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

共 1677 条记录，以下是321-330 订阅

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Heart stroke risk analysis: A deep learning approach 10

Heart stroke risk analysis: A deep learning approach

引用

10th IEEE international conference on Communication Systems and Network Technologies, CSNT 2021

作者： Sharma, Neeraj Mishra, Manas Kumar Chadha, Jasroop Singh Lalwani, Praveen School of Computing Science and Engg. VIT Bhopal University India

ISBN: (纸本)9780738105239

Heart Stroke is one of the severe health hazards;therefore, early heart stroke prediction helps the society to save human lives. this objective can be achieved using the machine learning techniques. In this research article, machine learning models are applied on well known heart stroke classification data-set. In addition, effect of pre-processing the data has also been summarized. In the experimental analysis, machine learning models and ANN with standard feature selection technique are tested on data-set, framingham and the obtained results are evaluated using the confusion metrics includes recall, F1-score, precision and accuracy. From the obtained results, it is observed that ANN performed the best on the pre-processed data, giving the highest accuracy of 87.95 % and F1-Score of 91.47%. © 2021 IEEE.

关键词： Classification (of information)

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Investigation and ordinal modelling of vocal features for stress detection in speech 9

Investigation and ordinal modelling of vocal features for st...

引用

9th international conference on Affective computing and Intelligent Interaction (ACII)

作者： Kalatzantonakis-Jullien, George-Marios Stefanakis, Nikolaos Giannakakis, Giorgos Hellen Open Univ Sch Sci & Technol Patras Greece Hellen Mediterranean Univ Dept Mus Technol & Acoust GR-74100 Rethimnon Greece Fdn Res & Technol Hellas FORTH Inst Comp Sci Iraklion Greece Hellen Mediterranean Univ Univ Res Ctr Inst AgriFood & Life Sci Iraklion Greece

ISBN: (纸本)9781665400190

this paper investigates a robust and effective automatic stress detection model based on human vocal features. Our study experimental dataset contains the voices of 58 Greek-speaking participants (24 male, 34 female, 26.9 +/- 4.8 years old), both in neutral and stressed conditions. We extracted a total of 76 speech-derived features after extensive study of the relevant literature. We investigated and selected the most robust features using automatic feature selection methods, comparing multiple feature ranking methods (such as RFE, mRMR, stepwise fit) to assess their pattern across gender & experimental phase factors. then, classification was performed both for the entire dataset, and then for each experimental task, for both genders combined and separately. the performance was evaluated using 10-fold cross-validation on the speakers. Our analysis achieved a best classification accuracy of 84.8% using linear SVM for the social exposure phase and 74.5% for the mental tasks phase using the gaussian SVM classifier. the ordinal modelling improved significantly our results, yielding a best on-subject basis 10-fold cross-validation classification accuracy of 95.0% for social exposure using gaussian SVM and 85.9% for mental tasks using the gaussian SVM. From our analysis, specific vocal features were identified as being robust and relevant to stress along with parameters to construct the stress model. However, it is was observed the susceptibility of speech to bias and masking and thus the need for universal speech markers for stress detection.

关键词： stress voice speech pairwise transformation Mel cepstral coefficients emotion recognition affective computing biosignals feature selection mRMR hyperparameter optimization

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Research on Fast Adaptive Transmission Models for international Inland Port Based on Edge Intelligence 10

Research on Fast Adaptive Transmission Models for Internatio...

引用

10th IEEE international conference on Cyber Security and Cloud computing and 9th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Yiwen, Liu Zhirong, Zhu Tangyan Wenkan, Wen Xiaoning, Peng Yuanquan, Shi Huaihua University School of Computer and Artificial Intelligence Huaihua Hunan418000 China Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing Huaihua Hunan418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9798350312461

In this paper, we propose a logistics international Inland Port resource allocation method based on intelligent edge scheduling, and we do a reasonable pre-allocation of logistics resources by building an edge network in the urban system. By using multiple integrated learning models and regional prevalence classification algorithms, the distribution demand of each sub-distribution point is predicted. the proposed method is able to cope with uncertainties such as high distribution volume, variable distribution situations or long transportation distances. We use the HuaiHua international Inland Port as the simulation object, and the simulation results show that the proposed method has the highest efficiency in logistics distribution and is still highly adaptive in emergency situations. © 2023 IEEE.

