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检索条件"任意字段=3rd IAPR International Conference on Pattern Recognition in Bioinformatics"

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Research on Radar Operation pattern recognition Method Based on MLP Optimization 24

Research on Radar Operation Pattern Recognition Method Based...

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3rd international conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Li, Hao Zhang, Fuqun Feng, Mingyue Zhang, Jiapeng Huang, Linxuan Tian, Tian Air Force Early Warning Acad Wuhan Peoples R China Xidian Univ Xian Peoples R China PLA Unit 93277 Shenyang Peoples R China

ISBN: (纸本)9798400716959

This article delves into an innovative radar working pattern recognition algorithm based on multi-layer perceptron (MLP). Through carefully designed optimization algorithms, we systematically searched and determined the optimal MLP network structure to solve the radar operating pattern recognition problem. In a detailed simulation experiment, we carefully analyzed the effects of various network parameters, including the number of network layers, number of neurons, learning rate, and batch rate. The experimental results show that the MLP network can exhibit optimal performance when the number of layers is 5, the number of neurons is 512, the learning rate is 0.006, and the batch rate is 10. This discovery provides us with a highly promising solution to the problem of radar working pattern recognition.

关键词： Multifunctional radar Radar working mode recognition neural network Optimization algorithm multi-layer perceptron

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3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2024) 3

3rd International Conference on Visual Pattern Extraction an...

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3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2024

作者： Amelio, Alessia Cupar, Drahomira Sangiorgio, Valentino InGeo University "G. d'Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DIS University of Zadar Ulica Mihovila Pavlinovića 1 ZadarHR-23 000 Croatia

This document presents the 3nd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2024), a premier forum for presenting academic and industry papers on big data, data mining and knowledge discovery, machine learning and deep learning, 3D modeling and simulation (structural, thermal, functional and acoustic analysis), software engineering, and, more generally, aspects related to digital transition for visual pattern extraction, analysis, and recognition to preserve cultural heritage. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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A Brief Review of Unsupervised Learning Algorithms for Zero-Day Attacks in Intrusion Detection Systems 3

A Brief Review of Unsupervised Learning Algorithms for Zero-...

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3rd international conference on Computing and Machine Intelligence (ICMI)

作者： Oluwadare, Sunkanmi ElSayed, Zag Adekoya, Oluwaseun Univ Cincinnati Dept Informat Technol Cincinnati OH 45221 USA Univ Cincinnati Dept Mech Engn Cincinnati OH 45221 USA

ISBN: (纸本)9798350372977;9798350372984

This research paper presents a brief review of ten popular unsupervised algorithms widely utilized in pattern recognition publications. The algorithms are assessed based on their popularity, strengths, limitations, and resource requirements. Considering these factors, we propose two most-preferred algorithms suitable for adoption in IDS (Intrusion Detection Systems) to address the problems associated with Zero Day exploits or attacks. Our review of the surveyed algorithms facilitated the recommendation of specific algorithms that can enhance IDS capabilities in detecting and mitigating Zero-Day attacks and anomalous intrusion attempts. These algorithms leverage unsupervised learning techniques to overcome the limitations of traditional signature-based approaches. By incorporating these algorithms, IDS can better handle sophisticated and evolving attacks that often evade detection. In conclusion, this research provides valuable insights into the strengths, limitations, and resource requirements of popular unsupervised algorithms used in pattern recognition. It highlights the potential of adopting these algorithms in IDS systems to bolster their ability to detect and respond to Zero-Day attacks. By recommending the integration of these algorithms, we contribute to the development of intelligent IDS solutions that can adapt to dynamic threat landscapes.

关键词： Intrusion Detection Zero-Day Attacks Machine Learning Unsupervised Learning pattern recognition

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Joint iapr international Workshops on Statistical Techniques in pattern recognition and Structural and Syntactic pattern recognition, S+SSPR 2024

Joint IAPR International Workshops on Statistical Techniques...

