datasets

The paper uses modern artificial intelligence methods to investigate models and methods for determining grape disease. The existing methodologies for classification and recognition by images of grape diseases using neural networks are analyzed. Several problems for improving recognition results are highlighted.

The classification of Border Gateway Protocol (BGP) anomalies is essential for maintaining Internet stability and security, as such anomalies can impair network functionality and reliability. Previous studies has examined the impact of key features on anomaly detection; however, current methodologies frequently demonstrate high computational costs, complexity, and usage challenges.