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

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Linguistic intellectual analysis methods for Ukrainian textual content processing 8

Linguistic intellectual analysis methods for Ukrainian textu...

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8th International conference on Computational Linguistics and Intelligent systems. Volume iv: Computational Linguistics Workshop, CLW-CoLInS 2024

作者： Vysotska, Victoria Lviv Polytechnic National University Stepan Bandera 12 Lviv79013 Ukraine

The peculiarities of the method of syntactic analysis of Ukrainian-language text content aimed at automatic detection of significant keywords of input texts are considered. The role and formal features of the parser in the process of identifying keywords of the content topic are defined, and the procedures of the proposed method are decomposed into 4 stages. Compared to well-known parsers, the proposed method provides self-improvement and self-learning of the automated keyword identification system due to the mechanism of identification of significant statistical parameters within the limits defined by the moderator. The experimental study confirmed the reliability of the method - for various methods of processing the primary text, the average coincidence of the lists of identified keywords with the authors varies in the range of 52.6-68.5%. The accuracy of matching keywords with the author's keywords ranges from 43.6 to 62.9%. The average match of meaningful keywords compared to all found by the system ranges from 38.9 to 75.8% according to the stages of article text analysis. The accuracy of matching keywords compared to all found by the system varies between 34.3-71.9% according to the stages of analysis of the texts of the articles. The reliability of scientific and practical results is confirmed by relevant materials on the implementation of dissertation research, as well as by comparing the obtained practical results on different samples of reliable input data. CLS was developed on the information resource http://*** using CMS Joomla! (for developing the e-framework of articles), PHP (for implementing text content processing methods), HTML (for implementing page markup), CSS (for describing page styles), and MySQL (for storing data and dictionaries). The experimental study confirmed the reliability of the method of determining Keywords - for different algorithms for processing the primary text, the average coincidence of the lists of identified key

关键词： Modeling languages

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A Novel Visual Communication Scenario Based on image processing algorithms 2

A Novel Visual Communication Scenario Based on Image Process...

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2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Yang, Jinhua Hou, Li Xijing University Shaanxi Xi'an710123 China

ISBN: (纸本)9798331505745

In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on image processing algorithms. The framework aims to optimize traditional key technologies such as the image generation, editing, style transfer and image compression. First, as the foundation of visual communication, this study proposes a generative adversarial network model based on text semantic information for image generation and editing. The model achieves stable image generation and efficient editing from a theoretical level through paired training of text and image pairs. Secondly, for image style transfer, this study designed an improved VGG19 convolutional neural network. At the same time, the adaptive instance normalization technology was combined to optimize the effect of style transfer. Finally, in terms of image compression, the study proposed an improved generative adversarial network (REVISED-GAN) model. This model can dynamically adjust the compression error based on structured information to improve image compression efficiency. Through comparative tests, the proposed image style transfer and image compression algorithms have shown excellent performance in terms of structural similarity, image quality and compression ratio. © 2025 IEEE.

关键词： Visual communication

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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...

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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...

引用

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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Enhancing Natural Language processing Capabilities in Geriatric Patient Care: An Annotation Scheme and Guidelines 29th

Enhancing Natural Language Processing Capabilities in Geriat...

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29th International conference on Applications of Natural Language to Information systems (NLDB)

作者： Guellil, Imane Andress, Salome Guthriel, Bruce Anand, Atul Zhang, Huayu Hasan, Abul Kalam Wu, Honghan Alex, Beatrice Univ Edinburgh Edinburgh Midlothian Scotland Univ Coll London UCL London England

ISBN: (纸本)9783031702419;9783031702426

Manual data annotation of information related to geriatric syndrome (GS), which includes various conditions affecting older adults, is required to train machine learning (ML) models to classify patients' health data for information that is otherwise poorly coded in structured electric health records. Such classification can be highly beneficial to support patients' healthcare in several ways, including early detection and diagnosis of geriatric syndrome, improving healthcare practices and patient empowerment and education (leading to improved adherence to treatment plans and better overall health outcomes). This paper presents an annotation scheme used for labelling information related to GS (i.e. falls, frailty, dementia, etc.) from electronic health records and its application to a regional, Scottish National Health Service (NHS) dataset. The annotation scheme also captures contextual information on GS. An initial pilot involving the annotation of 163 documents manually annotated by two annotators using this scheme yielded very encouraging inter-annotator-agreement (IAA) results (macro average f1-score = 0.750). This pilot was used to proceed to some fine-tuning of the annotation guidelines with input from clinicians. The resulting annotation scheme is now being used for annotating patient discharge summaries, radiology reports and referral letters provided by a Scottish regional trusted research environment. So far 719 documents have been annotated, resulting in 1,684 GS annotations. We have also begun the annotation of MIMIC iv data using the same annotation scheme. Our final aim is a cross-country comparison of GS-related concept detection using different algorithms and ML models as well as variations in language formulation by clinicians in the USA and Scotland/England.

关键词： Geriatric patients Natural Language processing Data annotation Automatic detection Annotation guideline

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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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