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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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Skeleton-based Human Action recognition by the Integration of Euclidean distance 2021

Skeleton-based Human Action Recognition by the Integration o...

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2021 the 9th international conference on Information Technology: IoT and Smart City

作者： Yi Gao Zhaokun Liu Xinmeng Wu Guangyuan Wu Jiahui Zhao Xiaofan Zhao College of Intelligence and Computing Tianjin University China School of Information Technology and Cyber Security People's Public Security University of China China

ISBN: (纸本)9781450384971

With the growing popularity of somatosensory interaction devices, human action recognition becomes more and more attractive in many application scenarios. Nowadays, it takes a long time for some behavior recognition methods to train a model. To solve the problem, this paper proposes a human skeleton model for human action recognition. the joint coordinates are extracted using OpenPose and the thermodynamic diagram, and then we extract partial features and overall features, as well as Euclidean distances between joints. All the data collected above makes up the motion features of independent video frames. What's more, we leverage Multilayer Perceptron (MLP) classifier to classify all the motion features. On the Kth and ICPR datasets, we test the accuracy of the mode verified by changing several parameters. When the image resolution is 320*240, weight is 12 and stride is 1, the highest accuracy rate of single-person behavior recognition is 0.821. When weight is 10 and stride is 1, the highest accuracy rate of multi-person behavior recognition is 0.812, and the high running speed enables the mode to be real-time.

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Measuring Variability in Acute Myocardial Infarction Coding Using a Statistical Process Control and Probabilistic Temporal Data Quality Control Approaches

Measuring Variability in Acute Myocardial Infarction Coding ...

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9th World conference on Information Systems and Technologies (WorldCIST)

作者： Souza, J. Caballero, I Santos, J., V Lobo, M. F. Pinto, A. Viana, J. Saez, C. Freitas, A. Univ Porto Fac Med Dept Community Med Informat & Hlth Decis Sci MEDC Porto Portugal Univ Porto Fac Med Ctr Hlth Technol & Serv Res CINTESIS Porto Portugal Univ Castilla La Mancha Ciudad Real Spain ACES Grande Porto VIII Espinho Gaia Publ Hlth Unit Vila Nova De Gaia Portugal Univ Politecn Valencia UPV Inst Univ Tecnol Informac & Comunicac ITACA Biomed Data Sci Lab Valencia Spain

ISBN: (纸本)9783030726508;9783030726515

Acute Myocardial Infarction (AMI) is frequently reported when coding hospital encounters, being commonly monitored through acute care outcomes. Variability in clinical coding within hospital administrative databases, however, may indicate data quality issues and thereby negatively affect quality assessment of care delivered to AMI patients, apart from impacting health care management, decision making and research as a whole. In this study, we applied statistical process control and probabilistic temporal data quality control approaches to assess inter-hospital and temporal variability in coding AMI episodes within a nationwide Portuguese hospitalization database. the application of the present methods identified affected data distributions that can be potentially linked to data quality issues. A total of 12 out of 36 institutions substantially differed in coding AMI when compared to their peers, mostly presenting lower than expected hospitalizations of AMI. Results also indicate the existence of abnormal temporal patterns demanding additional investigation, as well as dissimilarities of temporal data batches in the periods comprising the recent transition to the international Classification of Diseases, 10th revision, Clinical Modification (ICD-10-CM) and changes in the Diagnosis-Related Group (DRG) software. Hence, the main contribution of this paper is the use of reproducible, feasible and easy-to-interpret methods that can be employed to monitor the variability in clinical coding and that could be integrated into data quality assessment frameworks.

关键词： Data quality Clinical coding Data variability Acute myocardial infarction Clinical classification software international classification of diseases

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Multi-Scale Residual Pyramid Attention Network for Monocular Depth Estimation

Multi-Scale Residual Pyramid Attention Network for Monocular...

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international conference on pattern recognition

作者： Jing Liu Xiaona Zhang Zhaoxin Li Tianlu Mao College of Computer and Cyber Security Hebei Normal University Shijiazhuang Hebei China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728188089;9781728188096

Monocular depth estimation is a challenging problem in computer vision and is crucial for understanding 3D scene geometry. Recently, deep convolutional neural networks (DCNNs) based methods have improved the estimation accuracy significantly. However, existing methods fail to consider complex textures and geometries in scenes, thereby resulting in loss of local details, distorted object boundaries, and blurry reconstruction. In this paper, we proposed an end-to-end multi-scale residual pyramid attention network (MRPAN) to mitigate these problems. First, we propose a multi-scale attention context aggregation (MACA) module, which consists of spatial attention module (SAM) and global attention module (GAM). By considering the position and scale correlation of pixels from spatial and global perspectives, the proposed module can adaptively learn the similarity between pixels so as to obtain more global context information of the image and recover complex structures in the scene. then we proposed an improved residual refinement module (RRM) to further refine the scene structure, giving rise to deeper semantic information and retain more local details. Experimental results show that our method achieves more promising performance in object boundaries and local details compared with other state-of-the-art methods.

