Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many
business and research domains. Machine learning, deep learning, and natural language …

Imbalance in dataset enforces numerous challenges to implement data analytic in all
existing real world applications using machine learning. Data imbalance occurs when …

Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …

Medical datasets are usually imbalanced, where negative cases severely outnumber
positive cases. Therefore, it is essential to deal with this data skew problem when training …

Nowadays, network attacks are the most crucial problem of modern society. All networks,
from small to large, are vulnerable to network threats. An intrusion detection (ID) system is …

Traditional ML based IDS cannot handle high-speed and ever-evolving attacks.
Furthermore, these traditional IDS face several common challenges, such as processing …

Abstract Machine learning (ML) is increasingly used in cognitive, computational and clinical
neuroscience. The reliable and efficient application of ML requires a sound understanding of …

Network intrusion detection systems play an important role in protecting the network from
attacks. However, Existing network intrusion data is imbalanced, which makes it difficult to …

Abstract The Synthetic Minority Over-sampling Technique (SMOTE) is a well-known
resampling strategy that has been successfully used for dealing with the class-imbalance …

Attacks on networks are currently the most pressing issue confronting modern society.
Network risks affect all networks, from small to large. An intrusion detection system must be …