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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Research on the Methods of Data Mining based on the Edge Computing for the IoT

Research on the Methods of Data Mining based on the Edge Com...

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2023 IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2023

作者： Huang, Yan Zhang, Weimeng Xi'an Traffic Engineering Institute Shaanxi China Xian Technician College Shaanxi China

ISBN: (纸本)9798350398465

With the advent of the internet of everything, the number of network edge devices has increased rapidly, causing the data generated by such devices to increase rapidly. In order to enable the internet of things to provide users with information services and reliable decision-making more intelligently, valuable knowledge can be fully mined from these massive data. At present, the use of cloud computing models for centralized processing of big data generated by the internet of things has begun to show many drawbacks. Compared with cloud computing, edge computing migrates data computing or storage to the 'edge' of the network close to users, reducing the delay brought by network transmission. In addition, edge computing mode can better protect users' privacy data and reduce the risk of sensitive data privacy leakage. Edge computing has become a new paradigm to solve the needs of Internet of things and localized computing. The thesis studies the method of image data classification processing of the Internet, the edge agent is established by deploying the shallow part of the neural network on the edge device, and the cloud agent is established by deploying the deep part of the neural network on the cloud server device. The multi-agent is constructed by the way of edge cloud cooperation. The reasoning execution of convolutional neural network can complete the classification and exit on the local terminal for fast local inference;when additional processing is needed, the data can be transmitted to the cloud server for further processing by using the deep neural network in the cloud to improve the performance accuracy of the system. © 2023 IEEE.

关键词： Sensitive data

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Analysis of Plant Leaf Diseases Recognition using image processing with Machine Learning Techniques

Analysis of Plant Leaf Diseases Recognition using Image Proc...

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2022 IEEE International conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Vasudevan, N. Karthick, T. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9781665495295

Agriculture plays a foremost role in countries growth. The physical recognition of disease in the plant is more timeconsuming and necessity of expert labor is high. One of the most vital aspect in agriculture field is plant disease that reduces quality andquantity of agricultural crops. The machine learning approaches can easily recognize these diseases compared to physical method. So the machine learning techniques in addition toimage processing is used to identify diseased leaf. These methods will help in recognizing plant diseases thus increasing the yield of plants. This paper concentrates on the previous research work that used the characteristics of machine learning techniques in addition to image processing. It describes the various plant disease types, image processing methods and also various techniques of plant disease systems are analyzed with accuracy. Finally, the various challenges in the recognition of plant leaf diseases are identified and listed. © 2022 IEEE.

关键词： Learning algorithms

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Personal image Classifier Based Handy Pipe Defect Recognizer (HPD): Design and Test 7

Personal Image Classifier Based Handy Pipe Defect Recognizer...

引用

7th International conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Moshayedi, Ata Jahangir Khan, Amir Sohail Yang, Shuxin Zanjani, S. Mohammadali Jiangxi University of Science and Technology School of Information Engineering Ganzhou China Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran

ISBN: (纸本)9781665478571

Pipelines are known as a traditional solution for transporting various media such as gas, oil, and water. But pipes are always combined with the defects. Some of these defects are caused by the manufactory process, while some defects occur after installation due to the type of medium and environmental condition. Currently, various methods are used to detect these defects from industry to the site, but vision-based systems due to huge amount of data that can capture machine learning development algorithms have more demand. In this research paper Personal image Classifier (PIC) is used as the machine learning method combine with the MIT APP inventor to make the handy system called Handy Pipe Defect assistant recognizer (HPD) to address the defect name and help the user to investigate and somehow correct their process of product in industry (as the production stage) or the possible change in usage place (as the maintenance stage). The HPD designed based on the hypothesis of having the handy, portable and available tool for the operator in industries as the quality control and site engineers. The HPD can classify the pipe defects, especially the welding type by taking the picture of the defect and give the user feedback. The HPD focuses on the welding defect with the manufactory source over 150 image number trained and tested with the real 28 sample image. The design process and training model for HPD described and result based on sample images from manufactory and real situation are shown the 100% defect name detection of the defect along with image quality affected by environment light and similarity between the defects. © 2022 IEEE.

