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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

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Using Convolutional neural networks (CNN) to Realize Deep Recognition Analysis of Power Technology Standard images 4

Using Convolutional Neural Networks (CNN) to Realize Deep Re...

引用

4th International conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Liu, Shan Zhao, Jin Wang, Haiqi Li, Rui Lu, Zhumao State Grid Shanxi Electric Power Research Institute Shanxi Taiyuan030001 China

This article mainly studies the method of deep recognition analysis of power technology standard images using Convolutional neural Network (CNN). In this study, an efficient CNN model was constructed to quickly and accurately classify and recognize common technical standard images in the power industry. The experimental results indicate that the research method has high accuracy and good practicality in the recognition of power technology standard images. In the experimental stage, this study designed four experiments to explore the application of CNN models in power technology standard image recognition, with a focus on evaluating the impact of different network designs and data processing strategies on model performance. In the benchmark model comparison experiment, the AUC (Area Under the ROC Curve) value of the CNN model in the power technology standard image recognition task was 0.96, the AUC value of the Support Vector machine (SVM) was 0.88, and the AUC value of the decision tree model was 0.82. In experiments on the impact of different data augmentation strategies, the CNN model using scaling augmentation strategy achieved an accuracy of 90%. In the experiment on the impact of network depth on performance, the accuracy of the CNN model was highest improved to 95%. In real-time recognition capability testing, although the CNN model has the highest accuracy, the average processing time for each image is also relatively long. In the above data conclusion, the effectiveness of the CNN model in power technology standard image recognition tasks has been demonstrated, and possible paths for optimizing model performance in practical applications have also been revealed. © 2024 The Authors.

关键词： Convolutional neural networks

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CNN based image-Denoising Techniques-A Review 3

CNN based Image-Denoising Techniques-A Review

引用

3rd International conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Agarkar, Amal Shrivastava, Sudhanshu Bangde, Dhruva Bhise, Dhiraj SVKM'S NMIMS MPSTME IT Department Shirpur India

ISBN: (纸本)9798350396157

image denoising is a fundamental task within the realms of computer vision and image processing, with the objective of removing unwanted noise while preserving essential characteristics of the image. Convolutional neural networks (CNNs) have exhibited impressive capabilities across a range of computer vision tasks, with image denoising being one notable application. This research study gives a thorough overview of the many image denoising methods when CNN is used, with a particular emphasis on the developments and applications of this fascinating area. The importance of image denoising in numerous applications, including medical imaging, surveillance, and photography, is highlighted in the paper's introduction. By systematically examining CNN-based denoising techniques and their practical uses, the objective of this research study is to offer researchers and practitioners a thorough comprehension of the advancements in CNN-based image denoising application. © 2024 IEEE.

关键词： image denoising

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Training Deep neural Classifiers with Soft Diamond Regularizers 23

Training Deep Neural Classifiers with Soft Diamond Regulariz...

引用

23rd IEEE International conference on machine learning and applications, ICMLA 2024

作者： Adigun, Olaoluwa Kosko, Bart Signal and Image Processing Institute Department of Electrical and Computer Engineering Los AngelesCA90089-2564 United States

ISBN: (纸本)9798350374889

We introduce new soft diamond regularizers that both improve synaptic sparsity and maintain classification accuracy in deep neural networks. These parametrized regularizers outperform the state-of-the-art hard-diamond Laplacian regularizer of Lasso regression and classification. They use thick-tailed symmetric alpha-stable (Sa S) bell-curve synaptic weight priors that are not Gaussian and so have thicker tails. The geometry of the diamond-shaped constraint set varies from a circle to a star depending on the tail thickness and dispersion of the prior probability density function. Training directly with these priors is computationally intensive because almost all Sa S probability densities lack a closed form. A precomputed lookup table removed this computational bottleneck. We tested the new soft diamond regularizers with deep neural classifiers on the three datasets CIFAR-10, CIFAR-100, and Caltech-256. The regularizers improved the accuracy of the classifiers. The improvements included 4.57% on CIFAR-10, 4.27% on CIFAR-100, and 6.69% on Caltech-256. They also outperformed L2 regularizers on all the test cases. Soft diamond regularizers also outperformed L1 lasso or Laplace regularizers because they better increased sparsity while improving classification accuracy. Soft-diamond priors substantially improved accuracy on CIFAR-10 when combined with dropout, batch, or data-augmentation regularization. © 2024 IEEE.

