Convolutional neural的問題，透過圖書和論文來找解法和答案更準確安心。我們找到下列包括價格和評價等資訊懶人包

Q: 為了解決Convolutional neural的問題，作者何亞恩這樣論述

目錄誌謝 i摘要 iiAbstract iii目錄 v圖目錄 viii表目錄 xi第一章 緒論 11.1研究動機 11.2研究目的 21.3研究架構 2第二章 研究背景 3

書中字有黃金屋走在汽車革命的路上論文書籍站 Convolutional neural

Convolutional neural的問題，我們搜遍了碩博士論文和台灣出版的書籍，推薦陳昭明寫的開發者傳授PyTorch秘笈和的 Soft Computing and Signal Processing: Proceedings of 4th ICSCSP 2021都可以從中找到所需的評價。

另外網站Efficient Two-Stage Max-Pooling Engines for an FPGA- ...也說明：How to cite: Hong, E.; Choi, K.; Joo, A.J. Efficient Two-Stage Max-Pooling Engines for an FPGA-based Convolutional Neural Network.

這兩本書分別來自深智數位和所出版。

國立中正大學電機工程研究所余松年所指導何亞恩的一個使用智慧型手機實現深度學習心電圖分類的心臟疾病辨識系統（2022），提出Convolutional neural關鍵因素是什麼，來自於智慧型手機即時辨識、心電圖、深度學習、多卷積核模型、注意力機制。

而第二篇論文國立臺北科技大學電子工程系曾柏軒所指導林聖曄的考量CSI相位偏移偵測與校正之室內定位演算法（2021），提出因為有深度學習、通道狀態資訊、相位偏移、訊號強度、室內定位的重點而找出了 Convolutional neural的解答。

最後網站Convolutional Neural Network (CNN)則補充：A Convolutional Neural Network is a class of artificial neural network that uses convolutional layers to filter inputs for useful information.

Convolutional neural

開發者傳授PyTorch秘笈
為了解決Convolutional neural的問題，作者林聖曄 這樣論述
想知道Convolutional neural更多一定要看下面主題

開發者傳授PyTorch秘笈

為了解決Convolutional neural的問題，作者陳昭明這樣論述：

～ 2022 開發者唯一指定 PyTorch 祕笈！～深度學習【必備數學與統計原理】✕【圖表說明】✕【PyTorch 實際應用】　　★ 作者品質保證 ★ 　　經過眾多專家與學者試閱昭明老師著作皆給【5 顆星】滿分評價！　　～從基礎理解到 PyTorch 獨立開發，一氣呵成～　　本書專為 AI 開發者奠定扎實基礎，從數學統計 ► 自動微分 ► 梯度下降 ► 神經層，由淺入深介紹深度學習的原理，並透過大量 PyTorch 框架應用實作各種演算法：　　● CNN (卷積神經網路) 　　● YOLO (物件偵測) 　　● GAN (生成對抗網路) 　　● DeepFake (深

度偽造) 　　● OCR (光學文字辨識) 　　● ANPR (車牌辨識) 　　● ASR (自動語音辨識) 　　● BERT / Transformer 　　● 臉部辨識　　● Knowledge Graph (知識圖譜) 　　● NLP (自然語言處理) 　　● ChatBot 　　● RL (強化學習) 　　● XAI (可解釋的 AI) 本書特色　　入門深度學習、實作各種演算法最佳教材！　　★以【統計／數學】為出發點，介紹深度學習必備的數理基礎　　★以【程式設計取代定理證明】，讓離開校園已久的在職者不會看到一堆數學符號就心生恐懼，縮短學習歷程，增進學習樂趣　　★摒棄長篇大

論，輔以【大量圖表說明】介紹各種演算法　　★【完整的範例程式】及【各種演算法的延伸應用】！直接可在實際場域應用。　　★介紹日益普及的【演算法與相關套件】的使用　　★介紹 PyTorch 最新版本功能　　★與另一本姊妹作《深度學習–最佳入門邁向 AI 專題實戰》搭配，可同時學會 PyTorch 與 TensorFlow

