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pattern Match Query for Spatiotemporal RDF Graph 15th

Pattern Match Query for Spatiotemporal RDF Graph

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15th international conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th international conference on Harmony Search, soft computing and Applications, ICHSA 2019

作者： Di, Xiaofeng Wang, Jinyao Cheng, Shaohui Bai, Luyi Qinhuangdao066004 China

ISBN: (纸本)9783030325909

RDF is the W3C standard, whose model is defined as a triple. RDF is designed to provide a common way of describing resource so that it can be read and understood by computer applications. In RDF model, the statement in the resource description may correspond to a natural language statement, the resource corresponds to the subject in the natural language, the attribute type corresponds to the predicate, and the attribute value corresponds to the object. Meanwhile, RDF information has temporal attribute and spatial attribute. But classical RDF model can’t show the spatial and temporal properties of resources. So, combining spatiotemporal information with RDF is necessary. However, SPARQL, the W3C-recommended query language of RDF, only meets the classic RDF query. This paper presents a novel representation model of spatiotemporal RDF. Based on this model, a Find Isomorphic Graphs of the Query Graph algorithm is introduced to obtain some candidate isomorphic graph of the query graph. Finally, we define the process of pattern matching. © 2020, Springer Nature Switzerland AG.

关键词： pattern matching

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On the task of recognizing road infrastructure on devices with low computing power

On the task of recognizing road infrastructure on devices wi...

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2020 international conference on Engineering Management of Communication and Technology, EMCTECH 2020

作者： Moseva, M. Gorodnichev, M. Dzhabrailov, Kh. Gematudinov, R. Moscow Technical University of Communications and Informatics Moscow Russia Moscow Automobile and Road Construction State Technical University Moscow Russia

ISBN: (纸本)9780738130743

The article is devoted to the development of a client-server application for detecting road infrastructure objects. The main architectures of neural networks are considered, a database for collecting the necessary data is developed. A feature of the developed application is the binding of road infrastructure objects with geo-position and the formation of a map with the display of recognizable objects. © ©2020 IEEE.

关键词： Intellegence system Machine learning Mobile application Object detection pattern recognition Traffic flow

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Credit Card Fraud Detection and Prevention by Face recognition

Credit Card Fraud Detection and Prevention by Face Recogniti...

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2020 international conference on Smart Innovations in Design, Environment, Management, Planning and computing, ICSIDEMPC 2020

作者： Zinjurde, Ashwini.M. Kamble, Vilas.B. Pescoe Department of Cse Aurangabad India

ISBN: (纸本)9781728159706

Internet banking is now becoming the most commonly used form of banking transactions. Confidentiality can be compromised in the process of electronic purchases. We therefore introduced a new approach to prevent theft during online transactions in order to protect information through a two-step mechanism of authentication. The primary step of authentication is OTP verification. If the OTP has been checked, the face should be recognized. The details are collected and the authorization for both true and fraudulent transactions is submitted to the bank. The new credit card scanning device has beneficial characteristics such as certain health, user-friendliness, etc. The purpose of the application is to reduce credit card fraud by knowledge of the Face System. Customers get the most accessible and highly efficient electronic banking program. © 2020 IEEE.

关键词： Local binary pattern

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8th international conference on soft computing for Problem Solving, SocProS 2018

8th International Conference on Soft Computing for Problem S...

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8th international conference on soft computing for Problem Solving, SocProS 2018

ISBN: (纸本)9789811500343

The proceedings contain 163 papers. The special focus in this conference is on soft computing for Problem Solving. The topics include: Optimization of Target Oriented Network Intelligence Collection for the Social Web by Using k-Beam Search;compressed Air Energy Storage Driven by Wind Power Plant for Water Desalination Through Reverse Osmosis Process;fully Fuzzy Semi-linear Dynamical System Solved by Fuzzy Laplace Transform Under Modified Hukuhara Derivative;comparison of Performance of Four-Element Microstrip Array Antenna Using Electromagnetic Bandgap Structures;model Development for Strength Properties of Laterized Concrete Using Artificial Neural Network Principles;wind Power Forecasting Using Parallel Random Forest Algorithm;leukemia Cell Segmentation from Microscopic Blood Smear Image Using C-Mode;Implementation of Exploration in TONIC Using Non-stationary Volatile Multi-arm Bandits;fuzzy Logic-Based Model for Predicting Surface Roughness of Friction Drilled Holes;load Bearing Capacity for a Ferrofluid Squeeze Film in Double Layered Porous Rough Conical Plates;Face recognition and Classification Using GoogleNET Architecture;Enhancing Public Health Surveillance by Measurement of Similarity Using Rough Sets and GIS;data Analytics Implemented over E-commerce Data to Evaluate Performance of Supervised Learning Approaches in Relation to Customer Behavior;optimal Renewable Energy Resource Based Distributed Generation Allocation in a Radial Distribution System;PV Module Temperature Estimation by Using ANFIS;modified Artificial Potential Field Approaches for Mobile Robot Navigation in Unknown Environments;Analysis of BASNs Battery Performance at Different Temperature Conditions Using Artificial Neural Networks (ANN);ASIC Implementation of Fixed-Point Iterative, Parallel, and Pipeline CORDIC Algorithm;adaptive Sensor Ranking Based on Utility Using Logistic Regression.