关键词： Probability density function

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Satellite image Classification using CNN with Particle Swarm Optimization Classifier 5

Satellite Image Classification using CNN with Particle Swarm...

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5th International conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Vidhya, S. Balaji, M. Kamaraj, V. Dept. of Computing Technologires School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

Disaster relief, police work, and environmental monitoring all benefit from satellite images. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that need to be searched and the limited number of accessible analysts, automation is essential. However, the accuracy and dependability of existing object recognition and classification algorithms renders them inadequate for the task. One family of machine learning algorithms called "deep learning" has showed immense potential for automating these kinds of jobs. Convolutional neural networks have been successful in the area of image recognition. Here, convolutional neural networks (CNNs) and a particle swarm optimization classifier is utilized to develop efficient algorithms for classifying satellite images. The results of this classifier model are better than those of existing approaches. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Satellite image Classification Using Deep learning 2

Satellite Image Classification Using Deep Learning

引用

2nd International conference on Disruptive Technologies, ICDT 2024

作者： Saini, Shikhar Dahiya, Raghav Ratna, Sanatan Singh, Archana Amity University Department of Artificial Intelligence Uttar Pradesh Noida India

ISBN: (纸本)9798350371055

Satellite images have become a vital resource in a variety of fields, including environmental monitoring, urban planning, and disaster management. The adoption of deep learning algorithms has substantially improved the capacity to process and comprehend this large quantity of data. This abstract provides a concise summary of a detailed paper on satellite image classification using deep learning. Traditional object detection and classification algorithms have historically fallen short in delivering the accuracy and reliability required to address this problem effectively. The complexity and variability of objects and scenes in satellite imagery demand a more sophisticated approach. This is where deep learning, a subset of machine learning, has emerged as a promising solution. Deep learning techniques have demonstrated substantial potential for automating tasks that involve image analysis and understanding, largely thanks to Convolutional neural networks (CNNs). This report describes a research project that focuses on the creation of a deep-learning model for satellite image classification. The goal is to use deep learning skills to automate and improve image classification accuracy. We created a sophisticated tool capable of recognizing patterns and objects in satellite data by combining convolutional neural networks (CNNs). In this context, our study focuses on harnessing the power of deep learning, particularly CNN s, to address the automation of object detection and classification in satellite imagery. The ability of deep learning to learn intricate patterns and features from vast datasets and generalize to new, unseen examples makes it a compelling choice for applications that demand the scale, accuracy, and reliability that traditional algorithms cannot consistently provide. This report outlines our approach to employing CNN s for the automation of satellite image classification, which is essential for enhancing the efficiency and effectiveness of critical tasks

关键词： CNN model Deep learning image classification

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Research on Multi-scale Printed Formula Recognition Methods 24

Research on Multi-scale Printed Formula Recognition Methods

引用

2024 International conference on Advanced Robotics, Automation Engineering and machine learning, ARAEML 2024

作者： Xu, Xiaoyun Chen, Yuehui Cao, Yi Zhao, Yaou School of Information Science and Engineering University of Jinan No.336 Jinan No. 336 China Artificial Intelligence Institute School of Information Science andEngineering University of Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science & Engineering University of Jinan No. 336 China

ISBN: (纸本)9798400717116

Mathematical formula recognition has wide applications in intelligent document processing, significantly reducing the time required to input mathematical formulas. We provide a deep neural network model based on an encoder-decoder architecture to handle the complexity of formula input. This model can convert mathematical formula image into the appropriate LaTeX markup sequences. The encoder, a convolutional neural network, converts the raw image into a collection of feature maps. The contribution of this paper is the introduction of the Feature Pyramid Transformer (FPT) into the encoder, combined with a residual network, to extract features at different scales, thereby improving the accuracy of the LaTeX sequence output. The attention-based Transformer decoder translates the encoder's output into a sequence of LaTeX symbols. The im2latex-100k dataset was used to train and evaluate this model. The experimental results confirm the effectiveness and superiority of this method. © 2024 ACM.

关键词： Convolutional neural networks

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Convolutional neural Network (CNN) to Reduce Construction Loss in JPEG Compression Caused by Discrete Fourier Transform (DFT) 1st

Convolutional Neural Network (CNN) to Reduce Construction L...

