A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges
A Khraisat, A Alazab - Cybersecurity, 2021 - Springer
Abstract The Internet of Things (IoT) has been rapidly evolving towards making a greater
impact on everyday life to large industrial systems. Unfortunately, this has attracted the …
impact on everyday life to large industrial systems. Unfortunately, this has attracted the …
EffectorP 3.0: prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes
J Sperschneider, PN Dodds - Molecular plant-microbe …, 2022 - Am Phytopath Society
Many fungi and oomycete species are devasting plant pathogens. These eukaryotic
filamentous pathogens secrete effector proteins to facilitate plant infection. Fungi and …
filamentous pathogens secrete effector proteins to facilitate plant infection. Fungi and …
[PDF][PDF] H2o automl: Scalable automatic machine learning
E LeDell, S Poirier - Proceedings of the AutoML Workshop at ICML, 2020 - automl.org
H2O is an open source, distributed machine learning platform designed to scale to very
large datasets, with APIs in R, Python, Java and Scala. We present H2O AutoML, a highly …
large datasets, with APIs in R, Python, Java and Scala. We present H2O AutoML, a highly …
The role of ai, machine learning, and big data in digital twinning: A systematic literature review, challenges, and opportunities
Digital twinning is one of the top ten technology trends in the last couple of years, due to its
high applicability in the industrial sector. The integration of big data analytics and artificial …
high applicability in the industrial sector. The integration of big data analytics and artificial …
Explainable machine learning in materials science
Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …
their exceptional accuracy. However, the most accurate machine learning models are …
Machine learning and molecular dynamics simulation-assisted evolutionary design and discovery pipeline to screen efficient small molecule acceptors for PTB7-Th …
Organic solar cells are the most promising candidates for future commercialization. This goal
can be quickly achieved by designing new materials and predicting their performance …
can be quickly achieved by designing new materials and predicting their performance …
Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review
M Sheykhmousa, M Mahdianpari… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …
classification during the past two decades. Among these machine learning algorithms …
Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences
Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
The random forest algorithm for statistical learning
M Schonlau, RY Zou - The Stata Journal, 2020 - journals.sagepub.com
Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical-or machine-
learning algorithm for prediction. In this article, we introduce a corresponding new …
learning algorithm for prediction. In this article, we introduce a corresponding new …