关键词： Defects

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PRIVATE LEARNING VIA KNOWLEDGE TRANSFER WITH HIGH-DIMENSIONAL TARGETS 47

PRIVATE LEARNING VIA KNOWLEDGE TRANSFER WITH HIGH-DIMENSIONA...

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47th IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Fay, Dominik Sjolund, Jens Oechtering, Tobias J. KTH Royal Inst Technol Dept Intelligent Syst Stockholm Sweden Uppsala Univ Dept Informat Technol Uppsala Sweden Elekta Instrument Stockholm Sweden

ISBN: (纸本)9781665405409

Preventing unintentional leakage of information about the training set has high relevance for many machine learning tasks, such as medical image segmentation. While differential privacy (DP) offers mathematically rigorous protection, the high output dimensionality of segmentation tasks prevents the direct application of state-of-the-art algorithms such as Private Aggregation of Teacher Ensembles (PATE). In order to alleviate this problem, we propose to learn dimensionality-reducing transformations to map the prediction target into a bounded lower-dimensional space to reduce the required noise level during the aggregation stage. To this end, we assess the suitability of principal component analysis (PCA) and autoencoders. We conclude that autoencoders are an effective means to reduce the noise in the target variables.

关键词： Differential Privacy Machine Learning Knowledge Transfer image Segmentation Compression

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A Novel Pixel Block-Based Data Hiding Method for Improved Payload Capacity Using 2-Dimensional Block Segmentation 16

A Novel Pixel Block-Based Data Hiding Method for Improved Pa...

引用

16th IEEE International conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Tahara, Enrico Almer Dlayla, Adifa Widyadhani Chandra De La Croix, Ntivuguruzwa Jean Ahmad, Tohari Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia College of Science and Technology University of Rwanda Kigali Rwanda

ISBN: (纸本)9798331505264

Steganography is the practice of hiding information by embedding it as secret data within various types of digital media to strengthen security. Numerous algorithms have been proposed for image steganography with a common purpose of minimizing the perceptibility of the secret data existence to the human vision system. The most used in images steganography is the Least Significant Bit (LSB) technique;however, it is vulnerable to steganalysis and lacks robust verification processes. To address this, in this article, a new steganographic approach is proposed to optimize the image's pixels use for embedding the secret data. The proposed method, considering adjacent pixels, introduces a new data-hiding technique that enhances payload capacity by using two-dimensional block segmentation to embed information within pixel blocks. Experimental results show that the method outperforms previous studies reviewed in this work, achieving a Peak Signal-to-Noise Ratio (PSNR) between 49.7054 and 73.0848 decibels (dB) and Structural Similarity Index Measure (SSIM) between 0.9977 to 1.0000 for the selected difference range between -4 and 4, indicating high-quality stego images. © 2024 IEEE.

关键词： data hiding digital images processing information security steganalysis steganography

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Identification and Classification of Intestinal Parasitic Eggs in Animals Through Microscopic image Analysis 3rd

Identification and Classification of Intestinal Parasitic Eg...

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3rd International conference on Emergent Converging Technologies and Biomedical systems, ETBS 2023

作者： Mishra, Ketan Kavitha, C. Kannan, Devi Department of Computer Science and Engineering Atria Institute of Technology Bangalore India

ISBN: (纸本)9789819986453

Intestinal parasitic infections in animals can cause a range of symptoms, including diarrhea, weight loss, anemia, and malnutrition. This project aims to classify parasitic eggs belonging to the Monezia and Strongyles species using microscopic images from a veterinary hospital. The dataset consisted of stool samples from animals infected with these parasites. image processing and machine learning techniques, including data preprocessing, feature extraction, and feature selection are used to detect and classify the eggs. The goal was to accurately classify the eggs into either the Monezia or Strongyles species. To achieve the goal, two classification algorithms: K-Nearest Neighbors (KNN) and Decision Tree have been used. Using these algorithms, the model achieved accuracies of 91% and 60%, respectively. However, by incorporating Alyuda NeuroIntelligence for classification, a significantly improved accuracy of 92% was obtained. The results demonstrate that the use of Alyuda NeuroIntelligence in conjunction with machine learning techniques can greatly enhance the accuracy of parasitic egg classification. This approach can aid in the diagnosis and treatment of animal infections, improving animal health and well-being. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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processing of Analyzed Textual Information to a Meaningful Structure

Processing of Analyzed Textual Information to a Meaningful S...

