Steel Plate Defect Recognition of Deep Neural Network Recognition Based on Space-Time Constraints

Zhang, Chi; Liu, Baiting; Wang, Xiaolei

doi:10.1155/2022/9595286

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Blockchain technology allows it to create and maintain information blocks known as ledgers and to analyze data in a safe and appropriate model [3]. Blockchain helps in the examination and forecasting of medical data.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Enabled Deep Learning Approach to Improve Healthcare System

Das,

Singh,

Verma

2024

J Multimed Inf Syst

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The healthcare industry has advanced its digitalization and use of electronic medical records during the past ten years (EMRs). The EHR system gives the information's proprietor authority over their data and allows them to communicate it to certain individuals. It is challenging for data to maintain security and diagnostic processes because of the enormous volume of data in the medical field. This research presents a novel blockchain-based encryption system using deep learning (BcEs-DLM) for secure medical data management. The concept that is being described encompasses many phases of activities, including safe data management via blockchain, encryption, and optimal key generation. It provides individuals with the ability to manage data accessibility, granting read/write access to hospital authorities, and triggering precautionary agreements. Our recommended approach offers a reliable methodology for generating secure encryption keys and effectively safeguarding sensitive medical data using the block cipher technique. By following this method, you can ensure that patient information remains confidential and protected from unauthorized access. The detection process is performed using medical record sharing. In this paper, we achieved a 97 percent accuracy after training our deep learning model. Additionally, every node of this system is registered and updated on the blockchain

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Section: Introductionmentioning

confidence: 99%

Blockchain-Enabled Deep Learning Approach to Improve Healthcare System

Das,

Singh,

Verma

2024

J Multimed Inf Syst

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Contrastive self-supervised representation learning framework for metal surface defect detection

Zabin,

Kabir,

Kabir

et al. 2023

J Big Data

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Automated detection of defects on metal surfaces is crucial for ensuring quality control. However, the scarcity of labeled datasets for emerging target defects poses a significant obstacle. This study proposes a self-supervised representation-learning model that effectively addresses this limitation by leveraging both labeled and unlabeled data. The proposed model was developed based on a contrastive learning framework, supported by an augmentation pipeline and a lightweight convolutional encoder. The effectiveness of the proposed approach for representation learning was evaluated using an unlabeled pretraining dataset created from three benchmark datasets. Furthermore, the performance of the proposed model was validated using the NEU metal surface-defect dataset. The results revealed that the proposed method achieved a classification accuracy of 97.78%, even with fewer trainable parameters than the benchmark models. Overall, the proposed model effectively extracted meaningful representations from unlabeled image data and can be employed in downstream tasks for steel defect classification to improve quality control and reduce inspection costs.

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Steel Plate Defect Recognition of Deep Neural Network Recognition Based on Space-Time Constraints

Cited by 2 publications

References 13 publications

Blockchain-Enabled Deep Learning Approach to Improve Healthcare System

Blockchain-Enabled Deep Learning Approach to Improve Healthcare System

Contrastive self-supervised representation learning framework for metal surface defect detection

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