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Fake news pattern recognition using linguistic analysis 7

Fake news pattern recognition using linguistic analysis

引用

Joint 7th International conference on Informatics, Electronics and vision and 2nd International conference on Imaging, vision and pattern recognition, ICIEV-IVPR 2018

作者： Dey, Amitabha Rafi, Rafsan Zani Hasan Parash, Shahriar Arko, Sauvik Kundu Chakrabarty, Amitabha School of Engineering and Computer Science BRAC University Dhaka Bangladesh

ISBN: (纸本)9781538651612

In the wake of the 2016 US Presidential Election, the upsurge of fake news has been a subject of increased discussion and debate. In this paper, we propose a general framework that can been adopted in future elections worldwide to augment humans in making better decisions when it comes to recognizing news deception and identifying hidden bias of the author. For our study, we constructed a dataset comprising 200 tweets on »Hilary Clinton», while performing veracity assessment. We initially perform »text normalization» on tweets, explore techniques for feature extraction to classify news into categories, perform a comprehensive linguistic analysis on tweets, extract bag-of-words to find noticeable pattern, and finally apply k-nearest neighbor algorithm for classifying polarized news from credible. We later turn to some popular evaluation metrics to quantify the success rate of our framework, discuss the results of implementing knn algorithm and discuss interconnected research domains and future research directions for constructing an ideal model for fake news detection system around social media. © 2018 IEEE.

关键词： Classification (of information)

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Automatic Docking Design of Flange Pipe Based on Spatial Information

Automatic Docking Design of Flange Pipe Based on Spatial Inf...

引用

International conference on Information Science and Control Engineering (ICISCE)

作者： Xingxun Li Xiuhua Zhang Jinmeng Wu Jian Huang Pianpian Chen Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China Hubei Engineering Research Center of Video Image and HD Projection Wuhan Institute of Technology Wuhan China

With the development of ship industry, pipeline system has been more and more widely used. However, the pipeline site space is narrow. Mechanical and electrical equipments are of all kinds. the pipeline cable is densely distributed. All problems mentioned above seriously affects the pipeline construction. In order to solve the problem of pipeline docking, the method of making model and comprehensive lofting is usually adopted. However, this method is technically backward. the operation is inconvenient due to the narrow onsite space. the required period is long. With low information utilization and accuracy, the method of making model and comprehensive lofting has a great influence on the development cycle and design quality. In order to solve these problems, this paper presents a design method and system of flange pipe docking. A binocular camera is used to obtain the image of the pipe joint. the center position of the flange surface and array holes on the flange surface are extracted from the image, which is bias of subsequent work. First, the path of the intermediate connecting pipe is calculated. then, according to the pipe standards of intermediate connection pipe and production specification of enterprise, the model of intermediate connecting pipe is automatically generated by using secondary development technology of CAD. In this paper, the binocular vision technology is combined with pipeline docking design, which significantly improves the utilization rate of field space information and design efficiency, and solves the problems of inconvenient operation and safety risk of traditional profiling method.

关键词： Industries Solid modeling Design methodology Pipelines Production Flanges Feature extraction

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Singular Spectrum Analysis based Long Short-Term Memory for Predicting Bitcoin Price 7

Singular Spectrum Analysis based Long Short-Term Memory for ...

引用

7th IEEE International conference on computer Science and Network Technology (ICCSNT)

作者： Qiao, Zhaoying Chai, Tianrui Gu, Jialu Zhou, Xinyi Dai, Shuning Zhang, Xuanming Nanjing Univ Chinese Med Coll Hlth Econ & Management Nanjing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Univ New South Wales Business Sch Sydney NSW Australia Nanjing Univ Post & Telecommun Coll Commun & Informat Engn Nanjing Peoples R China Australian Natl Univ Coll Business & Econ Canberra ACT Australia

ISBN: (纸本)9781728132990

Bitcoin, a leading cryptocurrency in the financial market, is full of non-linearity, non-stationarity and high volatility. To make risk management strategies, emphasis on cryptocurrency price predicting is truly needy. However, studies about cryptocurrency prediction are lacking. In this paper, a novel hybrid model combining long short-term memory (LSTM), a state-of-the-art sequence learning method, with singular spectrum analysis (SSA) was proposed to predict Bitcoin price. SSA was employed to decompose the original time series into independent signals in term of trend, market fluctuation and noise. A smoothed series with valid information was reconstructed with reduction of noise. By introducing the smoothed series sequence into LSTM, prediction value is obtained. Empirical analysis shows that the proposed hybrid SSA-LSTM model outperforms baseline single LSTM model, according to root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). the result suggests that the proposed hybrid model has satisfactory ability to grasp pattern of Bitcoin price series since SSA can extract valid information from the original series and avoid overfitting.

