The Role of Machine Learning in IIoT Through FPGAs

2021 ◽  
pp. 121-137
Author(s):  
Behzad Joudat ◽  
Mina Zolfy Lighvan
Keyword(s):  
Machine Learning

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Role of machine learning algorithms over heart diseases prediction

2020 ◽  
Author(s):  
Siva Kumar Jonnavithula ◽  
Abhilash Kumar Jha ◽  
Modepalli Kavitha ◽  
Singaraju Srinivasulu
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

The role of machine learning analytics and metrics in retailing research

2020 ◽  
Author(s):  
Xin (Shane) Wang ◽  
Jun Hyun (Joseph) Ryoo ◽  
Neil Bendle ◽  
Praveen K. Kopalle
Keyword(s):  
Machine Learning ◽  
Learning Analytics

scholarly journals Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

2021 ◽  
Author(s):  
Doris Xin ◽  
Eva Yiwei Wu ◽  
Doris Jung-Lin Lee ◽  
Niloufar Salehi ◽  
Aditya Parameswaran
Keyword(s):  
Machine Learning

scholarly journals 180 The role of social constructs in placental insufficiency outcomes: an unsupervised machine learning approach

2021 ◽  
Vol 224 (2) ◽  
pp. S121-S122
Author(s):  
Ramamurthy Siripuram ◽  
Nathan R. Blue ◽  
Robert M. Silver ◽  
William A. Grobman ◽  
Uma M. Reddy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Placental Insufficiency ◽  
Learning Approach ◽  
Social Constructs ◽  
Unsupervised Machine Learning ◽  
Machine Learning Approach

scholarly journals Getting ahead of the Arms Race: Hothousing the Coevolution of VirusTotal with a Packer

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 395
Author(s):  
Héctor D. Menéndez ◽  
David Clark ◽  
Earl T. Barr
Keyword(s):  
Machine Learning ◽  
Evolutionary Computation ◽  
Malware Detection ◽  
Cloud Service ◽  
False Positives ◽  
Arms Race ◽  
System Calls ◽  
A New Technique ◽  
Coevolutionary Arms Race

Malware detection is in a coevolutionary arms race where the attackers and defenders are constantly seeking advantage. This arms race is asymmetric: detection is harder and more expensive than evasion. White hats must be conservative to avoid false positives when searching for malicious behaviour. We seek to redress this imbalance. Most of the time, black hats need only make incremental changes to evade them. On occasion, white hats make a disruptive move and find a new technique that forces black hats to work harder. Examples include system calls, signatures and machine learning. We present a method, called Hothouse, that combines simulation and search to accelerate the white hat’s ability to counter the black hat’s incremental moves, thereby forcing black hats to perform disruptive moves more often. To realise Hothouse, we evolve EEE, an entropy-based polymorphic packer for Windows executables. Playing the role of a black hat, EEE uses evolutionary computation to disrupt the creation of malware signatures. We enter EEE into the detection arms race with VirusTotal, the most prominent cloud service for running anti-virus tools on software. During our 6 month study, we continually improved EEE in response to VirusTotal, eventually learning a packer that produces packed malware whose evasiveness goes from an initial 51.8% median to 19.6%. We report both how well VirusTotal learns to detect EEE-packed binaries and how well VirusTotal forgets in order to reduce false positives. VirusTotal’s tools learn and forget fast, actually in about 3 days. We also show where VirusTotal focuses its detection efforts, by analysing EEE’s variants.

Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation

2021 ◽  
Author(s):  
Chun Xiang Tang ◽  
Bang Jun Guo ◽  
Joseph U. Schoepf ◽  
Richard R. Bayer ◽  
Chun Yu Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stent Implantation ◽  
Prognostic Role
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