一個使用智慧型手機實現深度學習心電圖分類的心臟疾病辨識系統

為了解決Convolutional neural的問題，作者何亞恩這樣論述：

目錄誌謝 i摘要 iiAbstract iii目錄 v圖目錄 viii表目錄 xi第一章緒論 11.1研究動機 11.2研究目的 21.3研究架構 2第二章研究背景 32.1心電圖與疾病介紹 32.1.1心臟導程 32.1.2心臟疾病介紹 52.2Android系統 102.2.1 Android的基礎 102.2.2 Android系統框架 102.3相關文獻探討 11第三章研究方法 173.1資料庫介紹 173.2訊號前處理 193.2.1小波濾波 193.2.2訊號正規化 213.3一維訊號轉二維影像 213.3.1手機螢幕上

繪製圖形 213.3.2影像儲存於智慧型手機 233.3.3資料擴增Data Augmentation 243.4深度學習架構 253.4.1多卷積核架構 253.4.2注意力模型 283.4.2.1通道注意力模組Channel attention 293.4.2.2空間注意力模組Spatial attention 303.4.2.3激活函數Activation function 303.5損失函數Loss function 313.6交叉驗證Cross validation 323.7優化訓練模型 333.8移動端應用 343.9硬體設備、軟體環境與開發環境 36

3.9.1硬體設備 363.9.2軟體環境與開發環境 37第四章研究結果與討論 3834.1評估指標 384.2訓練參數設定 404.3實驗結果 414.3.1深度學習模型之辨識結果 414.3.1.1比較資料擴增前後之分類結果 414.3.1.2不同模型架構之分類結果 424.3.2智慧型手機應用結果 464.4相關文獻比較 48第五章結論與未來展望 525.1結論 525.2未來展望 53參考文獻 54

Soft Computing and Signal Processing: Proceedings of 4th ICSCSP 2021

為了解決Convolutional neural的問題，作者這樣論述：

Data Preprocessing and finding optimal value of K for KNN Model.- Prediction of Cardiac Diseases using Machine Learning Algorithms.- A Comprehensive Approach to Misinformation Analysis and Detection of Low-Credibility News.- Evaluation of Machine Learning Algorithms for Electroencephalography based

Epileptic Seizure State Recognition.- Lung Disease Detection and Classification from Chest X-Ray Images using Adaptive Segmentation and Deep Learning.- A Quantitative analysis for Breast Cancer prediction using Artificial Neural Network and Support Vector Machine.- Tracking Misleading News of COVID-

19 within Social Media.- Energy aware Multi-chain PEGASIS in WSN: A Q-Learning Approach.- TEXTLYTIC: Automatic Project Report Summarization using NLP Techniques.- Management of Digital Evidence for Cybercrime Investigation- A Review.- Realtime Human Pose Detection and Recognition using Mediapipe.- C

harge the Missing Data with Synthesized Data by using SN-Sync technique.- Discovery of Popular Languages from GitHub Repository: A Data Mining.- Performance Analysis of Flower Pollination Algorithms using Statistical Methods: An Overview.- Counterfactual causal analysis on structured data.- Crime An

alysis Using Machine Learning.- Multi-Model Neural Style Transfer for Audio and Image (MMNST).- Feature Extraction from Radiographic Skin Cancer Data using LRCS.- Shared Filtering-Based Advice Of Online Group Voting.- Mining Challenger From Bulk Preprocessing Datasets.- Prioritized Load Balancer for

minimization of VM and Data Transfer Cost in Cloud Computing.- Smart Underground Drainage Management System using Internet of Things.- Iot Based System For Health Monitoring Of Arrhythmia Patients Using Machine Learning Classification Techniques.- EHR-Sec: A Blockchain based Security System for Ele

