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

共 1678 条记录，以下是281-290 订阅

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Supervised Feature Embedding for Classification by Learning Rank-based Neighborhoods 25

Supervised Feature Embedding for Classification by Learning ...

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25th international conference on pattern recognition (ICPR)

作者： Sheikhi, Ghazaal Altncay, Hakan Eastern Mediterranean Univ Dept Comp Engn Via Mersin 10 Famagusta North Cyprus Turkey

ISBN: (纸本)9781728188089

In feature embedding, the recovery of associated discriminative information in the reduced subspace is critical for downstream classifiers. In this study, a supervised feature embedding method is proposed inspired by the well-known word embedding technique, word2vec. Proposed embedding method is implemented as representative learning of rank-based neighbor-hoods. the notion of context words in word2vec is extended into neighboring instances within a given window. Neighborship is defined using ranks of instances rather than their values so that regions with different densities are captured properly. Each sample is represented by a unique one-hot vector whereas its neighbors are encoded by several two-hot vectors. the two-hot vectors are identical for neighboring samples of the same class. A feed-forward neural network with a continuous projection layer, then learns the mapping from one-hot vectors to multiple two-hot vectors. the hidden layer determines the reduced subspace for the train samples. the obtained transformation is then applied on test data to find a lower-dimensional representation. Proposed method is tested in classification problems on 10 UCI data sets. Experimental results confirm that the proposed method is effective in finding a discriminative representation of the features and outperforms several supervised embedding approaches in terms of classification performance.

关键词： embedding representative learning neighborhoods hot vectors

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Soft computing techniques for dengue prediction 10

Soft computing techniques for dengue prediction

引用

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

作者： Choubey, Dilip Kumar Mishra, Adweat Pradhan, Sambeet Kumar Anand, Naman Department of Computer Science & Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9780738105239

Dengue is a mosquito borne viral disease which occurs frequently affecting in large numbers of people in the tropical and sub-tropical regions. the disease requires medical treatment and may turn out to be fatal if not treated properly, or a delay in treatment is reported. therefore, there is a need for the early prediction of the disease so that treatment is being availaible as and when required. In this paper, we intend to study, implement and review the performance of various prediction techniques coupled with bioinformatics for the early prediction of Dengue disease. the primary objective of the paper is to analyze and compare the performance of the soft computing prediction techniques, the advantages and the issues associated with them on the Dengue datasets. the paper would be helpful in motivating the researchers and various government institutions to predict the number of likely Dengue cases, ramp up the medical and healthcare facilities and put various programs and plans into being. the applied approach may be used in some other medical diseases. © 2021 IEEE.

关键词： Forecasting

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Multi-objective emperor penguin optimizer for tuning the quality-of-service in cloud computing 10

Multi-objective emperor penguin optimizer for tuning the qua...

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

作者： Kaur, Harsimran Bhadoria, Robin Singh Dept. of CSE Uttarakhand Technical University Dehradun India Bhimtal Uttarakhand India

ISBN: (纸本)9780738105239

In the present situation, researchers have the task of developing a high quality features based cloud computing system in the real world if service is desired on request. Quality of service (QoS) describes such parameters which carry out the non-functional activities for any real time based systems. Such services are normally sufficient to achieve these QoS. Most of the information technology (IT) companies offer different cloud based services to its customers. there is no standard methodology available for calculating the actual use of cloud based resources and determining the rankings. From the customers’ point of view, it is not an easy task to find the reliable service provider that meets customer’s needs. this paper presents the QoS parameter especially on ‘throughput’ using Cloud Rank 3 with aid of Multi-Objective Emperor Penguin Optimizer (MOEPO) approach. this approach helps in finding the adorable and competent cloud service provider (CSP) available in the market. the different experiments have been carried out with actual QoS results, using Amazon EC2 services and its comparison. © 2021 IEEE.

