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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

共 2642 条记录，以下是371-380 订阅

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Retrospective Review of Activation Functions in artificial neural networks 3rd

Retrospective Review of Activation Functions in Artificial N...

引用

3rd International conference on Communication, Computing and Electronics Systems, ICCCES 2021

作者： Badiger, Manjunatha Mathew, Jose Alex Sahyadri College of Engineering & Management Adyar India Srinivas Institute of Technology Mangalore India

ISBN: (纸本)9789811688614

Deep learning is a subfield of machine learning and artificial intelligence technique. It employs neural network tasks like image processing, computer vision, voice recognition, machine translation, medical information processing, self-driving vehicles, predictive forecasting, robotics and control, cybersecurity, natural language processing, bioinformatics, and countless others. The performance of a neural network is determined by a variety of factors, and activation functions are an essential element in the design of a neural network. The hidden layer’s activation feature defines the extent to which the network model learns the training data set. The type of prognostications made by the model is regulated by the activation functions present at the output layer. This paper presents a comprehensive review of research studies on different activation functions aimed toward deep learning applications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： neural networks

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Detection of Polycystic Ovary Syndrome Using VGG-16 and Inception-V3 2

Detection of Polycystic Ovary Syndrome Using VGG-16 and Ince...

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2nd IEEE International conference on Communication, Security and artificial Intelligence, ICCSAI 2023

作者： Castelino, Karen Bagoriya, Sachin Gaikwad, Pankaj Kurhade, Swapnali Bhavathankar, Prasenjit Sardar Patel Institute of Technology Information Technology Mumbai India

ISBN: (纸本)9798350369960

Polycystic Ovary Syndrome (PCOS) is a major health concern amongst a majority of women. Many issues, including infertility, acne, hirsutism, irregular menstrual cycles, and obesity, may result from it. It could also result in type 2 diabetes, endometrial cancer, heart problems, and high blood pressure. Early diagnosis of PCOS may help prevent and treat such health concerns. This paper proposes using ultrasound imaging of the follicles to extract vital details about the ovary, such as the kind of cyst, the variety of follicles present, and how the size of the follicle reacts to hormonal imbalance. Using image processing techniques, morphological operations, and machine learning, the models identify the valuable characteristics in the ultrasound pictures, highlight them, and then build a suitable classification model for the same. On using transfer learning models such as VGG-16 and Inception-V3, an accuracy of 100% each is obtained. © 2023 IEEE.

关键词： Convolutional neural networks Deep Learning Feature Extraction image Classification Machine Learning Polycystic Ovary Syndrome

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E-Patcher: A Patch-Based Efficient Network for Fast Whole Slide images Segmentation 32nd

E-Patcher: A Patch-Based Efficient Network for Fast Whole Sl...

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32nd International conference on artificial neural networks (ICANN)

作者： Huang, Xiaoshuang Wang, Shuo Huang, Jinze Wei, Yaoguang Dai, Xinhua Zhao, Yang An, Dong Fang, Xiang China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Natl Inst Metrol Mass Spectrometry Engn Technol Res Ctr Ctr Adv Measurement Sci Beijing 102200 Peoples R China China Agr Univ Beijing Engn & Technol Res Ctr Internet Things Ag Beijing 100083 Peoples R China

