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KR-102964093-B1 - FUZZY LOGIC AND DISTANCE METRICS BASED APPROACH FOR MEASURING SPATIAL SIMILARITY AMONG COVID-19 EPICENTERS

KR102964093B1KR 102964093 B1KR102964093 B1KR 102964093B1KR-102964093-B1

Abstract

A fuzzy logic and distance indicator-based approach for measuring spatial similarity between COVID-19 epicenters is disclosed. The method for measuring COVID-19 epicenter similarity may include: collecting target data including proximity data, demographic data, weather data, and COVID-19 data for a plurality of cities selected as target cities; and measuring spatial similarity between the target cities using fuzzy logic and similarity metrics based on the target data.

Inventors

  • 아볼가셈
  • 최수미
  • 타메르 이스 아부메드

Assignees

  • 세종대학교산학협력단
  • 성균관대학교산학협력단

Dates

Publication Date
20260512
Application Date
20230601

Claims (15)

  1. In a method for measuring the similarity of the origin of COVID-19 executed on a computer device, The above computer device includes at least one processor configured to execute computer-readable instructions contained in memory, and The above method for measuring the similarity of the COVID-19 epicenter is, A step of collecting target data including proximity data, demographics data, weather data, and COVID-19 data for a plurality of cities selected as target cities by the above-mentioned at least one processor; and A step of measuring spatial similarity between the target cities using fuzzy logic and similarity metrics based on the target data by the at least one processor. Includes, The above-mentioned measuring step is, Step of ranking the importance of variables for measuring similarity between the above target cities A method for measuring COVID-19 origin similarity including
  2. In paragraph 1, The above-mentioned collecting step is, A step of generalizing the above target data using a fuzzy membership function A method for measuring COVID-19 origin similarity including
  3. In paragraph 1, The above-mentioned collecting step is, A step of generalizing the above target data using the MS small fuzzy membership function. A method for measuring COVID-19 origin similarity including
  4. In paragraph 1, The above-mentioned measuring step is, A step of calculating spatial similarity between the above target cities through a similarity model based on a distance metric. A method for measuring COVID-19 origin similarity including
  5. In paragraph 1, The above-mentioned measuring step is, A step of calculating spatial similarity between the above target cities through a similarity model based on distance indicators such as Manhattan distance, Euclidean distance, Minkowski distance, Mahalanobis distance, Chebyshev distance, and correlation distance. A method for measuring COVID-19 origin similarity including
  6. In paragraph 5, The above-mentioned measuring step is, The step of aggregating the similarity calculation results based on each distance indicator using the Copeland method. A method for measuring COVID-19 origin similarity that further includes
  7. In paragraph 1, The above-mentioned measuring step is, Step of granulating the target data using an atomic formula A method for measuring COVID-19 origin similarity including
  8. In paragraph 1, The above-mentioned measuring step is, A step of comparing the criterion distribution for the target data between the target cities using Ripley's K-function. A method for measuring COVID-19 origin similarity including
  9. delete
  10. In paragraph 1, The above ranking step is, A step of ranking the above variables using at least one modeling method among a Regressive Neural Network (GRNN), a Random Forest (RF), and Support Vector Regression (SVR). A method for measuring COVID-19 origin similarity including
  11. In Paragraph 10, The step of assigning ranks to the above variables is, The step of determining the rank of the above variables using cross-validation and the RMSE (root mean squared error) index. A method for measuring COVID-19 origin similarity including
  12. In a COVID-19 epicenter similarity measurement system implemented as a computer device, At least one processor configured to execute computer-readable instructions contained in memory Includes, The above at least one processor is, A process of collecting target data including proximity data, demographic data, weather data, and COVID-19 data for multiple cities selected as target cities; and A process of measuring spatial similarity between the aforementioned target cities using fuzzy logic and similarity indicators based on the aforementioned target data. Process, The above at least one processor is, Ranking the importance of variables for measuring similarity between the target cities using at least one modeling method among Regressive Neural Network (GRNN), Random Forest (RF), and Support Vector Regression (SVR). A COVID-19 epicenter similarity measurement system characterized by
  13. In Paragraph 12, The above at least one processor is, Calculating spatial similarity between the aforementioned target cities through a similarity model based on distance indicators such as Manhattan distance, Euclidean distance, Minkowski distance, Mahalanobis distance, Chebyshev distance, and correlation distance. A COVID-19 epicenter similarity measurement system characterized by
  14. In Paragraph 12, The above at least one processor is, Comparing the reference distribution for the target data between the target cities using the Ripley K-function A COVID-19 epicenter similarity measurement system characterized by
  15. delete