关键词： long short-term memory singular spectrum analysis Bitcoin cryptocurrency prediction

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Fully Automatic Faulty Weft thread Detection using a Camera System and Feature-based pattern recognition 7

Fully Automatic Faulty Weft Thread Detection using a Camera ...

引用

7th International conference on pattern recognition Applications and Methods (ICPRAM)

作者： Kopaczka, Marcin Saggiomo, Marco Guettler, Moritz Gries, thomas Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Rhein Westfal TH Aachen Inst Text Tech Aachen Germany

ISBN: (纸本)9789897582769

In this paper, we present a novel approach for the fully automated detection of faulty weft threads on airjet weaving machines using computer vision. the proposed system consists of a camera array for image acquisition and a classification pipeline in which we use different image processing and machine learning methods to allow precise localization and reliable classification of defects. the camera system is introduced and its advantages over other approaches are discussed. Subsequently, the processing steps are motivated and described in detail, followed by an in-depth analysis of the impact of different system parameters to allow chosing optimal algorithm combinations for the problem of faulty weft yarn detection. To analyze the capabilities of our solution, system performance is thoroughly evaluated under realistic production settings, showing excellent detection rates.

关键词： Automated Visual Inspection Industrial Image Processing Air-jet Weaving Machine

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Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion 4th

Word-Wise Handwriting Based Gender Identification Using Mult...

引用

4th Workshop on Document Analysis and recognition, DAR 2018, held in Conjunction with the 11th Indian conference on vision, Graphics, and Image Processing, ICVGIP 2018

作者： Asadzadeh Kaljahi, Maryam Vidya Varshini, P.V. Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Guru, D.S. Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Vellore Institute of Technology VelloreTamil Nadu India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China Department of Studies in Computer Science Manasagangotri University of Mysuru Mysore India

ISBN: (纸本)9789811393600

Handwriting based gender identification at the word level is challenging due to free style writing, use of different scripts, and inadequate information. this paper presents a new method based on Multi-Gabor Response (MGR) fusion for gender identification at the word level. It first explores weighted-gradient features for word segmentation from text line images. For each word, the proposed method obtains eight Gabor response images. then it performs sliding window operation over MGR images to smooth the values. For each smoothed MGR images, we perform fusion operation that chooses the Gabor response value which contributes to the highest peak in the histogram. this process results in a feature matrix, which is fed to CNN for gender identification. Experimental results on our dataset (multi scripts) apart from English, and benchmark databases, namely, IAM, KHATT, and QUWI, which contain handwritten English and Arabic text, show that the proposed method outperforms the existing methods. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Image segmentation

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Research on a Method of Airport Runway Detection Algorithm 4

Research on a Method of Airport Runway Detection Algorithm

引用

4th IEEE International conference on Image, vision and Computing, ICIVC 2019

作者： Xie, Kun Chen, Jinlong Yang, Minghao University of Electronic Technology Guangxi Key Laboratory of Trusted Software of Guilin Guilin541004 China University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security of Guilin Guilin541004 China Chinese Academy of Sciences Key Laboratory of Pattern Recognition Institute of Automation Beijing100190 China

ISBN: (纸本)9781728123257

In view of the long and narrow characteristics of the airport runway function area, this paper proposes a method combining the Haar -like feature and the spherical neural network to detect the airport runway in remote sensing images. the algorithm first uses Haar-like to extract the target features and trains the strong classifier, and then further uses a spherical neural network model to eliminate false alarms. the experimental results show that the algorithm achieves good accuracy in detecting airport runways. © 2019 IEEE.

关键词： Signal detection

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FindImplant: An Online Application for Visualizing the Dental Implants from X-Ray Images 16th

FindImplant: An Online Application for Visualizing the Denta...

引用

16th International conference on Cooperative Design, Visualization, and Engineering (CDVE)

作者： Huanca Marin, Julio Cesar Ponce Atencio, Yalmar Univ Nacl Jose Maria Arguedas Andahuaylas Apurimac Peru

ISBN: (纸本)9783030309480;9783030309497

Web applications are very popular today and many fields of knowledge have been benefited, however, still there are many study fields which there are not helpful tools. In this research, we propose the implementation of a collaborating tool for identifying dental implants in X-Ray images, due the identification of dental implant images is a tedious intensive manual task. Our proposal is based on computer vision techniques such as features detection, description and matching. the query image can be obtained from X-Ray image data using an ordinary camera, like the ones found on a mobile phone. then we extract features on the image in order to aid in the search for the best matches in the catalogue. the digital catalogue has a few hundred images of dental implants, and finding the best match takes a few seconds. the application has a high success rate and is helpful for dentists, returning the three best candidates, therefore reducing the work of a Dentist from analyzing hundreds of image to just a few.

