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3rd international conference on Artificial Intelligence, 2022

3rd International Conference on Artificial Intelligence, 202...

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3rd international conference on Artificial Intelligence, 2022

ISBN: (纸本)9789811694226

The proceedings contain 89 papers. The special focus in this conference is on Artificial Intelligence. The topics include: Medical Image Segmentation Using Transformer;satellite Online Scheduling Algorithm Based on Proximal Policy;generation Method of Control Strategy for Aircrafts Based on Hierarchical Reinforcement learning;finding Significant Influencing Factors of Core Quality and Ability Development of Teachers Based on Improved Genetic Algorithm;a New Augmented Method for Processing Video Datasets Based on Deep Neural Network;small-Object Detection with Super Resolution Embedding;graph-Based Anomaly Detection of Wireless Sensor Network;identifying Important Attributes for Secondary School Student Performance Prediction;research on Smart Home System Based on Internet of Things;a Cross-Modal Attention and Multi-task learning Based Approach for Multi-modal Sentiment Analysis;research on Problem Matching Classification Based on Artificial Intelligence Technology;research on the Application of Natural Language Processing in the Virtual Comment’s Classification;Design and Application of Endangered Animal Monitoring System Based on Mobile APP;a Rapid Image Semantic Segment Method Based on Deeplab V3+;analysis and Research on China’s New Energy Vehicles Industry Policy Based on Policy Subjects, Tools and Objectives;research on Semantic Retrieval System of Scientific Literature Based on Deep learning;research on the Evaluation System Construction of County Innovation Driven Development-Based on Evaluation and Measurement Model;research on the Method of Policy Text Knowledge Mining Based on Neural Network;concept Drift Detection Based on Restricted Boltzmann machine in Multi-class Classification System;Autoencoder-Based Baseline Parameterized by Central Limit Theorem for ICS Cybersecurity;Classroom Speech Emotion recognition Based on Multi-channel Convolution and SEnet Network;LFMCW Milimeter Radar Detection.

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An Encoder-Decoder Approach to Offline Handwritten Mathematical Expression recognition with Residual Attention 3rd

An Encoder-Decoder Approach to Offline Handwritten Mathemati...

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Lin, Qiqiang Wang, Chunyi Bi, Ning Suen, Ching Y. Tan, Jun School of Mathematics Sun Yat-Sen University Guangzhou510275 China Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Guangzhou510275 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783031090363

Unlike handwritten numeral recognition and handwritten text recognition, the recognition of handwritten mathematical expressions is more difficult because of their complex two-dimensional spatial structure. Since the "watch, attend and parse (WAP)" method was proposed in 2017, encoder-decoder models have made significant progress on handwritten mathematical expression recognition. Our model is improved based on the WAP [1] model. In this paper, the attention module is reasonably added to the encoder to make the extracted features are more informative. The new network is called Dense Attention Network (DATNet), which allows for an adequate extraction of the structural information from handwritten mathematical expressions. To prevent the model from overfitting during the training process, we use label smoothing [2] as the loss. Experiments showed that our model (DATWAP) improved WAP expression recognition from 48.4%/46.8%/45.1% to 54.72%/52.83%/52.54% on the CROHME 2014/2016/2019 test sets. © 2022, Springer Nature Switzerland AG.

关键词： Decoding

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Document analysis for music scores via machine learning 2016

Document analysis for music scores via machine learning

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3rd international Workshop on Digital Libraries for Musicology, DLfM 2016

作者： Calvo-Zaragoza, Jorge Vigliensoni, Gabriel Fujinaga, Ichiro Pattern Recognition and Artificial Intelligence Group Department of Software and Computing Systems University of Alicante Alicante Spain Centre for Interdisciplinary Research in Music Media and Technology Schulich School of Music McGill University MontréalQC Canada

ISBN: (纸本)9781450347518

Content within musical documents not only contains musical notation but can also include text, ornaments, annotations, and editorial data. Before any attempt at automatic recognition of elements in these layers, it is necessary to perform a document analysis process to detect and classify each of its constituent parts. The obstacle for this analysis is the high heterogeneity amongst collections, which makes it difficult to propose methods that can be generalizable to a broader range of sources. In this paper we propose a data-driven document analysis framework based on machine learning, which focuses on classifying regions of interest at pixel level. The main advantage of this approach is that it can be exploited regardless of the type of document provided, as long as training data is available. Our preliminary experimentation includes a set of specific tasks that can be performed on music such as the detection of staff lines, isolation of music symbols, and the layering of the document into its elemental parts. © 2016 ACM.