关键词： Geometry Image segmentation Correlation three-dimensional displays Semantics Estimation pattern recognition

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Evaluation of Soil Moisture for Estimation of Irrigation pattern by Using Machine Learning Methods 5th

Evaluation of Soil Moisture for Estimation of Irrigation Pat...

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5th international conference on Advances in computing and Data Sciences, ICACDS 2021

作者： Khanna, Abhishek Kaur, Sanmeet Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala147004 India

ISBN: (纸本)9783030882433

the presence of soil moisture is of paramount importance in agricultural domain, as it constitutes largely towards variations in soil texture, and development of crops. Hence, evaluation of this parameter can turn out to be very effective while performing agricultural activities. Mainly, evaluations for the parameter is done with an aim to estimate and reduce water consumption within the fields. In this article, the presence of soil moisture has been evaluated at three different levels, i.e., 10 cm, 45 cm, and 80 cm through Autoregressive Integrated Moving Average (ARIMA) modeling technique based on Time Series Analysis, to predict the future possible values so that precise distribution water can be done within the fields. the intermediate diminution in error rates attained by the modeling technique attained between 74%–77% in comparison to other modeling techniques, depicting its superiority. Based on the results, distribution of water was scheduled in advance as per the minimal requirement, resulting lesser consumption and better crop yields © 2021, Springer Nature Switzerland AG.

关键词： Soil moisture

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A serious game and negotiation skills in nursing students: A pilot study 10th

A serious game and negotiation skills in nursing students: A...

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10th international conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, MIS4TEL 2020

作者： Zeffiro, Valentina Di Fuccio, Raffaele Vellone, Ercole Alvaro, Rosaria D’Agostino, Fabio University of Rome Tor Vergata Rome Italy Istituto di Scienze e Tecnologie della Cognizione – Consiglio Nazionale delle Ricerche Rome Italy Saint Camillus International University of Health Sciences Rome Italy

ISBN: (纸本)9783030522865

Negotiation emerges in our lives whenever a decision needs to be made together with someone else. In the nursing field, negotiation is well represented in the relationship between the nurse and the patient. For this reason, it is important to help nurses to develop their communication skills, starting from the training period. the aims of this study were to identify the negotiation styles of nursing students with a serious game and to evaluate the effect of a tailored intervention on negotiation skills. the serious game was based on Rahim and Bonoma’s theory of conflict management and included five scenarios of daily life situations. Descriptive and inferential statistics were performed to evaluate the students’ negotiation styles before and after the intervention and the times spent to complete the game’s scenarios. Results showed that nursing students preferred an integrating negotiation style and that they improved their compromising style after the intervention. the students were generally oriented towards using problem-solving reasoning and, after the intervention, renouncing something in order to reach a mutually acceptable decision in the included scenarios. To our knowledge, this is the first study to use a negotiation serious game in the nursing learning field and serves as an example of how technology can be accepted and integrated in education to help nursing students develop not only their technical but also relational skills. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Serious games

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Comparative Analysis of Supervised Learning Techniques of Machine Learning for Software Defect Prediction

Comparative Analysis of Supervised Learning Techniques of Ma...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Anurag Gupta Ratnesh Kumar Shukla Abhishek Bhola Alok Singh Sengar Computer Science & Engineering College of Computing Science & Information Technology Moradabad India Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

ISBN: (纸本)9781665439718

Software Bug prediction or defect prediction is very important for the organizations to detect the bugs in the early stage of Software development process because software developers can know the vulnerable areas where the defects may be *** this research paper we have compared different statistical techniques like Linear Regression, Naïve Bayes, Random Forest, Decision Tree, Artificial Neural Networks etc. and come up with the best among them for the Bug prediction. Comparison is made using Performance Measures like Accuracy, precision, recall and F-measure.

关键词： Computer bugs Supervised learning Linear regression Organizations Artificial neural networks Market research Software

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Customized Particle Swarm Optimization Algorithm for Frequent Itemset Mining 10

Customized Particle Swarm Optimization Algorithm for Frequen...

