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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 585 条记录，以下是291-300 订阅

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Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks 26

Automatic Foot Ulcer Segmentation Using an Ensemble of Convo...

引用

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

作者： Mahbod, Amirreza Schaefer, Gerald Ecker, Rupert Ellinger, Isabella Med Univ Vienna Inst Pathophysiol & Allergy Res Vienna Austria Loughborough Univ Dept Comp Sci Loughborough Leics England TissueGnost GmbH Dept Res & Dev Vienna Austria

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pretrained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. The Dockerised guidelines, inference codes and saved trained models are publicly available at https://***/masih4/Foot Ulcer Segmentation.

关键词： Foot ulcer medical image analysis image segmentation machine learning deep learning ensemble

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Blind Print-Cam data Hiding Exploiting Color Perception 2nd

Blind Print-Cam Data Hiding Exploiting Color Perception

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

作者： Baldessari, Federico Boato, Giulia Lago, Federica University of Trento TrentoTN38123 Italy

ISBN: (纸本)9783030307530

Augmented Reality is becoming a fundamental technique to provide an easy access to additional information directly from the surrounding environment. It is however crucial that the mean through which the information is accessed is as integrated in the environment as possible. To this end, several data hiding techniques have been devised in the years to encode information in images in an imperceptible way. However, these techniques are frequently strongly affected by printing and re-acquisition process. This work presents an application developing a data hiding technique robust to printing and camera acquisition (print-cam), thus allowing to recover inserted data (hundreds or thousands of information bits) from printed images in a robust way (e.g., for different size of printed cover image, in different illumination conditions, with various geometric distortions). Performance and robustness of the proposed solution are tested with respect to different metrics to prove the feasibility of the technique. © 2019, Springer Nature Switzerland AG.

关键词： Augmented reality

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A learning procedure for detection of process anomalies in the production of metal long products and a new industrial case study 1

A learning procedure for detection of process anomalies in t...

引用

1st IFAC workshop on Control of Complex Systems (COSY)

作者： Weber, Andre Denker, Joachim Jelali, Mohieddine Mannstaedt GmbH Mendener Str 51 D-53840 Troisdorf Germany ASINCO GmbH Adv Solut Ind Control Kiekenbusch 11a D-47269 Duisburg Germany TH Koln Cologne Lab Artificial Intelligence & Smart Autom Betzdorfer Str 2 D-50679 Cologne Germany

The highly individualized production processes for long products in the steel industry is subject to a variety of influencing variables with mutual interactions in a complex manner. To handle this complexity, modern data mining methods can be used for a highly efficient analysis of process data, to detect process anomalies in the process data, e.g. from rolling mills by statistical pattern recognition. This paper proposes a data-based strategy for detecting process anomalies within a hot rolling mill for long products. Suitable data are identified and selected from existing sensors and processed within a new database. This central database is used to train classification algorithms. The reliability of two prominent classifiers based on Principal Component Analysis (PCA) and One-Class Support Vector machines (OC-SVM) has been evaluated. From the comparison in this respective use case, it has been concluded that satisfying results can be obtained, but PCA is highly dependent on the data distribution. The OC-SVM has also been implemented and tested and offers advantages when the data sets have a more complex distribution. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： industry4.0 data mining machine learning anomaly detection steel production PCA SVM OC-SVM

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1st ACM international workshop on Privacy and Anonymity for Very Large datasets, PAVLAD'09, Co-located with the 18th ACM international Conference on Information and Knowledge Management, CIKM 2009

1st ACM International Workshop on Privacy and Anonymity for ...

引用

1st ACM international workshop on Privacy and Anonymity for Very Large datasets, PAVLAD'09, Co-located with the 18th ACM international Conference on Information and Knowledge Management, CIKM 2009

ISBN: (纸本)9781605588049

The proceedings contain 8 papers. The topics discussed include: a new framework to automate constrained microaggregation;record linkage performance for large data sets;SAX: a privacy preserving general pupose method applied to detection of intrusions;applying differential privacy to search queries in a policy based interactive framework;weighted network decapitation: the economics of iterated attack and defense;incremental privacy preservation for associative classification;a novel approach for privacy mining of generic basic association rules;and on privacy preservation in text and document-based active learning for named entity recognition.

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The Scaffolded Sound Beehive 24

The Scaffolded Sound Beehive

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1st international workshop on Social Influence Analysis / 24th international Joint Conference on Artificial Intelligence (IJCAI)

作者： Maes, AnneMarie OKNO Brussels Urban Bee Lab Vlaamse Steenweg 66 B-1000 Brussels Belgium

ISBN: (纸本)9781577357384

The Scaffolded Sound Beehive is an immersive multi-media installation which provides viewers an artistic visual and audio experience of activities in a beehive. data were recorded in urban beehives and processed using sophisticated pattern recognition, AI technologies, and sonification and computer graphics software. The installation includes an experiment in using Deep learning to interpret the activities in the hive based on sound and micro-climate recording.

关键词： Deep learning

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machine learning Methods for Extraction and Classification for Biometric Authentication 1st

Machine Learning Methods for Extraction and Classification f...

