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检索条件"任意字段=5th International Conference on Pattern Recognition in Bioinformatics"

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Patch-based carcinoma detection on confocal laser endomicroscopy images - a cross-site robustness assessment 5

Patch-based carcinoma detection on confocal laser endomicros...

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5th international conference on Bioimaging, BIOIMAGING 2018 - Part of 11th international Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018

作者： Aubreville, Marc Goncalves, Miguel Knipfer, Christian Oetter, Nicolai Würfl, Tobias Neumann, Helmut Stelzle, Florian Bohr, Christopher Maier, Andreas Pattern Recognition Lab Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Otorhinolaryngology Head and Neck Surgery University Hospital Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Oral and Maxillofacial Surgery University Medical Center Hamburg-Eppendorf Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Oral and Maxillofacial Surgery University Hospital Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Germany First Department of Internal Medicine University Medical Center Mainz Johannes Gutenberg-Universität Mainz Germany

ISBN: (纸本)9789897582783

Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accuracy of 89.45% and an area-under-the-curve (AUC) value of 0.955, when applied on the vocal cords data. Compared to the state of the art, we achieve very similar results, yet with an algorithm that was trained on a completely disjunct data set. Concatenating both data sets yielded further improvements in cross-validation with an accuracy of 90.81% and AUC of 0.970. In this study, for the first time to our knowledge, a deep learning mechanism for the identification of oral carcinomas using CLE Images could be applied to other disciplines in the area of head and neck. this study shows the prospect of the algorithmic approach to generalize well on other malignant entities of the head and neck, regardless of the anatomical location and furthermore in an examiner-independent manner. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Image recognition

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Electromyography Assessment of Forearm Muscles: Towards the Control of Exoskeleton Hand 5

Electromyography Assessment of Forearm Muscles: Towards the ...

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5th international conference on Control, Decision and Information Technologies (CoDIT)

作者： Abas, N. Bukhari, W. M. Abas, M. A. Tokhi, M. O. Univ Sheffield Automat Control & Syst Engn Dept Sheffield S1 3JD S Yorkshire England Natl Univ Malaysia Univ Hlth Centre Selangor Malaysia London South Bank Univ Sch Engn London SE1 0AA England

ISBN: (纸本)9781538650653

Hand plays an important role in a human's life by offering physical interaction and grasping capabilities. In most stroke cases, the hand is the most vulnerable part of the body that has a high chance of suffering. this has led to the development of a numerous wearable robotic devices such as exoskeleton hands. the exoskeleton hands can provide physical assistance for stroke survivors to regain their abilities in performing basic activities of daily living and to improve their quality of life. the key challenges in developing such a device do not only lie in designing its mechanical but also in designing its controller. In controlling the exoskeleton hand, the principal criterion is to work according to the user's motion intention. It can be done by utilizing the electromyogram (EMG) signals generated by forearm muscles contributed from the movement and/or grasping abilities of the hand. In this paper, electromyography assessment of forearm muscles towards the control of an exoskeleton hand is presented. the EMG signals are collected non-invasively using multi-channel surface EMG sensors. the contractions of the muscles are detected from several forearm (flexion and extensor) muscles and the data is processed through several pattern recognition steps, before being mapped to various pinching/gripping forces and angular joints. the adaptability and learning process is done through a neural network. the experimental results show separable classes of features and significant range of control inputs that represent the inter-relation between forearm EMG signals, various pinching/gripping forces and angular joints for exoskeleton hand control.

关键词： Exoskeletons Muscles thumb Electromyography Joints Robots

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Application of data mining for behavior pattern recognition in telecommunication 3rd

Application of data mining for behavior pattern recognition ...

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3rd international conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th international conference on Swarm Intelligence, ICSI 2018

作者： Wu, Xingshen Zhao, Yu Gu, Qiang Gao, Li China Mobile Group Jiangsu Co. Ltd Nanjing China China Mobile Research Institute Beijing China

ISBN: (纸本)9783319938028

In telecom industry, mobile subscribers produce data traffic while online every day. these data traffic suggests that certain characteristics of the behavior. the application of data mining helped to analyze and identify the features from the data traffic. In this paper, we use exponential binning of data preprocessing technology to smooth the data sets and keep reduce the noise. By using K-means algorithm to cluster the data traffic stream, we aim to mining subscribers’ behavior characteristics from clusters, provide support for churn prediction, target marketing, and fraud detection. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Data mining

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the Accuracy of a Scene Geometry Based on the Terrestrial Laser Scanner Measurements 5th

The Accuracy of a Scene Geometry Based on the Terrestrial La...

