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Event Extraction in Vertical Domain Based on Similar Semantics and Dependency Syntax 22

Event Extraction in Vertical Domain Based on Similar Semanti...

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Proceedings of the 2022 5th international conference on Artificial Intelligence and pattern recognition

作者： Ying Cui Xiang Dai Lican Dai Second laboratory Southwest China Institute of Electronic Technology China

ISBN: (纸本)9781450396899

Event extraction has become an unavoidable bottleneck in the process of event map construction and event analysis and prediction. the accuracy and completeness of the extracted determine the quality of event knowledge map construction and the credibility of event prediction. Aiming at the difficulties of extracting events based on traditional pattern matching, the low recall rate and accuracy rate, and the low accuracy of event extraction based on in-depth learning method in specific fields, this paper proposes a method for extracting news events in vertical domain based on similar semantics and dependency syntax, and the algorithm is verified by taking the field of political diplomacy as an example. By calculating the similarity between the trigger word set and the Syntax descriptor, the trigger word list is extended to lay a foundation for identifying event types accurately. Further, based on the guidance of pattern, through the construction of meta-event template, we can recognize and extract events in specific domain, so as to achieve a structured description of events. the accuracy of extraction results is obviously better than that of end-to-end event extraction model based on deep neural network, and it can be used for reference and implementation in other specific fields. Firstly, this paper makes theoretical and practical research on news sentence level meta-event extraction, and introduces in detail the methods of event extraction in political diplomacy based on similar semantics and dependency syntax. Finally, we summarized the main difficulties in event extraction, and discussed the application prospects of event extraction.

关键词： Event Extraction Meta-event pattern matching Political diplomacy areas Syntax Vertical domain

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Morphological Classification of Galaxies using Enhanced Deep Learning Model

Morphological Classification of Galaxies using Enhanced Deep...

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Communication, Computing and Industry 4.0 (C2I4), international conference on

作者： Rohan Nemade Suraj Jha Harsh Anand Saravanan Chandrasekaran Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379891

Galaxies in astrophysics are one of its fundamental, their morphological classification remains an essential key to unlock countless doors about the structure, formation and history of our Universe. For nearly a century, astronomers have assigned galaxies to perceptual categories such as ellipticals, spirals and irregulars. However, the extraordinary expansion of the galaxy database catalysed and instigated by sky surveys of ever-increasing scales rendered this classification more manual than practical. Because of the learning capability and its scaling nature, to learn hierarchical abstract features from the visual data Convolutional Neural Networks (CNN) are powerful tools to recognize complex patterns and structures in images, such as spiral arms or central bulges that contribute significantly to accurate morphological classification. that is why CNN are particularly well suited to the problem of galaxy classification. While traditional classification work used to solve the problem, it is not feasible for today's huge amounts of generated data. this attempt to build and train a CNN model using galaxy images as our domain. the Enhanced CNN model can achieve the high accuracy to morphological type of galaxies. the Enhanced CNN increase both efficiency and classification accuracy, particularly when processing large dataset. the Galaxy Zoo dataset indicated that our study can provide a tool for other astronomical research in future. the Enhanced CNN gain the overall accuracy rate are 95.29% with the performance indicator were calculated including average precision, recall, F1-score and Area under curve (AUC) is 0.9513, 0.9531, 0.9525 and 0.9853 respectively. the Enhanced CNN outperforms the various existing state-of-the models.

关键词： Deep learning Surveys Representation learning Visualization Accuracy Spirals Shape Robustness pattern recognition Convolutional neural networks

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Skin Detection in Images based on pattern Matching Algorithms - A Review 5

Skin Detection in Images based on Pattern Matching Algorithm...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Harsha, B. K. Indumathi, G. CMR Inst Technol VTU RC Dept ECE Bengaluru India Cambridge Inst Technol VTU RC Dept ECE Bengaluru India

ISBN: (纸本)9781728146850

Detection of skin in images is a very important clue in many image processing applications such as biometnc system, medical imaging, face recognition and many more. An eye's perception of human skin depends on sensing the color, texture, feel and many more factors. this paper is a survey paper. Some popular approaches that are used to identify human skin are endowed. To distinguish skin from the given image based on colors, various methods are discussed that are based on different color models. Based on the survey, a comparison of various methods is provided.

关键词： Human skin detection methods Haar cascade color models face recognition

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Entropy Analysis of Two Phase Flow pattern Identification in Vertical Tube 5

Entropy Analysis of Two Phase Flow Pattern Identification in...

