Kwara State University
Kadri Akeem
Designation: Assistant Lecturer
Department: Computer Science
| My Publications | ||||||||
|---|---|---|---|---|---|---|---|---|
| S/N | Title | Abstract | Authors | Volume Numbers | Publication Type | Publication Date | Link | |
| 1 | Machine Learning Approach using KPCA-SVMs for Predicting COVID-19’’ |
This study proposes a machine learning method and exploits for prediction approach for COVID-19 occurrence in humans as artificial intelligence and machine learning are healthily incisive for prediction and diagnostics methods to fight COVID-19 pandemic. An enhanced kernel principal component analysis (KPCA) is used to fetch relevant kernel latent component features of genes that clinicians can efficiently use by clinicians to discover prescriptions, predictions, and diagnosis of patients suspected to be infected. Extracted features are classified by passing them into SVM-RBF classifier to classify the COVID-19 for prediction and the system performance is evaluated using the confusion matrix. The remaining part of the paper is structured as thus: Sect. 10.2 addressed the overview of the investigation and of related investigations. Section 10.3 provides research materials and methods. Section 10.4 provides experimental research outcomes. Section 10.5 provides the conclusion of the investigation. 10.2 | 3. MO Arowolo, RO Ogundokun, S Misra, A F Kadri, TO Aduragba | Volume 1 | Book Chapter | 2022-03-15 | ||