Leanna House

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 Huthaifa I. Ashqar

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Publications

2022
[j13]
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Huthaifa I. Ashqar, Mohammed Elhenawy, Hesham A. Rakha, Mohammed H. Almannaa, Leanna House:
Network and station-level bike-sharing system prediction: a San Francisco bay area case study. J. Intell. Transp. Syst. 26(5): 602-612 (2022)
[j12]
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Huthaifa I. Ashqar, Mohammed Elhenawy, Hesham A. Rakha, Leanna House:
Quality of Service Measure for Bike Sharing Systems. IEEE Trans. Intell. Transp. Syst. 23(9): 15841-15849 (2022)
2020
[i3]
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  - journals/corr/abs-2006-06945
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Huthaifa I. Ashqar, Mohammed H. Almannaa, Mohammed Elhenawy, Hesham A. Rakha, Leanna House:
Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains. CoRR abs/2006.06945 (2020)
[i2]
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  - journals/corr/abs-2006-08352
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Huthaifa I. Ashqar, Mohammed Elhenawy, Mohammed H. Almannaa, Ahmed Ghanem, Hesham A. Rakha, Leanna House:
Modeling bike availability in a bike-sharing system using machine learning. CoRR abs/2006.08352 (2020)
[i1]
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  - journals/corr/abs-2009-09367
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Huthaifa I. Ashqar, Mohammed Elhenawy, Hesham A. Rakha, Mohammed H. Almannaa, Leanna House:
Network and Station-Level Bike-Sharing System Prediction: A San Francisco Bay Area Case Study. CoRR abs/2009.09367 (2020)
2019
[j9]
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Huthaifa I. Ashqar, Mohammed H. Almannaa, Mohammed Elhenawy, Hesham A. Rakha, Leanna House:
Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains. IEEE Trans. Intell. Transp. Syst. 20(1): 244-252 (2019)
2017
[c6]
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  - conf/mtits/AshqarEAGRH17
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Huthaifa I. Ashqar, Mohammed Elhenawy, Mohammed H. Almannaa, Ahmed Ghanem, Hesham A. Rakha, Leanna House:
Modeling bike availability in a bike-sharing system using machine learning. MT-ITS 2017: 374-378

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