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共 1523 条记录，以下是321-330 订阅

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Application of Data Mining Methods for pattern recognition in Negotiation Support Systems 19th

Application of Data Mining Methods for Pattern Recognition i...

引用

1st Joint Meeting of the 19th Annual international conference on Group Decision and Negotiation (GDN) / Behavioral OR conference

作者： Kaya, Muhammed-Fatih Schoop, Mareike Univ Hohenheim D-70599 Stuttgart Germany

ISBN: (纸本)9783030217112;9783030217105

Data mining methods have long been used to support organisational decision making by analysing organisational data from large databases. The present paper follows this tradition by discussing two different data mining techniques that are being implemented for pattern recognition in Negotiation Support Systems (NSSs), thereby providing process assistance to human negotiators. To this end, data from several international negotiation experiments via NSS Negoisst is used. Consequently, a suitable data representation of the underlying utility data and communication data has to be created for the applicability of data mining Each generated data type needs individual processing treatments and almost all data mining methods lose their feasibility without a correct data representation as consequence. Once a correct data representation is found, the potential for pattern recognition in electronic negotiation data can be evaluated using descriptive and predictive methods. Whilst Association Rule Discovery is used as a descriptive technique to generate essential sets of strategic association patterns, the Decision Tree is applied as a supervised learning technique for the prediction of classification patterns. The extent to which reliable as well as valuable patterns can be derived from the electronic negotiation data and valuable predictions can be generated is examined in this paper.

关键词： Negotiation Support Systems Data mining Text Mining Association Rule Discovery Decision Tree

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steganography: An Efficient Data Hiding Method

Steganography: An Efficient Data Hiding Method

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4th international conference on Computer Science and Informatics, JCIS 1998, 1st international Workshop on High Performance, 1st international Workshop on Computer Vision, pattern recognition and image processing Volume 4

作者： Hou, Young-Chang Chen, Pei-Min Chiao, Yang-Fu Department of Information Management National Central University 38 Wu-Chuan Li Chungli 32054 Taiwan Department of Computer Science Soochow University

This article presents an efficient data hiding method that can embed more than one image into the counterfeit image. The bintree representation, image smoothing, codebook, and LSB-like techniques are applied to this proposed method. PSNR is also used to evaluate the effect of image smoothing approach. Since the value of distorted counterfeit image is over 30%, it is not easy for the snatcher to recognize the distortion and thus this data hiding method provides another layer of protection.

关键词： Data processing

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An Effective Automatic recognition Approach of Chinese Herbal Medicine by Micro image processing 21

An Effective Automatic Recognition Approach of Chinese Herba...

引用

21st IEEE international conference on Ubiquitous Computing and Communications / 5th IEEE international conference on Data Science and Computational Intelligence / 21st IEEE international conference on Computer and Information Technology

作者： Pang, Guangyao Yu, Zhenming Pan, Honglin Xie, Chengwen Gong, Ping Zhu, Xiaoying Peng, Zizhen Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China Wuzhou Univ Guangxi Coll & Univ Key Lab Ind Software Technol Wuzhou 543002 Peoples R China

ISBN: (纸本)9781665477260

The identification of traditional Chinese medicine is the key to control the quality of traditional Chinese medicine and ensure the safety and effectiveness of clinical medication. Compared with the physical and chemical identification methods with expensive equipment and complex operation, microscopic image identification of traditional Chinese medicine is an effective method with low cost. However, this method still has a high learning cost and identification errors due to staff fatigue. Therefore, this paper designs an effective automatic recognition approach of Chinese herbal medicine by micro image processing. The core of this method is the introduction of transfer learning and data enhancement methods, which effectively alleviates the problem of insufficient number of microscopic image data samples in the microscopic recognition of traditional Chinese medicine, and realizes the automatic recognition of traditional Chinese medicine. We construct a library of microscopic recognition features of Chinese herbal medicine, and designe evaluation experiments on this basis. The results show that the recognition performance of our method is better than that of SSD method, especially the F1 value is increased by 7.25 %.

关键词： image recognition identification of traditional Chinese medicine transfer learning data enhancement microscopic image

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learning Representations through Ensemble of Fuzzy C-Means for Identification of Retinal Pathologies 17

Learning Representations through Ensemble of Fuzzy C-Means f...