ctronic Health.- End to End Speaker Verication For Short Utterances.- A Comprehensive Analysis on Multi-class Imbalanced Bigdata Classification.- Efficient Recommender System for Kid’s Hobby using Machine Learning.- Programming Associative Memories.- Novel Associative Memories based on Spherical Sep

erability.- An Intelligent Fog-IoT based Disease Diagnosis Healthcare System.- Pre-processing of linguistic divergence in English- Marathi language pair in Machine Translation.- Deep Learning Approach for Image Based Plant Species Classification.- Inventory, Storage and Routing Optimization with Hom

ogeneous Fleet in the Secondary Distribution Network Using a Hybrid VRP, Clustering and MIP Approach.- Evaluation and Comparison of various static and dynamic load balancing strategies used in cloud computing.- Dielectric Resonator Antenna with Hollow Cylinder for Wide Bandwidth.- Recent Techniques

in Image Retrieval: A Comprehensive Survey.- Medical Image Fusion Based On Energy Attribute and PA-PCNN in NSST Domain.- Electrical Shift and Linear Trend artifacts removal from single channel EEG using SWT-GSTV model.- Forecasting Hourly Electrical Energy output of a Power plant using parametric mo

dels.- Cataract detection using Deep Convolutional Neural Networks.- Comparative Analysis of Body Biasing Techniques for Digital Integrated Circuits.- Optical Mark Recognition with Facial Recognition System.- Evaluation of Antenna Control System for Tracking Remote Sensing Satellites.- Face Recognit

ion using Cascading of HOG and LBP Feature Extraction.- Design of wideband patch Antenna using metamaterial and Dielectric resonator Structures.- Call Admission Control for Interactive Multimedia Applications in 4G Networks.- AI-based Pro-Mode in Smartphone Photography.- A ML-Based Model to Quantify

Ambient Air Pollutant.- Multimodal biometric system using Undecimated Dual-Tree Complex Wavelet Transform.- Design of Modified Dual - Coupled Linear Congruential Generator Method Architecture for Pseudorandom Bit Generation.- Performance Analysis of PAPR and BER in FBMC-OQAM With Low-complexity Usi

ng Modified Fast Convolution.- Sign Language Recognition using Convolution Neural Network.- Key Bas

考量CSI相位偏移偵測與校正之室內定位演算法

為了解決Convolutional neural的問題，作者林聖曄這樣論述：

通道狀態資訊(Channel StateInformation, CSI)可用於室內定位，起到監視人們生活的作用。它使用Wi-Fi多通道訊號，不受光源、聲音干擾，並具備優異的角度、距離感測能力。本文研究中心頻率5.22GHz，頻寬20MHz，56子載波的CSI量測值。在9個不同位置，收集實驗室中57個位置傳送的CSI訊號。在本研究中，我們發現隨機π跳動問題，使得每根天線的相位可能出現±π偏移，這主要是硬件的鎖相環造成的。由於相位的不同，三根天線之間有四種可能的相位差組合。為了估計使用者的位置，我們把CSI量測值轉化為熱力圖作為深度學習網路模型的輸入，來解決本問題。為了克服多路徑效應，經由多訊

號分類(Multiple Signal Classification, MUSIC)計算出到達角(Angle of Arrival, AoA)與飛行時間(Time of Flight, ToF)的熱力圖。然而，由於ToF量測平台存在延時偏移，在本研究中，把熱力圖最大值對應的距離平移到信號強度(Received Signal Strength Indicator, RSSI)對應的距離，再以接入點(access point, AP)的位置為中心，朝向為AoA參考方向，把極坐標轉為直角坐標。由於每根天線可能有π相位偏移，三根天線之間有四種相位組合，所以每筆資料的Rx有四張熱力圖。本文以卷積神經網路

