In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

The problem of class imbalance is extensive for focusing on numerous applications in the
real world. In such a situation, nearly all of the examples are labeled as one class called …

In this paper, we introduce two independent hybrid mining algorithms to improve the
classification accuracy rates of decision tree (DT) and naïve Bayes (NB) classifiers for the …

Abstract Anomaly based Intrusion Detection Systems (IDS) learn normal and anomalous
behavior by analyzing network traffic in various benchmark datasets. Common challenges …

Classifiers deployed in the real world operate in a dynamic environment, where the data
distribution can change over time. These changes, referred to as concept drift, can cause the …

Intrusion is the violation of information security policy by malicious activities. Intrusion
detection (ID) is a series of actions for detecting and recognising suspicious actions that …

Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

In this research, we undertake intelligent skin cancer diagnosis based on dermoscopic
images using a variant of the Particle Swarm Optimization (PSO) algorithm for feature …

Network security is one of the major concerns of the modern era. With the rapid development
and massive usage of internet over the past decade, the vulnerabilities of network security …

In this research, we propose a variant of the Particle Swarm Optimization (PSO) algorithm,
namely hybrid learning PSO (HLPSO), for skin lesion segmentation and classification …