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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

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A comprehensive review towards appropriate feature selection for moving object detection using aerial images 6th

A comprehensive review towards appropriate feature selection...

引用

6th international conference on Advances in Visual Informatics, IVIC 2019

作者： Mahayuddin, Zainal Rasyid Saif, A. F. M. Saifuddin Faculty of Information Science and Technology University Kebangsaan Malaysia BangiSelangor Malaysia Faculty of Science and Technology American International University–Bangladesh Dhaka Bangladesh

ISBN: (纸本)9783030340315

Efficient feature extraction for moving object using aerial images is still an unsolved issue in computer vision, image processing and pattern recognition research domains. Aerial types of images contain various environmental constraints due to capture frames from various altitudes level, i.e. illumination, shadows, occlusion. For this reason, appropriate feature selection for those types of images needs more attention by the researchers to improve detection rate with fast and computationally less complex features extraction method. this research performed comprehensive review with critical analysis for using various features with various methods for moving object detection using aerial images. In this context, three aspects for critical analysis of using various features are identified followed by challenges of using various features. After that, existing methods with advantages and barriers are comprehensively described with various constraints claimed by the previous research. Next, justification for the need of new feature selection is elaborated for optimum detection performance. Later, adequate validation matrics are illustrated to evaluate various features based moving object detection using aerial images performed in the previous research. the overall review performed in this paper have been comprehensively studied and expected to contribute significantly in computer vision, image processing pattern recognition research field. © Springer Nature Switzerland AG 2019.

关键词： Extraction

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A novel method for contrast enhancement of gray scale image based on shadowed sets

A novel method for contrast enhancement of gray scale image ...

引用

international conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Meysam Alavi Mehrdad Kargari Information Technology Management Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

Image processing is considered to be one of the basic components in intelligent and decision support systems. On the other hand, improving image contrast is one of the most challenging issues in the field of image processing, pattern recognition and machine vision. this process improves understanding the information in the image for the human viewer. this is especially of great importance for medical imaging because low light intensity and high noise levels make it difficult to accurately diagnose specific diseases as well as to document and analyze them. the main motivation in image enhancement is to modify the image properties so that it is suitable for viewing and application, which can be done by removing noise, sharpening the edge and improving contrast enhancement. Due to the importance of the subject, various methods have been proposed by researchers in this field. In this paper, a new method based on shadowed sets is presented to improve image contrast. the results show that this method has acceptable accuracy and efficiency compared to other image enhancement techniques.

关键词： Machine vision Image edge detection Signal processing pattern recognition Intelligent systems Image enhancement Noise level

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A secret sharing scheme based on game theory and BP neural network*

A secret sharing scheme based on game theory and BP neural n...

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Dependability in Sensor, Cloud and Big Data Systems and Application (DependSys), IEEE international conference on

作者： Qiuxian Li Quanxing Zhou Qixin Pan Lingyun Wang Hongfa Ding College of Big Data Engineering Kaili University Kaili China College of Information Guizhou University of Finance and Economics Guiyang China

ISBN: (数字)9781728176512

ISBN: (纸本)9781728176529

In order to solve the problem of low efficiency of traditional secret sharing communication, we design a new rational secret sharing scheme based on BP neural network under the framework of game theory. We first propose an extended system game model for rational secret sharing, and define the set of participants, information set, feasible strategies, and utility functions of the secret sharing game. Secondly, using the characteristics of pattern recognition and classification of BP neural network, the secret input and output training patterns are correlated to realize nonlinear mapping in the neural network, thereby identifying the sub-secrets owned by honest and rational participants and recovering the correct secret. We design a communication channel by using the random function of bilinear pair on elliptic curve to ensure interactive communication among participants and reduce the communication complexity of the solution. Finally, the analysis proves the effectiveness and security of this scheme. By comparing with the existing scheme, this scheme not only has higher communication efficiency, but also meets its security requirements.

关键词： Training Neural networks Games Resists pattern recognition Cryptography Game theory

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6th international conference on Advances in Visual Informatics, IVIC 2019

6th International Conference on Advances in Visual Informati...

