Adam J. Hall

Name: dblp XML data dump
Creator: Schloss Dagstuhl - Leibniz Center for Informatics
Published: 1993
License: https://creativecommons.org/publicdomain/zero/1.0/
Keywords: dblp, XML, computer science, scholarly publications, metadata

◀ ▶ joint publications with William J. Buchanan

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Publications

2021
[j5]
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Pavlos Papadopoulos, Will Abramson, Adam J. Hall, Nikolaos Pitropakis, William J. Buchanan:
Privacy and Trust Redefined in Federated Machine Learning. Mach. Learn. Knowl. Extr. 3(2): 333-356 (2021)
[i7]
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  - journals/corr/abs-2103-15753
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Pavlos Papadopoulos, Will Abramson, Adam J. Hall, Nikolaos Pitropakis, William J. Buchanan:
Privacy and Trust Redefined in Federated Machine Learning. CoRR abs/2103.15753 (2021)
2020
[j3]
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  - journals/jifs/AhmadTLASHB20
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Jawad Ahmad, Ahsen Tahir, Hadi Larijani, Fawad Ahmed, Syed Aziz Shah, Adam James Hall, William J. Buchanan:
Energy demand forecasting of buildings using random neural networks. J. Intell. Fuzzy Syst. 38(4): 4753-4765 (2020)
[c4]
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  - conf/icissp/BowditchABPH20
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William Bowditch, Will Abramson, William J. Buchanan, Nikolaos Pitropakis, Adam J. Hall:
Privacy-preserving Surveillance Methods using Homomorphic Encryption. ICISSP 2020: 240-248
[c3]
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  - conf/trustbus/AbramsonHPPB20
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Will Abramson, Adam James Hall, Pavlos Papadopoulos, Nikolaos Pitropakis, William J. Buchanan:
A Distributed Trust Framework for Privacy-Preserving Machine Learning. TrustBus 2020: 205-220
[i3]
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  - journals/corr/abs-2006-02456
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Will Abramson, Adam James Hall, Pavlos Papadopoulos, Nikolaos Pitropakis, William J. Buchanan:
A Distributed Trust Framework for Privacy-Preserving Machine Learning. CoRR abs/2006.02456 (2020)
2019
[i1]
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  - journals/corr/abs-1907-10272
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Adam James Hall, Nikolaos Pitropakis, William J. Buchanan, Naghmeh Moradpoor:
Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier. CoRR abs/1907.10272 (2019)
2018
[c2]
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  - conf/bigdataconf/HallPBM18
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Adam James Hall, Nikolaos Pitropakis, William J. Buchanan, Naghmeh Moradpoor:
Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier. IEEE BigData 2018: 5034-5039

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