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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3327 条记录，以下是611-620 订阅

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Efficient acne classification based on Resnet18 3

Efficient acne classification based on Resnet18

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Cheng, Shi Sun, Mingze Huang, Zhongyi Wang, Yushan Fu, Jiaxuan School of Engineering University of Manchester ManchesterM13 9PL United Kingdom School of Computing Science University of Glasgow GlasgowG12 8RZ United Kingdom School of Data Science and Big Data Nanjing Institute of Technology Nanjing210000 China The Affiliated High School Renmin University of China Beijing100083 China International Department Nanjing Jinling High School Nanjing21000 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

In this study, we investigated the mechanical performance of lightweight composite slabs, specifically focusing on the application of an integrated face recognition and acne classification pipeline. We propose a comprehensive approach that commences with facial detection, proceeds to the acne region localization, and concludes with acne classification utilizing ResNet18 architecture. The aim is to develop a highly accurate and efficient system for acne classification, particularly beneficial for remote patients or those seeking a convenient self-assessment tool. Our dataset, though limited in size and diversity, is iteratively refined and augmented to mitigate data scarcity issues. Through experimental evaluations, we demonstrate that the combination of ResNet18 and Support Vector machine (SVM) achieves an accuracy rate of 96%, significantly outperforming traditional manual feature extraction methods. Additionally, visualization techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are employed to gain insights into the data distribution and the effectiveness of automatically extracted features. This study highlights the potential of deep learning for acne detection and underscores the importance of dataset refinement for enhancing model performance. © 2025 SPIE.

关键词： Support vector machines

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2022 3rd international conference on Big data Analytics and Practices, IBDAP 2022

2022 3rd International Conference on Big Data Analytics and ...

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3rd international conference on Big data Analytics and Practices, IBDAP 2022

The proceedings contain 21 papers. The topics discussed include: performance analysis of na39;ve bayes classifier algorithm with Chi-square and Confix stripping stemmer selection features in HADITS translation clas...

ISBN: (纸本)9781665469388

The proceedings contain 21 papers. The topics discussed include: performance analysis of naïve bayes classifier algorithm with Chi-square and Confix stripping stemmer selection features in HADITS translation classification system;2021 monthly rice production in Chinese coastal provinces;deep learning approaches for unobtrusive human activity recognition using insole-based and smartwatch sensors;determination of land suitability for rice cultivation using random forest analysis in central region Thailand;new technology adoption of e-learning: model of perceived usefulness;continuance intention of e-learning: new model of technology adoption;STARA is in sight: the effect of STARA on job outcome through job insecurity perception about future workplace on private companies in East Java, Indonesia;nonconvulsive seizure and status epilepticus detection with deep learning in high-risk adult critically ill;and comparative analysis of machine learning algorithms in detection of brain tumor.

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data Augmentation for Code Analysis 3

Data Augmentation for Code Analysis

引用

3rd international conference on Intelligent data Science Technologies and Applications, IDSTA 2022

作者： Shroyer, Alexander Martin Swany, D. Indiana University Intelligent Systems Engineering Bloomington United States

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

A key challenge of applying machine learning techniques to binary data is the lack of a large corpus of labeled training data. One solution to the lack of real-world data is to create synthetic data from real data through augmentation. In this paper, we demonstrate data augmentation techniques suitable for source code and compiled binary data. By augmenting existing data with semantically-similar sources, training set size is increased, and machine learning models better generalize to unseen data. © 2022 IEEE.

关键词： data mining

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Anomalous Sound pattern Detection for machine Health Monitoring 1

引用

3rd international conference on Artificial Intelligence and Knowledge Processing (AIKP)