(Convolutional Neural Network, CNN)、殘差神經網路(Residual Neural Network, ResNet)等神經網絡組成的深度學習網路(Deep Learning based wireless localization, DLoc)，用訓練出的模型對不同位置的預測準確度，來探究AP數量、相位校正等因素對深度學習效能的影響，並與深度卷積網路(Deep Neural Network, DNN)和SpotFi的方法在校正π相位偏移的效能上作對比。

#1.A Comprehensive Guide to Convolutional Neural Networks

Convolutional neural networks (CNN) are particularly well-suited for image classification and object detection. Learn the basics of CNNs and ... 於 www.v7labs.com
#2.Convolutional Neural Networks, Explained

A Convolutional Neural Network, also known as CNN or ConvNet, is a class of neural networks that specializes in processing data that has a grid-like ... 於 towardsdatascience.com
#3.Efficient Two-Stage Max-Pooling Engines for an FPGA- ...

How to cite: Hong, E.; Choi, K.; Joo, A.J. Efficient Two-Stage Max-Pooling Engines for an FPGA-based Convolutional Neural Network. 於 www.preprints.org
#4.Convolutional Neural Network (CNN)

A Convolutional Neural Network is a class of artificial neural network that uses convolutional layers to filter inputs for useful information. 於 developer.nvidia.com
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#6.The History of Convolutional Neural Networks - Glass Box

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#7.Using Convolutional Neural Networks for Image Recognition

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#11.(PDF) A Review of Convolutional Neural Networks

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Data Science, Machine Learning, Deep Learning, Data Analytics, Tutorials, AI, Convolutional neural network, CNN, Applications, Examples. 於 vitalflux.com
#15.Deep Learning – Introduction to Convolutional Neural Networks

Convolutional Neural Networks – Convolutional neural networks (CNNs) are motivated by the architecture of the cerebral cortex. Which is a higher ... 於 vinodsblog.com
#16.Convolutional Neural Network Language Models

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#20.CS 230 - Convolutional Neural Networks Cheatsheet

Architecture of a traditional CNN Convolutional neural networks, ... The convolution layer and the pooling layer can be fine-tuned with respect to ... 於 stanford.edu
#21.Convolutional Neural Network Definition

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What Are Convolutional Neural Networks (CNNs)?. A Convolutional Neural Network (CNN) is a type of deep learning algorithm specifically designed for image ... 於 datagen.tech
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#26.Convolutional Neural Networks (CNNs) and Layer Types

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A convolutional neural network (CNN or convnet) is a subset of machine learning. It is one of the various types of artificial neural networks which are used ... 於 www.techtarget.com
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#31.Convolutional neural networks for vision neuroscience

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#35.Convolutional Neural Networks for Dummies

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#36.Convolutional Neural Networks & Computer Vision

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#37.Convolutional neural network - Wiki - Golden

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Convolutional Neural Networks relates to DevelopmentIT & Software. 279,462 learners. Featured course. Deep Learning: Convolutional Neural Networks in Python. 於 www.udemy.com
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1 Motivation for Convolutional Neural Networks · 2 Image Processing and Convolution · 3 Layers · 4 Convolutional Layer · 5 Weblinks. 於 collab.dvb.bayern
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Convolutional neural networks are distinguished from other neural networks by their superior performance with image, speech, or audio signal inputs. They have ... 於 www.ibm.com
#50.ImageNet Classification with Deep Convolutional Neural ...

ImageNet Classification with Deep Convolutional. Neural Networks. Alex Krizhevsky. University of Toronto [email protected]. Ilya Sutskever. 於 proceedings.neurips.cc
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#55.8. Modern Convolutional Neural Networks

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Bigjpg - Image Super-Resolution for Anime-style artworks using the Deep Convolutional Neural Networks without quality loss. Photos are also supported. 於 bigjpg.com
#57.7 Applications of Convolutional Neural Networks - FWS

This guide on the convolutional neural networks talks about how the 3-dimensional CNN replicates the simple and complex cells of the human brain, ... 於 www.flatworldsolutions.com
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Convolutional Neural Network (CNN) is a deep learning approach that is widely used for solving complex problems. It overcomes the limitations of traditional ... 於 www.sciencedirect.com
#59.Theoretical Understanding of Convolutional Neural Network

Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, ... 於 www.mdpi.com
#60.Convolutional neural network

Convolutional Neural Networks are deep learning models designed specifically for processing & analyzing visual data such as images & videos. 於 www.engati.com
#61.Convolutional Neural Networks (PyTorch).ipynb at master ...