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International Russian Automation conference, RusAutoCon 2022

作者： Semenov, R.E. MIREA – Russian Technological University 78 Vernadskogo Ave. Moscow119454 Russia

ISBN: (纸本)9783031223105

The results of studies of the morphological analysis of the text are demonstrated. Application of the technology of automatic processing of Russian-language texts to determine the parts of speech presented in the digital image. Definition of significant and non-essential information in the text. Identification and construction of a concept for implementation into an intelligent automated text recognition and analysis system. The correspondence of each word of the text to a certain part of speech according to a predetermined structure. The automatic selection of the necessary algorithms for bringing the text to the final form is carried out to get a real-time response. The designed system uses a certain structure, according to which, at the same time, text analysis is carried out to identify the main idea of a group of proposals, and presents the information received in an ordered form. The system can be upgraded and receive constant support in the form of updates to the relevance databases and calculation of criteria for weight characteristics. It can be implemented in the systems of Internet resources, used as an independent project supporting autonomous operation, or for implementation in intelligent systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intelligent systems

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image Based Plant Disease Classification with Cause and Severity Analysis and Remedial Action

Image Based Plant Disease Classification with Cause and Seve...

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2023 International conference on Intelligent systems for Communication, IoT and Security, ICISCoIS 2023

作者： Rani, Gopika N Divya Darshini, R. Monirhithikka, S.P. Samsitha Banu, S. Saravu Miththira, V.C. Subashini, B. PSG College of Technology Department of CSE Coimbatore India

ISBN: (纸本)9798350335835

Unlike diseases of the human body, plant diseases don't camouflage themselves within the body of the crop. The leaves reflect the infection with a change in color, shape, texture or a combination of the three. Hence, predictive modelling of the leaf images dataset using machine learning can help detect the disease. Mostly the image processing algorithms is used for predicting the diseases. The Random forest algorithm is used for color segmentation and Convolutional neural networks mainly focuses on disease classification. Vision based plant infection showed efficient results and promising performance. The features are extracted such that they are feasible for a severity analysis. The percentage of severity is extracted from the percentage which is 94.6% of the classification accuracy after which suitable remedial actions are suggested. This offers a comprehensive strategy for addressing the threat of crop diseases that lower agricultural output and waste. (Choice of crop depends on accessibility of dataset). © 2023 IEEE.

关键词： Convolutional neural networks

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AI-SMLA: An Artificial Intelligence based Smart Machine Learning Algorithm for Complex image Segmentation Issues in Vertex image processing 3

AI-SMLA: An Artificial Intelligence based Smart Machine Lear...

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3rd International conference on Electronics and Sustainable Communication systems, ICESC 2022

作者： Reddy, R. Pradeep Kumar Kiran, S. Kumar, A. Ashok Y.S.R Engineering College of Yvu Andhra Pradesh Proddatur India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Today's machine learning is considered as one of the artificial intelligence technologies used in many ways. Its functions are very accurate, from receiving the given input data to calculating, measuring and outputting it. It is used to calculate a valid result without user intervention. In that order the applications of this machine learning method in digital image processing technology are constantly increasing. This technology is used for everything from overcoming facial identities to calculating the various photo identities of mobile devices currently in use. In this paper a smart machine learning based algorithm has been designed to effectively handle vertex image processing environment and complex image segmentation functions. Its special feature is that it examines the given photos based on its dead pixels and calculates the correct results. There will be no human intervention in this system. The proposed smart algorithm was compared with some existing algorithms. © 2022 IEEE.

关键词： image segmentation

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FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide image Classification 38

FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Fu, Kexue Luo, xiaoyuan Qu, Linhao Wang, Shuo xiong, Ying Maglogiannis, Ilias Gao, Longxiang Wang, Manning Qilu University of Technology Shandong Academy of Sciences Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China Fudan University China University of Piraeus Greece Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST. © 2024 Neural information processing systems foundation. All rights reserved.

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