关键词： Quality of service

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Instance Segmentation in Mobile computing Environments for Identification of Specific Characteristics in Endangered Species 17

Instance Segmentation in Mobile Computing Environments for I...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Ribeiro, Morgana C. O. Monteiro, Rhayane S. Moreira, Mario W. L. Rodrigues, Joel J. P. C. Fed Inst Educ Sci & Technol Ceara IFCE Aracati CE Brazil Fed Univ Piaui UFPI Teresina PI Brazil Inst Telecomunicacoes Aveiro Portugal

ISBN: (纸本)9781728186160

the segmentation of instances is a key topic in image processing and computer vision. there are numerous applications such as medical image analysis, video surveillance, image compression among others, in which its algorithms show significant results. Due to the COVID-19 pandemic, most countries have been affected mainly by their economy. In the food production sector, including fishing and aquaculture, it was no different. In this context, this research has as main objective to contribute to the 2030 Agenda for Sustainable Development suggested by the United Nations (UN), through a fish detection and segmentation model based on the framework Detectron2, optimizing the time of professionals in identifying specific characteristics of a particular species. To achieve this objective, this research seeks to facilitate the recognition of patterns of parts of the fish from the segmentation of instances and to stimulate scientific research in the area through the morphological information collection of certain species. the results present an accuracy, based on the Intersection over Union (IoU) indicator, of 88.4%, providing an effective solution for the collection of these characteristics.

关键词： Mobile computing pattern recognition Instance Segmentation Convolutional Neural Networks Detectron2

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Efficiently Predicting Frequent patterns over Uncertain Data Streams 10

Efficiently Predicting Frequent Patterns over Uncertain Data...

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10th international conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) / 9th international conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICth)

作者： Liu, Chuan-Ming Liao, Kuan-Teng Natl Taipei Univ Technol Dept Comp Sci & Informat Engn 1Sec 3Zhongxiao E Rd Taipei 10608 Taiwan

In recent decades, frequent pattern mining has become popular in real time computing because it helps users extract meaningful business or technical trends over uncertain data streams. Generally, mainstream frequent pattern mining approaches can accurately explore frequent patterns whether the approaches are used in expected support or probabilistic frequentness mechanisms. However, whichever are implemented, they all have time issues when extracting frequent patterns if there exists a volume of data tuples in each time instance. To explore frequent patterns over uncertain data streams efficiently, we propose a predicted approach for forecasting frequent patterns with hidden Markov models. the model consists of a construction phase and a testing phase. In the former phase, the hidden Markov model is constructed by the history of past data, and in the latter phase, the results of evaluations and adjustments of transition probabilities occur. By constructing models of patterns, frequent patterns can be easily obtained. Experimental results show the proposed approach can save time cost when exploring frequent patterns and provide approximate 72% average accuracy in 50 sequential predictions;thereby, our approach can efficiently and accurately predict frequent patterns over uncertain data streams. (C) 2019 the Authors. Published by Elsevier B.V.

关键词： Hidden Markov model frequent pattern analysis uncertain data streaming computing

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2021 IEEE 10th international conference on Cloud Networking, CloudNet 2021

2021 IEEE 10th International Conference on Cloud Networking,...

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10th IEEE international conference on Cloud Networking, CloudNet 2021

ISBN: (纸本)9781665435383

the proceedings contain 23 papers. the topics discussed include: longer stay less priority: flow length approximation used in information-agnostic traffic scheduling in data center networks;where is the light(ning) in the taproot dawn? unveiling the bitcoin lightning (IP) network;using distributed tracing to identify inefficient resources composition in cloud applications;leveraging partial model extractions using uncertainty quantification;super-resolution on edge computing for improved adaptive http live streaming delivery;secure distributed storage on cloud-edge infrastructures;and a machine learning approach for service function chain embedding in cloud datacenter networks.

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Directed Graph Evolution from Euler-Lagrange Dynamics 24

Directed Graph Evolution from Euler-Lagrange Dynamics

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24th international conference on pattern recognition (ICPR)

作者： Wang, Jianjia Wilson, Richard C. Hancock, Edwin R. Univ York Dept Comp Sci Comp Vis & Pattern Recognit York YO10 5DD N Yorkshire England

ISBN: (纸本)9781538637883

In this paper, we develop a variational principle from the von Neumann entropy for directed graph evolution. We minimise the change of entropy over time to investigate how directed networks evolve under the Euler-Lagrange equation. We commence from our recent work in which we show how to compute the approximate von Neumann entropy for a directed graph based on simple in and out degree statistics. To formulate our variational principle we commence by computing the directed graph entropy difference between different time epochs. this is controlled by the ratios of the in-degree and out-degrees at the two nodes forming a directed edge. It also reveals how the entropy change is related to correlations between the changes in-degree ratio and in-degree, and their initial values. We conduct synthetic experiments with three widely studied complex network models, namely Erdos-Renyi random graphs, Watts-Strogatz small-world networks, and Barabasi-Albert scale-free networks, to simulate the in-degree and out-degree distribution. Our model effectively captures the directed structural transitions in the dynamic network models. We also apply the method to the real-world financial networks. these networks reflect stock price correlations on the New York Stock Exchange(NYSE) and can be used to characterise stable and unstable trading periods. Our model not only effectively captures how the directed network structure evolves with time, but also allows us to detect periods of anomalous network behaviour.

