declarations received (see patents.iec.ch).
Any trade name used in this document is information given for the convenience of users and does not constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions related to conformity assessment, as well as information about ISO's adherence to the World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www.iso.org/iso/foreword.html. In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology, Subcommittee SC 42, Artificial intelligence.
Any feedback or questions on this document should be directed to the user’s national standards body. A complete listing of these bodies can be found at www.iso.org/members.html and www.iec.ch/national-committees.

Introduction

This document provides a collection of use cases of artificial intelligence (AI) applications in a variety of domains.
In total, 132 AI use cases were submitted by experts between July 2018 and the end of November 2019. In this document, the term “use cases” means “collection of submitted use cases”.
The rationale for this document is as follows:
illustrating the applicability of the AI standardization work across a variety of application domains;
input to and reference for AI standardization work;
sharing the collected use cases in support of AI standardization work with external organizations and internal entities to foster collaboration;
reach out to new stakeholders interested in AI applicability;
establishment of liaison organizations to collect requirements for AI via use cases;
by investigating use cases, it is possible to find the new technical requirements (standardized demand) from the market, accelerating the transformation of science and technology achievements.
While a bottom-up approach was used to collect use cases, a top-down approach is used in this document to identify AI applications, and their deployment models, and their application domains., which is shown in Clause 5.
The first step taken to collect use cases was to identify application domains of AI systems (described in Clause 5) and to provide a use case template (described in 6.4 and Annex B). Contributors were requested to submit use cases using the provided template.
For improving the quality of use cases, a guidance was provided for contributors. The guidance included identified acceptable sources (described in 6.3) and AI characteristics (described in 6.4) for preparing use cases.
In this document, subclause 6.5 includes basic statistics of use cases. Subclause 6.6 and Annex C describe the findings from use case analysis.
The use cases were grouped and categorized according to the identified application domains. In this document, use cases are summarized and grouped according to the application domains in Clause 7. Readers of this document can find use cases of specific application domains and their original submissions at https://standards.iso.org/iso-iec/tr/24030/ed-1/en.
AI is an emerging field with use cases and solutions with a wide range of maturity and success. The descriptions are given for the convenience of users of this document and does not constitute an endorsement by ISO.

ISO/IEC TR 24030:2021(en)
ISO/IEC TR 24030:2021(en)
© GSO 2024 © ISO/IEC 2021 – All rights reserved.
© GSO 2024 © ISO/IEC 2021 – All rights reserved.
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
GSO ISO/IEC TR 24030:2024 Gulf Standard
Technical Report ISO/IEC TR 24030:2021(en)
Information technology — Artificial intelligence (AI) — Use cases

1 Scope

This document provides a collection of representative use cases of AI applications in a variety of domains.

2 Normative references

There are no normative references in this document.

