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检索条件"任意字段=8th International Conference on Computing and Pattern Recognition, ICCPR 2019"

共 394 条记录，以下是11-20 订阅

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8th international conference on pattern recognition Applications and Methods, ICPRAM 2019

8th International Conference on Pattern Recognition Applicat...

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8th international conference on pattern recognition Applications and Methods, ICPRAM 2019

ISBN: (纸本)9783030400132

the proceedings contain 7 papers. the special focus in this conference is on pattern recognition Applications and Methods. the topics include: Attributes for Understanding Groups of Binary Data;annealing by Increasing Resampling;implications of Z-Normalization in the Matrix Profile;enforcing the General Planar Motion Model: Bundle Adjustment for Planar Scenes;deep Multi-biometric Fusion for Audio-Visual User Re-Identification and Verification.

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Memory Matching is Not Enough: Jointly Improving Memory Matching and Decoding for Video Object Segmentation 27th

Memory Matching is Not Enough: Jointly Improving Memory Matc...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Zheng, Jintu Liang, Yun Zhang, Yuqing Su, Wanchao University of Chinese Academy of Sciences Beijing China South China Agricultural University Guangdong Guangzhou China Beijing University of Technology Beijing China Department of Human Centered Computing Faculty of Information Technology Monash University Melbourne Australia

ISBN: (纸本)9783031783111

Memory-based video object segmentation methods model multiple objects over long temporal-spatial spans by establishing memory bank, which achieve the remarkable performance. However, they struggle to overcome the false matching and are prone to lose critical information, resulting in confusion among different objects. In this paper, we propose an effective approach which jointly improving the matching and decoding stages to alleviate the false matching issue. For the memory matching stage, we present a cost aware mechanism that suppresses the slight errors for short-term memory and a shunted cross-scale matching for long-term memory which establish a wide filed matching spaces for various object scales. For the readout decoding stage, we implement a compensatory mechanism aims at recovering the essential information where missing at the matching stage. Our approach achieves the outstanding performance in several popular benchmarks (i.e., DAVIS 2016 &2017 Val (92.4% &88.1%), and DAVIS 2017 Test (83.9%)), and achieves 84.8% &84.6% on YouTubeVOS 2018 &2019 Val. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decoding

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ICPRAM 2019 - Proceedings of the 8th international conference on pattern recognition Applications and Methods

ICPRAM 2019 - Proceedings of the 8th International Conferenc...

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8th international conference on pattern recognition Applications and Methods, ICPRAM 2019

ISBN: (纸本)9789897583513

the proceedings contain 109 papers. the topics discussed include: planar motion bundle adjustment;deep learning for radar pulse detection;fast non-minimal solvers for planar motion compatible homographies;the growing n-gram algorithm: a novel approach to string clustering;pluggable drone imaging analysis framework for mob detection during open-air events;all together now! the benefits of adaptively fusing pre-trained deep representations;a cellular automata based classification algorithm;automatic information extraction from piping and instrumentation diagrams;annealing by increasing resampling in the unified view of simulated annealing;detecting and tracking surgical tools for recognizing phases of the awake brain tumor removal surgery;stochastic phase estimation and unwrapping;accelerated algorithm for computation of all prime patterns in logical analysis of data;adversarial alignment of class prediction uncertainties for domain adaptation;using recurrent neural networks for action and intention recognition of car drivers;and a fast and efficient method for solving the multiple closed curve detection problem.

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pattern Classification Can Detect Prodromal Alzheimer’s Neurodegenerations 27th

Pattern Classification Can Detect Prodromal Alzheimer’s Neu...