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5th international conference on Man-Machine Interactions (ICMMI)

作者： Kowalski, Przemyslaw Skabek, Krzysztof IITiS Pan Gliwice Poland

ISBN: (纸本)9783319677927;9783319677910

the article presents the theoretical estimation of the accuracy of the distance measurements between 3D points measured by a scanning system, a description of the experiments carried out and results for Faro X, Z+F Imager and Trimble TX scanners. the result of the experiments shows the correctness of the theoretical estimation and terrestrial laser scanner parameters. the experiments investigated the scanners' accuracy as applied to the crime scene description. We also present an algorithm for the location of centres of spheres.

关键词： Terrestrial scanners pattern recognition 3D point clouds

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Methodology for the design of a student pattern recognition tool to facilitate the teaching - Learning process through knowledge data discovery (big data) 3rd

Methodology for the design of a student pattern recognition ...

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3rd international conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th international conference on Swarm Intelligence, ICSI 2018

作者： Viloria, Amelec Lis-Gutiérrez, Jenny-Paola Gaitán-Angulo, Mercedes Godoy, Abel Ramiro Meza Moreno, Gloria Cecilia Kamatkar, Sadhana J. Universidad de la Costa Barranquilla Colombia Fundación Universitaria Konrad Lorenz Bogotá Colombia University of Mumbai Mumbai India

ISBN: (纸本)9783319938028

Imagine a platform in which the teacher can access to identify patterns in the learning styles of students attached to their course, and in turn this will allow you to know which pedagogical techniques to use in the teaching process - learning to increase the probability of success in your classroom?. What if this tool could be used by students to identify the teacher that best suits their learning style?. Yes, was the tool able to improve its prediction regarding academic performance as time passes? It is obvious that this would require specialized software in the handling of large data. this research-development aims to answer these questions, proposing a design methodology of a student pattern recognition tool to facilitate the teaching-learning process through Knowledge Data Discovery (Big Data). After an extensive document review and validation of experts in various areas of knowledge, the methodology obtained was structured in four phases: identification of patterns, analysis of the teaching-learning process, Knowledge Data Discovery and Development, implementation and validation of software. © Springer international Publishing AG, part of Springer Nature 2018.

关键词： Students

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5th international conference on Future Data and Security Engineering, FDSE 2018

5th International Conference on Future Data and Security Eng...

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5th international conference on Future Data and Security Engineering, FDSE 2018

ISBN: (纸本)9783030031916

the proceedings contain 38 papers. the special focus in this conference is on Future Data and Security Engineering. the topics include: A secure and efficient kNN classification algorithm using encrypted index search and Yao’s garbled circuit over encrypted databases;a security model for IoT networks;comprehensive study in preventive measures of data breach using thumb-sucking;intrusion prevention model for WiFi networks;security for the internet of things and the bluetooth protocol;a light-weight tightening authentication scheme for the objects’ encounters in the meetings;a privacy preserving authentication scheme in the intelligent transportation systems;Higher performance IPPC+ tree for parallel incremental frequent itemsets mining;A sample-based algorithm for visual assessment of cluster tendency (VAT) with large datasets;an efficient batch similarity processing with mapreduce;vietnamese paraphrase identification using matching duplicate phrases and similar words;automatic hyper-parameters tuning for local support vector machines;detection of the primary user’s behavior for the intervention of the secondary user using machine learning;text-dependent speaker recognition system based on speaking frequency characteristics;Static PE malware detection using gradient boosting decision trees algorithm;comparative study on different approaches in optimizing threshold for music auto-tagging;using machine learning for news verification;a short review on deep learning for entity recognition;an analysis of software bug reports using random forest;motorbike detection in urban environment;comprehensive review of classification algorithms for medical information system;new method of medical incomplete information system optimization based on action queries;Cloud media DJ platform: Functional perspective;Cloud media DJ platform: Performance perspective.

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5th international conference on HCI in Business, Government, and Organizations, HCIBGO 2018 Held as Part of HCI international 2018

5th International Conference on HCI in Business, Government,...

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5th international conference on HCI in Business, Government, and Organizations, HCIBGO 2018 Held as Part of HCI international 2018

ISBN: (纸本)9783319917153

the proceedings contain 60 papers. the special focus in this conference is on HCI in Business, Government, and Organizations. the topics include: Testing the convergent validity of continuous self-perceived measurement systems: An exploratory study;towards measuring the potential for semantically enriched texts in knowledge working environments;Evaluating the two-speed IT concept for digitalization;system response time as a stressor in a digital world: Literature review and theoretical model;health belief model and organizational employee computer abuse;deception detection in online automated job interviews;the effects of 360-degree VR videos on audience engagement: Evidence from the New York Times;a Structure-behavior coalescence method for human-computer interaction system requirements specification;data breaches and trust rebuilding: Moderating impact of signaling of corporate social responsibility;activity simulation for experiential learning in cybersecurity workforce development;the effect of LOGO location in navigation bar on web brand recognition based on event-related potential;comparing interface influence on users with varying expertise;initial trust in mobile apps based on landing page information: Results of an online experiment;the impact of usability, functionality and sociability factors on user shopping behavior in social commerce design;effect of social media product reviews on buying decision when presented in augmented reality;product web page design: A psychophysiological investigation of the influence of product similarity, visual proximity on attention and performance;Emotions and feelings: Some aspects for the HCI-community – A work in progress paper;are you worried about personalized service? an empirical study of the personalization-privacy paradox.

