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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 3245 条记录，以下是341-350 订阅

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data mining application for predictive learning from visiting nurse association data

Data mining application for predictive learning from visitin...

引用

3rd international conference on machine learning and Applications

作者： Vyas, P Elmaghraby, A Topp, R Univ Louisville Louisville KY 40292 USA

ISBN: (纸本)0780388232

In the following paper the process of knowledge generation from the Visiting Nurse Association database for elderly care is explored This inquiry is concerned with predicting falls. Predicting which patients are likely to fall can assist the clinician in the identification of high-risk patients and suggest the need for early falls prevention programs. The entire data mining process is described beginning with data gathering followed by cleaning, aggregation, and integration. Issues faced while conducting the research are discussed. Results of decision trees and decision rules, and artificial neural networks used to predict falls are presented.

关键词： falls community gerontology fall prevention predictive data mining and health informatics

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Meta learning application in rank aggregation feature selection 3

Meta learning application in rank aggregation feature select...

引用

3rd international conference on Soft Computing and machine Intelligence (ISCMI)

作者： Smetannikov, Ivan Deyneka, Alexander Filchenkov, Andrey ITMO Univ Comp Technol Lab St Petersburg Russia

ISBN: (纸本)9781509036967

One of the main tasks of machine learning and data mining is feature selection. Depending on the task different methods applied to find optimal balance between speed and feature selection quality. MeLiF algorithm effectively solves feature selection problem by building ensemble of feature ranking filters. It reduces filters aggregation problem to linear form optimization problem and works as a wrapper, but not on feature space as classical wrappers do, but on linear form coefficients space, which is much smaller. In this paper we tried to apply meta-learning to provide good starting optimization points for MeLiF method and as a result we increased not only speed but in some cases feature selection quality of this method.

关键词： machine learning feature selection rank-aggregation meta-learning MeLiF

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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...

引用

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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Keyword extraction using machine learning approaches 3

Keyword extraction using machine learning approaches

引用

3rd international conference on Advances in Computing, Communication and Automation (Fall), ICACCA 2017

作者： Kaur, Bhavneet Jain, Sushma Department of Computer Science and Engineering Thapar University Patiala India

ISBN: (纸本)9781509064038

data mining is the procedure to separate concealed prescient data from database and change it into a reasonable structure for sometime later. The varying areas in information mining are Web mining, Text mining, Sequence mining, graph mining, temporal data mining, Spatial data mining, Distributed data mining and Multimedia mining. A portion of the utilizations of information mining, it is utilized for the budgetary information examination, retail and media transmission ventures, science and designing and interruption location and counteractive action. In this exploration paper a calculation is proposed for ensembling and catchphrase extraction in content mining. In the proposition the above methods are upgraded which in turns, enhance the exactness, review, f-measure and accuracy. © 2017 IEEE.

关键词： Extraction

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Automatic Poincare section 3

Automatic Poincare section

引用

3rd international conference on Applied machine learning, ICAML 2021

作者： Cheng, Jie Wang, Ruike Li, Wenshi School of Electronics and Information En Soochow University Suzhou215006 China Faculty of Science Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781665421256

An attractor-lock-in Poincare section is formed through three space points of the minimum, mean and maximum of the attractor under test, with parameter needless manual selection. The key algorithm steps of automatic Poincare section include (a) digging out three geometry feature points as minimum, mean and maximum;(b) through three basic statistical points making a plane needed;(c) cut-off points counting by close contact measure and close range fusion;(d) using the spectral entropy complexity and construction creep rate in chaos spring test as gold standards of control experiments. Test data are provided by classical Chua’s equation and unified chaotic equation and random number instruction in Matlab tool. The results show that the automatic Poincare section and its cut-off point number can finish three classifying recognition on period, chaos or random states of the test three dimensional data. This case study will promote economically the information geometry, and further be applied to a wide range of pattern recognition fields. © 2021 IEEE

关键词： pattern recognition

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Dynamic data Augmentation with Gating Networks for Time Series recognition 26

Dynamic Data Augmentation with Gating Networks for Time Seri...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - Theory and Applications (IMTA)

作者： Oba, Daisuke Matsuo, Shinnosuke Iwana, Brian Kenji Kyushu Univ Dept Adv Informat Technol Fukuoka Japan

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

data augmentation is a technique to improve the generalization ability of machine learning methods by increasing the size of the dataset. However, since every augmentation method is not equally effective for every dataset, you need to select an appropriate method carefully. We propose a neural network that dynamically selects the best combination of data augmentation methods using a Gating Network and a mutually beneficial feature consistency loss. The Gating Network is able to control how much of each data augmentation is used for the representation within the network. The feature consistency loss gives a constraint that augmented features from the same input pattern should be in similar. In the experiments, we demonstrate the effectiveness of the proposed method on the 12 largest time-series datasets from 2018 UCR Time Series Archive and reveal the relationships between the data augmentation methods through analysis of the proposed method.

