SustainOnt

Edgar, Vatricia; Place, Cecilia La; Schmidt, Julia; Bansal, Ajay; Bansal, Srividya K.

doi:10.1145/3391274.3393640

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…In their work [91], researchers used indicators of wellbeing from the Better Life Index to propose a fuzzy model to assist in the formulation of strategies for a better development of life. The authors of [92] proposed an ontology for neighborhood-level sustainability that covers three domains: social, environmental, and economic. This ontology is designed to help organize and analyze data from multiple sources.…”

Section: E Semantic Web and Other Approaches To Model Qolmentioning

confidence: 99%

“…Modeling approach Ontology [81], [82], [83], [84], [86], [85], [87], [88], [89], [90], [92], [93] Fuzzy model [91] METHONTOLOGY [81] MIMIC approach [80] Data management system [94] Ontology building tool Prolog [87] Protégé [81], [82], [87], [92], [93] WebProtégé [83], [86] Semantic web language OWL/OWL2 [82], [86], [84], [87], [92] RDF [92], [93] SPARQL [82], [87], [92], [93] V. Discussion…”

Section: Used Techniques Research Workmentioning

confidence: 99%

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Artificial Intelligence for Quality of Life Study: A Systematic Literature Review

Jannani,

Sael,

Benabbou

2024

IEEE Access

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The concept of "quality of life" (QoL) encompasses all aspects of people's standard of living, including economic, social, or health-related factors, as well as their perceptions of their own lives. Although the growing application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in processing and modeling the diverse datasets associated with these domains, there remains a significant challenge in addressing different issues in QoL area of research and in fully harnessing these technologies to improve QoL research outcomes. Despite the technological advancements, current research endeavors often overlook the complex, multifaceted nature of QoL study. This oversight results in fragmented insights and leaves significant areas underexplored. In this work, we conducted a systematic literature review (SLR) to investigate the contribution of AI to QoL studies. For this, we collected 68 research works published between 2008 and 2022. This review covers a range of research questions about the objectives and methods of studies on QoL, the sources and types of data utilized, and the advancements made through the application of natural language processing (NLP), ML, deep learning (DL), statistical models, and semantic approaches. The goal of this review is to tackle the prevalent ambiguity in QoL dimensions, synthesize the research findings, and highlight the contributions, advancements, and most innovative approaches in the field. Moreover, we identify gaps and limitations in the current literature and suggest potential areas for future research, aiming to inspire more cohesive and comprehensive approaches to studying QoL using AI and ML techniques.

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Section: E Semantic Web and Other Approaches To Model Qolmentioning

confidence: 99%