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27th international conference on pattern recognition, ICPR 2024

作者： Przybyszewski, Andrzej W. Nowacki, Jerzy P. Drabik, Aldona Polish-Japanese Academy of Information Technology Warszawa02-008 Poland Department of Neurology UMass Medical School WorcesterMA01655 United States

ISBN: (纸本)9783031876592

Alzheimer’s disease (AD)-related neurodegeneration can begin several decades before the first symptoms appear, and AD biomarkers have only recently been identified. Our research focused on identifying AD-related biomarkers in healthy subjects. We analyzed Biocard data using a group of 150 subjects in different AD stages, with 40 AD patients compared to normal subjects. Using the granular computing method, we identified sets of attributes related to various stages of the disease. We applied this classification to the psychophysical test results of normal subjects to determine if some cases might show granular pattern similarities to groups with AD. Earlier detection can uncover patterns invisible to neuropsychologists in the cognitive like executive or immediate/delayed memory functions. these patterns may help to determine if a patient is getting amnestic or non-amnestic early symptoms that lead to dementia or not. Our analysis of Biocard data identified granules that classify cognitive attributes with disease stage scores (CDRSUM - Clinical Dementia Rating Scale Sum of Boxes). By applying these rules to 21 normal subjects with a CDRSUM of 0, we predicted that one subject might develop mild dementia (CDRSUM > 4.5) with a mixture of executive and cognitive test results. Another patient might get very mild dementia (CDRSUM > 2.25), but with dominating memory problems which might lead to serious amnestic symptoms. Other patients might develop questionable impairment (CDRSUM > 0.75). Our AI-powered method can identify patterns in cognitive attributes in normal subjects that might indicate their pre-dementia amnestic or non-amnestic stages, which may be invisible to neuropsychologists. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Granular computing

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QiMINT: Quantum-Inspired Mobile Intelligence - Advancing Complex Signal Processing with Machine Learning

QiMINT: Quantum-Inspired Mobile Intelligence - Advancing Com...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Vinothkumar Kolluru E. Helen Parimala Kokilavani T Sunil Raj Y Bastin Jesuraj Afrig Aminuddin Department of Data Science Stevens Institute of Technology NJ USA Department of Computer Science Gitam Deemed to be University Bangalore India Department of Computer Science School of Sciences Christ (Deemed) University Bangalore India Department of Data Science St. Xavier's College (Autonomous) Palayamkottai India Department of Computer Science Mother Theresa Arts and Science College Theni Tamilnadu India Department of Information System Faculty of Computer Science Universitas Amikom Yogyakarta Sleman Indonesia

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Integrating mobile intelligence with quantum-inspired machine learning (QiML) opens space for challenging mobile signal processing tasks. By utilizing superposition and entanglement, quantum computing principles, QiML boosts the agility of mobile devices by allowing real-time data processing, pattern recognition, and decision making. this work introduces Quantum-Inspired Mobile Intelligence (QiMINT) a new mobile computing framework that integrates quantum-inspired designs with classical machine learning, which increases mobile devices' accuracy, latency, and energy efficiency. Results show that medical QiML based models exceeded traditional machine learning methods in most key performance indicators. the quantum convolutional neural network (QCNN) achieved an accuracy of 92.3% in contrast with the CNNs' 87.5%, but with a processing time of 80 ms as to 120 ms, energy consumption of 10 mJ in comparison to the CNNs' 15mJ. Also, quantum-inspired random forests lowered processing delay by 40%, sustaining superior accuracy than the classical-base system. these results demonstrate that QiML can effectively balance computation complexity while making it suitable for edge computing, IoT, and mobile intelligence systems.

关键词： Quantum computing Accuracy Decision making Machine learning Signal processing Real-time systems Mobile handsets pattern recognition Optimization Edge computing

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8th international conference on pattern recognition and Machine Intelligence, PReMI 2019

8th International Conference on Pattern Recognition and Mach...

