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检索条件"任意字段=12th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2023"

共 89 条记录，以下是41-50 订阅

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Face recognition Based on Local Binary pattern Auto-correlogram 15th

Face Recognition Based on Local Binary Pattern Auto-correlog...

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15th international conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th international conference on Frontiers of Information Technology, applications and Tools, FITAT 2019

作者： Li, Zimei Yu, Ping Yan, Hui Jiang, Yixue School of Computer Technology and Engineering Changchun Institute of Technology Changchun130012 China Jilin Province S&T Innovation Center for Physical Simulation and Security of Water Resources and Electric Power Engineering Changchun130012 China

ISBN: (纸本)9789811397097

Face recognition mainly includes face feature extraction and recognition. Color is an important visual feature. Color correlogram (CC) algorithm is commonly used in the color-based image retrieval as a feature descriptor, but most of the existing methods based on CC have problems of high computational complexity and low retrieval accuracy. Aiming at this problem, this paper proposes an image retrieval algorithm based on color auto-correlogram. the new color feature vector which describes the global and spatial distribution relation among different colors is obtained in the CC feature matrix, thus reducing the computational complexity. Inter-feature normalization is applied in color auto-correlogram (CAC) to enhance the retrieval accuracy. the experimental result shows that this integrated method can reduce the computational complexity and improves real-time response speed and retrieval accuracy. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Local binary pattern

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Biomedical named entity recognition: A poor knowledge HMM-based approach

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12th international conference on applications of Natural Language to Information Systems

作者： Ponomareva, Natalia Pla, Ferran Molina, Antonio Rosso, Paolo Univ Politecn Valencia Dept Sistemas Informat & Computac Valencia Spain

ISBN: (纸本)9783540733508

With a recent quick development of a molecular biology domain it becomes indispensable to promote different resources as databases and ontologies that represent the formal knowledge of the domain. As these resources have to be permanently updated, due to a constant appearance of new data, the Information Extraction (IE) methods become very useful. Named Entity recognition (NER), that is considered 14 to be the easiest task of IE, still remains very challenging in molecular biology domain because of the special phenomena of biomedical entities. In this paper we present our Hidden Markov Model (HMM)-based biomedical NER system that takes into account only parts-of-speech as an additional feature, which are used both to tackle the problem of non-uniform distribution among biomedical entity classes and to provide the system with an additional information about entity boundaries. Our system, in spite of its poor knowledge, has proved to obtain better results than some of the state-of-the-art systems that employ a greater number of features.

关键词： pattern recognition

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DBGAN: A Data Balancing Generative Adversarial Network for Mobility pattern recognition 25th

DBGAN: A Data Balancing Generative Adversarial Network for M...

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25th international conference on Big Data Analytics and Knowledge Discovery (DaWaK)

作者： Zhang, Ke Liu, Hengchang Clarke, Siobhan Trinity Coll Dublin Dublin D02 R123 Ireland Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9783031398308;9783031398315

Mobility pattern recognition is a central aspect of transportation and data mining research. Despite the development of various machine learning techniques for this problem, most existing methods face challenges such as reliance on handcrafted features (e.g., user has to specify a feature such as "travel time") or issues with data imbalance (e.g., fewer older travelers than commuters). In this paper, we introduce a novel Data Balancing Generative Adversarial Network (DBGAN), which is a specifically designed attention mechanism-based GAN model to address these challenges in mobility pattern recognition. DBGAN captures both static (e.g., travel locations) and dynamic (e.g., travel times) features of different passenger groups, and avoids using handcrafted features that may result in information loss, based on a sequence-to-image embedding method. Our model is then applied to overcome the data imbalance issue and perform mobility pattern recognition. We evaluate the proposed method on real-world public transportation smart card data from Suzhou, China, and focus on recognizing two different passenger groups: older people and students. the results of our experiments demonstrate that DBGAN is able to accurately identify the different passenger groups in the data, with the detected mobility patterns being consistent with the ground truth. these results highlight the effectiveness of DBGAN in overcoming data imbalance in mobility pattern recognition, and demonstrate its potential for wider use in transportation and data mining applications.

