Author:Michael Lornwood

Publications
Advanced Techniques for Strengthening Adversarial Robustness in Deep Learning Models Leonardo Delviz, John Francis, Mo Chen, Hou Zhang and Michael Lornwood EasyChair Preprint 15624
An Innovative Hybrid Approach for Efficient Anomaly Detection with Machine Learning and Optimization Piter Wen, Li Wei, Mo Zhang, Hoo Wang and Michael Lornwood EasyChair Preprint 15623
Integrating Mathematical Models for Enhanced Object Detection: a Hybrid Deep Learning Approach Mo Zhang, Kon Yang, Mo Chang and Michael Lornwood EasyChair Preprint 15596
Deep Learning-Driven Real-Time Anomaly Detection in SDNs: a Performance Comparison Hoo Wang, Che Leo, John Davis, Sarah Smith, Daniel Taylor and Michael Lornwood EasyChair Preprint 15583
A Machine Learning-Driven Approach to XML-Based IP Network Configuration Optimization Hoo Wang, Che Leo, John Davis, Sarah Smith, Daniel Taylor and Michael Lornwood EasyChair Preprint 15560
Advancing Machine Learning: Comparative Analysis of Techniques Alex Wang, Maria Kin, Piter Wen, Rusel Deniz, Li Wei and Michael Lornwood EasyChair Preprint 15546
A Novel Algorithmic Approach for Privacy-Preserving Data Synthesis via Randomized Mixing Michael Lornwood, Li Wei and Emily Wilson EasyChair Preprint 15513
Mathematics-Driven Enhancements in Object Detection: a Hybrid Deep Learning Framework Michael Lornwood EasyChair Preprint 15504

Keyphrases

adaptive systems, AI-driven Framework, Algorithms⁴, analysis, anomaly detection², CNN², Configuration Management, Convolutional Neural Networks (CNN), data privacy, dataset synthesis, deep learning⁴, differential privacy, hybrid algorithm, machine learning⁵, model², Network Security, neural networks, Particle Swarm Optimization, Privacy-Preserving Algorithms, Randomized Mixing, Reinforcement Learning, Software Defined Networking (SDN), ViT².