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8th international conference on pattern recognition and Machine Intelligence, PReMI 2019

ISBN: (纸本)9783030348687

the proceedings contain 131 papers. the special focus in this conference is on pattern recognition and Machine Intelligence. the topics include: Spectral Feature Based Kannada Dialect Classification from Stop Consonants;Incorporation of Neighborhood Concept in Enhancing SOM Based Multi-label Classification;Prediction of Performance Indexes in CNC Milling Using Regression Trees;dyscalculia Detection Using Machine Learning;temporal Dynamics of the Brain Using Variational Bayes Hidden Markov Models: Application in Autism;robust Identification of Dense or Sparse Crowd Based on Classifier Fusion;sustained Self-Supervised Pretraining for Temporal Order Verification;retraining Conditions: How Much to Retrain a Network After Pruning?;m-ary Random Forest;exponentially Weighted Random Forest;automatic Attribute Profiles for Spectral-Spatial Classification of Hyperspectral Images;instance Ranking Using Data Complexity Measures for Training Set Selection;A Multi-class Deep All-CNN for Detection of Diabetic Retinopathy Using Retinal Fundus Images;real-Time Vehicle Detection in Aerial Images Using Skip-Connected Convolution Network with Region Proposal Networks;Dual CNN Models for Unsupervised Monocular Depth Estimation;Deep CNN for Single Image Super Resolution Using Skip Connections;detection of Image Manipulations Using Siamese Convolutional Neural Networks;DaNSe: A Dilated Causal Convolutional Network Based Model for Load Forecasting;Multichannel CNN for Facial Expression recognition;data Driven Sensing for Action recognition Using Deep Convolutional Neural Networks;gradually Growing Residual and Self-attention Based Dense Deep Back Projection Network for Large Scale Super-Resolution of Image;enhanced Depression Detection from Facial Cues Using Univariate Feature Selection Techniques;3D-GCNN - 3D Object Classification Using 3D Grid Convolutional Neural Networks;a New Steganalysis Method Using Densely Connected ConvNets.

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8th international conference on pattern recognition and Machine Intelligence, PReMI 2019

8th International Conference on Pattern Recognition and Mach...

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8th international conference on pattern recognition and Machine Intelligence, PReMI 2019

ISBN: (纸本)9783030348717

the proceedings contain 131 papers. the special focus in this conference is on pattern recognition and Machine Intelligence. the topics include: QIBDS Net: A Quantum-Inspired Bi-Directional Self-supervised Neural Network Architecture for Automatic Brain MR Image Segmentation;colonoscopic Image Polyp Classification Using Texture Features;GRaphical Footprint Based Alignment-Free Method (GRAFree) for Classifying the Species in Large-Scale Genomics;density-Based Clustering of Functionally Similar Genes Using Biological Knowledge;impact of the Continuous Evolution of Gene Ontology on Similarity Measures;DEGnet: Identifying Differentially Expressed Genes Using Deep Neural Network from RNA-Seq Datasets;Survival Analysis with the Integration of RNA-Seq and Clinical Data to Identify Breast Cancer Subtype Specific Genes;Deep Learning Based Fully Automated Decision Making for Intravitreal Anti-VEGF therapy;Biomarker Identification for ESCC Using Integrative DEA;critical Gene Selection by a Modified Particle Swarm Optimization Approach;automated Counting of Platelets and White Blood Cells from Blood Smear Images;neuronal Dendritic Fiber Interference Due to Signal Propagation;Two-Class in Silico Categorization of Intermediate Epileptic EEG Data;employing Temporal Properties of Brain Activity for Classifying Autism Using Machine Learning;Time-Frequency Analysis Based Detection of Dysrhythmia in ECG Using Stockwell Transform;Block-Sparsity Based Compressed Sensing for Multichannel ECG Reconstruction;a Dynamical Phase Synchronization Based Approach to Study the Effects of Long-Term Alcoholism on Functional Connectivity Dynamics;RBM Based Joke Recommendation System and Joke Reader Segmentation;TagEmbedSVD: Leveraging Tag Embeddings for Cross-Domain Collaborative Filtering;On Applying Meta-path for Network Embedding in Mining Heterogeneous DBLP Network;an Improved Approach for Sarcasm Detection Avoiding Null Tweets.

