Research proposal content extraction using natural language processing and semi-supervised clustering: A demonstration and comparative analysis

Funding institutions often solicit text-based research proposals to evaluate potential recipients. Leveraging the information contained in these documents could help institutions understand the supply of research within their domain. In this work, an end-to-end methodology for semi-supervised docume...

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Veröffentlicht in:	Scientometrics Jg. 128; H. 5; S. 3197 - 3224
Hauptverfasser:	Knisely, Benjamin M., Pavliscsak, Holly H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.05.2023 Springer Nature B.V
Schlagworte:	Algorithms Annotations Army Averages Bidirectionality Classification Clustering Coherence Comparative analysis Computer Science Distinctiveness Documents Embedding Extraction Funding Information Storage and Retrieval Innovations Institutions Library Science Machine learning Medical technology Medicine Methodology Military technology Natural language processing Proposals Ratings Ratings & rankings Research facilities Research methodology Research proposals Subject specialists Technological change Telecommunications Telemedicine Trends Text mining Cluster validation Document clustering Research portfolio Machine learning
ISSN:	0138-9130, 1588-2861
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Abstract	Funding institutions often solicit text-based research proposals to evaluate potential recipients. Leveraging the information contained in these documents could help institutions understand the supply of research within their domain. In this work, an end-to-end methodology for semi-supervised document clustering is introduced to partially automate classification of research proposals based on thematic areas of interest. The methodology consists of three stages: (1) manual annotation of a document sample; (2) semi-supervised clustering of documents; (3) evaluation of cluster results using quantitative metrics and qualitative ratings (coherence, relevance, distinctiveness) by experts. The methodology is described in detail to encourage replication and is demonstrated on a real-world data set. This demonstration sought to categorize proposals submitted to the US Army Telemedicine and Advanced Technology Research Center (TATRC) related to technological innovations in military medicine. A comparative analysis of method features was performed, including unsupervised vs. semi-supervised clustering, several document vectorization techniques, and several cluster result selection strategies. Outcomes suggest that pretrained Bidirectional Encoder Representations from Transformers (BERT) embeddings were better suited for the task than older text embedding techniques. When comparing expert ratings between algorithms, semi-supervised clustering produced coherence ratings ~ 25% better on average compared to standard unsupervised clustering with negligible differences in cluster distinctiveness. Last, it was shown that a cluster result selection strategy that balances internal and external validity produced ideal results. With further refinement, this methodological framework shows promise as a useful analytical tool for institutions to unlock hidden insights from untapped archives and similar administrative document repositories.
AbstractList	Funding institutions often solicit text-based research proposals to evaluate potential recipients. Leveraging the information contained in these documents could help institutions understand the supply of research within their domain. In this work, an end-to-end methodology for semi-supervised document clustering is introduced to partially automate classification of research proposals based on thematic areas of interest. The methodology consists of three stages: (1) manual annotation of a document sample; (2) semi-supervised clustering of documents; (3) evaluation of cluster results using quantitative metrics and qualitative ratings (coherence, relevance, distinctiveness) by experts. The methodology is described in detail to encourage replication and is demonstrated on a real-world data set. This demonstration sought to categorize proposals submitted to the US Army Telemedicine and Advanced Technology Research Center (TATRC) related to technological innovations in military medicine. A comparative analysis of method features was performed, including unsupervised vs. semi-supervised clustering, several document vectorization techniques, and several cluster result selection strategies. Outcomes suggest that pretrained Bidirectional Encoder Representations from Transformers (BERT) embeddings were better suited for the task than older text embedding techniques. When comparing expert ratings between algorithms, semi-supervised clustering produced coherence ratings ~ 25% better on average compared to standard unsupervised clustering with negligible differences in cluster distinctiveness. Last, it was shown that a cluster result selection strategy that balances internal and external validity produced ideal results. With further refinement, this methodological framework shows promise as a useful analytical tool for institutions to unlock hidden insights from untapped archives and similar administrative document repositories. Funding institutions often solicit text-based research proposals to evaluate potential recipients. Leveraging the information contained in these documents could help institutions understand the supply of research within their domain. In this work, an end-to-end methodology for semi-supervised document clustering is introduced to partially automate classification of research proposals based on thematic