Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …
Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks
N Moustafa, J Slay, G Creech - IEEE Transactions on Big Data, 2017 - ieeexplore.ieee.org
The prevalence of interconnected appliances and ubiquitous computing face serious threats
from the hostile activities of network attackers. Conventional Intrusion Detection Systems …
from the hostile activities of network attackers. Conventional Intrusion Detection Systems …
Hawkes processes for events in social media
This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …
processes, for modeling discrete, inter-dependent events over continuous time. We start by …
Deep learning for video classification and captioning
Deep learning for video classification and captioning Page 1 IPART MULTIMEDIA
CONTENT ANALYSIS Page 2 Page 3 1Deep Learning for Video Classification and …
CONTENT ANALYSIS Page 2 Page 3 1Deep Learning for Video Classification and …
Dimensionality reduction methods for brain imaging data analysis
The past century has witnessed the grand success of brain imaging technologies, such as
electroencephalography and magnetic resonance imaging, in probing cognitive states and …
electroencephalography and magnetic resonance imaging, in probing cognitive states and …
QCore: Data-efficient, on-device continual calibration for quantized models
We are witnessing an increasing availability of streaming data that may contain valuable
information on the underlying processes. It is thus attractive to be able to deploy machine …
information on the underlying processes. It is thus attractive to be able to deploy machine …
Compressed linear algebra for large-scale machine learning
Large-scale machine learning (ML) algorithms are often iterative, using repeated read-only
data access and I/O-bound matrix-vector multiplications to converge to an optimal model. It …
data access and I/O-bound matrix-vector multiplications to converge to an optimal model. It …
Designing an online and reliable statistical anomaly detection framework for dealing with large high-speed network traffic
N Moustafa - 2017 - unsworks.unsw.edu.au
Abstract Despite a Network Anomaly Detection System (NADS) being capable of detecting
existing and zero-day attacks, it is still not universally implemented in industry and real …
existing and zero-day attacks, it is still not universally implemented in industry and real …
Multimodal emotion and sentiment modeling from unstructured Big data: Challenges, architecture, & techniques
JKP Seng, KLM Ang - IEEE Access, 2019 - ieeexplore.ieee.org
The exponential growth of multimodal content in today's competitive business environment
leads to a huge volume of unstructured data. Unstructured big data has no particular format …
leads to a huge volume of unstructured data. Unstructured big data has no particular format …
Analysis of PCA algorithms in distributed environments
Classical machine learning algorithms often face scalability bottlenecks when they are
applied to large-scale data. Such algorithms were designed to work with small data that is …
applied to large-scale data. Such algorithms were designed to work with small data that is …