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Titlebook: Recent Trends in Image Processing and Pattern Recognition; 5th International Co KC Santosh,Ayush Goyal,Satish K Singh Conference proceeding

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51#
發(fā)表于 2025-3-30 10:36:03 | 只看該作者
52#
發(fā)表于 2025-3-30 15:36:10 | 只看該作者
53#
發(fā)表于 2025-3-30 19:36:25 | 只看該作者
Federated Learning for?Lung Sound Analysisient confidentiality is upheld across all sites. Additionally, the additional supervision gained from partner sites’ results enhances the global model’s overall detection capabilities. This study’s primary goal is to determine how the federated learning (FL) approach may offer a machine learning ave
54#
發(fā)表于 2025-3-30 22:47:23 | 只看該作者
Performance Analysis of CNN and Quantized CNN Model for Rheumatoid Arthritis Identification Using Thompatible with the Convolution Neural Network (CNN) of the Deep Learning (DL) model and statistical parameters such as mean, mode, mode, kurtosis, etc. are derived and the correlation between the parameters is drawn using the covariance matrix. The dataset is then visualized using graphical plots to
55#
發(fā)表于 2025-3-31 04:03:29 | 只看該作者
Image Processing and Pattern Recognition of Micropores of Polysulfone Membrane for the Bio-separatio the pore perimeter (contour) for the ‘front’, ‘back’ and ‘cross-section’ of the membrane employing the morphological operations for image processing. The retrieved perimeter pixelart is then employed for modeling the membrane structure in two domains viz. ‘solid content’ and ‘porous content’, for t
56#
發(fā)表于 2025-3-31 06:57:52 | 只看該作者
An Extreme Learning Machine-Based AutoEncoder (ELM-AE) for Denoising Knee X-ray Images and Grading K are later classified, based on KL grades. In experimentation, evaluation of performance is carried out for the model with and without using autoencoders. It is observed that with autoencoders the overall performance is enhanced significantly for standard as well as the local dataset.
57#
發(fā)表于 2025-3-31 10:29:54 | 只看該作者
58#
發(fā)表于 2025-3-31 13:56:31 | 只看該作者
59#
發(fā)表于 2025-3-31 18:37:39 | 只看該作者
Targeted Clean-Label Poisoning Attacks on?Federated Learningarget image without significantly affecting the model’s overall accuracy. In addition, the attack’s impact grows in direct proportion to the number of injected poisonous images and malicious client (i.e. controlled by adversaries) participating in the FL process.
60#
發(fā)表于 2025-3-31 21:59:26 | 只看該作者
Communications in Computer and Information Sciencehttp://image.papertrans.cn/r/image/823454.jpg
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