ISBN: (纸本)9783031442094;9783031442100

UNeXt is a leading medical image segmentation method that employs convolutional and multi-layer perceptron (MLP) structure for its segmentation network. It outperforms other image recognition algorithms, such as MedT and TransUNet, in terms of faster computation speed and has shown great potential for clinical applications. However, its reliance on limited pixel neighborhood information for pixel-level segmentation of large pathological imagesmay lead to inaccurate segmentation of the entire image and overlook important features, resulting in suboptimal segmentation results. To this end, we designed a slight and universal plug-and-play block based patch-level for fully considering local features and global features simultaneously, named "Digging" and "Filling" ViT (DF-ViT) block. Specifically, a "Digging" operation is introduced to randomly select sub-blocks from each sub-patch. Multi-Head Attention (MHA) is then applied to integrate global information into these sub-blocks. The resulting sub-blocks with global semantic features are reassembled into the original feature map, and feature fusion is performed to combine the local and global features. This approach achieves global representation while maintaining a low computational complexity of the model at 0.1424G. Compared to UNeXt, it improves mIoU by 1.07%. Moreover, it reduces the parameter count by 58.50% and the computational workload by 68.09%. Extensive experiments on PAIP 2019 WSI dataset demonstrate that the DF-ViT block significantly enhances computation efficiency while maintaining a high level of accuracy.

关键词： Efficient Transformer Medical image Segmentation Whole Slide images

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A Study of Convolutional neural networks in Face Recognition

A Study of Convolutional Neural Networks in Face Recognition

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2021 International conference on Computer Graphics, artificial Intelligence, and Data processing, ICCAID 2021

作者： Song, Zhiben Yu, Yilun Department of Computer Science and Technology University of Sydney Sydney Australia Department of Automation University of Science and Technology Beijing Beijing China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

Face recognition technology has attracted much attention in the past few years because of its potential value of both research and applications in the fields of pattern recognition, image analysis, computer vision and other fields. Meanwhile, as an important branch of the deep learning family, convolutional neural network (CNN) methods has yield remarkable achievements in many large-scale recognition tasks in the field of computer vision, especially face recognition. This is because, compared to classic neural networks, CNN has more hidden layers, a more complicated network topology, and more robust feature learning and feature expression capabilities. This paper gives an overview of the development on face recognition using CNN approaches, including categories of current face recognition technologies, description of the CNN method, and applications and research progress on CNN for face recognition. © 2022 SPIE.

关键词： Face recognition

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Deep neural Network Inference via Edge Computing: On-Demand Accelerating 1

Deep Neural Network Inference via Edge Computing: On-Demand ...

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1st International conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

作者： Singh, Mahesh K. Karthik, M. Ramesh, P. Rama Naidu, G. Department of ECE Aditya Engineering College AP. Surampalem India Department of ECE Aditya College of Engineering AP. Surampalem India

ISBN: (纸本)9781643683607

Deep neural networks (DNN) are a vital technology for allowing artificial Intelligence applications in the 5G future, and they've gotten a lot of press. Complex DNN-based activities are difficult to run on mobile devices. Edge computing was introduced in this research as a solution to these problems. Edge makes use of two design features: DNN partitioning and DNN right-sizing. The training technique provides information about the training. Preserving Edge is a very dynamic filtering approach for video images. Filters for edge preservation are vital tools for the many tasks involved in image processing and transformation. Nonlinear algorithms calculated the filtered grey value according to the contents of a certain neighborhood. Only for the average pixels evaluated with the same grey values are these taken on the basis of the list of neighborhood pixels. While one of their common features is the conservation of the rim, each edge preserving filter is characterized by its own individual algorithm. © 2023 The authors and IOS Press.

关键词： Deep neural networks

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InceptionNeXt Network with Relative Position Information for Microexpression Recognition 18th

InceptionNeXt Network with Relative Position Information fo...

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18th International conference on Wireless artificial Intelligent Computing Systems and applications, WASA 2024

作者： Cao, Zhilong Dong, Anming Yu, Jiguo Li, Sufang Tian, Xiang Zhang, Li Jinan250014 China Jinan250353 China Jinan250014 China