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5th international conference on Advances in Energy Resources and Environment Engineering (ICAESEE)

作者： Lu, Kun Ma, Longxin Li, Gen Shang, Wei Feng, Yixuan China Huadian Elect Power Res Inst Co Ltd Hangzhou 310030 Zhejiang Peoples R China

this paper aims to analyze the time domain marginal spectral entropy of differential pressure signals collected from the gas-liquid two-phase flow pattern vertical tube bundle channel on the experimental platform. By comparing the visualized and measured differential pressure signals, four flow patterns were found, namely, bubble flow, bubble-mixed flow, agitated flow, and annular flow. the marginal spectral entropy values of the four flow patterns are calculated, and it is found that the spectral entropy distribution regions of the different flow patterns are different. Putting the aforementioned four flow patterns that is regarded as the feature quantity into artificial neural network support vector machine for flow pattern recognition. the experimental results show that the marginal spectral entropy can better reflect the flow difference between different flow patterns. the support vector machine has the advantages of fast calculation speed and high recognition rate. the overall recognition rate of the four flow patterns (the transition flow is included) is as high as 95.31% by combing the marginal spectral entropy with the support vector machine, which is suitable for online identification of flow patterns.

关键词： Flow patterns

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5th international conference on Intelligent Computing and Applications, ICICA 2019

5th International Conference on Intelligent Computing and Ap...

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5th international conference on Intelligent Computing and Applications, ICICA 2019

ISBN: (纸本)9789811555657

the proceedings contain 71 papers. the special focus in this conference is on Intelligent Computing and Applications. the topics include: Performance Estimation and Analysis Over the Supervised Learning Approaches for Motor Imagery EEG Signals Classification;fully Automated Digital Mammogram Segmentation;empirical Study of Computational Intelligence Approaches for the Early Detection of Autism Spectrum Disorder;Intelligent Monitoring of Bearings Using Node MCU Module;image Denoising Using Various Image Enhancement Techniques;energy Consumption Analysis and Proposed Power-Aware Scheduling Algorithm in Cloud Computing;relationship Between Community Structure and Clustering Coefficient;digital Image Forensics-Image Verification Techniques;energy Efficient Data Centre Selection Using Service Broker Policy;using Automated Predictive Analytics in an Online Shopping Ecosystem;design and Development of Home Automation System;performance Analysis Study of Stochastic Computing Based Neuron;Scheduling of Parallel Tasks in Cloud Environment Using DAG MODEL;GREENIE—Smart Home with Smart Power Transmission;ANAVI: Advanced Navigation Assistance for Visually Impaired;thIRD EYE—Shopfloor Data Processing and Visualization Using Image recognition;A Hybrid Search Group Algorithm and pattern Search Optimized PIDA Controller for Automatic Generation Control of Interconnected Power System;Fractional Order PID Controlled PV Fed Quadratic Boost Converter TZ Source Inverter Fed Permanent Magnet Brushless Motor Drive;co-ordinate Measurement of Roll-Cage Using Digital Image Processing;peformance Analysis of Joysticks Used in Infotainment Control System in Automobiles;Probabilistic Principal Component Analysis (PPCA) Based Dimensionality Reduction and Deep Learning for Cancer Classification;blockchain Technology for Data Sharing in Decentralized Storage System;Understanding Concepts of Blockchain Technology for Building the DApps;chennai Water Crisis—Data Analysis.

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Footprint recognition and Feature Extraction Method Based on Artificial Intelligence 5

Footprint Recognition and Feature Extraction Method Based on...

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5th international conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Cao, Hanhua Zhang, Huanping Liu, Zhendan Lai, Jianhuang Sun Yat Sen Univ Sch Informat Sci Xinhua Coll Guangzhou Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9781665423144

In the modern society where artificial intelligence is gradually being used, identity recognition is widely used in various places. Footprint recognition is different from face recognition, its feature extraction is more difficult, and it is often used in related criminal investigation fields. In this study, Haar and Local Binary pattern (LBP) are selected as the feature extraction methods. these two methods are used to extract the feature vectors of digging and tread marks respectively, and Adaptive Boosting (Adaboost) is selected as the classification algorithm, based on Haar features and LBP feature training respectively. Algorithms are developed and tested for detecting pick marks and tread marks. the test results show that for pick marks and tread marks, the artificial intelligence method using Haar and LBP features has a better detection effect.

关键词： identity recognition Haar Local Binary pattern (LBP) Adaptive Boosting (Adaboost)

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Ayurvedic Medicinal Plants Identification: A Comparative Study on Feature Extraction Methods 5th

Ayurvedic Medicinal Plants Identification: A Comparative Stu...