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4th international conference on soft computing: Theories and Applications, SoCTA 2019

4th International Conference on Soft Computing: Theories and...

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4th international conference on soft computing: Theories and Applications, SoCTA 2019

ISBN: (纸本)9789811540318

The proceedings contain 98 papers. The special focus in this conference is on soft computing: Theories and Applications. The topics include: Hoax and Faux of Information Credibility in Social Networks: Explored, Exemplified and Experimented;Minimize Power Ratio (PR) in OFDM Using Tone Reservation Method;a Single-Phase Multi-level Inverter Using a Lesser Number of Switching Devices;symmetric Key Generation and Distribution Using Diffie-Hellman Algorithm;Design of Controllers Using PSO Technique for Second-Order Stable Process with Time Delay;a Green Dynamic Internet of Things (IoT)-Battery Powered Things Aspect-Survey;An Efficient Layout of Single-Layer Full Adder Using QCA;a Review of mm-Wave Power Amplifiers for Next-Generation 5G Communication;vision-Based Automated Traffic Signaling;Effectiveness of Whale Optimization Based I+PD Controller for LFC of Plug-in Electric Vehicle Included Multi-area System;Performance Comparison of SVM and ANN for Reversible ECG Data Hiding;application of Multi-criteria Decision-Making Method for the Evaluation of Tamilnadu Private Bus Companies;CNC Machine Shop Floor Facility Layout Design Using Genetic Algorithm;source of Treatment Selection for Different States of India and Performance Analysis Using Machine Learning Algorithms for Classification;Ant Lion Optimization Technique for Minimization of Voltage Deviation Through Optimal Placement of Static VAR Compensator;on Vector Variational Inequalities and Vector Optimization Problems;characterizations of the Solution Sets for Constrained Pseudolinear Semi-infinite Programming Problems;novel Chaotic Elephant Herding Optimization for Multilevel Thresholding of Color Image;Forecasting Groundwater Fluctuation from GRACE Data Using GRNN;android Application for recognition of Indian Origin Agricultural Products;dual Band Printed Rectangular Ring-Shaped Monopole Antenna for Wireless Communication.

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MicroRNA pattern recognition for bile duct cancer using programmable DNA and biological nanopore 21

MicroRNA pattern recognition for bile duct cancer using prog...

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21st international conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2017

作者： Hiratani, M. Kawano, R. Department of Biotechnology and Life Science Tokyo University of Agriculture and Technology Japan

ISBN: (纸本)9780692941836

This paper describes a programmable DNA system which recognizes patterns of microRNA (miRNA) overexpressed from a bile duct cancer. Recently, miRNA has attracted attentions as a next-generation of the early diagnostic marker for every cancers and been applied to the cancer diagnosis. Here, on the base of DNA computing technique and nanopore sensing, we have constructed the barcode-like DNA which coded the information of overexpressed miRNAs. Nanopore decoded it as the ionic current, and it enabled us to recognize miRNA pattern with label-free and in real-time. We believe that our system can be applied to a simple cancer diagnosis. © 17CBMS-0001.

关键词： Nanopores

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Complete Statistical Analysis to Weather Forecasting 1st

Complete Statistical Analysis to Weather Forecasting

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1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Datta, Anisha Si, Shukrity Biswas, Sanket Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri735102 India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

ISBN: (纸本)9789811390418

The primary objective of the model applied in this work is to predict the weather of a city named Austin in Texas using supervised machine learning algorithms. In this case, artificial neural networks and gradient boosting classifier were implemented to build models to predict weather and comparisons between these two models are also made for this dataset. Here, average temperature, average dew point, average pressure of sea level, average percentage of humidity, etc. are the parameters taken into consideration which influence the weather of the place. Using these parameters, the trained models performed a classification to predict whether the weather is rainy (thunderstorm or not), not rainy, snowy, or foggy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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EEG-Based Hand Motion pattern recognition Using Deep Learning Network Algorithms 2020

EEG-Based Hand Motion Pattern Recognition Using Deep Learnin...

