Chaisri Tharasawatpipat
Biographic Data
| ID | 10120165 |
|---|---|
| NAME | Chaisri Tharasawatpipat |
| GIVEN NAMES | Chaisri |
| FAMILY NAME | Tharasawatpipat |
| SIGNATURE | THARASAWATPIPAT C |
| AFFILIATIONS | Suan Sunandha Rajabhat University |
| ORCID | 0000-0002-4850-3964 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Unraveling the threads of adaptability
This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while ha…
Deciphering the complexity of Covid-19 transmission
In the realm of COVID-19 transmission data, scientists are scrutinizing policies to identify the ideal vaccination rate for halting the virus. This study aimed to pinpoint the minimal vaccinated percentage needed to break the virus cycle within communities. The underlying motivation stems from the urgent need to contain COVID-19’s spread and reduce the strain on healthcare systems worldwide. With fluctuating infection rates and the emergence of n…
AI-driven telemedicine
Amidst the COVID-19 pandemic, the imperative of physical distancing has underscored the necessity for telemedicine solutions. Traditionally, telemedicine systems have operated synchronously, requiring scheduled appointments. This study introduces an innovative telemedicine system integrating Artificial Intelligence (AI) to enable asynchronous communication between physicians and patients, eliminating the need for appointments and providing round-…
No prominent works on this page.
Unraveling the threads of adaptability
This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while ha…
Deciphering the complexity of Covid-19 transmission
In the realm of COVID-19 transmission data, scientists are scrutinizing policies to identify the ideal vaccination rate for halting the virus. This study aimed to pinpoint the minimal vaccinated percentage needed to break the virus cycle within communities. The underlying motivation stems from the urgent need to contain COVID-19’s spread and reduce the strain on healthcare systems worldwide. With fluctuating infection rates and the emergence of n…
AI-driven telemedicine
Amidst the COVID-19 pandemic, the imperative of physical distancing has underscored the necessity for telemedicine solutions. Traditionally, telemedicine systems have operated synchronously, requiring scheduled appointments. This study introduces an innovative telemedicine system integrating Artificial Intelligence (AI) to enable asynchronous communication between physicians and patients, eliminating the need for appointments and providing round-…
Computer Science (3 works) · 2019-20 coronavirus outbreak (2 works) · Medicine (2 works) · Outbreak (2 works) · Virology (2 works) · Adaptability (1 works) · Artificial Intelligence (1 works) · Biology (1 works) · Computer security (1 works) · COVID-19 and healthcare impacts (1 works)