Understanding the user-generated geographic information by utilizing big data analytics for health care

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dc.authoridAlaa Ali Hameed / 0000-0002-8514-9255en_US
dc.authorwosidAlaa Ali Hameed / GPB-6682-2022en_US
dc.contributor.authorUllah, Hidayat
dc.contributor.authorHameed, Alaa Ali
dc.contributor.authorRizvi, Sanam Shahla
dc.contributor.authorJamil, Akhtar
dc.contributor.authorKwon, Se Jin
dc.date.accessioned2022-11-11T07:52:41Z
dc.date.available2022-11-11T07:52:41Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThere are two main ways to achieve an active lifestyle, the first is to make an effort to exercise and second is to have the activity as part of your daily routine. The study's major purpose is to examine the influence of various kinds of physical engagements on density dispersion of participants in Shanghai, China, and even prototype check-in data from a Location-Based Social Network (LBSN) utilizing a mix of spatial, temporal, and visualization methodologies. This paper evaluates Weibo used for big data evaluation and its dependability in some types rather than physically collected proofs by investigating the relationship between time, class, place, frequency, and place of check-in built on geographic features and related consequences. Kernel density estimation has been used for geographical assessment. Physical activities and frequency allocation are formed as a result of hour-to-day consumption habits. Our observations are based on customer check-in activities in physical venues such as gyms, parks, and playing fields, the prevalence of check-ins, peak times for visiting fun parks, and gender disparities, and we applied relative difference formulation to reveal the gender difference in a much better way. The purpose of this research is to investigate the influence of physical activity and health-related standard of living on well-being in a selection of Shanghai inhabitants. Keywordsen_US
dc.identifier.citationUllah, H., Hameed, A. A., Rizvi, S. S., Jamil, A., & Kwon, S. J. (2022). Understanding the User-Generated Geographic Information by Utilizing Big Data Analytics for Health Care. Computational Intelligence and Neuroscience, 2022.en_US
dc.identifier.doi10.1155/2022/2532580en_US
dc.identifier.issn1687-5265en_US
dc.identifier.issn1687-5273en_US
dc.identifier.scopus2-s2.0-85139886512en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttp://dx.doi.org/10.1155/2022/2532580
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3318
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000870009300013en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorHameed, Alaa Ali
dc.language.isoenen_US
dc.publisherHINDAWI LTDen_US
dc.relation.ispartofCOMPUTATIONAL INTELLIGENCE AND NEUROSCIENCEen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleUnderstanding the user-generated geographic information by utilizing big data analytics for health careen_US
dc.typeArticleen_US

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