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pattern recognition and machine learning (prml), IEEE international conference on

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Detecting the boundary curve of planar random point set

3rd international conference on machine learning and Data Mining in pattern recognition

作者： Imiya, A Tatara, K Ootani, H Hlavác, V Chiba Univ Inst Media & Informat Technol Chiba Japan Chiba Univ Sch Sci & Technol Chiba Japan Czech Tech Univ Ctr Machine Percept CR-16635 Prague Czech Republic

In this paper, we develop an algorithm for the learning of the boundary and the medial axis of random point sets, employing the principal curve analysis. The principal curve analysis is a generalization of principal a... 详细信息

ISBN: (纸本)3540405046

关键词： pattern recognition

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A Neuro-Genetic Framework for pattern recognition in Complex Systems

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FUNDAMENTA INFORMATICAE 2008年第2期87卷 207-226页

作者： Bandini, Stefania Vanneschi, Leonardo Wuensche, Andrew Shehata, Alessandro Bahgat Univ Milano Bicocca Complex Syst & Artificial Intelligence CSAI Res C Dept Informat Syst & Commun DISCo I-20126 Milan Italy Univ Sussex Dept Informat Sch Sci & Technol Brighton BN1 9RH E Sussex England

This paper presents a general framework to automatically generate rules that produce given spatial patterns in complex systems. The proposed framework integrates Genetic Algorithms with Artificial Neural Networks or Support Vector machines. Here, it is tested on a well known 3-values, 6-neighbors, k-totalistic cellular automata rule called the "burning paper" rule. Results are encouraging and should pave the way for the use of the proposed framework in real-life complex systems models.

关键词： Complex Systems Cellular Automata pattern recognition Automatic Rule Generation machine learning Genetic Algorithms Artificial Neural Networks Support Vector machines

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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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BLS: A learning Search Algorithm with Bayesian learning 3

BLS: A Learning Search Algorithm with Bayesian Learning

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3rd IEEE international conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Zhang, Wei Northeastern Univ Coll Comp Sci & Engn Shenyang Liaoning Peoples R China

ISBN: (纸本)9781538643013

In learning search, it is assumed that a search algorithm has adequate opportunities to traverse the problem state space, and to sense and learn the topological (or probability) knowledge about the state space. In this paper, a new search algorithm based on Bayesian learning (algorithm BLS) is proposed, which finds the solution path for any given state pair (s, t) in the state space efficiently. The algorithm depicts the search as a process of pattern recognition for the current path. During its depth-first search, the BLS classifies the current path, depending on the probability distribution functions acquired in previous Bayesian learning, and prunes it if necessary. It is proved that the mean complexity of BLS grows linearly with the length of the solution path after sufficient training.

关键词： machine learning learning search big graph search and mining heuristic search Bayesian learning pattern recognition

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2022 3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

2022 3rd International Conference on Computer Vision, Image ...

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3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

ISBN: (纸本)9781665459112

The proceedings contain 253 papers. The topics discussed include: testing the feasibility of quantizing the progress of stroke patients’ rehabilitation with a computer vision method;recognition of surface defects of aluminum profiles based on convolutional neural network;a novel generative adversarial network simulating the complementary structure of DNA genetic information;an improved lightweight method of convolutional neural network for face recognition;a calculation method for line-of-sight stable attitude of networked optical transceiver based on depth feedforward neural network;a grapevine virus disease detection method based on convolution neural network;a predict method of measuring equipment operation performance based on improved local weighted partial least squares;research on multi-scale network computer modeling based on mobile vendor management;and identification method of dress pattern drawing based on machine vision algorithm.

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Analyses of the Technological Accumulation over the 2nd and the 3rd AI Boom and the Issues Related to AI Adoption by Firms

Analyses of the Technological Accumulation over the 2nd and ...

