Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …

Due to the lack of experimental toxicity data for environmental chemicals, there arises a
need to fill data gaps by in silico approaches. One of the most commonly used in silico …

Simple Summary This paper provides a review of recent studies exploring the application of
artificial intelligence (AI) in the early detection and monitoring of respiratory disease in …

Potato diseases such as early and late blight are the most lethal diseases that can cause
significant damage to potato production. Detecting these diseases early and making a …

In the age of sophisticated cyber threats, botnet detection remains a crucial yet complex
security challenge. Existing detection systems are continually outmaneuvered by the …

Ensemble learning has become a cornerstone in various classification and regression tasks,
leveraging its robust learning capacity across disciplines. However, the computational time …

Distributed denial-of-service (DDoS) attacks pose a significant threat to computer networks
and systems by disrupting services through the saturation of targeted systems with traffic …

Diabetes poses a global health challenge, demanding continuous monitoring and expert
care for effective management. Conventional monitoring methods lack real-time insights and …

The recent availability of satellite hyperspectral imaging combined with the developments in
the classification techniques have paved the way towards improving our ability to obtain …

Weather is essential to human life, but it is difficult to forecast due to its diverse nature. We
evaluated and compared the accuracy of two machine learning algorithms, GRU and …