Design of MPCANet fire image recognition model for deep learning

Deep learning (DL) has been used globally in almost every sector of technology and society. Despite its huge success, DL models and applications have been susceptible to adversarial attacks, impacting the accuracy and integrity of these models. Many state-of-the-art models are vulnerable to attacks by well-crafted adversarial examples, which are perturbed versions of clean data with a small amount of noise added, imperceptible to the human eyes, and can quite easily fool the targeted model. This paper introduces six most effective gradient-based adversarial attacks on the ResNet image recognition model, and demonstrates the limitations of traditional adversarial retraining technique. The authors then present a novel ensemble defense strategy based on adversarial retraining technique. The proposed method is capable of withstanding the six adversarial attacks on cifar10 dataset with accuracy greater than 89.31% and as high as 96.24%. The authors believe the design methodologies and experiments demonstrated are widely applicable to other domains of machine learning, DL, and computation intelligence securities.

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Multi-Semantic Image Recognition Model and Evaluating Index for Explaining the Deep Learning Models

10.1109/bigdia53151.2021.9619635 ◽

2021 ◽

Author(s):

Qianmengke Zhao ◽

Ye Wang ◽

Qun Liu

Keyword(s):

Deep Learning ◽

Image Recognition ◽

Learning Models ◽

Recognition Model

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Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189048 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5699-5711

Author(s):

Shirong Long ◽

Xuekong Zhao

Keyword(s):

Feature Extraction ◽

Particle Swarm Optimization ◽

Deep Learning ◽

Real Time ◽

Image Recognition ◽

Particle Swarm ◽

Learning Technology ◽

Search Performance ◽

Swarm Optimization ◽

Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096514666201224125653 ◽

2020 ◽

Vol 14 ◽

Author(s):

Dan Luo

Keyword(s):

Deep Learning ◽

Machine Vision ◽

Supervised Learning ◽

Image Recognition ◽

Production Efficiency ◽

Learning Algorithm ◽

Electronic Components ◽

Electron Device ◽

Actual Recognition ◽

The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

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STUDY ON VARIANT EXTRACTION OF CONCRETE STRUCTURES USING IMAGE RECOGNITION BY DEEP LEARNING

Journal of Japan Society of Civil Engineers Ser E2 (Materials and Concrete Structures) ◽

10.2208/jscejmcs.74.293 ◽

2018 ◽

Vol 74 (4) ◽

pp. 293-305

Author(s):

Kosuke AOSHIMA ◽

Shinya KAWAMURA ◽

Satoshi NAKANO ◽

Hideaki NAKAMURA

Keyword(s):

Deep Learning ◽

Image Recognition ◽

Concrete Structures

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These do not Look Like Those: An Interpretable Deep Learning Model for Image Recognition

IEEE Access ◽

10.1109/access.2021.3064838 ◽

2021 ◽

Vol 9 ◽

pp. 41482-41493

Author(s):

Gurmail Singh ◽

Kin-Choong Yow

Keyword(s):

Deep Learning ◽

Image Recognition ◽

Learning Model ◽

Deep Learning Model

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A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽

10.3390/electronics10010081 ◽

2021 ◽

Vol 10 (1) ◽

pp. 81

Author(s):

Jianbin Xiong ◽

Dezheng Yu ◽

Shuangyin Liu ◽

Lei Shu ◽

Xiaochan Wang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Plant Species ◽

Image Recognition ◽

Recurrent Neural Network ◽

Plant Diseases ◽

Learning Methods ◽

Smart Agriculture ◽

Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

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