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Portland international conference on Management of Engineering and Technology (PICMET) - Managing Technological Entrepreneurship - The Engine for Economic Growth

作者： Miyazaki, Kumiko Sato, Ryusuke Tokyo Inst Technol Dept Technol & Innovat Management Tokyo Japan IBM Corp Global Business Serv Watson Delivery 2 Tokyo Japan

ISBN: (纸本)9781890843373

Although AI was proposed by *** back in 1956, 60 years later, we are beginning to witness a surge of interest in the practical applications of AI in many sectors. However, the actual adoption rate of AI in businesses has been quite low. The current 3rd AI boom follows the 2nd AI boom in the 1980s which focused mainly on expert systems. In this paper, an attempt is made to analyze the technological accumulation process from the 2nd to the 3rd AI boom and to identify forthcoming key application areas. The methodology is based on a 3 step approach. In the first step, a bibliometric analyses of Artificial Intelligence was carried out to analyze the technological accumulation during the 2nd and the 3rd AI boom. Having done the desk work and interviewed several experts in AI, we decided that 2013 was the year which marked the boundary between the two AI booms. A bibliometric analyses based on countries and institutions over the 2 periods was followed by co-occurrence analyses of the author keywords in the 2 periods, before and after 2013. In the third stage, interviews were carried out with some corporate members to do a qualitative analysis on the possible application areas of AI and issues to be solved for adopting AI were identified. The results showed that in the 3rd AI boom, machine learning, deep learning, genetic algorithm have been identified as the key technologies. Furthermore, prediction, forecasting, data mining, fault diagnosis, pattern recognition were identified as important areas in the 3rd AI boom. It was also revealed that firms' R&D has been changing to focus more on AI applications.

关键词： machine learning Bibliometrics Companies Neural networks Market research Expert systems

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Facial Expression recognition Using Extreme learning machine 3

Facial Expression Recognition Using Extreme Learning Machine

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3rd international conference on Informatics and Computational Sciences (ICICoS)

作者： Shafira, Serenada Salma Ulfa, Nadya Wibawa, Helmie Arif Rismiyati Univ Diponegoro Fac Math & Sci Dept Informat Semarang Indonesia

ISBN: (纸本)9781728146102

Facial expression recognition is one of the technological capabilities in identifying a face image to follow up on research conducted by psychologists. The recognition of facial expressions is very important to know the emotions of someone who is experiencing it. In this study two datasets were used, namely the FER2013 and CK + datasets. The FER2013 dataset and CK+ are datasets designed to identify facial expressions. At the feature extraction stage, it uses the Histogram of Oriented Gradient (HOG) feature dan Local Binary pattern (LBP) feature. Whereas in the classification stage, the Extreme learning machine (ELM) classifier is used. The greatest accuracy by using HOG feature is 63.86% for the FER2013 dataset and 99.79% for the CK + dataset with sigmoid as an activation function. And the greatest accuracy by using LBP feature is 55.11% for the FER2013 dataset and 98.72% for the CK + dataset with RBF as an activation function.

关键词： facial expression recognition histogram of oriented gradient local binary pattern extreme learning machine

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A Novel Approach of Feature Vector Design for Financial Information Extraction Using Supervised learning 3

A Novel Approach of Feature Vector Design for Financial Info...

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

作者： Dadhich, Mahesh Lewis, James G. Bangalore India Los Angeles USA

ISBN: (纸本)9781509036967

Financial information extraction from big financial reports is a tedious task. This paper speaks about page-wise feature generation and applying learning algorithms for identifying financial information (balance sheets, cash flows, and income statements) in Form 10-K or annual reports of companies. Balance sheets, cash flows, and income statements have some structure in them and are semi-structured information. This approach employs selection of unigrams and bigrams based on frequency of occurrence and expert advice, generation of page wise features, and applying learning models for identifying patterns of specific financial information. Different supervised learning models are applied yielding results with very high accuracy (greater than 99%).

关键词： page wise Information extraction Feature generation SVM Random Forest logistic regression Naive Bayes cross validation 10k reports pattern recognition

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Proceedings of 3rd international conference on Intelligent Engineering and Management, ICIEM 2022

Proceedings of 3rd International Conference on Intelligent E...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

ISBN: (纸本)9781665467568

The proceedings contain 168 papers. The topics discussed include: design for ergonomics: application of single- passenger electric car via design of experiments;detection of breast cancer using machine learning and deep learning methods;automobile insurance fraud detection: an overview;a survey on detection of lung cancer using different image processing techniques;deep learning methods for automatic number plate recognition system: a review;skin cancer detection using machine learning: a survey;transforming education system through artificial intelligence and machine learning;model for retailers with price dependent demand rates;neuromarketing: an emerging domain in the formal education system;design of dual slot microstrip antenna for wireless application;analysis of ancillary services provided by distributed energy resources in smart distribution grid;and grey scale image skeletonization of vein pattern using Hausdroff image binarization technique in biometric security system.

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