DeepHist: Towards a Deep Learning-based Computational History of Trends in the NIPS

Amna Dridi; Mohamed Medhat Gaber; R. Muhammad Atif Azad; Jagdev Bhogal

doi:10.1109/IJCNN.2019.8852140

DeepHist: Towards a Deep Learning-based Computational History of Trends in the NIPS

Amna Dridi, Mohamed Medhat Gaber, R. Muhammad Atif Azad, Jagdev Bhogal

Birmingham City University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Original language	English
Title of host publication	2019 International Joint Conference on Neural Networks, IJCNN 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728119854
DOIs	https://doi.org/10.1109/IJCNN.2019.8852140
Publication status	Published (VoR) - Jul 2019
Event	2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary Duration: 14 Jul 2019 → 19 Jul 2019

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2019-July

Conference

Conference	2019 International Joint Conference on Neural Networks, IJCNN 2019
Country/Territory	Hungary
City	Budapest
Period	14/07/19 → 19/07/19

Funding

While we are not aware of previous works on predicting research trends in CS by drilling into paper content and following a fine-grained content analysis, there are few works addressing related research problems in investigating general publication trends, citation trends and evolution of research areas following a coarse-grained analysis. For instance, Hoonlor et al. [1] analysed data on proposals for grants supported by the U.S National Foundation and on CS publications in the ACM Digital Library and IEEE Xplore Digital Library using sequence mining and bursty word detection. Similarly, Hou et al. revealed the evolution of research topics between 2009 and 2016 using the timeline knowledge map through Document-Citation Analysis (DCA). In the same context, Effendy and Yap [15] performed trend analysis using the Microsoft Academic Graph (MAG) dataset. Both the above approaches to trend analysis in CS focus on citation analysis which fails to dig into the paper content and takes time to reveal trends.

Funders	Funder number
National Foundation
Anacostia Community Museum

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10.1109/IJCNN.2019.8852140

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Dridi, A., Gaber, M. M., Azad, R. M. A., & Bhogal, J. (2019). DeepHist: Towards a Deep Learning-based Computational History of Trends in the NIPS. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 Article 8852140 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852140

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author = "Amna Dridi and Gaber, {Mohamed Medhat} and Azad, {R. Muhammad Atif} and Jagdev Bhogal",

note = "Funding Information: While we are not aware of previous works on predicting research trends in CS by drilling into paper content and following a fine-grained content analysis, there are few works addressing related research problems in investigating general publication trends, citation trends and evolution of research areas following a coarse-grained analysis. For instance, Hoonlor et al. [1] analysed data on proposals for grants supported by the U.S National Foundation and on CS publications in the ACM Digital Library and IEEE Xplore Digital Library using sequence mining and bursty word detection. Similarly, Hou et al. revealed the evolution of research topics between 2009 and 2016 using the timeline knowledge map through Document-Citation Analysis (DCA). In the same context, Effendy and Yap [15] performed trend analysis using the Microsoft Academic Graph (MAG) dataset. Both the above approaches to trend analysis in CS focus on citation analysis which fails to dig into the paper content and takes time to reveal trends. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Joint Conference on Neural Networks, IJCNN 2019 ; Conference date: 14-07-2019 Through 19-07-2019",

year = "2019",

month = jul,

doi = "10.1109/IJCNN.2019.8852140",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

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Dridi, A, Gaber, MM, Azad, RMA & Bhogal, J 2019, DeepHist: Towards a Deep Learning-based Computational History of Trends in the NIPS. in 2019 International Joint Conference on Neural Networks, IJCNN 2019., 8852140, Proceedings of the International Joint Conference on Neural Networks, vol. 2019-July, Institute of Electrical and Electronics Engineers Inc., 2019 International Joint Conference on Neural Networks, IJCNN 2019, Budapest, Hungary, 14/07/19. https://doi.org/10.1109/IJCNN.2019.8852140

DeepHist: Towards a Deep Learning-based Computational History of Trends in the NIPS. / Dridi, Amna; Gaber, Mohamed Medhat; Azad, R. Muhammad Atif et al.
2019 International Joint Conference on Neural Networks, IJCNN 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8852140 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Funding Information: While we are not aware of previous works on predicting research trends in CS by drilling into paper content and following a fine-grained content analysis, there are few works addressing related research problems in investigating general publication trends, citation trends and evolution of research areas following a coarse-grained analysis. For instance, Hoonlor et al. [1] analysed data on proposals for grants supported by the U.S National Foundation and on CS publications in the ACM Digital Library and IEEE Xplore Digital Library using sequence mining and bursty word detection. Similarly, Hou et al. revealed the evolution of research topics between 2009 and 2016 using the timeline knowledge map through Document-Citation Analysis (DCA). In the same context, Effendy and Yap [15] performed trend analysis using the Microsoft Academic Graph (MAG) dataset. Both the above approaches to trend analysis in CS focus on citation analysis which fails to dig into the paper content and takes time to reveal trends. Publisher Copyright: © 2019 IEEE.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

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DeepHist: Towards a Deep Learning-based Computational History of Trends in the NIPS

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