Unsupervised label noise modeling and loss correction
Despite being robust to small amounts of label noise, convolutional neural networks trained
with stochastic gradient methods have been shown to easily fit random labels. When there …
with stochastic gradient methods have been shown to easily fit random labels. When there …
[HTML][HTML] Mutational landscape and significance across 12 major cancer types
Abstract The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis
methods to identify somatic variants across thousands of tumours. Here we present data and …
methods to identify somatic variants across thousands of tumours. Here we present data and …
IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities
J Ashraf, M Keshk, N Moustafa, M Abdel-Basset… - Sustainable Cities and …, 2021 - Elsevier
The rapid proliferation of the Internet of Things (IoT) systems, has enabled transforming
urban areas into smart cities. Smart cities' paradigm has resulted in improved quality of life …
urban areas into smart cities. Smart cities' paradigm has resulted in improved quality of life …
[HTML][HTML] SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution
The sensitivity of massively-parallel sequencing has confirmed that most cancers are
oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine …
oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine …
Subclonal reconstruction of tumors by using machine learning and population genetics
Most cancer genomic data are generated from bulk samples composed of mixtures of cancer
subpopulations, as well as normal cells. Subclonal reconstruction methods based on …
subpopulations, as well as normal cells. Subclonal reconstruction methods based on …
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 …
[HTML][HTML] A survey on machine learning-based mobile big data analysis: Challenges and applications
This paper attempts to identify the requirement and the development of machine learning-
based mobile big data (MBD) analysis through discussing the insights of challenges in the …
based mobile big data (MBD) analysis through discussing the insights of challenges in the …
Multiple feature reweight densenet for image classification
Recent network research has demonstrated that the performance of convolutional neural
networks can be improved by introducing a learning block that captures spatial correlations …
networks can be improved by introducing a learning block that captures spatial correlations …
[HTML][HTML] Statistical characterisation of the real transaction data gathered from electric vehicle charging stations
Despite the many environmental benefits that a massive diffusion of electric vehicles (EVs)
could bring to the urban mobility and to society as a whole, numerous are the challenges …
could bring to the urban mobility and to society as a whole, numerous are the challenges …
Dual cross-entropy loss for small-sample fine-grained vehicle classification
Fine-grained vehicle classification is a challenging topic in computer vision due to the high
intraclass variance and low interclass variance. Recently, considerable progress has been …
intraclass variance and low interclass variance. Recently, considerable progress has been …