作者： Dalmia, Shivali Rege, Manjeet Univ St Thomas St Paul MN 55105 USA

ISBN: (数字)9783031686177

ISBN: (纸本)9783031686160;9783031686177

Anomaly detection using audio signals from industrial machines in the manufacturing industry has gained broad interest over the last few years. For example, predictive maintenance solutions utilize raw analog signals to identify trends and patterns. In a few scenarios, an engineer working in a factory setting can tell when a machine is behaving abnormally just by hearing unexpected sounds (e.g., the loudness of sound) that are well within the human perceivable frequency range (20 Hz - lowest pitch to 20 kHz - highest pitch) which are typically concentrated in a narrow range of frequencies and amplitudes. The human perception of the amplitude of a sound is its loudness. However, the audio signal in its raw form is not always the best representation of the important features (e.g., frequencies, amplitude, peaks). Additionally, the machine learning applications which rely on using traditional digital signal processing techniques (e.g., digital signal processors, chips) have a lot of dependency on subject matter experts to tune the system for a better performance. Thus, we investigate how the digital transformation of waveform signals from microphone sensors (e.g., Audio recordings of industrial pumps, valves, slide rails) into Spectrograms (A spectrogram is a voiceprint of a signal which expresses an audio signal as an image using different colors to indicate the amplitude or strength of each frequency.) can help to monitor machine health (e.g., anomaly classification). In the pre-processing phase, raw audio signals (.WAV format) from each machine are converted to Mel Spectrogram images using short-term Fourier transformation. Then, comparative study of image classification techniques using deep convolutional neural networks (CNN) with and without data augmentation, is conducted to classify images as normal or abnormal. The approach is evaluated using Malfunctioning industrial machine investigation and inspection dataset [1-3] (MIMII dataset). Results show that the neu

关键词： Anomaly detection machine health monitoring Predictive maintenance

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Research on the Design Scheme Construction of Industrial Big data Platform Based on Enterprise Portrait 3

Research on the Design Scheme Construction of Industrial Big...

引用

3rd international conference on Intelligent Computing, Communication and Convergence, ICI3C 2023

作者： Yu, Xiangjun Wang, Chao Guangdong University of Science and Technology School of Management Dongguan China Wuhan University of Engineering Science Wuhan China

ISBN: (纸本)9798350343922

The complexity and numerous influencing factors of the industrial chain make precise monitoring, optimized control, and scientific management extremely difficult. However, utilizing big data technology to achieve full factor correlation analysis of industries is an effective way to support the modernization development of industries in the future. On the basis of analyzing the current situation of industrial big data applications, this article elaborates on the functions and objectives of the industrial big data platform, and constructs the overall architecture of the platform from four aspects: technical architecture, data architecture, application architecture, and physical architecture. At the same time, this article also analyzes the two key technologies involved in the platform, and combines machine learning related algorithms to profile enterprises, multi-dimensional mining of enterprise information for data display services. By building this platform, precise industrial management based on big data can be achieved, more scientific and objective industrial guidance policies can be formulated, supporting industrial management decisions, and providing valuable references for industrial informatization and intelligent transformation and upgrading. © 2023 IEEE.

关键词： Modernization

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Identify Hidden Phenotypes in Electronic Health Records Using machine learning Technique 3

Identify Hidden Phenotypes in Electronic Health Records Usin...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Takuli, Arun Kumar Joshi, Prakash Nayan, Sony Singh, Nitendra Saraswat, Manish Department of Information Technology U.P. Greater Noida201306 India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering U.P. Ghaziabad201003 India ABES Engineering College CSE-AIML UP Ghaziabad201002 India Hmritm Hamidpur Department of Computer Science & Engineering Delhi India Lloyd Institute of Engineering & Technology Affiliated to Aktu Mechanical Engineering UP Greater Noida India

ISBN: (纸本)9798350342338

scientific information, such as EHRs, have become increasingly famous because of their comfort and price savings related to their use. On the side of providing an extra efficient manner of storing and reviewing affected person facts, using EHRs allows healthcare companies to study and examine healthcare tendencies and outcomes extra effectively. As the use of EHRs grows, so does the need for brand-new methods to perceive and extract medically helpful facts from the facts. Lately, there has been a growing frame of labor that uses device learning strategies to discover hidden phenotypes in EHRs. These hidden phenotypes are defined as 'information or characteristics that are not conveniently obvious from the raw EHRs information however, may be gleaned through automatic data mining and evaluation.' This newsletter focuses on the various tactics used to pick out hidden phenotypes in EHRs through machine studying, natural language processing (NLP), deep gaining knowledge of, and supervised mastering techniques. It additionally gives an outline of the most promising algorithms and discusses the challenges and possibilities associated with the usage of this era. © 2023 IEEE.

关键词： data mining Health Record Information Medical Records NLP

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A Survey of Offline Handwritten Signature Verification Based on Deep learning 3

A Survey of Offline Handwritten Signature Verification Based...