关键词： Mobility pattern recognition Data imbalance Kernel embedding Sequence to image Generative adversarial networks Self-attention

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Artificial Intelligence applications and Innovations 1

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丛书名： IFIP Advances in Information and Communication Technology

1000年

作者： Lazaros Iliadis Ilias Maglogiannis

ISBN: (数字)9783319449449

ISBN: (纸本)9783319449432;9783319831688

this book constitutes the refereed proceedings of the 12th IFIP WG 12.5 international conference on Artificial Intelligence applications and Innovations, AIAI 2016, and three parallel workshops, held in thessaloniki, Greece, in September 2016. the workshops are the third Workshop on New methods and Tools for Big Data, MT4BD 2016, the 5th Mining Humanistic Data Workshop, MHDW 2016, and the First Workshop on 5G - Putting Intelligence to the Network Edge, 5G-PINE *** 30 revised full papers and 8 short papers presented at the main conference were carefully reviewed and selected from 65 submissions. the 17 revised full papers and 7 short papers presented at the 3 parallel workshops were selected from 33 submissions. the papers cover a broad range of topics such as artificial neural networks, classification, clustering, control systems - robotics, data mining, engineering application of AI, environmental applications of AI, feature reduction, filtering, financial-economics modeling, fuzzy logic, genetic algorithms, hybrid systems, image and video processing, medical AI applications, multi-agent systems, ontology, optimization, pattern recognition, support vector machines, text mining, and Web-social media data AI modeling.

关键词： Artificial Intelligence Data Mining and Knowledge Discovery pattern recognition Computer applications Algorithm Analysis and Problem Complexity Computer Communication Networks

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A Learning Framework to Improve Unsupervised Gene Network Inference 1

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12th international conference on Machine Learning and Data Mining (MLDM)

作者： Turki, Turki Bassett, William Wang, Jason T. L. King Abdulaziz Univ Dept Comp Sci POB 80221 Jeddah 21589 Saudi Arabia New Jersey Inst Technol Bioinformat Program Newark NJ 07102 USA Dept Comp Sci Newark NJ 07102 USA

ISBN: (数字)9783319419206

ISBN: (纸本)9783319419206;9783319419190

Network inference through link prediction is an important data mining problem that finds many applications in computational social science and biomedicine. For example, by predicting links, i.e., regulatory relationships, between genes to infer gene regulatory networks (GRNs), computational biologists gain a better understanding of the functional elements and regulatory circuits in cells. Unsupervised methods have been widely used to infer GRNs;however, these methods often create missing and spurious links. In this paper, we propose a learning framework to improve the unsupervised methods. Given a network constructed by an unsupervised method, the proposed framework employs a graph sparsification technique for network sampling and principal component analysis for feature selection to obtain better quality training data, which guides three classifiers to predict and clean the links of the given network. the three classifiers include neural networks, random forests and support vector machines. Experimental results on several datasets demonstrate the good performance of the proposed learning framework and the classifiers used in the framework.

关键词： Feature selection Graph mining Network analysis applications in biology and medicine

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MIPPR 2023: Remote Sensing Image Processing, Geographic Information Systems, and Other applications

MIPPR 2023: Remote Sensing Image Processing, Geographic Info...

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SPIE 12th international Symposium on Multispectral Image Processing and pattern recognition, MIPPR 2023

ISBN: (纸本)9781510674998

the proceedings contain 25 papers. the topics discussed include: a method for cloud removal in remote sensing images based on attention mechanism and residual symmetric connection structure;research on deep learning-based land cover extraction method using multi-source mixed samples;a lossless compression of remote sensing images based on ANS entropy coding algorithm;research on the impact of change features for remote sensing image change detection;supervised spiking neural network for SAR image recognition: analysis of encoding methods and performance under strong noise influence;an edge-guided network for building footprint extraction from optical remote sensing images;and planting areas extraction of sugarcane using multi-feature fusion method based on Sentinel-2A satellite images.

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Experimental of Circular Patch Microstrip Antenna Using Defected Ground Structure (DGS) methods in DVB-T2 applications 12

Experimental of Circular Patch Microstrip Antenna Using Defe...

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12th IEEE international conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Ashari, Sofian Dwi Praja, Muhammad Panji Kusuma Wicaksono, Agung Institut Teknologi Telkom Purwokerto Central Java Telecommunication Enginering Indonesia

ISBN: (纸本)9798350341102

In this digital era, television cannot be separated, as it has changed from analog to digital, as stated in the Ministry of Communication and Informatics regulations. Digital TV broadcasting in Indonesia uses the digital video broadcasting second-generation terrestrial (DVB-T2) standard, which operates in the frequency range of 478 MHz - 694 MHz. Switching from analog to digital TV requires a suitable antenna that can detect the signal. the microstrip antenna used has the advantage of being compact and easy to fabricate, but has weaknesses including the resulting narrow bandwidth;therefore, in this study, using a circular patch and using the defective ground structure (DGS) method, this method is used to widen the bandwidth. A simulation software was used in this design. From the simulation results that have been obtained, the value of the return loss at a frequency of 586 MHz -39.32, a bandwidth of 365 MHz, the lowest VSWR is 0.19, the gain at a frequency of 586 MHz is 1.18 dBi and the radiation pattern is bidirectional;then, on antenna measurements, there is a slight difference, where the value of the return loss at a frequency of 586 MHz -35.95, bandwidth of 2SS MHz, lowest VSWR 1.032, gain at 586 MHz 0.55 dBi and omnidirectional radiation pattern. © 2023 IEEE.

