Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

Shi, Yeqiong; Wang, Sainan; Shuna, Zhou; Kamruzzaman, M. M.

doi:10.1109/access.2020.3018180

Cited by 16 publications

(10 citation statements)

References 22 publications

(19 reference statements)

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“…We investigate the setting for the adversary network for the poison attack, the assumption, the first case, taken as perfect knowledge gained by the adversary on the target classifier ( P TC ) and known feature space t ( x ).The second case, is that adversaries gained the less or limited knowledge ( L TC ), target classifier. We assumed that attacker may have knowledge of features representation, but not the training dataset (Rathore et al, 2022; Poongodi, Bourouis et al, 2022; Ramesh, Lihore et al, 2022; Poongodi, Malviya, Hamdi et al, 2022; Poongodi, Malviya, Kumar et al, 2022; Poongodi, Hamdi, & Wang 2022; Poongodi et al, 2021; Ramesh, Vijayaragavan et al, 2022; Hamdi et al, 2022; Poongodi, Hamdi, Malviya et al, 2022; Kamruzzaman 2021; Hossain et al, 2022; Chen et al, 2019; Kamruzzaman 2013, 2014; Zhang et al, 2021; Hossain, Kamruzzaman et al, 2022; Sarker et al, 2021; Shi et al, 2020; Chen et al, 2020).…”

Section: Attack Modelsmentioning

confidence: 99%

SVM‐based generative adverserial networks for federated learning and edge computing attack model and outpoising

et al. 2022

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Machine learning are vulnerable to the threats. The Intruders can utilize the malicious nature of the nodes to attack the training dataset to worsen the process and manipulate the learning and make the over all system with less efficiency and performance. The optimized poison attack procedures are already introduced to estimate the overall bad scenario, design the intrusion as bi‐level optimization and it is considered computational complexity is high and demanding, in contrary the applicability is limited such models deep neural networks. In this research papers, we have proposed, novel proposed system, poisoning attacks against the Machine learning training dataset, including the genuine data points that reduce the accuracy of the classifier in the process of training. The proposed system have 3 components of Generative Adverserial networks (GAN) generator, discriminator, and the target classifier. The proposed system allows to detect the vulnerability easy and it can be found as similar as realistic attacks to detect the area where the underlying data distribution have more possibility of poising attack which cause vulnerability to the network. Our experimentation, proves the claim our that the proposed model is effective on compromising the classifiers uses the machine learning algorithms and also deep learning networks.

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Section: Attack Modelsmentioning

confidence: 99%

SVM‐based generative adverserial networks for federated learning and edge computing attack model and outpoising

et al. 2022

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“…Some scholars have proposed two-dimensional linear discriminant analysis, which uses the image matrix to construct the intraclass divergence matrix and interclass divergence matrix so as to overcome the problem. Then, the maximum interval criterion (MMC) is proposed, which takes the difference between the interclass divergence matrix and the intraclass divergence matrix as the criterion so as to avoid solving the inverse matrix of the intraclass divergence matrix and solve the problem theoretically [11].…”

Section: Related Workmentioning

confidence: 99%

Commodity Price Recognition and Simulation of Image Recognition Technology Based on the Nonlinear Dimensionality Reduction Method

Liu

Wang

Bai

2021

Advances in Mathematical Physics

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Dimensionality reduction of images with high-dimensional nonlinear structure is the key to improving the recognition rate. Although some traditional algorithms have achieved some results in the process of dimensionality reduction, they also expose their respective defects. In order to achieve the ideal effect of high-dimensional nonlinear image recognition, based on the analysis of the traditional dimensionality reduction algorithm and refining its advantages, an image recognition technology based on the nonlinear dimensionality reduction method is proposed. As an effective nonlinear feature extraction method, the nonlinear dimensionality reduction method can find the nonlinear structure of datasets and maintain the intrinsic structure of data. Applying the nonlinear dimensionality reduction method to image recognition is to divide the input image into blocks, take it as a dataset in high-dimensional space, reduce the dimension of its structure, and obtain the low-dimensional expression vector of its eigenstructure so that the problem of image recognition can be carried out in a lower dimension. Thus, the computational complexity can be reduced, the recognition accuracy can be improved, and it is convenient for further processing such as image recognition and search. The defects of traditional algorithms are solved, and the commodity price recognition and simulation experiments are carried out, which verifies the feasibility of image recognition technology based on the nonlinear dimensionality reduction method in commodity price recognition.

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“…Appropriate sizing is guaranteed by fittings and adjustments, which result in the creation of prototypes and test samples. The finished products are frequently displayed in fashion presentations or shows, and in order to remain relevant in the rapidly changing fashion business, successful designers constantly conduct market research and work on their brands [2][3][4]. A combination of technical skill, artistic vision, and a deep understanding of market trends are needed in this sector.…”

Section: Introductionmentioning

confidence: 99%

Clothing Design Style Recommendation Using Optimized Semantic-Preserved Generative Adversarial Network

Guannan Yang

2024

jes

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Recommendations for clothing design styles provide a tailored and efficient buying experience, decreasing consumers' decision fatigue and boosting interest. There are a number of obstacles to implementing clothing design style recommendations, including the requirement for diverse and accurate data to train efficient algorithms, the possibility of biases in recommendation systems, the complexity of comprehending subtle customer preferences, and the ever-changing nature of fashion trends. To overcome this complication, Clothing Design Style Recommendation Using Optimized Semantic-Preserved Generative Adversarial Network (ICMA-SPGAN-SFOA-CD) are proposed. Initially, the images collected from the clothing dataset are given as input. Afterward, the data are fed to pre-processing. In pre-processing, Reverse Image Filter (RIF) is used for redundant of data and noise removal. The pre-processing output is fed to Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) for clothing design style recommendation. The recommended clothing design style was given for classification using Semantic-Preserved Generative Adversarial Network (SPGAN) optimized with Sheep Flock Optimization Algorithm (SFOA)for classifying clothing design style as Basic clothing, Looseness Season for clothing, Collar type, Colour and Advanced clothing . MATLAB is used to implement the proposed ICMA-SPGAN-SFOA-CD method. The performance of the proposed ICMA-SPGAN-SFOA-CD approach attains 25%, 21.5%, and 22.5% high precision,25.3%, 27.5%, and 21.8% high recall and 27.6%, 24.8%, and 23.2% high F1-Scorecompared with existing methods such as RCNN Framework Using L-Softmax Loss (CAR-RCNN-LSL) ,Clothing Attribute Recognition Based on Clothing fashion style recognition with design issue graph (CFSR-DCNN-DIG), Personalized Smart Clothing Design Based on Multimodal Visual Data Detection (PSCD-DNN-MVDD) models respectively.

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Study on Modeling Method of Forest Tree Image Recognition Based on CCD and Theodolite

Cited by 16 publications

References 22 publications

SVM‐based generative adverserial networks for federated learning and edge computing attack model and outpoising

SVM‐based generative adverserial networks for federated learning and edge computing attack model and outpoising

Commodity Price Recognition and Simulation of Image Recognition Technology Based on the Nonlinear Dimensionality Reduction Method

Clothing Design Style Recommendation Using Optimized Semantic-Preserved Generative Adversarial Network

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