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3rd international conference on pattern recognition and machine learning, PRML 2022

作者： Muhtar, Yusnur Kang, Wenxiong Rexit, Aliya Mahpirat Ubul, Kurban Xinjiang University Information Science and Engineering Institute Xinjiang China South China University of Technology School of Automation Science and Engineering Guangzhou510641 China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Handwritten signatures are biometrics and a point of contention in the scientific community, the process used to verify whether a person's signature is genuine. In the past ten years, the application of handwritten signature technology in the fields of administration, finance, handling legal disputes, and security has been greatly developed, and many researchers have focused on applying systems based on handwritten signature analysis and processing to new fields on the possibility. After several years of disorderly development in this field of research, it is time to assess the applicability of its current developments to formulate a structured path forward. In this paper, we provide a systematic review of the literature on offline handwritten signatures over the past 10 years, focusing on the most prominent and promising deep learning-based signature verification methods, and attempt to elicit possible future research directions on this topic. © 2022 IEEE.

关键词： Deep learning

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A Multi-level Complex Feature mining Method Based on Deep learning for Automatic Modulation recognition 3

A Multi-level Complex Feature Mining Method Based on Deep Le...

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3rd international conference on Information Science, Parallel and Distributed Systems, ISPDS 2022

作者： Huang, Chenzhao Ji, Mingrui Zhang, Hang Luo, Ruisen Sichuan University Department of Software Engineering Sichuan Chengdu China

ISBN: (数字)9781665487474

ISBN: (纸本)9781665487474

The previous modulation recognition models based on deep learning ignore the signal's complex characteristics and only consider the information carried by the signal in a single dimension, resulting in poor performance. Aiming at the complex characteristics of in-phase/quadrature (I/Q) data, this paper adopts a combination of complex convolution and one-dimensional real convolution, emphasizing the feature interaction between I and Q and enriching the feature representation of the signal. Besides, a multi-level complex attention block is introduced to enhance the informative representation of the entire feature space. Experimental results indicate that the proposed method's recognition accuracy of MQAM is significantly improved. Furthermore, the proposed method also alleviates the poor performance under a low signal-to-noise ratio, which is overall better than other deep learning-based modulation recognition models. © 2022 IEEE.

关键词： Convolution

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Realization of Network Security Knowledge Base with Knowledge Graph 3rd

Realization of Network Security Knowledge Base with Knowledg...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Ouyang, Zepeng Li, Xiangyang Wang, Bing Ji, Zhenyan School of Software Engineering Beijing Jiaotong University No. 3 Shangyuan Village Beijing China Cyberspace Security Department China Railway Information Technology Group Co. Ltd. No. 10 Fuxing Avenue Beijing China

ISBN: (纸本)9789819616978

In the era of digital transformation, the sheer volume of network security data poses significant challenges in terms of organization and retrieval. Traditional search engines fail to provide contextual answers, necessitating the development of more advanced systems. This paper presents the design and implementation of a network security knowledge base enhanced with a knowledge graph. The proposed system aims to improve the efficiency of information management and retrieval for network security professionals. We discuss the multi-layer architecture of the knowledge base, which includes the UI, service, data access, and data storage layers. The knowledge graph is constructed through a four-layer architecture comprising the data, extraction, knowledge, and application layers. Using the Scrapy framework, we efficiently crawl and extract data from various online sources. This data is processed with the Jena tool and stored in Neo4j as structured triples, enabling complex queries and advanced analytic. Our system supports semantic queries, knowledge association discovery, and intelligent question answering, improving user experience and decision-making in network security management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Computer Vision and pattern recognition Technology on Account of Deep Neural Network 4th

Computer Vision and Pattern Recognition Technology on Accoun...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Ren, Yiming Han, Jiake Chen, Chongyu Xu, Yang Bao, Tingxuan Shenzhen MSU BIT Univ Shenzhen 518172 Guangdong Peoples R China Shanghai Univ Shanghai 201900 Peoples R China Tianjin Univ Sci & Technol 1038 Dagu South Rd Tianjin Hexi Tianjin 300222 Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the rapid development of computer, computer vision technology is also making rapid progress. In this paper, deep neural network algorithm is used to improve the technology of computer vision, improve the visual effect, and at the same time, innovative algorithm structure, improve the identification of pattern recognition. Computer vision and pattern recognition is a very cutting edge technology that has given people very advanced tools for vision and recognition. At present, the technology can control more accurate image resolution and improve the ability of pattern recognition. This article mainly explains the process of using deep neural network algorithm to improve computer vision and pattern recognition from the internal mechanism, and reveals the working principle and internal mechanism of computer vision and pattern recognition technology application. data analysis proves that the pattern recognition application established by deep neural network algorithm performs very well in the field of vision and pattern recognition.

关键词： Deep neural network algorithm Computer vision pattern recognition Technical application

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