A novel approach to voice of customer extraction using GPT-3.5 Turbo: linking advanced NLP and Lean Six Sigma 4.0

Shahin, Mohammad; Chen, F. Frank; Hosseinzadeh, Ali; Maghanaki, Mazdak; Eghbalian, Ayda

doi:10.1007/s00170-024-13167-w

Cited by 7 publications

(1 citation statement)

References 57 publications

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“…This approach departs from the CNNs that have traditionally dominated this domain. ViT demonstrates that transformers can achieve remarkable performance on image recognition tasks, challenging the supremacy of CNNs in computer vision and thus representing a novel approach to image classification [103,104]. The core idea behind ViT is to treat an image as a sequence of patches, akin to how a sentence is viewed as a sequence of words in NLP [105].…”

Section: Visual Transformer (Vit)mentioning

confidence: 99%

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Shahin,

Chen,

Maghanaki

et al. 2024

Sensors

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Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

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Section: Visual Transformer (Vit)mentioning

confidence: 99%