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10th international conference on Computer Communication and Informatics (ICCCI)

作者： Sukanya, N. S. thangaiah, P. Ranjit Jeba CMR Univ Sch Sci Studies Bangalore Karnataka India Karunya Inst Technol & Sci Dept Informat Technol Coimbatore Tamil Nadu India

ISBN: (纸本)9781728145136

In data analytics, frequent itemset mining is the key process for decision making. Frequent patterns are analyzed to increase the benefits, reduce the costs, maximize the income and to develop the overall resources. Every day there is an increase in data, which leads to a massive dataset. Mining the frequent itemset is a time and memory consuming task. Handling a massive transactional database is a challenging issue. there is a need for an efficient technique to retrieve the frequent patterns by reducing the computational time and memory utilization. In this paper, evolutionary computing technique is implemented to retrieve the frequent itemsets. Customized PSO is applied to mine the frequent patterns by maximizing the number frequent itemsets identified in the transactional database. First, the standard Particle Swarm Optimization (SPSO), second, Constriction Factor Particle Swarm Optimization (CFPSO), third, Customized PSO with Local Search (CPSO_LS) technique are applied to deal with the retrieval of frequent itemsets efficiently. Finally, the proposed techniques are investigated based on the number of frequent itemsets retrieved, and computational time. Experimental results illustrates that the customized PSO with local search outperforms the other two variants of the PSO algorithm.

关键词： Frequent itemset pattern mining Evolutionary computing PSO Local search

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Cost Volume Refinement for Depth Prediction

Cost Volume Refinement for Depth Prediction

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international conference on pattern recognition

作者： João L. Cardoso Nuno Gonçalves Michael Wimmer Institute of Visual Computing and Human-Centered Technology TU Wien Vienna Austria Institute of Systems and Robotics University of Coimbra Portuguese Mint and Official Printing Office Lisbon

Light-field cameras are becoming more popular in the consumer market. their data redundancy allows, in theory, to accurately refocus images after acquisition and to predict the depth of each point visible from the camera. Combined, these two features allow for the generation of full-focus images, which is impossible in traditional cameras. Multiple methods for depth prediction from light fields (or stereo) have been proposed over the years. A large subset of these methods relies on cost-volume estimates - 3D objects where each layer represents a heuristic of whether each point in the image is at a certain distance from the camera. Generally, this volume is used to regress a depth map, which is then refined for better results. In this paper, we argue that refining the cost volumes is superior to refining the depth maps in order to further increase the accuracy of depth predictions. We propose a set of cost-volume refinement algorithms and show their effectiveness.

关键词： three-dimensional displays Smoothing methods Volume measurement Refining Redundancy Pipelines Cameras

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Rethinking Experience Replay: a Bag of Tricks for Continual Learning

Rethinking Experience Replay: a Bag of Tricks for Continual ...

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international conference on pattern recognition

作者： Pietro Buzzega Matteo Boschini Angelo Porrello Simone Calderara University of Modena and Reggio Emilia Italy

In Continual Learning, a Neural Network is trained on a stream of data whose distribution shifts over time. Under these assumptions, it is especially challenging to improve on classes appearing later in the stream while remaining accurate on previous ones. this is due to the infamous problem of catastrophic forgetting, which causes a quick performance degradation when the classifier focuses on learning new categories. Recent literature proposed various approaches to tackle this issue, often resorting to very sophisticated techniques. In this work, we show that naive rehearsal can be patched to achieve similar performance. We point out some shortcomings that restrain Experience Replay (ER) and propose five tricks to mitigate them. Experiments show that ER, thus enhanced, displays an accuracy gain of 51.2 and 26.9 percentage points on the CIFAR-10 and CIFAR-100 datasets respectively (memory buffer size 1000). As a result, it surpasses current state-of-the-art rehearsal-based methods.

关键词： Degradation Neural networks pattern recognition Proposals Erbium Standards

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Critical Review on Data Mining in Healthcare Sector

Critical Review on Data Mining in Healthcare Sector

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Prabhjot Kaur Neha Dhariwal University Institue of Computing Chandigarh University India

ISBN: (纸本)9781665439718

With the growing need for technology to handle and analyze data, Data Mining (DM) is becoming an important tool to be used in healthcare for several purposes. Data mining refers to the processing of raw information to convert it into useful information by which certain decisions can be taken. In the healthcare sector, Data mining has great significance because due to the changes in lifestyles, various diseases affect the people and there is a need to use the efficient technique to predict the disease in time so that appropriate treatment can be provided to the patient. there are several algorithms of data mining that are used for this purpose including Naïve Bayes, support vector machine (SVM), decision tree, and neural networks. this paper attempts to identify the role of data mining in the healthcare sector to predict and classify diseases.

关键词： Support vector machines Neural networks Medical services Prediction algorithms Market research Data mining Noise measurement

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