3 Terms and definitions

For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
ISO Online browsing platform: available at https://www.iso.org/obp
IEC Electropedia: available at http://www.electropedia.org/
3.1
artificial intelligence
AI
<system> capability to acquire, process, create and apply knowledge, held in the form of a model, to conduct one or more given tasks
3.2
artificial intelligence
AI
<engineering discipline> discipline of developing and studying artificial intelligence (3.1)
3.3
quality
conformance to specified requirements
[SOURCE: ISO 13628-2:2006, 3.33]
3.4
machine learning
process using computational techniques to enable systems to learn from data or experience
3.5
deep learning
approach to creating rich hierarchical representations through the training of neural networks with many hidden layers
3.6
service
performance of activities, work, or duties
Note 1 to entry: A service is self-contained, coherent, discrete, and can be composed of other services.
Note 2 to entry: A service is generally an intangible product.
[SOURCE: ISO/IEC/IEEE 12207:2017, 3.1.50]
3.7
classification
task of assigning collected data to target categories or classes.
Note 1 to entry: Machine learning (ML) models can be designed/created either for binary classification, where they can learn to predict one of two different categories/classes; or for multiclass classification, where ML models learn to predict one of many different categories/classes.
Note 2 to entry: An example of classification is to predict if a photograph of an animal is a cat or a dog or even a different species. Classification employs supervised learning. Classification can employ supervised, semi-supervised, or unsupervised learning.
3.8
application
software or a program that is specific to the solution of an application problem
[SOURCE: ISO/IEC 11801:2002, 3.1.2]
3.9
neural network
network of primitive processing elements connected by weighted links with adjustable weights, in which each element produces a value by applying a nonlinear function to its input values, and transmits it to other elements or presents it as an output value
Note 1 to entry: Whereas some neural networks are intended to simulate the functioning of neurons in the nervous system, most neural networks are used in artificial intelligence as realizations of the connectionist model.
Note 2 to entry: Examples of nonlinear functions are a threshold function, a sigmoid function, and a polynomial function.
[SOURCE: ISO/IEC 2382: 2015, 2120625]
3.10
task
set of activities undertaken in order to achieve a specific goal
Note 1 to entry: These activities can be physical, perceptual and/or cognitive.
Note 2 to entry: While goals are independent of the means used to achieve them, tasks describe particular means of achieving goals.
Note 3 to entry: Examples of tasks include classification, regression, ranking, clustering and dimensionality reduction.
[SOURCE: ISO 9241-11:2018, 3.1.11, modified — Note 3 to entry has been added.]
3.11
parameter
<machine learning> variable of the model that affects its output any characteristic that can help in defining or classifying a particular system
3.12
artificial intelligence system
AI system
engineered information processing system featuring artificial intelligence
Note 1 to entry: AI systems are designed to operate with varying levels of autonomy.
3.13
training data
samples for training used to fit a machine learning model
3.14
cloud service
one or more capabilities offered through cloud computing invoked using a defined interface
[SOURCE: ISO/IEC 17788:2014, 3.2.8]
3.15
cloud computing
paradigm for enabling network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on-demand
Note 1 to entry: Examples of resources include servers, operating systems, networks, software, applications, and storage equipment.
[SOURCE: ISO/IEC 17788:2014, 3.2.5]
3.16
automation
characteristic of a system where work is performed that might previously have been done by a living being and that is governed by rules determined outside of the system
Note 1 to entry: Such systems are subject to external control and oversight.
Note 2 to entry: Automation implies the (revocable) delegation to a machine of a specific and defined set of “skills”, operations, processes, or procedures.
3.17
bias
systematic difference between true (or accepted) value and measured value
[SOURCE: ISO 14488:2007, 3.1]
3.18
data set
identifiable collection of data available for access or download in one or more formats
[SOURCE: ISO/IEC 20546:2019, 3.1.11]
3.19
natural language processing
<system> information processing based upon natural-language understanding
3.20
natural language processing
<engineering discipline> field of study within computer science and linguistics concerning automated processing, in whole or in part, of natural language
3.21
retraining
updating a trained model by training with different training data
3.22
computer vision
capability of a functional unit to acquire, process, and interpret visual data
Note 1 to entry: Computer vision involves the use of visual sensors to create an electronic or digital image of a visual scene.
Note 2 to entry: Not to be confused with machine vision.
Note 3 to entry: computer vision; artificial vision: terms and definition standardized by ISO/IEC [ISO/IEC 2382-28:1995].
Note 4 to entry: 28.01.19 (2382)
[SOURCE: ISO/IEC 2382:2015, 2123787]
3.23
trained model
result of model training
3.24
robot
automation system with actuators that performs intended tasks in the physical world, by means of sensing its environment and a software control system
Note 1 to entry: A robot includes a control system and interface of a control system.
Note 2 to entry: The classification of robot into industrial robot or service robot is done according to its intended application.
Note 3 to entry: In order to properly perform its tasks, a robot makes use of different kinds of sensors to confirm its current state and perceive the elements composing the environment in which it operates.
[SOURCE: ISO 18646-2:2019, 3.1, modified — Note 3 to entry has been added]
3.25
big data
extensive datasets — primarily in the data characteristics of volume, variety, velocity, and/or variability — that require a scalable technology for efficient storage, manipulation, management, and analysis
Note 1 to entry: Big data is commonly used in many different ways, for example as the name of the scalable technology used to handle big data extensive datasets.
[SOURCE: ISO/IEC 20546:2019, 3.1.2]
3.26
end user
individual person who ultimately benefits from the outcomes of the system
Note 1 to entry: The end user may be a regular operator of the software product or a casual user such as a member of the public.
[SOURCE: ISO/IEC 25000:2014, 4.7]
3.27
data analysis
systematic investigation of the data and their flow in a real or planned system
[SOURCE: ISO/IEC 2382:2015, 2122686]
3.28
pattern recognition
identification, by a functional unit, of physical or abstract patterns, and of structures and configurations
Note 1 to entry: This is an improved version of the definition in ISO/IEC 2382-12:1988.
Note 2 to entry: Pattern recognition: term and definition standardized by ISO/IEC 2382-28:1995.
Note 3 to entry: 28.01.13 (2382)
[SOURCE: ISO/IEC 2382:2015, 2123781]
3.29
anomaly detection
task of identifying data samples that do not conform to an expected pattern distribution, especially within data sets that appear to be homogeneous.
Note 1 to entry: Anomaly detection is mostly used for outlier detection, for example, fraud detection, detecting suspicious activities, etc. It may, therefore, also be called 'outlier detection'.
Note 2 to entry: With anomaly detection, the input data is all of one class and the ML model predicts if a data point is typical for a given distribution or not.
Note 3 to entry: Anomaly detection typically employs unsupervised learning.
3.30
stakeholder
person or organization that can affect, be affected by, or perceive themselves to be affected by a decision or activity
[SOURCE: ISO 35001:2019, 3.2]
3.31
model
physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, process or data
[SOURCE: ISO/IEC 18023-1:2006, 3.1.11, modified — In the definition, "or data" has been added.]
3.32
knowledge
<artificial intelligence> information about objects, events, concepts or rules, their relationships and properties, organized for goal-oriented systematic use.
Note 1 to entry: Information can exist in numeric or symbolic form.
Note 2 to entry: Information is data that has been contextualized, so that it is interpretable. Data is created through abstraction or measurement from the world.

ISO/IEC TR 24030:2021(en)
ISO/IEC TR 24030:2021(en)
© GSO 2024 © ISO/IEC 2021 – All rights reserved.
© GSO 2024 © ISO/IEC 2021 – All rights reserved.
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
GSO ISO/IEC TR 24030:2024 Gulf Standard
Bibliography
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Du Boulay B. Artificial Intelligence as an Effective Classroom Assistant, IEEE Intelligent Systems, V 31, p.7681. 2016.
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Schuelke-Leech B.A. and Marta Janczarski (2019), Incorporating Societal (Social) and Ethical Implications into the Design, Development, and Deployment of Technologies, IEEE ISTAS 2019 Conference Proceedings, November 15-16, 2019, Boston, MA.
[8]
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[9]
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ISO/IEC TR 20547-2:2018, Information technology — Big data reference architecture — Part 2: Use cases and derived requirements
[11]
IEC 62559 (all parts), Use case methodology
[12]
IEEE P7003, Algorithmic Bias Considerations
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
Gulf Standard GSO ISO/IEC TR 24030:2024
GSO ISO/IEC TR 24030:2024 Gulf Standard
ICS 35.020
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