关键词： Resource allocation

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Ophthalmic Medical Image Analysis 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Bhavna Antony Hao Chen Huihui Fang Huazhu Fu Cecilia S. Lee Yalin Zheng

this book constitutes the refereed proceedings of the 10th international Workshop on Ophthalmic Medical Image Analysis, OMIA 2023, held in conjunction with the 26th international conference on Medical Imaging and Comp... 详细信息

ISBN: (数字)9783031440137

ISBN: (纸本)9783031440120

关键词： Image Processing and Computer Vision Artificial Intelligence pattern recognition Computer Systems Organization and Communication Networks

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the Right Delay Detecting Specific Spike patterns with STDP and Axonal Conduction Delays

The Right Delay Detecting Specific Spike Patterns with STDP ...

引用

10th international conference on Adaptive and Natural computing Algorithms

作者： Datadien, Arvind Haselager, Pim Sprinkhuizen-Kuyper, Ida Radboud Univ Nijmegen Dept Artificial Intelligence Nijmegen Netherlands

ISBN: (纸本)9783642202810

Axonal conduction delays should not be ignored in simulations of spiking neural networks. Here it is shown that by using axonal conduction delays, neurons can display sensitivity to a specific spatiotemporal spike pattern. By using delays that complement the firing times in a pattern, spikes can arrive simultaneously at an output neuron, giving it a high chance of firing in response to that pattern. An unsupervised learning mechanism called spike-timing-dependent plasticity then increases the weights for connections used in the pattern, and decreases the others. this allows for an attunement of output neurons to specific activity patterns, based on temporal aspects of axonal conductivity.

关键词： Spiking neural networks STDP Axonal delay Spatiotemporal pattern

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GPGPU based Concurrent Classification using Trained Model of Handwritten Digits

GPGPU based Concurrent Classification using Trained Model of...

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10th international conference on Open Source Systems and Technologies (ICOSST)

作者： Rizvi, Syed Tahir Hussain Cabodi, Gianpiero Arif, Arslan Javed, Muhammad Yaqoob Gulzar, Muhammad Majid Politecn Torino Turin Italy Univ Sci & Technol China Hefei Peoples R China Univ Cent Punjab Lahore Pakistan

ISBN: (纸本)9781509055869

In this paper, General Purpose Graphical Processing Unit (GPGPU) based concurrent implementation of handwritten digit classifier is presented. Different styles of handwriting make it difficult to recognize a pattern but using neural network, it is not a difficult task to perform. Different softwares like torch and MATLAB provide the support of multiple training algorithms to train a network. By choosing an appropriate training algorithm for a specific application, speed of training can be increased. Furthermore, using computational power of GPUs, training and classification speed of neural network can be significantly improved. In this work, Modified National Institute of Standards and Technology (MNIST) database of handwritten digits is used to train the network. Accuracy and training time of digit classifier is evaluated for different algorithms and then concurrent training is performed by exploiting power of GPU. Trained parameters are imported and used for the concurrent classification with Compute Unified Device Architecture (CUDA) computing language which can be useful in numerous practical applications. Finally, the results of sequential and concurrent operations of training and classification are compared.

关键词： Concurrent Parallel architectures Neural network pattern recognition MATLAB Image Classification

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Core-set based product quantization for large-scale multimedia search 18

Core-set based product quantization for large-scale multimed...

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10th international conference on Internet Multimedia computing and Service, ICIMCS 2018

作者： Zhao, Tianli Jiang, Yanshu Wu, Jiaxiang Cheng, Jian Harbin University of Technology National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science China Tencent AI Lab China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science China

ISBN: (纸本)9781450365208

Approximate nearest neighbor (ANN) search has been wellstudied and weightily applied in the field of large scale multimedia search. In recent years, Product Quantization (PQ) based methods have achieved greate success in ANN search. However, both the time and space complexity for PQ learning are linear to the data amount, making it infeasible for large-scale datasets. In this paper, we propose a more efficient learning strategy for product quantization. Instead of learning PQ on the whole dataset, we construct a small set of representative elements, from which PQ can be approximated learned with much lower computation overhead. Extensive experiments are conducted on three large-scale datasets. the codebook learning process of our approach can achieve up to tens times speed-up than existing PQ-based methods, while the memory consumption is only logarithmic to the number of data points. At the same time, the ANN search accuracy of our method is still comparable with state-of-the-art methods on all datasets. © 2018 ACM.

