Understanding videos at scale: How to extract insights for business research

Schwenzow, Jasper; Hartmann, Jochen; Schikowsky, Amos; Heitmann, Mark

doi:10.1016/j.jbusres.2020.09.059

Cited by 40 publications

(15 citation statements)

References 68 publications

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“…The primary benefit of CNN architecture over predecessors like feedforward neural network (FNN) and recurrent neural network (RNN) is that it identifies key characteristics automatically without the need for human intervention (Gao et al. , 2021; Schwenzow et al. , 2021; Zhang et al.…”

Section: Science Mapping Of DL Applications In Manufacturing Operatio...mentioning

confidence: 99%

“…, 2020). Nowadays, video analytics is evolving into a sophisticated technology that utilizes ML algorithms to do various tasks by applying computer vision and DL to video footage or live video feeds (Schwenzow et al. , 2021).…”

Section: Science Mapping Of DL Applications In Manufacturing Operatio...mentioning

confidence: 99%

“…The CNN is a DL model that works exceptionally well when ML systems are dealing with huge amounts of data (Zhang et al, 2020a), most notably visual data (e.g. images, pictures) (Abobakr et al, 2019;Schwenzow et al, 2021). The primary application area for CNNs is thus image classification, where they demonstrate exceptional accuracy (Bergado et al, 2021).…”

Section: Deep Learning Applications Reviewmentioning

confidence: 99%

“…Instead of requiring humans to develop task-specific software programs, DL takes unstructured metadata and trains itself to perform incremental and predictable actions based on the information provided (Usuga Cadavid et al. , 2022; Schwenzow et al. , 2021).…”

Section: Introductionmentioning

confidence: 99%

“…DL is a subset of ML and a grand-subset of AI that reflects how machines mimic humans in acquiring certain types of information and develop situational knowledge (Zhao et al, 2020;Usuga-Cadavid et al, 2022). Instead of requiring humans to develop task-specific software programs, DL takes unstructured metadata and trains itself to perform incremental and predictable actions based on the information provided Schwenzow et al, 2021). Noteworthily, DL is reshaping the way organizations interact with technology, assisting individuals in the execution of both basic and complex operations (Chatterjee et al, 2022), thus improving core processes in a variety of business sectors such as banking and finance (Borovkova and Tsiamas, 2019;Feng and Qu, 2021), cybersecurity (Adebowale et al, 2020), retail and shopping (Qin et al, 2020), and transportation (Shen and Lin, 2020;Wang et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning applications in manufacturing operations: a review of trends and ways forward

Sahoo

Kumar

Abedin

et al. 2022

JEIM

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PurposeDeep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.Design/methodology/approachUsing bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.FindingsThis research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.Research limitations/implicationsThis research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.Originality/valueThis research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

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Section: Science Mapping Of DL Applications In Manufacturing Operatio...mentioning

confidence: 99%

Section: Science Mapping Of DL Applications In Manufacturing Operatio...mentioning

confidence: 99%