关键词： Entropy Mathematical model Correlation Data models Complex networks pattern recognition Computational modeling

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An improved method for cloth pattern cutting based on Holistically-nested Edge Detection 10

An improved method for cloth pattern cutting based on Holist...

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10th IEEE Data Driven Control and Learning Systems conference, DDCLS 2021

作者： Yu, Naigong Zhang, Zhen Xu, Qiao Firdaous, Essaf Lin, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent System Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781665424233

Image edge detection is the basis for precise positioning and accurate cutting of cloth pattern contours. Compared with the commonly used traditional edge detection methods, the Holistically-nested Edge Detection has clearer and more continuous detection results including the reduction of the false detection rate. However, this method extracts a coarser thick outline. In order to extract a high-precision cloth pattern outline, clearly distinguish the main body of the pattern from the background, provide convenience for the follow-up cutting machine for accurate cutting, this paper proposes an improved method for edge detection of cloth pattern cutting based on the holistically-nested Edge Detection method. the edge refinement and smoothing process are added, where the edge detection, edge refinement, and edge smoothing of the clothes images are carried out in sequences, so that the extracted cloth pattern contour is continuous, smooth, and detailed, allowing the respect of the cutting requirements of the cutting machine and the requirements of the factory production. © 2021 IEEE.

关键词： Machinery

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Neuromorphic pattern Generation Circuits for Bioelectronic Medicine 10

Neuromorphic Pattern Generation Circuits for Bioelectronic M...

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10th international IEEE-EMBS conference on Neural Engineering (NER)

作者： Donati, Elisa Krause, Renate Indiveri, Giacomo Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781728143378

Chronic diseases can greatly benefit from bioelectronic medicine approaches. Neuromorphic electronic circuits present ideal characteristics for the development of brain-inspired low-power implantable processing systems that can be interfaced with biological systems. these circuits, therefore, represent a promising additional tool in the tool-set of bioelectronic medicine. In this paper, we describe the main features of neuromorphic circuits that are ideally suited for continuously monitoring the physiological parameters of the body and interact with them in real-time. We propose examples of computational primitives that can be used for real-time pattern generation and present a neuromorphic implementation of neural oscillators for the generation of sequence activation patterns. We demonstrate the features of such systems with an implementation of a three-phase network that models the dynamics of the respiratory Central pattern Generator (CPG) and the heart chambers rhythm, and that could be used to build an adaptive pacemaker.

关键词： Timing circuits

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Study of robust facial recognition under occlusion using different techniques 5

Study of robust facial recognition under occlusion using dif...

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5th international conference on Intelligent computing and Control Systems, ICICCS 2021

作者： Jayant, Deepanshu Bhat, Aruna Delhi Technological University Department of Computer Science New Delhi India

ISBN: (纸本)9781665412728

this paper represents the different techniques used for the study of robust facial recognition. the need of facial recognition is expanding very rapidly in current technologies as it is possible to identify a humane face features through different digital mediums. Although, facial recognition is not done achieved easily in the real-world situation as most of the time the human face goes through various occlusions making hard for the system to complete the identification process. the sole purpose of this article is to compare the techniques used and identify the most accurate and efficient one from all of them. Robust facial recognition is a task of doing recognition under uncertain environments and features. the main aim of the robust facial recognition is to analyze and identify the face images where the picture proposes difficult viewpoints, angles, poses, illumination, noises and expressions. the techniques studied for this article are Local Binary patterns, HOG features, Occlusion-adaptive Deep Networks (ODN), Robust Principal Component Analysis (RPCA), Progressive Convolutional Neural Network and Region Attention Networks (RAN). In this article, a table is created summarizing the experiments and results of the techniques studied. © 2021 IEEE.

关键词： Local binary pattern

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