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@PHDTHESIS{Alia:1028429,
      author       = {Alia, Ahmed},
      title        = {{A}rtificial {I}ntelligence {F}ramework for {V}ideo
                      {A}nalytics: {D}etecting {P}ushing in {C}rowds},
      volume       = {61},
      school       = {Univ. Wuppertal},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2024-04610},
      isbn         = {978-3-95806-763-9},
      series       = {Schriften des Forschungszentrums Jülich IAS Series},
      pages        = {xviii, 151},
      year         = {2024},
      note         = {Dissertation, Univ. Wuppertal, 2024},
      abstract     = {In the modern era, data has become more complex, posing
                      additional challenges to conventional data analysis methods.
                      This is where Artificial Intelligence comes into play,
                      specifically Deep Learning algorithms. These algorithms can
                      analyze such data automatically, quickly, and accurately.
                      Moreover, they can explore complex relationships between
                      variables and identify non-linear patterns humans may not
                      perceive. Leveraging this potential, Deep Learning has
                      recently become pivotal in analyzing complex data, such as
                      video data, arising from human crowds to enhance safety.
                      Despite considerable advancements, some challenging problems
                      in crowd dynamics still need to be solved efficiently and
                      automatically.},
      cin          = {IAS-7},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      doi          = {10.34734/FZJ-2024-04610},
      url          = {https://juser.fz-juelich.de/record/1028429},
}