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5th international conference on Computer Vision and Image Processing, CVIP 2020

作者： Ahila Priyadharshini, R. Arivazhagan, S. Arun, M. Centre for Image Processing and Pattern Recognition Mepco Schlenk Engineering College Sivakasi Tamil Nadu India

ISBN: (纸本)9789811610912

Proper identification of medicinal plants is essential for agronomists, ayurvedic medicinal practitioners and for ayurvedic medicines industry. Even though many plant leaf databases are available publicly, no specific standardized database is available for Indian Ayurvedic Plant species. In this paper, we introduce a publicly available annotated database of Indian medicinal plant leaf images named as MepcoTropicLeaf. the research work also presents the preliminary results on recognizing the plant species based on the spatial, spectral and machine learnt features on the selected set of 50 species from the database. To attain the machine learnt features, we propose a six level convolutional neural network (CNN) and report an accuracy of 87.25% using machine learnt features. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Database systems

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5th international Joint conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021

5th International Joint Conference on Asia-Pacific Web and W...

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5th international Joint conference on Asia-Paciﬁc Web and Web-Age Information Management, APWeb-WAIM 2021

ISBN: (纸本)9783030858957

the proceedings contain 74 papers. the special focus in this conference is on Asia-Pacific Web and Web-Age Information Management. the topics include: A Distribution-Aware Training Scheme for Learned Indexes;AIR Cache: A Variable-Size Block Cache Based on Fine-Grained Management Method;learning an Index Advisor with Deep Reinforcement Learning;SardineDB: A Distributed Database on the Edge of the Network;DLSM: Distance Label Based Subgraph Matching on GPU;distributed Top-k pattern Mining;SQKT: A Student Attention-Based and Question-Aware Model for Knowledge Tracing;comparison Question Generation Based on Potential Compared Attributes Extraction;multimodal Encoders for Food-Oriented Cross-Modal Retrieval;FedMDR: Federated Model Distillation with Robust Aggregation;Data Cleaning for Indoor Crowdsourced RSSI Sequences;a Behavior-Aware Graph Convolution Network Model for Video Recommendation;GRHAM: Towards Group Recommendation Using Hierarchical Attention Mechanism;multi-interest Network Based on Double Attention for Click-through Rate Prediction;self-residual Embedding for Click-through Rate Prediction;GCNNIRec: Graph Convolutional Networks with Neighbor Complex Interactions for Recommendation;velocity-Dependent Nearest Neighbor Query;finding Geo-Social Cohorts in Location-Based Social Networks;modeling Dynamic Spatial Influence for Air Quality Prediction with Atmospheric Prior;learning Cooperative Max-Pressure Control by Leveraging Downstream Intersections Information for Traffic Signal Control;data Augmentation for Graph Convolutional Network on Semi-supervised Classification;privacy-Preserving Healthcare Analytics of Trajectory Data;PARROT: An Adaptive Online Shopping Guidance System;gStore-C: A Transactional RDF Store with Light-Weight Optimistic Lock;deep-gAnswer: A Knowledge Based Question Answering System;ALMSS: Automatic Learned Index Model Selection System;GPKRS: A GPU-Enhanced Product Knowledge Retrieval System;standard-Oriented Standard Knowledge Graph Constr

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5th international Joint conference on Asia-Pacific Web and Web-Age Information Management, APWeb-WAIM 2021

5th International Joint Conference on Asia-Pacific Web and W...

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5th international Joint conference on Asia-Paciﬁc Web and Web-Age Information Management, APWeb-WAIM 2021

ISBN: (纸本)9783030858988

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Face Detection and recognition of the Seven Emotions via Facial Expression: Integration of Machine Learning Algorithm into the NAO Robot 5

Face Detection and Recognition of the Seven Emotions via Fac...

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5th international conference on Control and Robotics Engineering (ICCRE)

作者： Reyes, Rosula S. J. Depano, Keanu M. Velasco, Aaron Matthew A. Kwong, John Chris T. Oppus, Carlos M. Ateneo Manila Univ Dept Elect Comp & Commun Engn Quezon City Philippines

ISBN: (数字)9781728167916

ISBN: (纸本)9781728167916

this study revolves around NAO, a programmable and interactive robot, to do Emotion recognition via Facial Expression, and to give an appropriate response for the identified emotion whilst accumulating images to further enhance the accuracy. To develop this cohesive system, the authors utilized Computer Vision and Haar-Cascade Classifier along with the feature descriptors, Histogram of Oriented Gradient and Local Binary pattern to train the Support Vector Machine Learning Algorithm. Gathering and increasing data with Asian/Filipino participants whilst being added to the CK+ database in different stages of retraining, optimization, and undertaking cross-fold validation yielded improved results with the highest average accuracy across the seven emotions of 87.14%. Suggesting that adding images with a specific ethnicity yields a higher accuracy model for a more diverse testing dataset and in the wild image classifications. With the machine learning model capable of accurately recognizing the emotion and reinforcing it with the accumulated local images, NAO equipped with emotion recognition capabilities can better aid and support the individuals in need.

关键词： NAO NAOqi Choregraphe Suite Human-Robot Interaction Support Vector Machine Histogram of Oriented Gradient Local Binary pattern Emotion recognition via Facial Expression

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