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Joint iapr international Workshops on Statistical Techniques in pattern recognition and Structural and Syntactic pattern recognition, S+SSPR 2024

ISBN: (纸本)9783031805066

The proceedings contain 19 papers. The special focus in this conference is on Statistical Techniques in pattern recognition and Structural and Syntactic pattern recognition. The topics include: Community-Hop: Enhancing Node Classification Through Community Preference;spatio-Temporal Graph Neural Networks for Water Temperature Modeling;enhancing IoT Network Security with Graph Neural Networks for Node Anomaly Detection;LSTM Networks and Graph Neural Networks for Predicting Events of Hypoglycemia;evaluation Metrics in Saliency Maps Applied to Graph Regression;LESI-GNN: An Interpretable Graph Neural Network Based on Local Structures Embedding;mixture of Variational Graph Autoencoders;multimodality Calibration in 3D Multi Input-Multi Output Network for Dementia Diagnosis with Incomplete Acquisitions;the Effect of Class Distribution on Multi-Modal Medical Images Classification in Meta-Learning;from Semantic Segmentation of Natural Images to Medical Image Segmentation Using ViT-Based Architectures;Chronic Wound Assessment with Semi-supervised Hierarchical CNNs;ZIRACLE: Zero-Shot Composed Image Retrieval with Advanced Component-Level Emphasis;improving Object Detection Performance on Low-Quality Images Using Histogram Matching and Model Stacking;comparing Learning Methods to Enhance Decision-Making in Simulated Curling;an Empirical Characterization of the Stability of Isolation Forest Results;automated Classification of Android Games Using Word Embeddings;an Interesting Property of Random Forest Distances with Respect to the Curse of Dimensionality.

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Research on Electricity Consumption Characteristic Analysis and Load Identification Method Based on Symmetrized Dot pattern Information Fusion 3

Research on Electricity Consumption Characteristic Analysis ...

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3rd international conference on Energy and Electrical Power Systems (ICEEPS)

作者： Li, Le Liu, Zhiyuan Han, Hao Li, Chaoran Liu, Nan Zhu, Xiaoxun State Grid Beijing Daxing Elect Power Supply Co Sales Dept Beijing Peoples R China North China Elect Power Univ Dept Power Engn Baoding Peoples R China

ISBN: (纸本)9798350375145;9798350375138

Based on multi-label load identification, power load feature analysis and intelligent identification method based on Symmetrized Dot pattern (SDP) information fusion. This method improves the decomposition efficiency and solves the problem of large reconstruction error;proposes the method of SDP fusion feature analysis, extracts the SDP image feature of each modal information of the load, improves the completeness of the information;proposes the SDP image recognition method based on YOLOv5 and builds on load intelligent recognition model. Through experimental research, the load identification accuracy of this method reached 98%, which ensured the level of non-invasive load monitoring.

关键词： load identification complementary ensemble mode decomposition symmetrized dot pattern YOLOv5 fusion features

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Architectural Decorative Image pattern recognition in the Context of Iconography for Efficient Information Mining 3

Architectural Decorative Image Pattern Recognition in the Co...

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3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Qin, Yu College of Art and Design Guangzhou Panyu Polytechnic Guangzhou City511483 China

ISBN: (纸本)9798350396157

This study addresses the crucial task of architectural decorative image pattern recognition in the context of iconography, with an emphasis on efficient information mining. The proposed research work presents a novel algorithm that combines edge detection, ResNet50, and the triplet attention mechanism for image style analysis. The proposed methodology is organized into comprehensive sections, including a literature review that explores various studies in image pattern recognition. The proposed algorithm focuses on overcoming the challenges faced by existing deep learning methods in capturing aesthetic style features. It introduces a multifaceted self-supervised task and incorporates edge detection using the Canny method, followed by the application of ResNet50 with a triplet attention mechanism. The paper then focuses on the optimization of the network structure for the targeted task of architectural decorative image recognition, introducing a feature embedding encoder (FEE) to effectively handle multi-level structures. In the experimental phase, the proposed model is tested against traditional CNN and FCM models, demonstrating superior performance with recognition accuracy consistently ranging from 97% to 99%. The comparative analysis highlights the effectiveness of the proposed algorithm in achieving high accuracy, positioning it as a promising solution for image recognition tasks. © 2024 IEEE.

关键词： Deep learning

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VIPERC 2024 - Proceedings of the 3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, co-located with 2024 IEEE 20th international conference on Automation Science and Engineering, CASE 2024

VIPERC 2024 - Proceedings of the 3rd International Conferenc...

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3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2024

The proceedings contain 12 papers. The topics discussed include: digital solutions for cultural heritage: preservation, interpretation, and engagement in line with the Venice charter principles;a discrete event approach to the simulation of selective laser sintering 3D printing large scale production;virtual resurrection leveraging 3D modeling for digital preservation of an ancient educational institution;a new methodology to automatically detect cracks in existing RC bridges;advanced non-linear 3D FEM modeling of masonry structures for the preservation of cultural heritage;measurement of displacement of petroglyphs of Bangudae Terrace in Daegok-ri, Ulju, using edge and region extraction;integrating coastal cultural heritage, blue economy, and one health: a holistic framework for sustainable coastal management;and pattern recognition and neural networks for acoustic monitoring and conservation of the historic port of Ancona.