关键词： Histograms Analytical models Time series analysis Neural networks Radar machine learning pattern recognition

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A Syllabus on data mining and machine learning with Applications to Cybersecurity 3

A Syllabus on Data Mining and Machine Learning with Applicat...

引用

3rd international conference on Digital Information Processing, data mining, and Wireless Communications (DIPDMWC)

作者： Epishkina, Anna Zapechnikov, Sergey Natl Res Nucl Univ MEPhI Moscow Russia

ISBN: (纸本)9781467393799

Big data analytics are very fruitful for solving problems in cybersecurity. We have analyzed modern trends in intelligent security systems research and practice and worked out a syllabus for a new university course in the area of data mining and machine learning with applications to cybersecurity. The course is for undergraduate and graduate students studying the cybersecurity. The main objective of the course is to provide students with fundamental concepts in data mining (in particular, mining frequent patterns, associations and correlations, classification, cluster analysis, outlier detection), machine learning (including neural networks, support vector machines etc.) and related issues, e.g. the basics of multidimensional statistics. Contrary to the traditional data mining and machine learning courses we illustrate course topics by cases from the area of cybersecurity including botnet detection, intrusion detection, deep packet inspection, fraud monitoring, malware detection, phishing detection, active authentication. We note that our course has great potential for development.

关键词： information technology associative rules data mining classification clustering outlier detection information security specialists training educational and methodical maintenance

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A comparative study of unsupervised machine learning and data mining techniques for intrusion detection 1

引用

5th international conference on machine learning and data mining in pattern recognition

作者： Sadoddin, Reza Ghorbani, Ali A. Univ New Brunswick Network Secur Lab Fredericton NB Canada

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

During the past number of years, machine learning and data mining techniques have received considerable attention among the intrusion detection researchers to address the weaknesses of knowledgebase detection techniques. This has led to the application of various supervised and unsupervised techniques for the purpose of intrusion detection. In this paper, we conduct a set of experiments to analyze the performance of unsupervised techniques considering their main design choices. These include the heuristics proposed for distinguishing abnormal data from normal data and the distribution of dataset used for training. We evaluate the performance of the techniques with various distributions of training and test datasets, which are constructed from KDD99 dataset, a widely accepted resource for IDS evaluations. This comparative study is not only a blind comparison between unsupervised techniques, but also gives some guidelines to researchers and practitioners on applying these techniques to the area of intrusion detection.

关键词： Intrusion detection

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A systematic review of techniques, tools and applications of machine learning 3

A systematic review of techniques, tools and applications of...

引用

3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Abhishek Dhankar, Abhishek Gupta, Neha Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9780738111834

machine learning is a part of Artificial Intelligence. A branch of artificial Intelligence(AI), that offers the capability to the system by learning on their own and work better from experience without human intervention. By using machine learning (ML), software applications become more precise for outcome prediction with no requirement of explicit programmed. machine learning has a fundamental concept i.e. building of algorithms for accepting input data and with the help of statistical analysis they can predict the output. Nowadays, machine learning has gained vast popularity, and its algorithms are used in every field like object detection, pattern recognition, text interpretation, and different research areas. One of the basic goals of machine learning is to teach computers to use the data to solve a particular problem. A fair range of machine-learning applications include e-mail classification training to distinguish spam from non-spam messages, fraud detection, etc. This paper aims at varied machine learning methods, algorithms, and tools required to run the machine learning projects. It also focuses on the advancements that are administered in order so that the current researchers can be benefited out of it. © 2021 IEEE.

关键词： Reinforcement learning

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Vehicle Theft Detection Using Neural pattern recognition 3

Vehicle Theft Detection Using Neural Pattern Recognition

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Kumar Rapuri, Anil Ravva, Vennela Rasala, Raghavendra Ray Adurgam, Sai Dcepak Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering TamilNadu Krishnankoil India SRM Madurai College for Engineering and Technology Dept of Computer Science and Engineering TamilNadu Sivagangai India

ISBN: (纸本)9798350322842

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge. © 2023 IEEE.

关键词： Image segmentation

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