CommuNety: deep learning-based face recognition system for the prediction of cohesive communities
Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shah, Syed Afaq Ali [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 82(2022), 7 vom: 10. Sept., Seite 10641-10659 |
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| Übergeordnetes Werk: |
volume:82 ; year:2022 ; number:7 ; day:10 ; month:09 ; pages:10641-10659 |
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| DOI / URN: |
10.1007/s11042-022-13741-y |
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OLC2134257563 |
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| 520 | |a Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities. | ||
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| 700 | 1 | |a Cheema, Muhammad Aamir |4 aut | |
| 700 | 1 | |a Bais, Abdul |4 aut | |
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10.1007/s11042-022-13741-y doi (DE-627)OLC2134257563 (DE-He213)s11042-022-13741-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Shah, Syed Afaq Ali verfasserin (orcid)0000-0003-2181-8445 aut CommuNety: deep learning-based face recognition system for the prediction of cohesive communities 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities. Deep learning Social communities Predictive modelling Deng, Weifeng aut Cheema, Muhammad Aamir aut Bais, Abdul aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 7 vom: 10. Sept., Seite 10641-10659 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:7 day:10 month:09 pages:10641-10659 https://doi.org/10.1007/s11042-022-13741-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 7 10 09 10641-10659 |
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10.1007/s11042-022-13741-y doi (DE-627)OLC2134257563 (DE-He213)s11042-022-13741-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Shah, Syed Afaq Ali verfasserin (orcid)0000-0003-2181-8445 aut CommuNety: deep learning-based face recognition system for the prediction of cohesive communities 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities. Deep learning Social communities Predictive modelling Deng, Weifeng aut Cheema, Muhammad Aamir aut Bais, Abdul aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 7 vom: 10. Sept., Seite 10641-10659 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:7 day:10 month:09 pages:10641-10659 https://doi.org/10.1007/s11042-022-13741-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 7 10 09 10641-10659 |
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10.1007/s11042-022-13741-y doi (DE-627)OLC2134257563 (DE-He213)s11042-022-13741-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Shah, Syed Afaq Ali verfasserin (orcid)0000-0003-2181-8445 aut CommuNety: deep learning-based face recognition system for the prediction of cohesive communities 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities. Deep learning Social communities Predictive modelling Deng, Weifeng aut Cheema, Muhammad Aamir aut Bais, Abdul aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 7 vom: 10. Sept., Seite 10641-10659 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:7 day:10 month:09 pages:10641-10659 https://doi.org/10.1007/s11042-022-13741-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 7 10 09 10641-10659 |
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10.1007/s11042-022-13741-y doi (DE-627)OLC2134257563 (DE-He213)s11042-022-13741-y-p DE-627 ger DE-627 rakwb eng 070 004 VZ Shah, Syed Afaq Ali verfasserin (orcid)0000-0003-2181-8445 aut CommuNety: deep learning-based face recognition system for the prediction of cohesive communities 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities. Deep learning Social communities Predictive modelling Deng, Weifeng aut Cheema, Muhammad Aamir aut Bais, Abdul aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2022), 7 vom: 10. Sept., Seite 10641-10659 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2022 number:7 day:10 month:09 pages:10641-10659 https://doi.org/10.1007/s11042-022-13741-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2022 7 10 09 10641-10659 |
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Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities. © The Author(s) 2022 |
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Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities. © The Author(s) 2022 |
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Abstract Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities. © The Author(s) 2022 |
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CommuNety: deep learning-based face recognition system for the prediction of cohesive communities |
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Deng, Weifeng Cheema, Muhammad Aamir Bais, Abdul |
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