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Convolutional Neural Networks. This course is part of Deep Learning Specialization. Andrew Ng. Kian Katanforoosh. Younes Bensouda Mourri. 於 www.coursera.org
#63.Convolutional Neural network 卷積神經網路(part1)

filter 是特殊的”neuron”. convolution 跟fully connected 有什麼關係。其實convolution就是一個neural network。 convolution這件事情，其實就是fully ... 於 wenwu53.com
#64.CNNs for Image Recognition: Pros, Cons, and Alternatives

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Convolutional neural networks are compared with other existing techniques, and the advantages and disadvantages of using CNN in agriculture are ... 於 www.cambridge.org
#67.Convolutional neural network: a review of models ...

The name “convolutional neural system” shows that the system utilizes a mathematical linear operation called convolution, instead of general ... 於 link.springer.com
#68.Deep Learning Book - Chapter 9: Convolutional Networks

CONVOLUTIONAL NETWORKS. 9.5 Variants of the Basic Convolution Function. When discussing convolution in the context of neural networks, we usually do. 於 www.deeplearningbook.org
#69.Convolutional Neural Network (CNN) | TensorFlow Core

Convolutional Neural Network (CNN) · On this page · Import TensorFlow · Download and prepare the CIFAR10 dataset · Verify the data · Create the ... 於 www.tensorflow.org
#70.What Is a Convolutional Neural Network?

Sometimes called ConvNets or CNNs, convolutional neural networks are a class of deep neural networks used in deep learning and machine learning. 於 www.wgu.edu
#71.What are convolutional neural networks (CNN)?

Convolutional neural networks are composed of multiple layers of artificial neurons. Artificial neurons, a rough imitation of their biological ... 於 bdtechtalks.com
#72.A Comprehensive Guide to Convolutional Neural Networks

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable ... 於 saturncloud.io
#73.Pathology Outlines - Convolutional neural networks

Convolutional neural networks · Convolution or pooling operations are carried out on information from 1 layer and the results are passed on to a ... 於 www.pathologyoutlines.com
#74.Convolutional Neural Network Tutorial [Update]

A convolutional neural network is a feed-forward neural network that is generally used to analyze visual images by processing data with grid- ... 於 www.simplilearn.com
#75.Build convolutional neural networks (CNNs) to enhance ...

Build convolutional neural networks (CNNs) to enhance computer vision ... What you'll build. Layers to enhance your neural network ... 於 developers.google.com
#76.Understanding Convolutional Neural Networks (CNNs)

A convolutional block is a general term used to describe a sequence of layers in a CNN that are often repeatedly used in the feature extractor. The feature ... 於 learnopencv.com
#77.What is the difference between a convolutional neural ...

TLDR: The convolutional-neural-network is a subclass of neural-networks which have at least one convolution layer. 於 ai.stackexchange.com
#78.Recent advances in convolutional neural networks

Convolutional Neural Network (CNN) is a well-known deep learning architecture inspired by the natural visual perception mechanism of the living creatures. 於 www.sciencedirect.com
#79.Convolutional Neural Networks (CNN) Introduction

While an artificial neural network could learn to recognize a cat on the left, it would not recognize the same cat if it appeared on the ... 於 algobeans.com
#80.卷積神經網路的運作原理 - 選擇一種語言

原文：How do Convolutional Neural Networks work? ... 學習又有什麼重大突破時，這些進展十有八九都和卷積神經網路（Convolutional Neural Networks，CNN）有關。於 brohrer.mcknote.com
#81.Learn Convolutional Neural Network with Online Courses ...