关键词： Dental implant X-Ray Shape matching Image descriptor pattern recognition Medial axis of polygonal shape Collaborative web application

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An IOT Edge-Fog-Cloud Architecture for vision Based Pallet Integrity 2nd

An IOT Edge-Fog-Cloud Architecture for Vision Based Pallet I...

引用

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： Vaira, Raffaele Pietrini, Rocco Pierdicca, Roberto Zingaretti, Primo Mancini, Adriano Frontoni, Emanuele Dipartimento di Ingegneria dell’Informazione Università Politecnica delle Marche Via Brecce Bianche 12 Ancona60131 Italy Dipartimento di Ingegneria Civile Edile e dell’Architettura Università Politecnica delle Marche Via Brecce Bianche 12 Ancona60131 Italy

ISBN: (纸本)9783030307530

Improving the availability of products in a store in order to avoid the OOS (out-of-stock) problem is a crucial topic nowadays. the reduction of OOS events leads to a series of consequences, including, an increase in customer satisfaction and loyalty to the store and brand, the production of positive advertising with a consequent growth in sales, and finally an increase in profitability and sales for a specific category. In this context, we propose the Pallet Integrity system for the automatic and real-time detection of OOS on promo pallets and promo forecasting using computer vision. the system uses two cameras placed in top-view configuration;one equipped with a depth sensor used to determines the number of pieces on the pallet and the other, a very high resolution web-cam, that is used for the facing recognition. the computer vision depth process takes place on edge, while the product recognition and promo OOS alarms runs on the fog, with a processing unit per store;the multi-promo forecasting service and the data aggregation and visualization is on cloud. the system was extensively tested on different real stores worldwide with accurate OOS detection and forecasting results. © 2019, Springer Nature Switzerland AG.

关键词： Cloud computing architecture

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Studio2Shop: From Studio Photo Shoots to Fashion Articles 7

Studio2Shop: From Studio Photo Shoots to Fashion Articles

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7th International conference on pattern recognition Applications and Methods (ICPRAM)

作者： Lasserre, Julia Rasch, Katharina Vollgraf, Roland Zalando Res Muehlenstr 25 D-10243 Berlin Germany

ISBN: (纸本)9789897582769

Fashion is an increasingly important topic in computer vision, in particular the so-called street-to-shop task of matching street images with shop images containing similar fashion items. Solving this problem promises new means of making fashion searchable and helping shoppers find the articles they are looking for. this paper focuses on finding pieces of clothing worn by a person in full-body or half-body images with neutral backgrounds. Such images are ubiquitous on the web and in fashion blogs, and are typically studio photos, we refer to this setting as studio-to-shop. Recent advances in computational fashion include the development of domain-specific numerical representations. Our model Studio2Shop builds on top of such representations and uses a deep convolutional network trained to match a query image to the numerical feature vectors of all the articles annotated in this image. Top-k retrieval evaluation on test query images shows that the correct items are most often found within a range that is sufficiently small for building realistic visual search engines for the studio-to-shop setting.

关键词： computer vision Deep Learning Fashion Item recognition Street-to-shop

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An Orientation-correction Detection Method for Scene Text Based on SPP-CNN 4

An Orientation-correction Detection Method for Scene Text Ba...

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4th IEEE International conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Dai, Jin Guo, Liang Wang, Zu Liu, Shuguang Chongqing Univ Posts & Telecommun Sch Software Engn Chongqing Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Peoples R China

ISBN: (纸本)9781728114101

Scene text detection plays an important role in computer vision and pattern recognition field in recent years due to extract the accurate and rich text information. At present, component-based methods have become the trend, and there are still some challenges because of illumination, blur and difficult background. In this paper, an orientation-correction detection method for scene text based on Spatial Pyramid Pooling Convolutional Neural Networks, SPP-CNN, is proposed. Firstly, an enhanced multi-channel MSER model, which is constructed from R, G, B, H, S, V and grey channels of manually blurred and sharpened images, is built. then, the manually-designed features are embedded in SPP-CNN as the effective text feature detector and classifier. Finally, a twolayer text grouping algorithm is achieved which can handle slightly-slanted text. Experiments on ICDAR 2011 show the f-measure has improved to 84%, the precision and f-measure of ICDAR 2013 have reached 87% and 85% respectively.

关键词： multi-channel MSER SPP-CNN scene text detection orientation correction

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