Description

Fuzzy Logic and Distance Metrics-Based Approach for Measuring Spatial Similarity Among COVID-19 Epicenters The following description concerns a technology for measuring spatial similarity between COVID-19 epicenters. Coronavirus disease 19 (COVID-19) is a global health problem due to its rapid worldwide spread. As of March 11, 2022 (WHO 2022), more than 450,229,635 confirmed cases and 6,019,085 deaths were recorded. COVID-19 was first discovered in Wuhan, China, in late 2019 and is currently affecting more than 225 countries. The outbreak of COVID-19 has caused many economic, political, and social problems in other countries. Economic, cultural, social, and human activities have also come to a halt. Therefore, it is very important to study the predictability for identifying the prevalence, spread rate, high-risk areas, and emotional factors of COVID-19. FIG. 1 is a block diagram illustrating an example of the internal configuration of a computer device in an embodiment of the present invention. FIG. 2 is a flowchart illustrating an example of a method for measuring the similarity of the epicenter of COVID-19 in an embodiment of the present invention. FIG. 3 is a graph showing the number of COVID-19 patients from 2020 to 2021 in one embodiment of the present invention. Figure 4 shows an example of COVID-19 data used as data to be collected in an embodiment of the present invention. FIG. 5 shows an example of a source of data to be collected in an embodiment of the present invention. Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. Embodiments of the present invention relate to a technique for measuring spatial similarity between COVID-19 epicenters. Embodiments including those specifically disclosed in this specification can implement a model that combines fuzzy logic and similarity indicators with different distance formulas for measuring the similarity of COVID-19 epicenters. A COVID-19 origin similarity measurement system according to embodiments of the present invention may be implemented by at least one computer device, and a COVID-19 origin similarity measurement method according to embodiments of the present invention may be performed through at least one computer device included in the COVID-19 origin similarity measurement system. In this case, a computer program according to one embodiment of the present invention may be installed and run on the computer device, and the computer device may perform a COVID-19 origin similarity measurement method according to embodiments of the present invention under the control of the run computer program. The above-described computer program may be stored on a computer-readable recording medium to be combined with the computer device to execute the COVID-19 origin similarity measurement method on the computer. FIG. 1 is a block diagram illustrating an example of a computer device according to an embodiment of the present invention. For example, a COVID-19 epicenter similarity measurement system according to embodiments of the present invention can be implemented by a computer device (100) illustrated in FIG. 1. As illustrated in FIG. 1, a computer device (100) may include a memory (110), a processor (120), a communication interface (130), and an input/output interface (140) as components for implementing a method for measuring the similarity of the origin of COVID-19 according to embodiments of the present invention. Memory (110) is a computer-readable recording medium and may include a non-perishable mass storage device such as RAM (random access memory), ROM (read only memory), and a disk drive. Here, a non-perishable mass storage device such as a ROM and a disk drive may be included in the computer device (100) as a separate permanent storage device distinct from memory (110). Additionally, an operating system and at least one program code may be stored in memory (110). These software components may be loaded into memory (110) from a computer-readable recording medium separate from memory (110). This separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, disk, tape, DVD/CD-ROM drive, or memory card. In another embodiment, software components may be loaded into memory (110) through a communication interface (130) rather than a computer-readable recording medium. For example, software components can be loaded into the memory (110) of the computer device (100) based on a computer program installed by files received through the network (160). The processor (120) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to the processor (120) via memory (110) or a communication interface (130). For example, the processor (120) may be configured to execute instructions received according to program code stored in a recording device such