What is a Convolutional Neural Network? ConvNet or CNN is a class of deep learning neural networks. They're used effectively in image recognition and ... 於 www.edx.org
#82.Convolutional Neural Networks (CNN) and Deep Learning

Learn about convolutional neural networks, their connection to deep learning and computer vision, and their value in the future. 於 www.intel.com
#83.Convolutional Neural Network | Deep Learning

Convolution Neural Network has input layer, output layer, many hidden layers and millions of parameters that have the ability to learn ... 於 developersbreach.com
#84.[資料分析&機器學習] 第5.1講: 卷積神經網絡介紹 ...

卷積神經網絡(Convolutional Neural Network)簡稱CNN，CNN是所有深度學習課程、書籍必教的模型(Model)，CNN在影像識別方面的威力非常強大，許多影樣辨識的模型也都是 ... 於 medium.com
#85.卷積神經網路(Convolutional Neural , CNN)

基於上面幾個理由便衍伸出Convolutional Neural Network ( CNN ) 卷積神經網路來進行圖像辨識。整個CNN 結構主要分成幾個部分: 卷積層( Convolution layer )、池化 ... 於 hackmd.io
#86.Convolutional Neural Networks: 1998-2023 Overview

A convolutional neural network consists of an input layer, an output layer, and several hidden layers. In this article, we will cover: Genesis ... 於 www.superannotate.com
#87.How do Convolutional Neural Networks work? - Brandon Rohrer

Nine times out of ten, when you hear about deep learning breaking a new technological barrier, Convolutional Neural Networks are involved. 於 e2eml.school
#88.An introduction to convolutional neural networks

One of the most impressive forms of ANN architecture is that of the Convolutional Neural Network (CNN). CNNs are primarily used to solve ... 於 arxiv.org
#89.An Overview of Convolutional Neural Networks

Method Year Papers Darknet‑53 · YOLOv3: An Incremental Improvement 2018 230 GoogLeNet · Going Deeper with Convolutions 2014 131 MobileNetV3 · Searching for MobileNetV3 2019 60 於 paperswithcode.com
#90.What is a convolutional neural network (CNN)? [Video]

Whereas a convolutional neural network is a feedforward network that filters spatial data, a recurrent neural network, as the name implies, ... 於 hub.packtpub.com
#91.Learning Convolutional Neural Networks for Graphs

have nodes and edges with multiple discrete and continuous attributes and may have multiple types of edges. Similar to convolutional neural network for images, ... 於 proceedings.mlr.press
#92.Convolutional Neural Networks in Python with Keras

Convolutional Neural Network: Introduction. By now, you might already know about machine learning and deep learning, a computer science branch that studies the ... 於 www.datacamp.com
#93.Fully Connected Layer vs Convolutional Layer: Explained

A fully connected layer refers to a neural network in which each input node is connected to each output node. In a convolutional layer, ... 於 builtin.com
#94.Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) ... 於 en.wikipedia.org
#95.Convolutional Neural Networks for Medical Image Processing ...

2.1 Convolutional Neural Networks Convolutional Neural Networks (CNN) are widely used learning models with three features: parameter sharing, ... 於 books.google.com.tw
#96.Using Deep Learning Models / Convolutional Neural Networks

With deep learning / convolutional neural networks complex problems can be solved and objects in images recognized. This chapter briefly outlines the ... 於 docs.ecognition.com
#97.What is a Convolutional Neural Network?

Convolutional neural networks are composed of a series of interconnected layers of artificial “neurons”. These artificial neurons are ... 於 blog.roboflow.com
#98.Convolutional neural networks: an overview and application in ...

Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting ... 於 insightsimaging.springeropen.com

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