ISBN: (纸本)9783031714696

Micro-expression recognition is one of the most challenging tasks in affective computing, aiming to identify subtle facial movements that are difficult for humans to perceive within a short period. In recent years, convolutional neural networks (CNNs) have been widely employed for micro-expression recognition. Despite achieving high recognition accuracy, these approaches still exhibit some limitations. Traditional CNNs learn solely from complete images, failing to accurately capture the fine details of facial micro-expressions. Traditional convolution uses small convolution kernels, which ignore global image features during feature extraction, affecting the accuracy of micro expression recognition. In response to the shortcomings of convolutional kernels, this paper proposes a large kernel convolutional neural network for micro expression recognition. Specifically: 1) Introducing a large-kernel convolutional neural network (LKCNN) that extends the model’s receptive field, enabling it to capture data relationships over a larger range. This enhances the model’s performance. To balance computational costs, the network utilizes Inception depthwise convolution, which decomposes expensive depthwise convolutions into three convolutional branches with small kernel sizes, along with an identity mapping branch. 2) The input is transformed from entire images to image blocks, followed by the incorporation of relative position attention (RPI). This not only magnifies detailed features but also filters important feature information, ensuring the full utilization of subtle information. Experiments were conducted on three publicly available databases: CASME ii, SMIC, and SAMM. The results demonstrate superior performance of the proposed method in three-class and five-class recognition compared to state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Modelling the COVID-19 infection rate through a Physics-Informed learning approach 31

Modelling the COVID-19 infection rate through a Physics-Info...

引用

31st Euromicro International conference on Parallel, Distributed and Network-Based processing, PDP 2023

作者： De Rosa, Mariapia Giampaolo, Fabio Piccialli, Francesco Cuomo, Salvatore University of Naples Federico Ii Dept. of Mathematics and Applications 'R. Caccioppoli' Napoli Italy

ISBN: (纸本)9798350337631

Over the past two years, the COVID-19 pandemic has been one of the most frequently and hotly debated social topics. Lockdowns and restrictions radically change the way of working and socializing due to social distancing and wearing masks;the ongoing pandemic impacts people's life and psychological health. Infection Rate Rt has been the main parameter used by national and local governments worldwide for describing the pandemic behavior synthetically. Rt was adopted to define containment policies (lockdowns, social distancing, intermittent regional strategies, etc.) that have affected social life. In the present paper, we propose an artificial Intelligence (AI) approach for the modeling of the COVID-19 Infection Rate Rt by exploiting the novel methodology of the Physics-Informed neural networks (PINNs) to compute the susceptible-infected-dead-recovered (SIDR) model. To test the accuracy of the neural network, we predicted the susceptible, infected, dead, and recovered on the next 30 days against the considered period. © 2023 IEEE.

关键词： COVID-19

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Optimized Deep neural Network for Accurate Detection of Malignant and Benign Brain Tumors 3

Optimized Deep Neural Network for Accurate Detection of Mali...

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3rd International conference on artificial Intelligence For Internet of Things, AiioT 2024

作者： Visumathi, J. Kalaivani, N. Hemanth, S.V. Balasubramanian, K. Karthikeyan, S. Amirthayogam, G. Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai600062 India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Mathematics Tamilnadu Chennai600062 India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad501401 India Kalasalingam Academy of Research and Education Deemed to be University Department of Computer Applications Tamil Nadu Krishnankoil626126 India Arunai Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvannamalai606603 India Hindustan Institute of Technology and Science Deemed To Be University Department of Information Technology Tamilnadu Chennai603103 India

ISBN: (纸本)9798350372120

The rapid development of medical technology has ushered in a new age of crucial medical records. A brain tumor is an aberrant mass of rapidly expanding brain cells, some of which have the potential to metastasize. Brain tumors are often diagnosed in clinical practice using magnetic resonance imaging (MRI) scans. These tumors appear as clusters of irregular cells that could endanger patients by invading nearby tissues. However, manually spotting and segmenting brain tumors via MRI scans present complex challenges and are prone to mistakes. Thus, analysis of such data has influenced the prognosis, monitoring, diagnosis, and treatment of tumor-related conditions. To overcome these issues, we propose an Optimized Deep neural Network (ODNN) method to accurately predict benign or malignant brain tumor diseases. Moreover, pre-processing uses an Adaptive Histogram Contrast Normalization (AHCN) technique to enhance the distribution and contrast of MRI image density. Afterward, features can be extracted using a Texture-Based Gray Level Co-Occurrence Matrix (TGLCM) method to separate the dark and bright parts of the image. Finally, a Machine Learning (ML) based ODNN method is employed to classify benign or malignant diseases. Utilizing sensitivity, specificity, recall, and accuracy metrics, the simulation results of the recommended ODNN approach are contrasted with those of preexisting approaches. © 2024 IEEE.