areas of interest. The methodology consists of three stages: (1) manual annotation of a document sample; (2) semi-supervised clustering of documents; (3) evaluation of cluster results using quantitative metrics and qualitative ratings (coherence, relevance, distinctiveness) by experts. The methodology is described in detail to encourage replication and is demonstrated on a real-world data set. This demonstration sought to categorize proposals submitted to the US Army Telemedicine and Advanced Technology Research Center (TATRC) related to technological innovations in military medicine. A comparative analysis of method features was performed, including unsupervised vs. semi-supervised clustering, several document vectorization techniques, and several cluster result selection strategies. Outcomes suggest that pretrained Bidirectional Encoder Representations from Transformers (BERT) embeddings were better suited for the task than older text embedding techniques. When comparing expert ratings between algorithms, semi-supervised clustering produced coherence ratings ~ 25% better on average compared to standard unsupervised clustering with negligible differences in cluster distinctiveness. Last, it was shown that a cluster result selection strategy that balances internal and external validity produced ideal results. With further refinement, this methodological framework shows promise as a useful analytical tool for institutions to unlock hidden insights from untapped archives and similar administrative document repositories. The online version contains supplementary material available at 10.1007/s11192-023-04689-3. Funding institutions often solicit text-based research proposals to evaluate potential recipients. Leveraging the information contained in these documents could help institutions understand the supply of research within their domain. In this work, an end-to-end methodology for semi-supervised document clustering is introduced to partially automate classification of research proposals based on thematic areas of interest. The methodology consists of three stages: (1) manual annotation of a document sample; (2) semi-supervised clustering of documents; (3) evaluation of cluster results using quantitative metrics and qualitative ratings (coherence, relevance, distinctiveness) by experts. The methodology is described in detail to encourage replication and is demonstrated on a real-world data set. This demonstration sought to categorize proposals submitted to the US Army Telemedicine and Advanced Technology Research Center (TATRC) related to technological innovations in military medicine. A comparative analysis of method features was performed, including unsupervised vs. semi-supervised clustering, several document vectorization techniques, and several cluster result selection strategies. Outcomes suggest that pretrained Bidirectional Encoder Representations from Transformers (BERT) embeddings were better suited for the task than older text embedding techniques. When comparing expert ratings between algorithms, semi-supervised clustering produced coherence ratings ~ 25% better on average compared to standard unsupervised clustering with negligible differences in cluster distinctiveness. Last, it was shown that a cluster result selection strategy that balances internal and external validity produced ideal results. With further refinement, this methodological framework shows promise as a useful analytical tool for institutions to unlock hidden insights from untapped archives and similar administrative document repositories.Funding institutions often solicit text-based research proposals to evaluate potential recipients. Leveraging the information contained in these documents could help institutions understand the supply of research within their domain. In this work, an end-to-end methodology for semi-supervised document clustering is introduced to partially automate classification of research proposals based on thematic areas of interest. The methodology consists of three stages: (1) manual annotation of a document sample; (2) semi-supervised clustering of documents; (3) evaluation of cluster results using quantitative metrics and qualitative ratings (coherence, relevance, distinctiveness) by experts. The methodology is described in detail to encourage replication and is demonstrated on a real-world data set. This demonstration sought to categorize proposals submitted to the US Army Telemedicine and Advanced Technology Research Center (TATRC) related to technological innovations in military medicine. A comparative analysis of method features was performed, including unsupervised vs. semi-supervised clustering, several document vectorization techniques, and several cluster result selection strategies. Outcomes suggest that pretrained Bidirectional Encoder Representations from Transformers (BERT) embeddings were better suited for the task than older text embedding techniques. When comparing expert ratings between algorithms, semi-supervised clustering produced coherence ratings ~ 25% better on average compared to standard unsupervised clustering with negligible differences in cluster distinctiveness. Last, it was shown that a cluster result selection strategy that balances internal and external validity produced ideal results. With further refinement, this methodological framework shows promise as a useful analytical tool for institutions to unlock hidden insights from untapped archives and similar administrative document repositories.The online version contains supplementary material available at 10.1007/s11192-023-04689-3.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11192-023-04689-3.
Author	Knisely, Benjamin M. Pavliscsak, Holly H.