关键词： Music

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3rd international conference on Soft Computing and its Engineering Applications, icSoftComp 2021

3rd International Conference on Soft Computing and its Engin...

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3rd international conference on Soft Computing and its Engineering Applications, icSoftComp 2021

ISBN: (纸本)9783031057663

The proceedings contain 33 papers. The special focus in this conference is on Soft Computing and its Engineering Applications. The topics include: Sentiment Analysis of Twitter Data Using machine learning Approaches;mining Spatio-Temporal Sequential patterns Using MapReduce Approach;a Split-Then-Join Lightweight Hybrid Majority Vote Classifier;identification of Barriers in Adoption of IoT: Commercial Complexes in India;a Dynamically Adapting Framework for Stock Price Prediction;evaluating Binary Classifiers with Word Embedding Techniques for Public Grievances;database Concentration Method for Efficient Image Retrieval Using Clustering and Image Tag Comparison;microstructure Image Classification of Metals Using Texture Features and machine learning;segregation of Areca Nuts Using Three Band Photometry and Deep Neural Network;Early Diagnosis of Alzheimer’s Disease from MRI Images Using Scattering Wavelet Transforms (SWT);constraint Pushing Multi-threshold Framework for High Utility Time Interval Sequential pattern Mining;KTSVidRec: A Knowledge-Based Topic Centric Semantically Compliant Approach for Video Recommendation on the Web;intelligent Facial Expression Evaluation to Assess Mental Health Through Deep learning;intelligent Mobility: A Proposal for Modeling Traffic Lights Using Fuzzy Logic and IoT for Smart Cities;crop Disease Prediction Using Multiple Linear Regression Modelling;radial Basis Function Network Based Intelligent Scheme for Software Quality Prediction;generating the Base Map of Regions Using an Efficient Object Segmentation Technique in Satellite Images;KCEPS: Knowledge Centric Entity Population Scheme for Research Document Recommendation;weighted Hybrid Recommendation System Using Singular Value Decomposition and Cosine Similarity;CT/MRI 3D Fusion for Cerebral System Analysis;text Analysis and Classification for Preprocessing Phase of Automatic Text Summarization Systems;arabic Cyberbullying Detection from Imbalanced Dataset Using machine learning;in

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Rough neurocomputing: A survey of basic models of neurocomputation 3rd

Rough neurocomputing: A survey of basic models of neurocompu...

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3rd international conference on Rough Sets and Current Trends in Computing, RSCTC 2002

作者： Peters, James F. Szczuka, Marcin S. Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 5V6 Canada Institute of Mathematics Warsaw University Banacha 2 Warsaw02-097 Poland

ISBN: (纸本)9783540442745

This article presents a survey of models of rough neurocomputing that have their roots in rough set theory. Historically, rough neurocomputing has three main threads: training set production, calculus of granules, and interval analysis. This form of neurocomputing gains its inspiration from the work of Pawlak on rough set philosophy as a basis for machine learning and from work on data mining and pattern recognition by Swiniarski and others in the early 1990s. This work has led to a variety of new rough neurocomputing computational models that are briefly presented in this article. The contribution of this article is a survey of representative approaches to rough neurocomputing. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Neural networks

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3rd EAI international conference on IoT and Big Data Technologies for Health Care, IotCARE 2022

3rd EAI International Conference on IoT and Big Data Technol...

