Saeed Mian Qaisar
Biographic Data
| ID | 9366476 |
|---|---|
| NAME | Saeed Mian Qaisar |
| GIVEN NAMES | Saeed Mian |
| FAMILY NAME | Qaisar |
| SIGNATURE | QAISAR S M |
| AFFILIATIONS | Effat University |
| ORCID | 0000-0002-4268-3482 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
Ontology-Enabled Emotional Sentiment Analysis on Covid-19 Pandemic-Related Twitter Streams
The exponential growth of social media users has changed the dynamics of retrieving the potential information from user-generated content and transformed the paradigm of information-retrieval mechanism with the novel developments on the concept of “web of data”. In this regard, our proposed Ontology-Based Sentiment Analysis provides two novel approaches: First, the emotion extraction on tweets related to COVID-19 is carried out by a well-formed t…
Performance Evaluation of Regression Models for the Prediction of the Covid-19 Reproduction Rate
This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to si…
No prominent works on this page.
Ontology-Enabled Emotional Sentiment Analysis on Covid-19 Pandemic-Related Twitter Streams
The exponential growth of social media users has changed the dynamics of retrieving the potential information from user-generated content and transformed the paradigm of information-retrieval mechanism with the novel developments on the concept of “web of data”. In this regard, our proposed Ontology-Based Sentiment Analysis provides two novel approaches: First, the emotion extraction on tweets related to COVID-19 is carried out by a well-formed t…
Performance Evaluation of Regression Models for the Prediction of the Covid-19 Reproduction Rate
This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to si…
Computer Science (2 works) · 2019-20 coronavirus outbreak (1 work) · Advanced Text Analysis Techniques (1 work) · Artificial Intelligence (1 work) · Complex Network Analysis Techniques (1 work) · Computer network (1 work) · COVID-19 diagnosis using AI (1 work) · COVID-19 epidemiological studies (1 work) · COVID-19 Pandemic Impacts (1 work) · Data science (1 work)