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6th international conference on Advances in Visual Informatics, IVIC 2019

ISBN: (纸本)9783030340315

the proceedings contain 65 papers. the special focus in this conference is on Advances in Visual Informatics. the topics include: Early intervention through identification of learners with dyscalculia as initial analysis to design AR assistive learning application;gamifying online knowledge sharing environment: A motivating user interface design;Ascertain quality attributes for design and development of new improved chatbots to assess customer satisfaction index (CSI): A preliminary study;Intrinsic face image decomposition from RGB images with depth cues;haze removal algorithm using improved restoration model based on dark channel prior;Initial investigation on affective 4D mathematics model for low vision learners (AM4LV);virtual reality ideal learning: A look into future medical classroom;hybrid requirement elicitation techniques with lean six sigma methodology for an enhanced framework;longevity risk profiling based on non-disease specific risk factors using association rules mining;Experts’ feedback on DST elements as infused in development process of mobile learning apps;ensemble of deep convolutional neural network for skin lesion classification in dermoscopy images;a comprehensive review towards appropriate feature selection for moving object detection using aerial images;SUFID: Sliced and unsliced fruits images dataset;A combined of fuzzy TOPSIS with z-number and alpha-cut for decision making problems;facial recognition adaptation as biometric authentication for intelligent door locking system;manufacturing lead time classification using support vector machine;iFR: A new framework for real-time face recognition with deep learning;algebraic-trigonometric nonlinear analytical inverse kinematic modeling and simulation for robotic manipulator arm motion control;Combining ARIZ with shape grammars to support designers.

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6th international Workshop on Artificial Intelligence and pattern recognition, IWAIPR 2018

6th International Workshop on Artificial Intelligence and Pa...

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6th international Workshop on Artificial Intelligence and pattern recognition, IWAIPR 2018

the proceedings contain 43 papers. the special focus in this conference is on Artificial Intelligence and pattern recognition. the topics include:

ISBN: (纸本)9783030011314

the proceedings contain 43 papers. the special focus in this conference is on Artificial Intelligence and pattern recognition. the topics include:

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A Framework for the Automatic Combination and Evaluation of Gene Selection Methods 12th

A Framework for the Automatic Combination and Evaluation of ...

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12th international conference on Practical Applications of Computational Biology and bioinformatics (PACBB)

作者： Grasnick, Bastien Perscheid, Cindy Uflacker, Matthias Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9783319987026;9783319987019

High-throughput RNA-Sequencing technologies produce large gene expression datasets whose analysis leads to a better understanding and treatment of diseases like cancer. the data's high dimensionality poses challenges to its computational analysis, which is addressed by applying gene selection. Traditional gene selection methods are based on the data only. In turn, integrative approaches include curated biological information from external knowledge bases in the gene selection process, which improves result accuracy and computational complexity. this paper presents a framework for comparing knowledge based and computational gene selection. Moreover, a novel integrative method of the automatic combination of both approaches is presented. Results on a cancer dataset show that simple computational methods enriched by external knowledge can compete with complex computational techniques.

关键词： Gene expression data analysis Gene selection pattern recognition

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Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks

Every Corporation Owns Its Image: Corporate Credit Ratings v...

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international conference on Computer and Communications (ICCC)

作者： Bojing Feng Wenfang Xue Bindang Xue Zeyu Liu Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China School of Astronautics Beihang University Beijing China Tianjin Academy for Intelligent Recognition Technologies Tianjin China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. there have emerged many studies that implement machine learning techniques to deal with corporate credit rating. However, the ability of these models is limited by enormous amounts of data from financial statement reports. In this work, we analyze the performance of traditional machine learning models in predicting corporate credit rating. For utilizing the powerful convolutional neural networks and enormous financial data, we propose a novel end-to-end method, Corporate Credit Ratings via Convolutional Neural Networks, CCR-CNN for brevity. In the proposed model, each corporation is transformed into an image. Based on this image, CNN can capture complex feature interactions of data, which are difficult to be revealed by previous machine learning models. Extensive experiments conducted on the Chinese public-listed corporate rating dataset which we build, prove that CCR-CNN outperforms the state-of-the-art methods consistently.

关键词： Neural networks Machine learning Predictive models Feature extraction Data models Convolutional neural networks Reliability

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6th international conference on Geo-informatics in Sustainable Ecosystem and Society, GSES 2018

6th International Conference on Geo-informatics in Sustainab...

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6th international conference on Geo-informatics in Sustainable Ecosystem and Society, GSES 2018

