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DC Field | Value | Language |
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dc.contributor.advisor | Σαμαράς, Νικόλας | el |
dc.contributor.author | Βαζακίδης, Δημήτριος | el |
dc.date.accessioned | 2023-10-11T07:27:17Z | - |
dc.date.available | 2023-10-11T07:27:17Z | - |
dc.date.issued | 2023 | el |
dc.identifier.uri | http://dspace.lib.uom.gr/handle/2159/29489 | - |
dc.description | Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2023. | el |
dc.description.abstract | The COVID-19 pandemic emerged in 2019 and has since evolved into a global crisis of paramount concern for governments and people worldwide. Its initial appearance marked the onset of significant disruptions, affecting both economies and daily lives. The repercussions have been profound, leading to the loss of lives and livelihoods, and even now, four years later, its impact continues to be felt. Comprehending the patterns and trends within the foundational data related to the pandemic holds immense importance. This knowledge is crucial for governments as they formulate policies and strategies to manage the crisis effectively. It is equally vital for individuals to stay informed and make informed decisions regarding their health and safety in these challenging times. The pandemic's enduring impact underscores the need for continued vigilance, research, and cooperation on a global scale to mitigate its effects and prevent similar crises in the future. | en |
dc.format.extent | 77 | el |
dc.language.iso | en | en |
dc.publisher | Πανεπιστήμιο Μακεδονίας | el |
dc.rights | CC0 1.0 Παγκόσμια | el |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | en |
dc.title | Prediction of cases and transmission rate of covid-19 with machine learning techniques | en |
dc.type | Electronic Thesis or Dissertation | en |
dc.type | Text | en |
dc.contributor.department | Πρόγραμμα Μεταπτυχιακών Σπουδών στην Τεχνητή Νοημοσύνη και Αναλυτική Δεδομένων | el |
Appears in Collections: | ΠΜΣ στην Τεχνητή Νοημοσύνη και Αναλυτική Δεδομένων (Μ) |
Files in This Item:
File | Description | Size | Format | |
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VazakidisDimitriosMsc2023.pdf | 2.55 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License