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Proceedings of the 2020 9th international conference on computing and pattern recognition

作者： Yongyu Jiang Christine Chen Xiaodong Zhang Wei Zhou Chaoyang Chen Stephen Lemos Xi'an Jiaotong University Xi'an China Department of Computer Science University of Michigan at Ann Arbor Ann Arbor Michigan USA Ximen University Xiamen China Department of Biomedical Engineering Wayne State University Detroit Michigan USA Orthopedic Surgery and Sports Medicine DMC Detroit MI USA

ISBN: (纸本)9781450387835

EEG signals contain information of limb motion patterns that are difficult to be extracted using conventional methods. The purpose of this study was to determine the feasibility of novel machine learning algorithms in extraction of hand motion intents from EEG. In this paper, 3 hand motion patterns (hand opening, closing, resting) were recognized using the filter bank common spatial pattern (FBCSP) method. The spatial filtering method and the CNN classifier were used to decode and classify the EEG signals. 15 healthy subjects' EEG datasets were used. Results demonstrated that the average correct rate of motion pattern recognition was 83.61%. The performance of these algorithms was validated using off-line computer-controlled robotic hand system, the average accuracy of a subject was 77.32%, and the average time of the system recognition action was 28.4ms. This study demonstrated that hand motion pattern recognition can be extracted from EEG signals and can be utilized by brain computer interface (BCI) to trajectory control a robotic hand motion.

关键词： pattern recognition electroencephalography (eeg) signal processing hand motion convolutional neural network

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The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection

The Devil is in the Conflict: Disentangled Information Graph...

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IEEE international conference on Data Mining (ICDM)

作者： Zhixun Li Dingshuo Chen Qiang Liu Shu Wu School of Computer Science and Technology Beijing Institute of Technology Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

Graph-based fraud detection has heretofore received considerable attention. Owning to the great success of Graph Neural Networks (GNNs), many approaches adopting GNNs for fraud detection has been gaining momentum. However, most existing methods are based on the strong inductive bias of homophily, which indicates that the context neighbors tend to have same labels or similar features. In real scenarios, fraudsters often engage in camouflage behaviors in order to avoid detection system. Therefore, the homophilic assumption no longer holds, which is known as the inconsistency problem. In this paper, we argue that the performance degradation is mainly attributed to the inconsistency between topology and attribute. To address this problem, we propose to disentangle the fraud network into two views, each corresponding to topology and attribute respectively. Then we propose a simple and effective method that uses the attention mechanism to adaptively fuse two views which captures data-specific preference. In addition, we further improve it by introducing mutual information constraints for topology and attribute. To this end, we propose a Disentangled Information Graph Neural Network (DIGNN) model, which utilizes variational bounds to find an approximate solution to our proposed optimization objective function. Extensive experiments demonstrate that our model can significantly outperform state-of-the-art baselines on real-world fraud detection datasets.

关键词： Adaptation models Network topology Fuses Semantics Graph neural networks Fraud Topology

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The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy

The Effect of Spectrogram Reconstruction on Automatic Music ...

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international conference on pattern recognition

作者： Kin Wai Cheuk Yin-Jvun Luo Emmanouil Benetos Dorien Herremans Institute of High Performance Computing Agency for Science Technology and Research School of Electronic Engineering and Computer Science Queen Mary University of London

ISBN: (纸本)9781728188089;9781728188096

Most of the state-of-the-art automatic music transcription (AMT) models break down the main transcription task into sub-tasks such as onset prediction and offset prediction and train them with onset and offset labels. These predictions are then concatenated together and used as the input to train another model with the pitch labels to obtain the final transcription. We attempt to use only the pitch labels (together with spectrogram reconstruction loss) and explore how far this model can go without introducing supervised sub-tasks. In this paper, we do not aim at achieving state-of-the-art transcription accuracy, instead, we explore the effect that spectrogram reconstruction has on our AMT model. Our proposed model consists of two U-nets: the first U-net transcribes the spectrogram into a posteriorgram, and a second U-net transforms the posteriorgram back into a spectrogram. A reconstruction loss is applied between the original spectrogram and the reconstructed spectrogram to constrain the second U-net to focus only on reconstruction. We train our model on three different datasets: MAPS, MAESTRO, and MusicNet. Our experiments show that adding the reconstruction loss can generally improve the note-level transcription accuracy when compared to the same model without the reconstruction part. Moreover, it can also boost the frame-level precision to be higher than the state-of-the-art models. The feature maps learned by our U-net contain gridlike structures (not present in the baseline model) which implies that with the presence of the reconstruction loss, the model is probably trying to count along both the time and frequency axis, resulting in a higher note-level transcription accuracy.

关键词： Training Measurement Time-frequency analysis Transforms Predictive models Market research pattern recognition

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