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House rent prediction based on joint model 8

House rent prediction based on joint model

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8th international conference on computing and pattern recognition, iccpr 2019

作者： Zhang, Kun Shen, Ling Cong Liu, Ningning School of Information Technology and Management University of International Business and Economics China

ISBN: (纸本)9781450376570

With the development of the house leasing market, the house rents of several large cities in China have experienced rapid growth due to the increasing demand. In this paper, we focus on investigating various machine learning approaches to predict the house rent. Firstly, we not only have investigated different rent-related features including community condition, location, traffic and house condition, but also have employed various prediction models including XGBoost, LightGBM and CatBoost algorithms. Based on these, we proposed a joint model, which is a combination of above three models by linear weighting learned from least square method. Our best model ranks in top3% in the public Data Castle competition, which proves the joint model can effectively improve the accuracy and stability of the rent prediction compared to other prediction models. © 2019 Association for computing Machinery.

关键词： Forecasting

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Bangla Handwritten Basic Character recognition Using Deep Convolutional Neural Network 8

Bangla Handwritten Basic Character Recognition Using Deep Co...

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Joint 8th international conference on Informatics, Electronics and Vision (ICIEV) / 3rd international conference on Imaging, Vision and pattern recognition (icIVPR) / international conference on Activity and Behavior computing (ABC)

作者： Saha, Chandrika Faisal, Rahat Hossain Rahman, Md Mostafijur Univ Barishal Dept Comp Sci & Engn Barishal Bangladesh

ISBN: (纸本)9781728107882

Bangla is one of the mostly spoken languages all over the world, nonetheless, efforts on Bangla handwritten character recognition is not adequate. the use of Deep Convolutional Neural Network (DCNN) based classifiers has become a triumph over the state of art machine learning techniques. Using DCNN model to classify Bangla isolated basic characters can provide better output because of its ability to detect many hidden features from an image. In this paper, a new DCNN model, namely, BBCNet-15 is proposed for Bangla handwritten basic character recognition. the proposed model consists of 6 convolution layers, 6 max pooling layers, 2 fully connected layers followed by the softmax output unit. To avoid overfitting dropout regularization technique is used. the implementation of the proposed DCNN model is evaluated on a benchmark dataset, CMATERdb 3.1.2 which contains 50 character classes including 39 consonants and 11 vowels. the experimental evaluation of BBCNet-15 on the dataset provides a recognition accuracy of 96.40% which outperforms some prominent techniques in existence.

关键词： Deep learning Convolutional Neural Network pattern recognition Optical Character recognition Bangla Handwritten Character recognition

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Credit card fraud detection based on csat-related adaboost 8

Credit card fraud detection based on csat-related adaboost

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8th international conference on computing and pattern recognition, iccpr 2019

作者： Yang, Yue Liu, Chenyuan Liu, Ningning School of Information Technology and Management University of International Business and Economics China School of Insurance and Economics University of International Business and Economics China

ISBN: (纸本)9781450376570

In the field of Financial Technology, machine learning provides important support for decision-making through the effective use of data. Credit card fraud detection technology is a good example, but it still faces two challenges: the unbalanced data sets and costsensitive characteristics. In this paper, we proposed an enhanced CSat (Customer Satisfaction)-related AdaBoost. Based on the traditional AdaBoost, we consider the expected loss of the impact of customer satisfaction and re-adjust the weight of different categories in the cost adjustment function of the basic classifier. Considering the serious consequences of fraud transactions, we also implemented a metric related to the Total Profit of Classification (TPC) to evaluate performance. the results show that the CSat-related AdaBoost performed better in F1-score and AUC score compared to the traditional AdaBoost and some mainstream models, the reliability and interpretability of TPC as an evaluation metric is also demonstrated in our paper. © 2019 Association for computing Machinery.

关键词： Customer satisfaction

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