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3rd EAI international conference on IoT and Big Data Technologies for Health Care, IotCARE 2022

ISBN: (纸本)9783031335440

The proceedings contain 23 papers. The special focus in this conference is on IoT and Big Data Technologies for Health Care. The topics include: Personalized Recommendation Method of Maternal and Child Health Education Resources Based on Association Rule Mining Algorithm;methods of Integrating Ideological and Political Education into Health Management in Colleges and Universities Based on Internet of Things Technology;data Mining of Psychological Tendency and Health of Ideological and Political Students in Higher Vocational Tourism English Courses;intelligent Imaging Method of Nuclear Magnetic Resonance Medical Devices Based on Compression Sensing;evaluation of Post Fitness of Employees in Health Care Enterprises Based on Big Data;design of Telemedicine and Health Care System Based on Embedded Technology;anomaly Detection Method of Healthcare Internet of Things Gateway Supporting Edge Computing;research on Fast Encryption of Electronic Health Record Data Based on Privacy Protection;research on Secure Storage of Healthcare Data in the Environment of Internet of Things;research on Cloud Health Privacy Information Protection Algorithm Based on Data Mining;architecture of Wide Area Health Monitoring System;internet of Things Technologies in Healthcare for People with Hearing Impairments;artificial Intelligence-Based Early Warning Method for Abnormal Operation and Maintenance Data of Medical and Health Equipment;abnormal Signal recognition Method of Wearable Sensor Based on machine learning;data Security Mining Method for Social Media Users’ Mental Health Status Test Based on machine learning Algorithm;early Warning Method of College Students Mental Subhealth Based on Internet of Things.

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Interpolation Kernel machine and Indefinite Kernel Methods for Graph Classification 3rd

Interpolation Kernel Machine and Indefinite Kernel Methods ...

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3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Zhang, Jiaqi Liu, Cheng-Lin Jiang, Xiaoyi Faculty of Mathematics and Computer Science University of Münster Einsteinstrasse 62 Münster48149 Germany National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing10049 China

ISBN: (纸本)9783031092817

Graph kernels have been studied for a long time and applied among others for graph classification. In this paper we bring two novel aspects into the graph processing community. Currently, the backbone for kernel-based classification is solely the support vector machine. We introduce the interpolation kernel machine for this purpose. In addition, for both support vector machine and interpolation kernel machine, many kernels used in practice do not satisfy the formal requirements (e.g. positive definiteness). We thus introduce extensions of the standard version to indefinite kernel methods. We argue and experimentally demonstrate why these two aspects should be considered for graph classification. One of our conclusions will be that the interpolation kernel machine is a good alternative of support vector machine. Consequently, we will propose an extended experimental protocol. With this work we contribute to increasing the methodological plurality in the graph processing community. © 2022, Springer Nature Switzerland AG.

关键词： Support vector machines

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A Novel Multi-view Fuzzy Clustering Algorithm Based on Fuzzy C-Means 1

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3rd international conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Lin, Ze Chen, Yixiang National Engineering Research Center of Trustworthy Embedded Software East China Normal University Shanghai200062 China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Fuzzy c-means (i.e., FCM) is a representative clustering method that is widely used in machine learning and pattern recognition. It can describe the degree of fuzziness of objects to clusters using memberships, but it cannot assess the degree of fuzziness of the clustering results. Motivated by such issues, in this paper, we propose a multi-view fuzzy clustering method based on FCM and a fuzzy assessment is introduced to assess the fuzziness of clustering results. Specifically, we constructed a multi-view FCM to obtain a reasonable membership matrix by fully integrating multi-view information. Based on this, we propose a fuzzy assessment method to evaluate the fuzziness of the clustering results and mark the objects between the different clusters (i.e., the object has the feature of multiple clusters). The experimental result shows that the proposed multi-view fuzzy clustering method achieves better performance than several related clustering methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： pattern recognition

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Joint IAPR international Workshops on Structural, Syntactic pattern recognition and Statistical Techniques in pattern recognition, SSPR 2000 and SPR 2000

Joint IAPR International Workshops on Structural, Syntactic ...