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A Novel Distributed Algorithm with Bitcoin Incentives for Phylogeny Analysis 5th

A Novel Distributed Algorithm with Bitcoin Incentives for Ph...

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5th international conference on Advanced Computing, Networking, and Informatics (ICACNI)

作者： Nair, B. J. Bipin Amrita Vishwa Vidyapeetham Dept Comp Sci Amrita Sch Arts & Sci Mysuru India

ISBN: (纸本)9789811086366;9789811086359

Phylogenetic tree reconstruction plays a key role in the field of bioinformatics for the understanding of evolutionary history of species and species traits. However, computation of such trees, especially for enormous data sets, is a challenging task. Maximum Parsimony method generates accurate results, although it is highly resource intensive. In this paper, we present a novel algorithm that improves over the original Maximum Parsimony method. Following a bottom-up approach of the divide-and-conquer methodology, the algorithm divides the input data set into sub-data sets. Sub-trees corresponding to the sub-data sets are generated and analysed in parallel via multiple threads of execution running over multiple cores of multiple processors. Finally, they are merged to generate the optimal evolution tree. Cluster cell formation is encouraged with a novel idea of utilizing bitcoins to reward participating users. We show that our method has reduces time complexity and analyse the impact of bitcoins on user participation willingness. the proposed algorithm is programming language independent.

关键词： Phylogenetic tree bioinformatics Maximum parsimony Bitcoin Parallel algorithm Distributed algorithm

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Head Ternary pattern-Head Shoulder Density Features Pedestrian Detection Algorithm Based on Depth Image 1st

Head Ternary Pattern-Head Shoulder Density Features Pedestri...

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1st international conference on Intelligent Manufacturing and Internet of things (IMIOT) / 5th international conference on Computing for Sustainable Energy and Environment (ICSEE)

作者： Wang, Haikuan Sun, Haoxiang Liu, Kangli Fei, Minrui Shanghai Univ Sch Mechatron Engn & Automat 99 Shangda Rd Shanghai 200444 Peoples R China

ISBN: (纸本)9789811323843;9789811323836

the existing pedestrian detection algorithms are mostly based on 2D image processing, which are susceptible to complex background, the changes of light intensity, and many other factors. For these reasons, the robustness and accuracy of traditional algorithms are not ideal. Moreover, for other few algorithms that use 3D vision, the core process is to extract features by converting 3D images to grayscale images, which cannot make full use of depth information. To solve these problems, a pedestrian detection algorithm based on depth image is proposed in this paper. For depth image preprocessing, a 3D dilation and erosion algorithm based on probability density is proposed. Combined with the Head Ternary pattern-Head Shoulder Density (HTP-HSD) features proposed in this paper, the implement of pedestrian detection can be ensured using SVM classifier to classify the features. Experimental results show that the HTP-HSD features meet the actual needs of pedestrian detection with higher recognition rate and recognition speed.

关键词： Depth image Pedestrian detection 3D dilation and erosion HTP-HSD

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Identifying the Responsible Group for Extreme Acts of Violence through pattern recognition 5th

Identifying the Responsible Group for Extreme Acts of Violen...

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5th international conference on HCI in Business, Government and Organizations (HCIBGO) Held as Part of the 20th international conference on Human-Computer Interaction (HCI international)

作者： Hashemi, Mahdi Hall, Margeret Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

ISBN: (纸本)9783319917160;9783319917153

the expansion of Internet has eased the broadcasting of data, information, and propaganda. the availability of myriads of social and televised media have turned the spotlight on violent extremism, widened the rift between different sides of the spectrum, and expanded the scope and impact of ideology-oriented acts of violence on citizens and nations. the human casualties and psychological impacts on societies make any study on such acts worthwhile, let alone attempting to detect patterns among them. this study focuses on mining the information about each violent act, including human casualties and fatalities, level of coordination and expertise, importance of the targeted process, and the extent of its impact on the process, to identify the responsible group. Decision tree, a non-linear classifier, reached 20% cross-validation accuracy in identifying the correct group among 38 groups. this is the highest accuracy achieved in comparison with other linear classifiers, including Perceptron, SVM, and least squares. Our results also underscored the human casualties and fatalities as the most important predictors. the other four variables, including level of coordination, level of expertise, importance of the targeted process, and the extent of the impact on the process were all partly correlated and less helpful. However, the single feature, generated by linear combination of these four features using PCA, was as good of a predictor as the human casualties and fatalities.

关键词： Decision tree Perceptron SVM Least squares Feature selection Machine learning

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