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Cites_doi	10.1016/j.cptl.2018.03.019 10.1016/j.envsci.2006.10.004 10.1109/TFUZZ.2018.2889010 10.1109/ASRU46091.2019.9003958 10.1007/s11192-017-2609-2 10.46743/2160-3715/2019.4120 10.5281/zenodo.831850 10.3233/JIFS-189871 10.1016/j.ipm.2019.04.002 10.1016/j.ipm.2022.103069 10.1016/j.knosys.2021.107342 10.1007/s10586-018-2023-4 10.1016/j.joi.2018.09.004 10.1109/TSMCA.2011.2172205 10.3115/v1/D14-1162 10.2307/2284239 10.1016/j.yjbinx.2019.100057 10.1109/ICISC.2017.8068581 10.1007/s10489-021-02376-5 10.1007/978-3-319-19369-4_5 10.1038/nmeth.1619 10.1016/j.eswa.2022.116551 10.1016/j.joi.2020.101018 10.1016/j.hfh.2022.100016 10.11613/BM.2012.031 10.18653/v1/D19-1383 10.1186/s40537-022-00564-9 10.1109/ICOASE51841.2020.9436540 10.1007/978-3-030-32381-3_16 10.1093/ptj/85.3.257 10.5220/0006541900290039 10.1177/1063293X20982973 10.1007/s11192-020-03396-7 10.1007/s11192-019-03126-8 10.1007/s11192-014-1319-2 10.1109/TSE.2020.3036108 10.1093/reseval/rvw016 10.1109/ICAICTA53211.2021.9640285 10.1111/coin.12064 10.1016/j.ipm.2021.102683 10.1109/HICSS.2015.153 10.1093/scipol/scy026 10.1007/978-3-642-30111-7_25 10.1016/j.eswa.2020.113679 10.1109/ACCESS.2020.2980942 10.1016/j.envsci.2006.10.001 10.1109/ITNEC48623.2020.9085059 10.5441/002/edbt.2014.31 10.1002/wics.1270 10.1016/j.ipm.2021.102816 10.1007/s10994-006-6540-7 10.18637/jss.v025.i04 10.1016/j.eswa.2008.06.093 10.18653/v1/N19-1423 10.19026/rjaset.8.1118 10.1007/s12559-019-09664-w 10.18653/v1/D19-1410 10.1109/ICACSIS53237.2021.9631364 10.1504/IJAIP.2021.116369 10.1109/COMITCon.2019.8862247 10.1016/j.sapharm.2012.04.004 10.1080/09537325.2013.850477 10.1002/widm.1300 10.5829/ije.2021.34.12C.10 10.1007/978-3-030-51935-3_34
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References	LG Nichols (4689_CR49) 2014; 100 Y Wang (4689_CR72) 2015 E Bair (4689_CR7) 2013; 5 4689_CR40 P Jiménez (4689_CR29) 2021; 58 SA Curiskis (4689_CR17) 2020; 57 FK Khattak (4689_CR32) 2019; 100 IO Arnarsson (4689_CR4) 2021; 29 4689_CR47 Y Qin (4689_CR56) 2019; 11 EC McNie (4689_CR41) 2007; 10 T Mikolov (4689_CR43) 2013 GT Reddy (4689_CR59) 2020; 8 M Pourrajabi (4689_CR54) 2014 K Kaya (4689_CR31) 2022; 59 KW Boyack (4689_CR12) 2018; 114 A Ebadi (4689_CR20) 2020; 14 4689_CR71 P Bhattacharya (4689_CR10) 2022; 59 CA Freyman (4689_CR23) 2016; 25 DS Priya (4689_CR55) 2014 MA Mutasodirin (4689_CR48) 2021; 2021 4689_CR34 4689_CR37 D Sarewitz (4689_CR65) 2007; 10 M Mittal (4689_CR45) 2019; 9 Y Hu (4689_CR28) 2016; 32 SK Mishra (4689_CR44) 2022; 52 J Ma (4689_CR38) 2012; 42 E Rendón (4689_CR61) 2011; 5 F Pedregosa (4689_CR51) 2011; 12 SM Sadjadi (4689_CR62) 2021; 34 4689_CR3 4689_CR60 4689_CR5 J Kim (4689_CR33) 2020; 123 WM Rand (4689_CR58) 1971; 66 4689_CR6 A Castleberry (4689_CR14) 2018; 10 J Bergstra (4689_CR9) 2012; 13 RA Saravanan (4689_CR64) 2021; 19 4689_CR1 R Pappagari (4689_CR50) 2019; 2019 N Kalpokaite (4689_CR30) 2019 K Rajput (4689_CR57) 2017; 2017 Y Zhou (4689_CR76) 2019; 120 S Bird (4689_CR11) 2009 R Sandhiya (4689_CR63) 2019; 22 N Gisev (4689_CR27) 2013; 9 S Gajawada (4689_CR24) 2012 ML McHugh (4689_CR39) 2012; 22 A Subakti (4689_CR68) 2022; 9 S Bajpai (4689_CR8) 2015; 41 WS El-Kassas (4689_CR22) 2021; 165 AJ Gates (4689_CR25) 2017; 18 J Edler (4689_CR21) 2018; 45 Y Li (4689_CR35) 2020; 1 J Sim (4689_CR66) 2005; 85 Z Ghasemi (4689_CR26) 2022; 195 C Sun (4689_CR69) 2019 J Wu (4689_CR73) 2009; 36 4689_CR52 S Choi (4689_CR15) 2014; 26 A Penta (4689_CR53) 2021; 229 A Starczewski (4689_CR67) 2015 M Allaoui (4689_CR2) 2020 G Brock (4689_CR13) 2008 4689_CR16 EM Talley (4689_CR70) 2011; 8 4689_CR18 Y Zhang (4689_CR74) 2018; 12 SM Mohammed (4689_CR46) 2020 J Dhanani (4689_CR19) 2021; 41 J-P Mei (4689_CR42) 2019; 27 M Li (4689_CR36) 2022; 48 S Zhong (4689_CR75) 2006; 65