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international Association of pattern recognition, IAPR 2000 held jointly with 8th international Workshop on Structural and Syntactic pattern recognition, SSPR 2000 and 3rd international Workshop on Statistical Techniques in pattern recognition, SPR 2000

ISBN: (纸本)3540679464

The proceedings contain 92 papers. The special focus in this conference is on Pierre Devijver Lecture and Hybrid and Combined Methods. The topics include: Some notes on twenty one 21 nearest prototype classifiers;adaptive graphical pattern recognition beyond connectionist-based approaches;current trends in grammatical inference;classifier's complexity control while training multilayer perceptrons;a framework for classifier fusion;image pattern recognition based on examples;a hybrid system for the recognition of hand-written characters;improving statistical measures of feature subsets by conventional and evolutionary approaches;selection of classifiers based on multiple classifier behaviour;the adaptive subspace map for image description and image database retrieval;adaptative automatic target recognition with SVM boosting for outlier detection;a multiresolution causal colour texture model;writer identification;syntactic pattern recognition by error correcting analysis on tree automata;offline recognition of syntax-constrained cursive handwritten text;structural classification for retrospective conversion of documents;segmentation of date field on bank cheques;grammars and discourse theory to describe and recognize mechanical assemblies;computation of the N best parse trees for weighted and stochastic context-free grammars;partitional vs hierarchical clustering using a minimum grammar complexity approach;encoding nondeterministic finite-state tree automata in sigmoid recursive neural networks;a structural matching algorithm using generalized deterministic annealing;alignment and correspondence using singular value decomposition;fast candidate elimination using machine learning techniques and efficient alignment and correspondence using edit distance.

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CNN- BASED FLOW pattern IDENTIFICATION BASED ON FLOW-INDUCED VIBRATION CHARACTERISTICS FOR MULTIPHASE FLOW PIPELINES 14

CNN- BASED FLOW PATTERN IDENTIFICATION BASED ON FLOW-INDUCED...

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14th international Pipeline conference (IPC)

作者： Chen, Haobin Dang, Zhuoran Hugo, Ron Park, Simon Univ Calgary Pipeline Engn Ctr PEC Calgary AB Canada

ISBN: (纸本)9780791886588

Structural health monitoring (SHM) techniques designed for single phase flow pipelines can be difficult to apply to multiphase flow pipelines. Data collected for pipeline SHM can be affected by intermittent changes in flow characteristics associated with flow patterns. Therefore, determining the flow pattern can help to better predict the unsteadiness of the flow parameters associated with pipeline integrity management. It is known that flow-induced vibration (FIV) in multiphase flow is highly correlated with flow pattern. In this paper, the characterization of FIV under various flow patterns is investigated experimentally, and a CNN-based flow pattern identification method is presented for horizontal gas-liquid pipe flow. Measurements were performed using two wall-mounted triaxial accelerometers with a high-speed camera, which simultaneously acquired both images and accelerometer signals. The difference of flow-induced vibration under various flow patterns can be explicitly shown by extracting morphological features using the Hilbert-Huang Transform (HHT) and Short Time Fourier Transform (STFT). In this paper, the HHT with 1st to 3rd IMFs is intended to emphasize the portion of FIV due to the unsteady fluctuations, while the STFT is intended to examine the FIV contributed to by both steady and unsteady fluctuations. To eliminate human interpretation errors, a convolutional neural network (CNN)-based machine learning model is built for the flow pattern identification task. To increase the size of the dataset used for flow pattern identification, the original time-series database is augmented by applying a series of data augmentation methods including sliding window, window stretch, denoising, and noise enhancement. These enable the flow pattern identification process to be more robust under various void fractions and flow geometries. The flow pattern identification results show that both HHT and STFT trained model have promising performance with overall accuracy abo

关键词： flow-induced vibration flow pattern identification horizontal gas-liquid pipeline Hilbert-Huang transform short time Fourier transform convolutional neural network

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