引用

1st International conference on AI-generated Content, AIGC 2023

作者： Kunwar, Suman Selinus University of Sciences and Literature Ragusa Italy

ISBN: (纸本)9789819975860

In recent decades, digital image processing has gained enormous popularity. Consequently, a number of data compression strategies have been put forth, with the goal of minimizing the amount of information required to represent images. Among them, JPEG compression is one of the most popular methods that has been widely applied in multimedia and digital applications. The periodic nature of DFT makes it impossible to meet the periodic condition of an image’s opposing edges without producing severe artifacts, which lowers the image’s perceptual visual quality. On the other hand, deep learning has recently achieved outstanding results for applications like speech recognition, image reduction, and natural language processing. Convolutional neural networks (CNN) have received more attention than most other types of deep neural networks. The use of convolution in feature extraction results in a less redundant feature map and a smaller dataset, both of which are crucial for image compression. In this work, an effective image compression method is purposed using autoencoders. The study’s findings revealed a number of important trends that suggested better reconstruction along with good compression can be achieved using autoencoders. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete Fourier transforms

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Sign Language Recognition using Spiking neural networks 2

Sign Language Recognition using Spiking Neural Networks

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2nd International conference on machine learning and Data Engineering, ICMLDE 2023

作者： Chaudhari, Pranav Vicente-Sola, Alex Basu, Amlan Manna, Davide L. Kirkland, Paul Caterina, Gaetano Di Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow United Kingdom

In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial neural networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking neural networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages. © 2024 Elsevier B.V.. All rights reserved.

关键词： neural networks

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Leveraging the Effect of CNN to Identify Severity of Lumpy Skin Disease in Cattle

Leveraging the Effect of CNN to Identify Severity of Lumpy S...

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2024 International conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Kumar, Vinesh Maurya, Jay Prakash Shrivastava, Abhishek School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computing Science Engineering VIT Bhopal University Sehore India School of Mechanical Engineering VIT Bhopal University Sehore India

ISBN: (纸本)9798331517953

There have been research articles in the past that identified the presence of Lumpy Skin Disease (LSD) in Cattle through machine-learning methods. This paper is focused not only on confirming the presence of the virus but also on predicting the stage of the LSD virus in a Cattle skin image sample. In this paper, several machine-learning classification algorithms including Support Vector machines, Naive Bayes, Random Forest, AdaBoost, K-nearest neighbors, and finally, Convolutional neural networks, were analyzed to classify the image samples of Cattle skin into 3 categories: Normal (LSD-negative), Mild LSD, and Severe LSD classes. A custom-made dataset consisting of 3454 image samples from the three classes was built to train the models. There is no cure for LSD, and the infected cattle's immune response is the only way out;however, as a supporting aid, primary anti-viral treatment can be given for a faster recovery. This should be administered to the cattle with a higher probability of getting cured and staying alive. Hence, the detection of the stage of the disease is significant in terms of the investment of time and treatment to needy cattle as healthcare facilities and government resources are limited. After analyzing the results from the models trained on the algorithms mentioned earlier, the best accuracy was obtained through Convolutional neural networks with pre-trained base model VGG-16 with 92.93% on the training dataset and 88.93% on the validation dataset. This model eliminates possibilities of human bias to empower the government in granting fair compensation in case of death due to LSD and to maximize resource utilization in the best possible way if integrated into a user-friendly application. © 2024 IEEE.

关键词： Convolutional neural networks

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Universal neural Functionals 38

Universal Neural Functionals

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38th conference on neural Information processing Systems, NeurIPS 2024

作者： Zhou, Allan Finn, Chelsea Harrison, James Stanford University United States Google DeepMind United Kingdom

A challenging problem in many modern machine learning tasks is to process weight-space features, i.e., to transform or extract information from the weights and gradients of a neural network. Recent works have developed promising weight-space models that are equivariant to the permutation symmetries of simple feedforward networks. However, they are not applicable to general architectures, since the permutation symmetries of a weight space can be complicated by recurrence or residual connections. This work proposes an algorithm that automatically constructs permutation equivariant models, which we refer to as universal neural functionals (UNFs), for any weight space. Among other applications, we demonstrate how UNFs can be substituted into existing learned optimizer designs, and find promising improvements over prior methods when optimizing small image classifiers and language models. Our results suggest that learned optimizers can benefit from considering the (symmetry) structure of the weight space they optimize. We open-source our library for constructing UNFs at https://***/AllanYangZhou/universal_neural_functional. © 2024 neural information processing systems foundation. All rights reserved.

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