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Data Processing and pattern recognition for Electronic Nose 3

Data Processing and Pattern Recognition for Electronic Nose

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Raymond, David Chaudhri, Shivnath Shaikh, Kahkasha Datta Meghe Institute of Higher Education and Research Faculty of Enfineering and Technology Department of Computer Science and Medical Engineering Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Enfineering and Technology Depertment of Computer Science and Design Wardha India

ISBN: (纸本)9798331509675

Electronic nose (e-nose) technology has become a powerful tool for identifying and evaluating complex scents in a variety of contexts, such as environmental monitoring, medical diagnostics, and food quality control. This review study looks at how important pattern recognition and data processing are to improving e-nose system performance. This study covered challenges, including data complexity, cross-sensitivity and interference, sensor drift, and a lack of standardization. The article covers several future directions, such as the use of artificial intelligence (AI) and machine learning to advance pattern recognition and predictive analytics, integration with the Internet of Things (IoT) and big data for real-time monitoring, and advancements in sensor technology for improved sensitivity and *** research work is underway towards the development of low power flexible sensors, improved multi sensor fusion Methodologies, smart adaptive algorithms, Application for personalized health monitoring and diagnostics as well to make it green technologies. To move forward with emerging technologies and for e-nose technology to be applicable, it will have to overcome these weaknesses. © 2025 IEEE.

关键词： Electronic nose

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Analysis of English Pronunciation Transformation Text Model based on Neural Network pattern recognition Algorithm 3

Analysis of English Pronunciation Transformation Text Model ...

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3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Gong, Yuanyuan Jiang Xi Teachers College NO.1 Binjiang Rd. Xin Jiang Xin district Jiangxi province Yintan City335000 China

ISBN: (纸本)9798350396157

This study proposes an English pronunciation transformation text model based on a neural network pattern recognition algorithm, which aims to address various challenges in speech processing through in-depth analysis and innovative approaches. The paper first introduces the basic principles of pattern recognition, emphasizing the transition from feature space to decision space. Subsequently, a literature review highlights the transformative impact of convolutional neural networks (CNNs) in diverse domains, including financial markets, neuroimaging, environmental monitoring, and more. Methodologically, the paper describes a novel pattern recognition algorithm based on spiking neural networks. The algorithm overcomes the limitations of multi-layer network learning through the Spike Propagation (Spike Prop) algorithm, which introduces error back-propagation for effective training. Ensemble methods are used to improve the generalization ability of the system, with a focus on neural network integration. For pronunciation transformation in text models, sound features are extensively studied, including features such as short-term energy, short-term average amplitude, and Mel-frequency cepstrum coefficients. The experimental part, conducted in a Windows 10 environment using Python, TensorFlow, and GTX1660 GPU, demonstrates positive results across 30 experimental groups. The system shows significant improvements in character recognition accuracy, a reduction in missing and multiple word occurrences, a reduction in typos, and an increase in word accuracy. These results provide a solid foundation for the future research and practical applications in natural language processing. © 2024 IEEE.

关键词： Character recognition

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A Review on Non-overlapping pattern Matching and Mining 24

A Review on Non-overlapping Pattern Matching and Mining

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3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Li, Yongyi Shen, Cong Wu, Youxi Tianjin University of Technology Tianjin China School of Artificial Intelligence Hebei University of Technology Tianjin China

ISBN: (纸本)9798400707308

pattern matching under non-overlapping condition denotes to the identical character in any two occurrences that cannot occupy the position of the pattern twice in an equivalent manner. It has advantages in pattern mining as it considers both matching validity and efficiency. Non-overlapping pattern mining is viewed as a related operation that is conducive to excavating potential patterns under the non-overlapping conditions. However, since the flexible wildcard in non-overlapping pattern matching and mining is equipped with both the traditional wildcard properties and complicated constraint construction, it involves more challenges in the calculation process. Non-overlapping research has made significant promotion, including theory, algorithms, and applications in bioinformatics, finance, health care, cyber-security, etc. For brevity, state-of-the-art works focused on sequential and graphic pattern matching and mining, which deal with patterns in sequential form or image to handle the matching and mining straightforwardly. This review summarizes the development of non-overlapping pattern matching and mining in three aspects. 1) available algorithms and tools;2) challenges, including indices construction w.r.t. matching problem, reasonable pattern, and threshold selection, the derivative problem types based on sequential data, etc.;3) future development opportunities in the background of massive data, booming of artificial intelligence, and rapidly growing computational power. © 2024 Copyright held by the owner/author(s).

关键词： Network security

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