Machine learning with big data: Challenges and approaches

A L'heureux, K Grolinger, HF Elyamany… - Ieee …, 2017 - ieeexplore.ieee.org
The Big Data revolution promises to transform how we live, work, and think by enabling
process optimization, empowering insight discovery and improving decision making. The …

The role of machine learning in cybersecurity

G Apruzzese, P Laskov, E Montes de Oca… - … Threats: Research and …, 2023 - dl.acm.org
Machine Learning (ML) represents a pivotal technology for current and future information
systems, and many domains already leverage the capabilities of ML. However, deployment …

Dsmt-net: Dual self-supervised multi-operator transformation for multi-source endoscopic ultrasound diagnosis

J Li, P Zhang, T Wang, L Zhu, R Liu… - … on Medical Imaging, 2023 - ieeexplore.ieee.org
Pancreatic cancer has the worst prognosis of all cancers. The clinical application of
endoscopic ultrasound (EUS) for the assessment of pancreatic cancer risk and of deep …

Domain generalization on medical imaging classification using episodic training with task augmentation

C Li, X Lin, Y Mao, W Lin, Q Qi, X Ding, Y Huang… - Computers in biology …, 2022 - Elsevier
Medical imaging datasets usually exhibit domain shift due to the variations of scanner
vendors, imaging protocols, etc. This raises the concern about the generalization capacity of …

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 …

Search result diversification

RLT Santos, C Macdonald, I Ounis - Foundations and Trends® …, 2015 - nowpublishers.com
Ranking in information retrieval has been traditionally approached as a pursuit of relevant
information, under the assumption that the users' information needs are unambiguously …

A comprehensive review of trends, applications and challenges in out-of-distribution detection

N Ghassemi, E Fazl-Ersi - arxiv preprint arxiv:2209.12935, 2022 - arxiv.org
With recent advancements in artificial intelligence, its applications can be seen in every
aspect of humans' daily life. From voice assistants to mobile healthcare and autonomous …

TKAGFL: a federated communication framework under data heterogeneity

J Pei, Z Yu, J Li, MA Jan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Federated learning still faces many problems from research to technology implementation
and the most critical problem is that the communication efficiency is not high. Therefore, the …

Non-iidness learning in behavioral and social data

L Cao - The Computer Journal, 2014 - ieeexplore.ieee.org
Most of the classic theoretical systems and tools in statistics, data mining and machine
learning are built on the fundamental assumption of IIDness, which assumes the …

Training keyword spotting models on non-iid data with federated learning

A Hard, K Partridge, C Nguyen, N Subrahmanya… - arxiv preprint arxiv …, 2020 - arxiv.org
We demonstrate that a production-quality keyword-spotting model can be trained on-device
using federated learning and achieve comparable false accept and false reject rates to a …