Retracted: Image Recognition Technology Based on Neural Network

Chen, Jianqiu

doi:10.1109/access.2020.3014692

Cited by 12 publications

(5 citation statements)

References 33 publications

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“…Among the numerous applications of ANN one can mention a few, e.g., medical diagnosis [ 60 , 61 ], image recognition [ 62 ], economics [ 63 ], biosystem engineering [ 64 ] or automobile guidance systems [ 65 ]. ANN is also utilized in the paper industry.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Edge Crush Resistance of Corrugated Board Using Artificial Intelligence

2023

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Recently, AI has been used in industry for very precise quality control of various products or in the automation of production processes through the use of trained artificial neural networks (ANNs) which allow us to completely replace a human in often tedious work or in hard-to-reach locations. Although the search for analytical formulas is often desirable and leads to accurate descriptions of various phenomena, when the problem is very complex or when it is impossible to obtain a complete set of data, methods based on artificial intelligence perfectly complement the engineering and scientific workshop. In this article, different AI algorithms were used to build a relationship between the mechanical parameters of papers used for the production of corrugated board, its geometry and the resistance of a cardboard sample to edge crushing. There are many analytical, empirical or advanced numerical models in the literature that are used to estimate the compression resistance of cardboard across the flute. The approach presented here is not only much less demanding in terms of implementation from other models, but is as accurate and precise. In addition, the methodology and example presented in this article show the great potential of using machine learning algorithms in such practical applications.

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Section: Introductionmentioning

confidence: 99%

Estimation of the Edge Crush Resistance of Corrugated Board Using Artificial Intelligence

2023

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“…Neural networks are computational models inspired by the functioning of the human brain. These networks comprise layers of interconnected nodes, called neurons, that process and transmit information [25]. Neural networks are widely used in machine learning to perform pattern recognition, classification, and prediction generation tasks.…”

Section: Concepts Usedmentioning

confidence: 99%

Evaluating the Robustness of Deep Learning Models against Adversarial Attacks: An Analysis with FGSM, PGD and CW

Villegas-Ch,

Jaramillo-Alcázar,

Luján-Mora

2024

BDCC

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This study evaluated the generation of adversarial examples and the subsequent robustness of an image classification model. The attacks were performed using the Fast Gradient Sign method, the Projected Gradient Descent method, and the Carlini and Wagner attack to perturb the original images and analyze their impact on the model’s classification accuracy. Additionally, image manipulation techniques were investigated as defensive measures against adversarial attacks. The results highlighted the model’s vulnerability to conflicting examples: the Fast Gradient Signed Method effectively altered the original classifications, while the Carlini and Wagner method proved less effective. Promising approaches such as noise reduction, image compression, and Gaussian blurring were presented as effective countermeasures. These findings underscore the importance of addressing the vulnerability of machine learning models and the need to develop robust defenses against adversarial examples. This article emphasizes the urgency of addressing the threat posed by harmful standards in machine learning models, highlighting the relevance of implementing effective countermeasures and image manipulation techniques to mitigate the effects of adversarial attacks. These efforts are crucial to safeguarding model integrity and trust in an environment marked by constantly evolving hostile threats. An average 25% decrease in accuracy was observed for the VGG16 model when exposed to the Fast Gradient Signed Method and Projected Gradient Descent attacks, and an even more significant 35% decrease with the Carlini and Wagner method.

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“…Researchers have proposed various algorithms and techniques to improve the accuracy and efficiency of facial recognition systems. One of the most widely used techniques is the Haar Cascade Classifier, which uses machine learning algorithms to detect the presence of specific facial features [2]. The Haar Cascade Classifier has been applied in various access control systems, including door locks, attendance systems, and surveillance systems.…”

Section: Literature Surveymentioning

confidence: 99%

Face Recognition and Raspberry Pi Powered Smart Door Unlocking System

Kumar,

Rishik

et al. 2023

E3S Web Conf.

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Security plays a major role in the well-being of people. It is not possible to hire a security guard in person and always ensure his presence on our premises. It is an inefficient investment. The main role of security personnel is to stay on patrol, monitor, inspect and defend against any breach of security. A smart lock is an electromechanical lock that allows entry based on the authorization device that gives it instructions to lock and unlock. This system is proved to be inefficient as well These locks are either based on Pin, Bluetooth, WIFI. We propose a smart locking system that unlocks based upon face recognition that pic up specific, distinctive details about a person’s face. This is a powerful library that can run even by taking up a single picture of the person provided that the facial features are distinctly identified. The model is proven to be reaching accuracy levels of 99.7% according to the Centre for Strategic and International Studies (CSIS). The database is stored on the cloud consisting of all the authorized personnel that can pass through the door. The client holds the power of customizing the entry and exit of an individual through an application on his mobile.

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Retracted: Image Recognition Technology Based on Neural Network

Cited by 12 publications

References 33 publications

Estimation of the Edge Crush Resistance of Corrugated Board Using Artificial Intelligence

Estimation of the Edge Crush Resistance of Corrugated Board Using Artificial Intelligence

Evaluating the Robustness of Deep Learning Models against Adversarial Attacks: An Analysis with FGSM, PGD and CW

Face Recognition and Raspberry Pi Powered Smart Door Unlocking System

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