ISBN: (纸本)9789811370243

the proceedings contain 46 papers. the special focus in this conference is on Geo-informatics in Sustainable Ecosystem and Society. the topics include: Research on ICS intrusion success rate algorithm based on attack and defense countermeasures;the review of task scheduling in cloud computing;Prediction model of river water quality time series based on ARIMA model;a Review of Gait Behavior recognition Methods Based on Wearable Devices;k-Means Optimization Algorithm Based on Tightness Mutation;Study of Coal Integrated Network Decision Support System Based on GIS;analysis on Spatio-Temporal Changes of the Land Covers in Shenyang;hierarchical Decision Tree for Change Detection Using High Resolution Remote Sensing Images;surface Features Classification of Airborne Lidar Data Based on TerraScan;Enhancement of Class Separability for Polarimetric TerraSAR-X Data and Its Application to Crop Classification in Leizhou Peninsula, Southern China;the Regionalization of Eco-Geological Environment System and Brief Function Evaluation of Luoyang City;cloud Detection in Landsat Imagery Using the Fractal Summation Method and Spatial Point-pattern Analysis;extraction of Target Geological Hazard Areas in Loess Cover Areas Based on Mixed Total Sieving Algorithm;research on Heat and Humidity Transfer Performance Evaluation of Spraying Mine Exhaust Air Heat Exchanger;land Cover Change Analysis in Wuhan, China Using Google Earth Engine Platform and Ancillary Knowledge;overview of Speed Sensorless Control of Permanent Magnet Synchronous Motors;experimental Study on Lateral Compaction Characteristics of Filled Gangue Under Limited Roof Condition;using Improved Genetic Algorithm to Solve the Equations;design of Security Alarm System Based on LoRa Technology.

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6th international Symposium on Model-Based Safety and Assessment, IMBSA 2019

6th International Symposium on Model-Based Safety and Assess...

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6th international Symposium on Model-Based Safety and Assessment, IMBSA 2019

ISBN: (纸本)9783030328719

the proceedings contain 24 papers. the special focus in this conference is on international Symposium on Model-Based Safety and Assessment. the topics include: Model synchronization: A formal framework for the management of heterogeneous models;DPN – dependability priority numbers;towards dependability and energy aware asset management framework for maintenance planning in smart grids;formal verification of network interlocking control by distributed signal boxes;SQUADfps: Integrated model-based machine safety and product quality for flexible production systems;a serverless architecture for wireless body area network applications;Automated model-based attack tree analysis using HiP-HOPS;what today’s serious cyber attacks on cars tell us: Consequences for automotive security and dependability;Safety and security aspects of fail-operational urban surround perceptION (FUSION);Model based approach for RAMS analyses in the space domain with capella open-source tool;an approach for validating safety of perception software in autonomous driving systems;Stochastic modelling of autonomous vehicles driving scenarios using PEPA;a runtime safety analysis concept for open adaptive systems;clustering environmental conditions of historical accident data to efficiently generate testing sceneries for maritime systems;pattern-based formal approach to analyse security and safety of control systems;modeling patterns for the assessment of maintenance policies with altaRica 3.0;A domain specific language to support HAZOP studies of SysML models;Integrating existing safety analyses into SysML;FDS-ML: A new modeling formalism for probabilistic risk and safety analyses;Integrating safety design artifacts into system development models using safeDeML;A conceptual framework to incorporate complex basic events in HiP-HOPS;compositionality of component fault trees.

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Improved Pathogen recognition using Non-Euclidean Distance Metrics andWeighted kNN 19

Improved Pathogen Recognition using Non-Euclidean Distance M...

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Proceedings of the 2019 6th international conference on Biomedical and bioinformatics Engineering

作者： Mukunthan tharmakulasingam Cihan Topal Anil Fernando Roberto La Ragione Centre for Vision Speech and Signal Processing University of Surrey United Kingdom CVSSP University of Surrey UK Eskisehir Technical University Turkey School of Veterinary Medicine University of Surrey United Kingdom

ISBN: (纸本)9781450372992

the timely identification of pathogens is vital in order to effectively control diseases and avoid antimicrobial resistance. Non-invasive point-of-care diagnostic tools are recently trending in identification of the pathogens and becoming a helpful tool especially for rural areas. Machine learning approaches have been widely applied on biological markers for predicting diseases and pathogens. However, there are few studies in the literature that have utilized volatile organic compounds (VOCs) as non-invasive biological markers to identify bacterial pathogens. Furthermore, there is no comprehensive study investigating the effect of different distance and similarity metrics for pathogen classification based on VOC data. In this study, we compared various non-Euclidean distance and similarity metrics with Euclidean metric to identify significantly contributing VOCs to predict pathogens. In addition, we also utilized backward feature elimination (BFE) method to accurately select the best set of features. the dataset we utilized for experiments was composed from the publications published between 1977 and 2016, and consisted of associations in between 703 VOCs and 11 *** performed extensive set of experiments with five different distance metrics in both uniform and weighted manner. Comprehensive experiments showed that it is possible to correctly predict pathogens by using 68 VOCs among 703 with 78.6% accuracy using k-nearest neighbour classifier and Sorensen distance metric.

关键词： pathogen detection bioinformatics feature selection Distance metrics machine learning backward feature elimination (BFE)

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