[HTML][HTML] A deep dive into membrane distillation literature with data analysis, bibliometric methods, and machine learning
Membrane distillation (MD) is a non-isothermal separation process applied mainly in
desalination for the treatment of saline aqueous solutions including brines for distilled water …
desalination for the treatment of saline aqueous solutions including brines for distilled water …
Machine learning for design and optimization of organic Rankine cycle plants: A review of current status and future perspectives
The organic Rankine cycle (ORC) is widely acknowledged as a sustainable power cycle.
However, the traditional approach commonly adopted for its optimal design involves …
However, the traditional approach commonly adopted for its optimal design involves …
SoK: Pragmatic assessment of machine learning for network intrusion detection
G Apruzzese, P Laskov… - 2023 IEEE 8th European …, 2023 - ieeexplore.ieee.org
Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …
An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles
S Gupta, C Maple, R Passerone - IEEE Access, 2023 - ieeexplore.ieee.org
Connected and autonomous vehicles (CAVs) can fulfill the emerging demand for smart
transportation on a global scale. Such innovations for transportation can bring manyfold …
transportation on a global scale. Such innovations for transportation can bring manyfold …
[HTML][HTML] On the Robustness of ML-Based Network Intrusion Detection Systems: An Adversarial and Distribution Shift Perspective
Utilizing machine learning (ML)-based approaches for network intrusion detection systems
(NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to …
(NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to …
Data Optimization in Deep Learning: A Survey
O Wu, R Yao - arXiv preprint arXiv:2310.16499, 2023 - arxiv.org
Large-scale, high-quality data are considered an essential factor for the successful
application of many deep learning techniques. Meanwhile, numerous real-world deep …
application of many deep learning techniques. Meanwhile, numerous real-world deep …
A survey on robustness in trajectory prediction for autonomous vehicles
J Hagenus, FB Mathiesen, JF Schumann… - arXiv preprint arXiv …, 2024 - arxiv.org
Autonomous vehicles rely on accurate trajectory prediction to inform decision-making
processes related to navigation and collision avoidance. However, current trajectory …
processes related to navigation and collision avoidance. However, current trajectory …
Overcoming Adversarial Attacks for Human-in-the-Loop Applications
R McCoppin, M Kennedy, P Lukyanenko… - arXiv preprint arXiv …, 2023 - arxiv.org
Including human analysis has the potential to positively affect the robustness of Deep Neural
Networks and is relatively unexplored in the Adversarial Machine Learning literature. Neural …
Networks and is relatively unexplored in the Adversarial Machine Learning literature. Neural …
A Survey of Artificial Intelligence Approaches to Safety and Mission-Critical Systems
C Thames, Y Sun - 2024 Integrated Communications …, 2024 - ieeexplore.ieee.org
Safety and mission-critical systems span across an extensive array of research areas,
including transportation (aviation, aerospace, automotive, rail), cybersecurity, robotics, and …
including transportation (aviation, aerospace, automotive, rail), cybersecurity, robotics, and …
A Survey of Security Mechanisms for Edge Computing based Connected Autonomous Vehicles
Connected and autonomous vehicles (CAVs) can fulfill the emerging demand for smart
transportation on a global scale. Such innovations for transportation can bring manyfold …
transportation on a global scale. Such innovations for transportation can bring manyfold …