PMID- 35890820
OWN - NLM
STAT- MEDLINE
DCOM- 20220728
LR  - 20220731
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 14
DP  - 2022 Jul 8
TI  - A Two-Phase Machine Learning Framework for Context-Aware Service Selection to 
      Empower People with Disabilities.
LID - 10.3390/s22145142 [doi]
LID - 5142
AB  - The use of software and IoT services is increasing significantly among people 
      with special needs, who constitute 15% of the world's population. However, 
      selecting appropriate services to create a composite assistive service based on 
      the evolving needs and context of disabled user groups remains a challenging 
      research endeavor. Our research applies a scenario-based design technique to 
      contribute (1) an inclusive disability ontology for assistive service selection, 
      (2) semi-synthetic generated disability service datasets, and (3) a machine 
      learning (ML) framework to choose services adaptively to suit the dynamic 
      requirements of people with special needs. The ML-based selection framework is 
      applied in two complementary phases. In the first phase, all available atomic 
      tasks are assessed to determine their appropriateness to the user goal and 
      profiles, whereas in the subsequent phase, the list of service providers is 
      narrowed by matching their quality-of-service factors against the context and 
      characteristics of the disabled person. Our methodology is centered around a 
      myriad of user characteristics, including their disability profile, preferences, 
      environment, and available IT resources. To this end, we extended the widely used 
      QWS V2.0 and WS-DREAM web services datasets with a fusion of selected 
      accessibility features. To ascertain the validity of our approach, we compared 
      its performance against common multi-criteria decision making (MCDM) models, 
      namely AHP, SAW, PROMETHEE, and TOPSIS. The findings demonstrate superior service 
      selection accuracy in contrast to the other methods while ensuring accessibility 
      requirements are satisfied.
FAU - Namoun, Abdallah
AU  - Namoun A
AUID- ORCID: 0000-0002-7050-0532
AD  - Faculty of Computer and Information Systems, Islamic University of Madinah, 
      Madinah 42351, Saudi Arabia.
FAU - Abi Sen, Adnan Ahmed
AU  - Abi Sen AA
AD  - Faculty of Computer and Information Systems, Islamic University of Madinah, 
      Madinah 42351, Saudi Arabia.
FAU - Tufail, Ali
AU  - Tufail A
AUID- ORCID: 0000-0003-4871-4080
AD  - School of Digital Science, Universiti Brunei Darussalam, Tungku Link, Gadong 
      BE1410, Brunei.
FAU - Alshanqiti, Abdullah
AU  - Alshanqiti A
AUID- ORCID: 0000-0002-6080-5236
AD  - Faculty of Computer and Information Systems, Islamic University of Madinah, 
      Madinah 42351, Saudi Arabia.
FAU - Nawaz, Waqas
AU  - Nawaz W
AUID- ORCID: 0000-0002-9989-6163
AD  - Faculty of Computer and Information Systems, Islamic University of Madinah, 
      Madinah 42351, Saudi Arabia.
FAU - BenRhouma, Oussama
AU  - BenRhouma O
AD  - Faculty of Computer and Information Systems, Islamic University of Madinah, 
      Madinah 42351, Saudi Arabia.
LA  - eng
GR  - 13/20/This research was funded by Deputyship for Research and Innovation, 
      Ministry of Education in Saudi Arabia/
PT  - Journal Article
DEP - 20220708
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - *Disabled Persons
MH  - Humans
MH  - Machine Learning
PMC - PMC9324550
OTO - NOTNLM
OT  - QoS
OT  - accessibility
OT  - assistive technologies
OT  - disabled people
OT  - machine learning
OT  - ontologies
OT  - quality of service
OT  - service selection
OT  - universal design
OT  - web services
COIS- The authors declare no conflict of interest.
EDAT- 2022/07/28 06:00
MHDA- 2022/07/29 06:00
PMCR- 2022/07/08
CRDT- 2022/07/27 01:41
PHST- 2022/06/02 00:00 [received]
PHST- 2022/06/24 00:00 [revised]
PHST- 2022/07/01 00:00 [accepted]
PHST- 2022/07/27 01:41 [entrez]
PHST- 2022/07/28 06:00 [pubmed]
PHST- 2022/07/29 06:00 [medline]
PHST- 2022/07/08 00:00 [pmc-release]
AID - s22145142 [pii]
AID - sensors-22-05142 [pii]
AID - 10.3390/s22145142 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Jul 8;22(14):5142. doi: 10.3390/s22145142.