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1st international Conference on data Science, machine learning and Applications, 2019

作者： Gunjan, Vinit Kumar Prasad, Puja S. Pathak, Rashmi Kumar, Amit Department of CSE CMR Institute of Technology Hyderabad India Department of CSE Geethanjali College of Engineering and Technology Hyderabad India Department of CSE Siddhant College of Engineering Pune India Department of CSE SJB Institute of Technology Bengaluru India

ISBN: (纸本)9789811514197

In this paper our main focus is to discover different machine learning techniques that are useful biometric System. As biometric authentication system is a combination of both image processing and pattern recognition, in this classification of pattern is a difficult task. machine learning have number of algorithm that makes classification task easy. machine learning is divided as supervised as well as unsupervised learning. In unsupervised learning the machine construct representation of input by getting inputs x1, x2, x3, …, and this is used for decision making, predicting future inputs or we say unsupervised learning finds patterns in the data and mainly solve clustering problem. In supervised learning set of output is already given, only we have to find this set of output from respective input value. In this paper we also discuss the area of machine learning where already work has done for biometric. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Unsupervised learning

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learning to rank typed graph walks: Local and global approaches

Learning to rank typed graph walks: Local and global approac...

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Joint 9th WebKDD and 1st SNA-KDD workshop 2007 on Web mining and Social Network Analysis. Held in conjunction with 13th ACM SIGKDD international Conference on Knowledge Discovery and data mining, KDD 2007

作者： Minkov, Einat Cohen, William W. Language Technologies Institute Carnegie Mellon University Pittsburgh PA Machine Learning Department Carnegie Mellon University Pittsburgh PA

ISBN: (纸本)9781595938480

We consider the setting of lazy random graph walks over directed graphs, where entities are represented as nodes and typed edges represent the relations between them. This framework has been used in a variety of problems to derive an extended measure of entity similarity. In this paper we contrast two different approaches for applying supervised learning in this framework to improve graph walk performance: a gradient descent algorithm that tunes the transition probabilities of the graph, and a reranking approach that uses features describing global properties of the traversed paths. An empirical evaluation on a set of tasks from the domain of personal information management and multiple corpora show that reranking performance is usually superior to the local gradient descent algorithm, and that the methods often yield best results when combined. Copyright 2007 ACM.

关键词： Graph theory

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Fuzzy proximal support vector classification via generalized eigenvalues

引用

1st international Conference on pattern recognition and machine Intelligence

作者： Jayadeva Khemchandani, R Chandra, S Indian Inst Technol Dept Elect Engn New Delhi 110016 India Indian Inst Technol Dept Math New Delhi 110016 India

ISBN: (纸本)3540305068

In this paper, we propose a fuzzy extension to proximal support vector classification via generalized eigenvalues. Here, a fuzzy membership value is assigned to each pattern, and points are classified by assigning them to the nearest of two non parallel planes that are close to their respective classes. The algorithm is simple as the solution requires solving a generalized eigenvalue problem as compared to SVMs, where the classifier is obtained by solving a quadratic programming problem. The approach can be used to obtain an improved classification when one has an estimate of the fuzziness of samples in either class.

关键词： support vector machines fuzzy data classification machine learning generalized eigenvalue problem proximal classifier

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Proceedings Sixth international Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS international workshop on Self-Assembling Wireless Networks SNPD/SAWN 2005

Proceedings Sixth International Conference on Software Engin...

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6th international Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and 1st ACIS international workshop on Self-Assembling Wireless Networks, SNPD/SAWN 2005

ISBN: (纸本)0769522947

The proceedings contain 71 papers. The topics discussed include: soft computing algorithms applied to the segmentation of nerve cell images;pattern recognition based on time-frequency distributions of radar micro-Doppler dynamics;a quantitative software quality evaluation model for the artifacts of component based development;a new approach to software requirements elicitation;using data mining technology to design an intelligent CIM system for IC manufacturing;data mining for imprecise temporal associations;analysis of breast cancer using data mining and statistical techniques;analyzing the conditions of coupling existence based on program slicing and some abstract information-flow;a study of model layers and reflection;a general scalable implementation of fast matrix multiplication algorithms on distributed memory computers;error prediction for multi-classification;an integer support vector machine;and layered neural networks computations.

关键词： Software engineering

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Design pattern detection using software metrics and machine learning

Design pattern detection using software metrics and machine ...

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Joint 1st Int. workshop on Model-Driven Software Migration, MDSM 2011 and the 5th international workshop on Software Quality and Maintainability, SQM 2011 - workshops at the 15th European Conf. on Software Maintenance and Reengineering, CSMR 2011

作者： Uchiyama, Satoru Washizaki, Hironori Fukazawa, Yoshiaki Kubo, Atsuto Dept. Computer Science and Engineering Waseda University Tokyo Japan Aoyama Media Laboratory Tokyo Japan

The understandability, maintainability, and reusability of object-oriented programs could be improved by automatically detecting well-known design patterns in programs. Many existing detection techniques are based on static analysis and use strict conditions composed of class structure data. Hence, it is difficult for them to detect design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. We propose a design pattern detection technique using metrics and machine learning. Our technique judges candidates for the roles that compose the design patterns by using machine learning and measurements of metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. We conducted experiments that showed that our technique was more accurate than two previous techniques.

关键词： static analysis

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