引用

international conference on Internet of Things and machine learning (IML)

作者： Karri, S. P. K. Chakraborty, Debjani Ray, Ajoy K. Chatterjee, Jyotirmoy IIT Kharagpur Kharagpur W Bengal India IIEST Sibpur Howrah India

ISBN: (纸本)9781450352437

Retinal image analysis is employed to automate screening process through low-level feature extraction and classification. Supervised classification approaches are dependent on kernels or distance metrics to handle complex manifolds as they warp feature space for effective classification with less complex boundaries between classes. Proposed approach identifies control points (Voronoi diagram) by exploring the structures of class specific manifolds which constructs complex boundaries with piecewise linear nature. Such a framework has less number of hyperparameters to tweak resulting easy control and understanding of the system. The learning characteristics of the proposed algorithm has been depicted on toy and optical coherence tomography data set. It has illustrated effective performance in identification of retinal pathologies and compared against off-the-shelf classifiers with various parameters. Proposed algorithm is capable of accommodating unsupervised approaches other than Fuzzy C-Means reflecting its adaptability.

关键词： pattern recognition Fuzzy Logic Fractals Voronoi

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Floor plan recognition and vectorization using combination unet, faster-rcnn, statistical component analysis and ramer-douglas-peucker 1st

Floor plan recognition and vectorization using combination u...

引用

1st international conference on Computing Science, Communication and Security, COMS2 2020

作者： Surikov, Ilya Y. Nakhatovich, Mikhail A. Belyaev, Sergey Y. Savchuk, Daniil A. Polytechnicheskaya 29 St. Petersburg195251 Russia ITSociety LTD Diagonalnaya 4-1-170 St. Petersburg194100 Russia

ISBN: (纸本)9789811566479

The floor plan recognition and vectorization problem from the image has a high market response due to the ability to be applied in such domains as design, automatic furniture fitting, property cost estimation, etc. Several approaches already exist on the market. Many of them are using just statistical or deep machine learning methods capable of recognizing a limited set of floor plan types or providing a semi-automatic tool for recognition. This paper introduces the approach based on the combination of statistical image processing methods in a row of machine learning techniques that allow training robust model for the different floor plan topologies. Faster R-CNN for the floor object detection with a mean average precision of 86% and UNet for the wall segmentation has shown the IoU metric results of about 99%. Both methods, combined with functional and component filtration, made it possible to implement the new approach for vectoring the floor plans. © Springer Nature Singapore Pte Ltd 2020.

关键词： Deep learning

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Heritage image classification by convolution neural networks 1

Heritage image classification by convolution neural networks

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1st international conference on Multimedia Analysis and pattern recognition, MAPR 2018

作者： Vu, Manh-Tu Beurton-Aimar, Marie Le, Van-Linh LaBRI-CNRS 5800 France PUF - Pôle Universitaire Francophone Ho Chi Minh City Viet Nam LaBRI-CNRS 5800 Universite Bordeaux Talence France LaBRI-CNRS 5800 France ITDLU - Dalat University France

ISBN: (纸本)9781538641804

In this project, we have explored the use of Convolution Neural Networks for semantic classification of heritage images. Recently four architectures, AlexNet, GoogLeNet, ResNet, and SENet have been proposed. We have tested them with two different learning modalities. Besides conventional training from scratch, we have resorted to pre-trained networks that have been fine-tuned on a huge amount of data in order to avoid overfitting problems and to reduce design/computing time. Experiments on a dataset we have built by ourselves from Heritage repository show that with the help of this step of fine-tuning, one of the simplest (and oldest) networks AlexNet is able to produce similar results than more sophisticated ones as ResNet which is pretty 3 times more consuming in computing time, mainly due to the number of the layer it uses. With this model, we have been able to show that our model can classify in the right way more than 98% of the images belonging to the dataset test. From now, this model can be used to classify the full dataset of the Heritage repository. © 2018 IEEE.

关键词： image classification

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On Topological Analysis of Cells Organization in Biological images 1st

On Topological Analysis of Cells Organization in Biological ...

引用

1st IAPR international Joint conference on Discrete Geometry and Mathematical Morphology (DGMM)

作者： Jimenez, Maria-Jose Univ Seville Dept Matemat Aplicada 1 Seville Spain

ISBN: (纸本)9783030766566;9783030766573

There are many problems inside the field of biomedical image analysis that can be dealt from a topological (and geometric) point of view. One of them refers to the way in which cells are self-organised inside a tissue, mainly motivated by the changes that may occur in such an organization in case of disease. This problem can be faced from different perspectives in terms, first, of how to model the cells and their 'connections' and second, how to computationally characterise their organization. We will discuss some topological approaches to this topic as well as future lines of research.

