| Autor: |
Srishti Choubey, Snehlata Barde, Abhishek Badholia |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Measurement: Sensors, Vol 24, Iss , Pp 100505- (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2665-9174 |
| DOI: |
10.1016/j.measen.2022.100505 |
| Popis: |
The influence of virus-borne diseases (VBDs) is one of the key factors contributing to the health concern. Annual outbreaks of VBDs have been reported throughout the past few years in several places, providing proof of continued spreading. It is probably the arbovirus that is spread the most broadly worldwide. The spreading of VBD is impacted by an increase in several viral fevers. This study suggested using an Enhanced Back Propagation with Artificial Neural Network algorithm (EBP-ANN) to detect viruses at an early stage. This suggested approach is mainly used to improve the prediction efficiency of viruses. Initially, the virus dataset is collected. The Z-score normalization technique is employed for preprocessing. The feature is extracted using a dynamic angle projection pattern (DAPP). A genetic algorithm is utilized for the feature selection process. The proposed algorithm efficiently predicts the disease with greater accuracy. Finally, the performance of the system is carried out. The following metrics are compared with existing techniques such as prediction accuracy, prediction time, precision, and recall. The proposed method shows that it is an efficient technique for enhancing the prediction efficiency of virus-borne disease. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