关键词： Nearest neighbor search

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Algorithmic Identification of Tone Color A Comparison of Algorithmic Identification and Identification by Survey Respondents 10

Algorithmic Identification of Tone Color A Comparison of Alg...

引用

10th international conference on Digital and Interactive Arts (ARTECH)

作者： Haron, Anis Onn, Wong Chee Hin, Hew Soon Multimedia Univ Fac Creat Multimedia Cyberjaya Malaysia

ISBN: (纸本)9781450384209

Tone color is an important timbral descriptor for sound that is commonly referred to using semantic descriptors such as warm, dark and bright. this article introduces a novel algorithm using hidden Markov models to computationally identify a best fit semantic descriptor of tone color for a given sound file. the algorithm is developed using dataset annotated by trained individuals and tested against thousands of sound samples obtained from an online database. A survey is conducted to alongside the algorithm development in order to compare identification results from the algorithm against identification results by survey respondents. Results from the algorithm shows positive match in general suggesting its viability. Algorithmic identification of tone color finds its application in music production, where it can be used as a parameter recommendation tool for synthesizers.

关键词： Music Information Retrieval Tone color Timbre Sound and Music computing

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DeepSpotCloud: Leveraging Cross-Region GPU Spot Instances for Deep Learning 10

DeepSpotCloud: Leveraging Cross-Region GPU Spot Instances fo...

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10th IEEE international conference on Cloud computing (CLOUD)

作者： Lee, Kyungyong Son, Myungjun Kookmin Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9781538619933

Cloud computing resources that are equipped with GPU devices are widely used for applications that require extensive parallelism, such as deep learning. When the demand of cloud computing instance is low, the surplus of resources is provided at a lower price in the form of spot instance by AWS EC2. this paper proposes DeepSpotCloud that utilizes GPU-equipped spot instances to run deep learning tasks in a cost efficient and fault-tolerant way. thorough analysis about spot instance price history logs reveals that GPU spot instances show more dynamic price change pattern than other general types of cloud computing resources. To deal with the price dynamicity of the GPU spot instance, DeepSpotCloud utilizes instances in different regions across continents as a single resource pool. this paper also proposes a task migration heuristic by utilizing a checkpointing mechanism of existing deep learning analysis platform to conduct fast task migration when a running spot instance is interrupted. Extensive experiments using real AWS services prove that the proposed task migration method is effective even in a WAN environment with limited network bandwidth. Comprehensive simulations by replaying AWS EC2 price history logs reveal that DeepSpotCloud can achieve 13% more cost gain than a state-of-the-art interrupt-driven scheduling policy. the prototype of DeepSpotCloud is implemented using various cloud computing services provided by AWS to serve real deep learning tasks.

关键词： Graphics processing units Machine learning Cloud computing Fault tolerance Fault tolerant systems Checkpointing Continents

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xDaaS: Any Data as a Service 10

xDaaS: Any Data as a Service

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10th IEEE international conference on Semantic computing (ICSC)

作者： Palavalli, Amarnath Karri, Durgaprasad Pasupuleti, Swarnalatha VMware Palo Alto CA 94304 USA

ISBN: (纸本)9781509006625

Integration of products in enterprises comes with hard challenges due to several factors such as products developed in house, off the shelf, developed over different time lines, available as services over internet, ever changing product APIs, disconnected data models among the products, extensions developed by partners and customers and many more. We propose Semantic Data Platform (SDP), built around the semantics of the information derived out of product integration. this approach of semantic model based data sharing provides extreme loose coupling among products, makes extension of information from a given product possible with zero impact on rest of the system. Easy certification of integration enables evolution of products fairly independent of each other. SDP offers semantic federation and querying, therefore it can fetch any data irrespective of the product where the data lives. thus, 'Any Data from a product can be offered as a Service'.

关键词： Cloud computing Enterprise Data Integration Fact Oriented Modeling Object Role Modeling Ontology Ontology Design pattern ORM Semantic Data Platform

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