关键词： Microstrip antennas

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Automated Machine Learning Evaluation of Squat Movement patterns Using Smartphone and CoreML 12

Automated Machine Learning Evaluation of Squat Movement Patt...

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12th IEEE international conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications, IDAACS 2023

作者： Sokolowska, Marta Podpora, Michal Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Opole45-758 Poland

ISBN: (纸本)9798350358056

there is a vast diversity of Machine Learning use cases, and both the hardware and the methodology itself are constantly getting better. this article presents a very specific use case - the recognising, classifying, and evaluation of human physical activity by using just a video stream from a smartphone and potentially instructing people how to exercise to get better results. the paper presents two key aspects of the challenge: (1) defining the problem in the physical activity domain and choosing a proper ML approach and tool, and (2) conducting the experiment using consumer-grade hardware (a smartphone) to evaluate if the proposed methodology is applicable using an easy-to-use tool and widely accessible hardware for users with limited (or without) Machine Learning experience. Chapter II presents the process of gathering and preparing the dataset, while the next chapter presents the creation of a Machine Learning model, model training using the acquired dataset, and the model performance. It was possible to obtain the result of 92.4% validation accuracy for the squat movement pattern classification model. the trained model can be used in an ML smartphone application for evaluation of user-provided examples of squats. © 2023 IEEE.

关键词： action classiication Artiicial Intelligence Machine Learning movement patterns pattern recognition video analysis

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Pose-Based Temporal-Spatial Network (PTSN) for Gait recognition with Carrying and Clothing Variations 12th

Pose-Based Temporal-Spatial Network (PTSN) for Gait Recognit...

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12th Chinese conference on Biometric recognition, CCBR 2017

作者： Liao, Rijun Cao, Chunshui Garcia, Edel B. Yu, Shiqi Huang, Yongzhen College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China 7ma A 21406 PlayaHavana Cuba

ISBN: (纸本)9783319699226

One of the most attractive biometric techniques is gait recognition, since its potential for human identification at a distance. But gait recognition is still challenging in real applications due to the effect of many variations on the appearance and shape. Usually, appearance-based methods need to compute gait energy image (GEI) which is extracted from the human silhouettes. GEI is an image that is obtained by averaging the silhouettes and as result the temporal information is removed. the body joints are invariant to changing clothing and carrying conditions. We propose a novel pose-based gait recognition approach that is more robust to the clothing and carrying variations. At the same time, a pose-based temporal-spatial network (PTSN) is proposed to extract the temporal-spatial features, which effectively improve the performance of gait recognition. Experiments evaluated on the challenging CASIA B dataset, show that our method achieves state-of-the-art performance in both carrying and clothing conditions. © 2017, Springer international Publishing AG.

关键词： Gesture recognition

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12th IFIP WG 12.5 international conference and Workshops on Artificial Intelligence applications and Innovations, AIAI 2016

12th IFIP WG 12.5 International Conference and Workshops on ...

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12th IFIP WG 12.5 international conference and Workshops on Artificial Intelligence applications and Innovations, AIAI 2016

ISBN: (纸本)9783319449432

the proceedings contain 62 papers. the special focus in this conference is on Medical Artificial Intelligence Modeling, Classification - pattern recognition, Ontology-Web and Social Media AI Modeling, Environmental AI Modeling, Machine Learning-Learning, Optimization-SVM, Agents-Robotics-Control, Artificial Neural Network Modeling and New methods and Tools for Big Data wokshop. the topics include: A cumulative training approach to schistosomiasis vector density prediction;mobile and evolving tool to predict colorectal cancer survivability;machine learning preprocessing method for suicide prediction;increasing diversity in random forests using naive bayes;identifying asperity patterns via machine learning algorithms;combining prototype selection with local boosting;modeling economic open data;web image indexing using wice and a learning-free language model;an intelligent internet search assistant based on the random neural network;deep neural networks for web page information extraction;environmental impact on predicting olive fruit fly population using trap measurements;a hybrid soft computing approach producing robust forest fire risk indices;applying artificial neural networks to short-term pm2.5 forecasting modeling;convolutive audio source separation using robust ica and reduced likelihood ratio jump;use of flight simulators in analyzing pilot behavior;active learning algorithms for multi-label data;design of an advanced smart forum for tesys e-learning platform;efficient support vector machine classification using prototype selection and generation;genetic search of pickup and delivery problem solutions for self-driving taxi routing and introducing chemotaxis to a mobile robot.

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