关键词： Deep neural networks

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DenseNet201 based Feature Extraction for Remote Sensing image Classification in Land Cover Monitoring 3

DenseNet201 based Feature Extraction for Remote Sensing Imag...

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3rd IEEE International conference on Mobile networks and Wireless Communications, ICMNWC 2023

作者： Aluvala, Srinivas Issa, Ali Ashoor Saranya, K. Almoussawi, Zainab Abed Shakir, Aboothar Mahmood School of Computer Science & Artificial Intelligence SR University Telangana Warangal India National University of Science and Technology Dhi Qar Iraq Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India College of Islamic Science Ahl Al Bayt University Karbala Iraq The Islamic University Najaf Iraq

ISBN: (纸本)9798350317022

The expedition of the classification of the remote sensing images based on their freights have a wide range of applications in the number of fields. The manual yield interpretation for the land cover monitoring is the challenging task, as well as it has consumed much time to classify the Land Cover (LC) image. To address these challenges, this research proposed a pre-trained DenseNet201 architecture of the Deep Learning (DL) technique-based feature extraction for the land cover monitoring. This research utilized the UCM dataset which contains 2100 aerial images clipped from aerial ortho imagery is used for evaluated the performance of the proposed DenseNet201 framework. In pre-processing stage, the noise removal technique is used for removing the unwanted noise in the collected dataset. After pre-processing, the DenseNet201is used for the feature extraction and Convolutional neural Network (CNN) is used for the classification of the LC aerial images. The proposed DenseNet201 framework achieves better results and attains the values of accuracy of 93.67%, precision of 96.28%, recall of 94.32% as well as F1-score of 94.88% when compared to the existing methods like Prototype based Memory Network (PM-Net), Pass Over (POEP) and Recurrent neural Network-Random Forest (RNN-RF). © 2023 IEEE.

关键词： Convolutional neural Network Deep Learning DenseNet201 Land Cover Monitoring Noise removal

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Rethinking and Improving Robustness of Convolutional neural networks: a Shapley Value-based Approach in Frequency Domain 36

Rethinking and Improving Robustness of Convolutional Neural ...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Chen, Yiting Ren, Qibing Yan, Junchi Shanghai Jiao Tong Univ Dept Comp Sci & Engn MoE Key Lab Artificial Intelligence Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781713871088

The existence of adversarial examples poses concerns for the robustness of convolutional neural networks (CNN), for which a popular hypothesis is about the frequency bias phenomenon: CNNs rely more on high-frequency components (HFC) for classification than humans, which causes the brittleness of CNNs. However, most previous works manually select and roughly divide the image frequency spectrum and conduct qualitative analysis. In this work, we introduce Shapley value, a metric of cooperative game theory, into the frequency domain and propose to quantify the positive (negative) impact of every frequency component of data on CNNs. Based on the Shapley value, we quantify the impact in a fine-grained way and show intriguing instance disparity. Statistically, we investigate adversarial training(AT) and the adversarial attack in the frequency domain. The observations motivate us to perform an in-depth analysis and lead to multiple novel hypotheses about i) the cause of adversarial robustness of the AT model;ii) the fairness problem of AT between different classes in the same dataset;iii) the attack bias on different frequency components. Finally, we propose a Shapley-value guided data augmentation technique for improving the robustness. Experimental results on image classification benchmarks show its effectiveness. The code for this paper is at https://***/Ytchen981/CSA

关键词： Convolutional neural networks

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