References_xml	– volume: 10 start-page: 807 issue: 6 year: 2018 ident: 4689_CR14 publication-title: Currents in Pharmacy Teaching and Learning doi: 10.1016/j.cptl.2018.03.019 – volume: 10 start-page: 17 issue: 1 year: 2007 ident: 4689_CR41 publication-title: Environmental Science & Policy doi: 10.1016/j.envsci.2006.10.004 – volume: 27 start-page: 1726 issue: 9 year: 2019 ident: 4689_CR42 publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/TFUZZ.2018.2889010 – volume: 41 start-page: 20 year: 2015 ident: 4689_CR8 publication-title: Journal of the Indian Academy of Applied Psychology – volume: 2019 start-page: 838 year: 2019 ident: 4689_CR50 publication-title: IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) doi: 10.1109/ASRU46091.2019.9003958 – ident: 4689_CR37 – volume: 114 start-page: 449 issue: 2 year: 2018 ident: 4689_CR12 publication-title: Scientometrics doi: 10.1007/s11192-017-2609-2 – year: 2019 ident: 4689_CR30 publication-title: The Qualitative Report doi: 10.46743/2160-3715/2019.4120 – volume: 13 start-page: 281 issue: 10 year: 2012 ident: 4689_CR9 publication-title: Journal of Machine Learning Research – ident: 4689_CR6 doi: 10.5281/zenodo.831850 – volume: 41 start-page: 5497 issue: 5 year: 2021 ident: 4689_CR19 publication-title: Journal of Intelligent & Fuzzy Systems doi: 10.3233/JIFS-189871 – volume: 57 start-page: 102034 issue: 2 year: 2020 ident: 4689_CR17 publication-title: Information Processing & Management doi: 10.1016/j.ipm.2019.04.002 – ident: 4689_CR71 – volume: 59 start-page: 103069 issue: 6 year: 2022 ident: 4689_CR10 publication-title: Information Processing & Management doi: 10.1016/j.ipm.2022.103069 – volume: 229 start-page: 107342 year: 2021 ident: 4689_CR53 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2021.107342 – volume: 22 start-page: 3213 issue: 2 year: 2019 ident: 4689_CR63 publication-title: Cluster Computing doi: 10.1007/s10586-018-2023-4 – volume: 12 start-page: 1099 issue: 4 year: 2018 ident: 4689_CR74 publication-title: Journal of Informetrics doi: 10.1016/j.joi.2018.09.004 – volume: 42 start-page: 784 issue: 3 year: 2012 ident: 4689_CR38 publication-title: IEEE Transactions on Systems, Man, and Cybernetics - Part a: Systems and Humans doi: 10.1109/TSMCA.2011.2172205 – ident: 4689_CR52 doi: 10.3115/v1/D14-1162 – volume: 66 start-page: 846 issue: 336 year: 1971 ident: 4689_CR58 publication-title: Journal of the American Statistical Association doi: 10.2307/2284239 – volume: 100 start-page: 100057 year: 2019 ident: 4689_CR32 publication-title: Journal of Biomedical Informatics doi: 10.1016/j.yjbinx.2019.100057 – volume: 2017 start-page: 1 year: 2017 ident: 4689_CR57 publication-title: International Conference on Inventive Systems and Control (ICISC) doi: 10.1109/ICISC.2017.8068581 – volume: 52 start-page: 1520 issue: 2 year: 2022 ident: 4689_CR44 publication-title: Applied Intelligence doi: 10.1007/s10489-021-02376-5 – start-page: 49 volume-title: Artificial intelligence and soft computing year: 2015 ident: 4689_CR67 doi: 10.1007/978-3-319-19369-4_5 – volume: 8 start-page: 443 issue: 6 year: 2011 ident: 4689_CR70 publication-title: Nature Methods doi: 10.1038/nmeth.1619 – volume: 195 start-page: 116551 year: 2022 ident: 4689_CR26 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2022.116551 – volume: 14 start-page: 101018 issue: 2 year: 2020 ident: 4689_CR20 publication-title: Journal of Informetrics doi: 10.1016/j.joi.2020.101018 – ident: 4689_CR34 doi: 10.1016/j.hfh.2022.100016 – volume: 22 start-page: 276 issue: 3 year: 2012 ident: 4689_CR39 publication-title: Biochemia Medica doi: 10.11613/BM.2012.031 – ident: 4689_CR16 doi: 10.18653/v1/D19-1383 – volume: 18 start-page: 3049 issue: 1 year: 2017 ident: 4689_CR25 publication-title: The Journal of Machine Learning Research – volume: 9 start-page: 15 issue: 1 year: 2022 ident: 4689_CR68 publication-title: Journal of Big Data doi: 10.1186/s40537-022-00564-9 – year: 2020 ident: 4689_CR46 publication-title: International Conference on Advanced Science and Engineering (ICOASE) doi: 10.1109/ICOASE51841.2020.9436540 – start-page: 194 volume-title: Chinese Computational Linguistics year: 2019 ident: 4689_CR69 doi: 10.1007/978-3-030-32381-3_16 – volume: 85 start-page: 257 issue: 3 year: 2005 ident: 4689_CR66 publication-title: Physical Therapy doi: 10.1093/ptj/85.3.257 – ident: 4689_CR47 doi: 10.5220/0006541900290039 – volume: 29 start-page: 142 issue: 2 year: 2021 ident: 4689_CR4 publication-title: Concurrent Engineering doi: 10.1177/1063293X20982973 – volume: 123 start-page: 563 issue: 2 year: 2020 ident: 4689_CR33 publication-title: Scientometrics doi: 10.1007/s11192-020-03396-7 – volume: 120 start-page: 167 issue: 1 year: 2019 ident: 4689_CR76 publication-title: Scientometrics doi: 10.1007/s11192-019-03126-8 – volume: 100 start-page: 741 issue: 3 year: 2014 ident: 4689_CR49 publication-title: Scientometrics doi: 10.1007/s11192-014-1319-2 – volume: 48 start-page: 1771 issue: 5 year: 2022 ident: 4689_CR36 publication-title: IEEE Transactions on Software Engineering doi: 10.1109/TSE.2020.3036108 – volume: 25 start-page: 442 issue: 4 year: 2016 ident: 4689_CR23 publication-title: Research Evaluation doi: 10.1093/reseval/rvw016 – ident: 4689_CR5 doi: 10.1109/ICAICTA53211.2021.9640285 – volume-title: Advances in neural information processing systems year: 2013 ident: 4689_CR43 – volume: 32 start-page: 480 issue: 3 year: 2016 ident: 4689_CR28 publication-title: Computational Intelligence doi: 10.1111/coin.12064 – volume: 58 start-page: 102683 issue: 6 year: 2021 ident: 4689_CR29 publication-title: Information Processing & Management doi: 10.1016/j.ipm.2021.102683 – year: 2015 ident: 4689_CR72 publication-title: 48th Hawaii International Conference on System Sciences doi: 10.1109/HICSS.2015.153 – volume: 45 start-page: 433 issue: 4 year: 2018 ident: 4689_CR21 publication-title: Science and Public Policy doi: 10.1093/scipol/scy026 – start-page: 267 volume-title: Advances in Computer Science, Engineering & Applications year: 2012 ident: 4689_CR24 doi: 10.1007/978-3-642-30111-7_25 – volume: 165 start-page: 113679 year: 2021 ident: 4689_CR22 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113679 – volume: 8 start-page: 54776 year: 2020 ident: 4689_CR59 publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2980942 – volume: 10 start-page: 5 issue: 1 year: 2007 ident: 4689_CR65 publication-title: Environmental Science & Policy doi: 10.1016/j.envsci.2006.10.001 – volume: 1 start-page: 1426 year: 2020 ident: 4689_CR35 publication-title: IEEE 4th Information Technology, Networking Electronic and Automation Control Conference (ITNEC) doi: 10.1109/ITNEC48623.2020.9085059 – year: 2014 ident: 4689_CR54 publication-title: 17th International Conference on Extending Database Technology (EDBT) doi: 10.5441/002/edbt.2014.31 – volume: 5 start-page: 349 issue: 5 year: 2013 ident: 4689_CR7 publication-title: Wiley Interdisciplinary Reviews. Computational Statistics doi: 10.1002/wics.1270 – volume: 59 start-page: 102816 issue: 1 year: 2022 ident: 4689_CR31 publication-title: Information Processing & Management doi: 10.1016/j.ipm.2021.102816 – volume: 65 start-page: 3 issue: 1 year: 2006 ident: 4689_CR75 publication-title: Machine Learning doi: 10.1007/s10994-006-6540-7 – volume: 5 start-page: 27 issue: 1 year: 2011 ident: 4689_CR61 publication-title: International Journal of Computers and Communications – year: 2008 ident: 4689_CR13 publication-title: Journal of Statistical Software doi: 10.18637/jss.v025.i04 – volume: 36 start-page: 6050 issue: 3, Part 2 year: 2009 ident: 4689_CR73 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2008.06.093 – ident: 4689_CR18 doi: 10.18653/v1/N19-1423 – year: 2014 ident: 4689_CR55 publication-title: Research Journal of Applied Sciences, Engineering and Technology, doi: 10.19026/rjaset.8.1118 – volume: 11 start-page: 599 issue: 5 year: 2019 ident: 4689_CR56 publication-title: Cognitive Computation doi: 10.1007/s12559-019-09664-w – volume-title: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit year: 2009 ident: 4689_CR11 – ident: 4689_CR60 doi: 10.18653/v1/D19-1410 – volume: 2021 start-page: 1 year: 2021 ident: 4689_CR48 publication-title: International Conference on Advanced Computer Science and Information Systems (ICACSIS) doi: 10.1109/ICACSIS53237.2021.9631364 – volume: 12 start-page: 2825 issue: 85 year: 2011 ident: 4689_CR51 publication-title: Journal of Machine Learning Research – ident: 4689_CR3 – volume: 19 start-page: 422 issue: 3–4 year: 2021 ident: 4689_CR64 publication-title: International Journal of Advanced Intelligence Paradigms doi: 10.1504/IJAIP.2021.116369 – ident: 4689_CR1 doi: 10.1109/COMITCon.2019.8862247 – ident: 4689_CR40 – volume: 9 start-page: 330 issue: 3 year: 2013 ident: 4689_CR27 publication-title: Research in Social and Administrative Pharmacy doi: 10.1016/j.sapharm.2012.04.004 – volume: 26 start-page: 241 issue: 3 year: 2014 ident: 4689_CR15 publication-title: Technology Analysis & Strategic Management doi: 10.1080/09537325.2013.850477 – volume: 9 start-page: e1300 issue: 3 year: 2019 ident: 4689_CR45 publication-title: Wires Data Mining and Knowledge Discovery doi: 10.1002/widm.1300 – volume: 34 start-page: 2648 issue: 12 year: 2021 ident: 4689_CR62 publication-title: International Journal of Engineering doi: 10.5829/ije.2021.34.12C.10 – start-page: 317 volume-title: Image and Signal Processing year: 2020 ident: 4689_CR2 doi: 10.1007/978-3-030-51935-3_34
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