This paper addresses the problem of PAC learning half spaces under constant malicious
noise, where a fraction of the training data is adversarially corrupted. While traditional …

One of the most well-known hybrid machine learning approaches is the Adaptive Neuro
Fuzzy Inference System (ANFIS), combining neural networks and fuzzy logic, which creates …

Abstract Machine learning (ML) has become a transformative technology across multiple
domains, offering advanced capabilities for predictive analytics, decision-making, and …

This paper explores advancements in Machine Learning (ML) models, focusing on
comparing different techniques, including supervised and unsupervised learning methods …

In this paper, we review and advance the application of deep learning algorithms for
anomaly detection in Software Defined Networks (SDN). As SDN environments become …

Dynamic environments challenge traditional machine learning (ML) models due to their
inability to adapt to non-stationary data distributions. This paper introduces a mathematically …

In recent years, the e-commerce sector has witnessed an explosive growth, with vast
amounts of data generated from customer interactions. Predicting customer behavior is …

In this paper, we explore and advance deep learning algorithms for anomaly detection in
Software Defined Networks (SDN). As SDNs gain prominence in modern networking, their …

This study examines recent advancements in Machine Learning (ML) by comparing various
techniques, including both supervised and unsupervised learning methods. It provides a …

Abstract Software-Defined Networking (SDN) revolutionizes network management and
adaptability by separating the control and data planes. However, its centralized nature …