关键词： Topological data analysis Persistent homology Epithelial organization

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A unified smart surveillance system incorporating adaptive foreground extraction and deep learning-based classification 1

A unified smart surveillance system incorporating adaptive f...

引用

1st international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Muchtar, Kahlil Rahman, Faris Munggaran, Muhammad Rizky Dwiyantoro, Alvin Prayuda Juniarta Dharmadi, Richard Nugraha, Indra Nodeflux Jakarta Indonesia Syiah Kuala Univ Dept Elect & Comp Engn Banda Aceh Indonesia

ISBN: (纸本)9781538678220

The smart surveillance system is one of the most important services provided in a smart city. The smart surveillance applications are equipped with camera sensors to automatically detect and identify potential regions through automated object detection methods. Usually, such methods require high-complexity image processing techniques and algorithms. Hence, the design of low-complexity automated object detection algorithms becomes an important topic in this area. We consider a foreground extraction and deep learning to achieve these goals. A novel unified technique is proposed to detect a moving object from the surveillance videos based on CPU (central processing units). Unlike most of the existing methods that are relying on pixel information, we use a block-based texture and spatial-temporal information. We use this method to determine the area of the detected moving object(s) only. Furthermore, the area will be processed through a deep learning-based image classification in GPU (graphics processing units) in order to ensure high efficiency and accuracy. It cannot only help to detect object rapidly and accurately, but also can reduce big data volume needed to be stored in smart surveillance systems.

关键词： conferences Computer vision pattern recognition Surveillance Object detection Graphics processing units Big Data

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Research on CCD infrared image method

Research on CCD infrared image method

引用

1st international conference on Pervasive Computing, Signal processing and Applications, PCSPA 2010

作者： Ye, Zhang Wei, Yang Wendong, Zhang Wang, Zhengyan Xingling, Shao Qiang, Yin Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of Education North University of China Xueyuan Road 3 Taiyuan Shanxi Province 030051 China National Key Laboratory of Science and Technology on Electronic Test and Measurement Xueyuan Road 3 Taiyuan Shanxi Province 030051 China

ISBN: (纸本)9780769541808

Infrared image pattern recognition system consists of image enhancement, segmentation and pattern classification. image segmentation is realized through Threshold Segmentation. Generally speaking, there are several methods of threshold segmentation such as double-hump method, iterated method and maximum entropy method and the otsu method and so on. These segmentation methods are used for the automatic segmentation of pedestrians in the infrared images. The results show that the Otsu threshold segmentation method can achieve infrared image segmentation better than others. © 2010 IEEE.

关键词： image segmentation

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Analysis of images Obtained by Unmanned Aerial Vehicle by Deep learning Methods 1

Analysis of Images Obtained by Unmanned Aerial Vehicle by De...

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1st international Informatics and Software Engineering conference, IISEC 2019

作者： Kutlu, Ozgur Demir, Onder Dogan, Baris Marmara Üniversitesi Fen Bilimleri Enstitüsü Istanbul Turkey Marmara Üniversitesi Teknoloji Fakültesi Bilgisayar Mühendisliǧi Istanbul Turkey Marmara Üniversitesi Teknoloji Fakültesi Mekatronik Mühendisliǧi Istanbul Turkey

ISBN: (纸本)9781728139920

In artificial intelligence applications, many sub-methods such as machine learning, artificial neural networks, classification, clustering algorithms are used. One of these methods is deep learning. Deep learning is an advanced machine learning class. Using the Deep learning method, video analysis, image classification, speech recognition and natural language processing are very successful. The data and experiences to be provided by the projects covering Deep learning and Unmanned Aerial Vehicles will increase the number of qualified studies on these issues and contribute to the development of high value-added products in these technologies. In this study, a control software that evaluates image data from unmanned aerial vehicles and makes various inferences (classification, positioning, marking) is created. By using the method of retraining the last layers of the pre-trained artificial neural network models with our data set, it has been tried to reduce the training time and increase the success. In these studies, 2 pre-educated models were used and as a result of training of these models, as a result of 190 thousand steps of training, 25.39 and 27.87 mAP values were reached. © 2019 IEEE.

关键词： Antennas

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