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[{"key": "dc.contributor.advisor", "value": "Terziyan, Vagan", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.advisor", "value": "Khriyenko, Oleksiy", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Ibironke, Emiola Ololade", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2021-06-02T07:47:17Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2021-06-02T07:47:17Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2021", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/76146", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Artificial intelligence is being used in the health sector of developed countries and it has improved the lives of humans greatly such as detecting cancers and different types of brain tumours and diagnosing cardiovascular diseases within a short period. AI has been used to make decisions for humans based on some observed symptoms identified in the human body. AI is also used in speech technology for healthcare to process human speech in order to identify languages, gender and age and also for the purpose of detecting some physical and mental disease which have been known to cause changes in human speech. For example, the Automatic Speech Recognition (ASR) technology has been used in the healthcare sector to fix speech and hearing impairments of patients. AI is also used to study the emotions of humans and it has been used to handle complex situations difficult for health specialists, such as tracking and detecting patients with corona virus through an application and the development of vaccines.\nOn the other hand, developing countries are still struggling to keep up with the use of AI in their health sectors. Embedding AI in improving the services provided by the health sector in Nigeria is a long-overdue transformation. Most especially in the maternity section. Pregnant women ought to be given proper health care attention irrespective of whether they live in the urban area, metropolitan area, semi-rural area, or rural area. It is based on this that this research was carried to compare the present distribution system of maternity kits to pregnant women and a designed proposed model.\nThis research work focused on identifying the issues related to the distribution of maternity kits in Nigeria and the design and development of an AI system that would help to ensure the effective distribution of maternity kits to pregnant women. Two types of questionnaires were administered. The use of online questionnaires and paper-based questionnaires enabled participants of this study to state the issues they face as regards the collection of maternity kits in government hospitals. It was discovered that many of the participants believed that services in the health sector of Nigeria need to be improved. Based on the survey carried out, about 50% of the participants were willing to share their addresses to be used for maternity kit distribution and about a quarter of the participants were willing to share their photos and fingerprints to be used in the AI system for maternity kit distribution. Participants' opinions varied in the information they were willing to share with the government hospitals and Non-governmental Organisations (NGOs) as regards the use of text, voice, and fingerprints for AI purposes.\nIn the design and development of the system, the pregnant woman\u2019s due date was predicted by the Machine Leaning Algorithm. Photos were detected to determine if she has been registered in the system. Google Map API was used to navigate to her location, and Twilio messaging API was used to send her messages to inform her of the maternity kit distribution date.\nIn the development of a proposed model, AI for facial detection was used. It was discovered that the facial recognition model was not 100% efficient in identifying people of the black race especially people from the same family because they have similar facial features.\nSeveral strategies are recommended for the full functioning of the proposed model. Firstly, it is important to note that only one researcher carried out this design hence some other technical skills which the researcher did not possess is required for full deployment and implementation of the model. Secondly, most of the APIs used were the free versions, for a more efficient model, paid versions may be required.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Paivi Vuorio (paelvuor@jyu.fi) on 2021-06-02T07:47:16Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2021-06-02T07:47:17Z (GMT). No. of bitstreams: 0\n Previous issue date: 2021", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "99", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.format.mimetype", "value": "application/pdf", "language": null, "element": "format", "qualifier": "mimetype", "schema": "dc"}, {"key": "dc.language.iso", "value": "eng", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "AI tracking", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "maternity kits", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.subject.other", "value": "Nigeria", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "An AI tracking and delivery system for the distribution of maternity kits : case study: the health sector \u2013 Nigeria", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "master thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202106023382", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Pro gradu -tutkielma", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Master\u2019s thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietotekniikka", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Mathematical Information Technology", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_bdcc", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "masterThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "602", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "\u00e4itiyspakkaukset", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "terveys", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "jakelu", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "teko\u00e4ly", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "terveysala", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "maternity packages", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "health", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "distribution of delivery", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "machine learning", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "artificial intelligence", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "health sector", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.format.content", "value": "fulltext", "language": null, "element": "format", "qualifier": "content", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}, {"key": "dc.type.okm", "value": "G2", "language": null, "element": "type", "qualifier": "okm", "schema": "dc"}]
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